kuprel
/
min-dalle
Fast, minimal port of DALL·E Mini to PyTorch
Prediction
kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7IDik7huhsfyjcfdmhfjlf742n5h4StatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- a bob ross painting of a an alien spaceship over a from a lake
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- 4
{ "text": "a bob ross painting of a an alien spaceship over a from a lake", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": 4 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", { input: { text: "a bob ross painting of a an alien spaceship over a from a lake", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: 4 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", input={ "text": "a bob ross painting of a an alien spaceship over a from a lake", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": 4 } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", "input": { "text": "a bob ross painting of a an alien spaceship over a from a lake", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": 4 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7 \ -i 'text="a bob ross painting of a an alien spaceship over a from a lake"' \ -i 'grid_size="4"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor=4'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "a bob ross painting of a an alien spaceship over a from a lake", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": 4 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-06T18:31:39.316486Z", "created_at": "2022-07-06T18:31:20.268652Z", "data_removed": false, "error": null, "id": "ik7huhsfyjcfdmhfjlf742n5h4", "input": { "text": "a bob ross painting of a an alien spaceship over a from a lake", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": 4 }, "logs": "tokenizing text\n['Ġa']\n['Ġbob']\n['Ġross']\n['Ġpainting']\n['Ġof']\n['Ġa']\n['Ġan']\n['Ġalien']\n['Ġspaceship']\n['Ġover']\n['Ġa']\n['Ġfrom']\n['Ġa']\n['Ġlake']\ntext tokens [0, 58, 3118, 4473, 1545, 111, 58, 101, 8925, 28566, 709, 58, 314, 58, 1586, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 15.179313, "total_time": 19.047834 }, "output": [ "https://replicate.delivery/mgxm/953dcd54-b293-4fd2-bcb7-0a63a8baa941/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/da910ca1-4243-4627-b49e-db6f51222166/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/f9c85e2d-c3c8-4ab1-909d-6af9515d11e0/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/4d092a4d-f36b-4f86-910c-56c86d6d87f4/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/b0da95f2-9d1a-42a8-a89e-61325aa5f72f/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/532de2f0-19bf-482d-8031-f3e77056d868/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/a7182bd7-8e7a-47cc-b232-484aa7fdb942/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/7b34983c-ffc1-4958-a33e-8e9d2310a294/min-dalle-iter-8.jpg" ], "started_at": "2022-07-06T18:31:24.137173Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ik7huhsfyjcfdmhfjlf742n5h4", "cancel": "https://api.replicate.com/v1/predictions/ik7huhsfyjcfdmhfjlf742n5h4/cancel" }, "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7" }
Generated intokenizing text ['Ġa'] ['Ġbob'] ['Ġross'] ['Ġpainting'] ['Ġof'] ['Ġa'] ['Ġan'] ['Ġalien'] ['Ġspaceship'] ['Ġover'] ['Ġa'] ['Ġfrom'] ['Ġa'] ['Ġlake'] text tokens [0, 58, 3118, 4473, 1545, 111, 58, 101, 8925, 28566, 709, 58, 314, 58, 1586, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7Input
- text
- A photo of a real-life Pikachu, Studio Lighting, High Detail, 4K, Title-Shift, Hyperrealism
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "A photo of a real-life Pikachu, Studio Lighting, High Detail, 4K, Title-Shift, Hyperrealism", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", { input: { text: "A photo of a real-life Pikachu, Studio Lighting, High Detail, 4K, Title-Shift, Hyperrealism", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", input={ "text": "A photo of a real-life Pikachu, Studio Lighting, High Detail, 4K, Title-Shift, Hyperrealism", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", "input": { "text": "A photo of a real-life Pikachu, Studio Lighting, High Detail, 4K, Title-Shift, Hyperrealism", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7 \ -i 'text="A photo of a real-life Pikachu, Studio Lighting, High Detail, 4K, Title-Shift, Hyperrealism"' \ -i 'grid_size="4"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor="6"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "A photo of a real-life Pikachu, Studio Lighting, High Detail, 4K, Title-Shift, Hyperrealism", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-06T19:07:51.000430Z", "created_at": "2022-07-06T19:07:07.608820Z", "data_removed": false, "error": null, "id": "tongk5frsfdm5pysqziq3kb5om", "input": { "text": "A photo of a real-life Pikachu, Studio Lighting, High Detail, 4K, Title-Shift, Hyperrealism", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġa']\n['Ġphoto']\n['Ġof']\n['Ġa']\n['Ġreal', '-', 'life']\n['Ġpikachu', ',']\n['Ġstudio']\n['Ġlighting', ',']\n['Ġhigh']\n['Ġdetail', ',']\n['Ġ4', 'k', ',']\n['Ġtitle', '-', 'shift', ',']\n['Ġhyper', 'real', 'ism']\ntext tokens [0, 58, 564, 111, 58, 639, 3, 3210, 20478, 11, 1984, 4352, 11, 524, 5854, 11, 252, 38, 11, 4440, 3, 30637, 11, 6139, 4646, 822, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 15.132197, "total_time": 43.39161 }, "output": [ "https://replicate.delivery/mgxm/d0d6334e-3016-497f-b4aa-9dc27a56ed66/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/d9fb385e-1a28-4e6f-8644-51b87bfad0e4/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/4a818a7a-d745-4728-aeba-92d2162ed74f/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/e6d9f46f-5540-4534-a6ea-cb147c11c987/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/8175efc1-e6e3-43ae-85a2-fb4c7013f2be/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/16858e56-28f5-44f7-b59e-3de72c67091a/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/6975584b-7117-40f9-8a17-ae867ad0c72c/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/fe1a8ad7-cd34-4c48-8446-a8f7c8c9c74d/min-dalle-iter-8.jpg" ], "started_at": "2022-07-06T19:07:35.868233Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tongk5frsfdm5pysqziq3kb5om", "cancel": "https://api.replicate.com/v1/predictions/tongk5frsfdm5pysqziq3kb5om/cancel" }, "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7" }
Generated intokenizing text ['Ġa'] ['Ġphoto'] ['Ġof'] ['Ġa'] ['Ġreal', '-', 'life'] ['Ġpikachu', ','] ['Ġstudio'] ['Ġlighting', ','] ['Ġhigh'] ['Ġdetail', ','] ['Ġ4', 'k', ','] ['Ġtitle', '-', 'shift', ','] ['Ġhyper', 'real', 'ism'] text tokens [0, 58, 564, 111, 58, 639, 3, 3210, 20478, 11, 1984, 4352, 11, 524, 5854, 11, 252, 38, 11, 4440, 3, 30637, 11, 6139, 4646, 822, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7IDgmk3rbxtnrhlreqbja7f35x76qStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- Photo realistic, 8K UHD,high resolution : (background = the mushroom kingdom in the style of futuristic 18th/19th/20th century concept art detailed realistic )
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- 4
{ "text": "Photo realistic, 8K UHD,high resolution : (background = the mushroom kingdom in the style of futuristic 18th/19th/20th century concept art detailed realistic )", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": 4 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", { input: { text: "Photo realistic, 8K UHD,high resolution : (background = the mushroom kingdom in the style of futuristic 18th/19th/20th century concept art detailed realistic )", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: 4 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", input={ "text": "Photo realistic, 8K UHD,high resolution : (background = the mushroom kingdom in the style of futuristic 18th/19th/20th century concept art detailed realistic )", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": 4 } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", "input": { "text": "Photo realistic, 8K UHD,high resolution : (background = the mushroom kingdom in the style of futuristic 18th/19th/20th century concept art detailed realistic )", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": 4 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7 \ -i 'text="Photo realistic, 8K UHD,high resolution : (background = the mushroom kingdom in the style of futuristic 18th/19th/20th century concept art detailed realistic )"' \ -i 'grid_size="4"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor=4'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "Photo realistic, 8K UHD,high resolution : (background = the mushroom kingdom in the style of futuristic 18th/19th/20th century concept art detailed realistic )", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": 4 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-06T19:26:53.732823Z", "created_at": "2022-07-06T19:25:53.408297Z", "data_removed": false, "error": null, "id": "gmk3rbxtnrhlreqbja7f35x76q", "input": { "text": "Photo realistic, 8K UHD,high resolution : (background = the mushroom kingdom in the style of futuristic 18th/19th/20th century concept art detailed realistic )", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": 4 }, "logs": "tokenizing text\n['Ġphoto']\n['Ġrealistic', ',']\n['Ġ8', 'k']\n['Ġuhd', ',', 'high']\n['Ġresolution']\n['Ġ', ':']\n['Ġ', '(', 'back', 'ground']\n['Ġ', '=']\n['Ġthe']\n['Ġmushroom']\n['Ġkingdom']\n['Ġin']\n['Ġthe']\n['Ġstyle']\n['Ġof']\n['Ġfuturistic']\n['Ġ18', 'th', '/', '19', 'th', '/', '20', 'th']\n['Ġcentury']\n['Ġconcept']\n['Ġart']\n['Ġdetailed']\n['Ġrealistic']\n['Ġ', ')']\ntext tokens [0, 564, 10573, 11, 416, 38, 20531, 11, 19374, 7790, 54, 3, 54, 3, 1735, 1116, 54, 3, 99, 6987, 3154, 91, 99, 1155, 111, 18196, 666, 184, 13, 1254, 184, 13, 601, 184, 2730, 3319, 241, 8461, 10573, 54, 3, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 15.127496, "total_time": 60.324526 }, "output": [ "https://replicate.delivery/mgxm/2ebb9b20-45b6-434e-bce5-53a3b8ba7b21/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/bd96ac65-1fa2-4887-9b73-606fe6d1507e/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/985be0fe-93fb-4bb0-87ca-d3afffd16581/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/53185b7f-745c-40d5-882e-612227010474/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/4ac3481c-2b09-49d9-93cc-97e14758e9a0/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/43e3b3b3-c49d-49f3-874a-0976f050663a/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/0e54869f-b050-44ad-825e-d18d1368ece8/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/447e90cb-48d1-41a7-8118-92ed1320bf55/min-dalle-iter-8.jpg" ], "started_at": "2022-07-06T19:26:38.605327Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/gmk3rbxtnrhlreqbja7f35x76q", "cancel": "https://api.replicate.com/v1/predictions/gmk3rbxtnrhlreqbja7f35x76q/cancel" }, "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7" }
Generated intokenizing text ['Ġphoto'] ['Ġrealistic', ','] ['Ġ8', 'k'] ['Ġuhd', ',', 'high'] ['Ġresolution'] ['Ġ', ':'] ['Ġ', '(', 'back', 'ground'] ['Ġ', '='] ['Ġthe'] ['Ġmushroom'] ['Ġkingdom'] ['Ġin'] ['Ġthe'] ['Ġstyle'] ['Ġof'] ['Ġfuturistic'] ['Ġ18', 'th', '/', '19', 'th', '/', '20', 'th'] ['Ġcentury'] ['Ġconcept'] ['Ġart'] ['Ġdetailed'] ['Ġrealistic'] ['Ġ', ')'] text tokens [0, 564, 10573, 11, 416, 38, 20531, 11, 19374, 7790, 54, 3, 54, 3, 1735, 1116, 54, 3, 99, 6987, 3154, 91, 99, 1155, 111, 18196, 666, 184, 13, 1254, 184, 13, 601, 184, 2730, 3319, 241, 8461, 10573, 54, 3, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7Input
- text
- a creepy possessed doll sitting on a wooden floor, 4K, hyperdetailed, horror, art by Beksiński and Tim Burton, dark art,
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "a creepy possessed doll sitting on a wooden floor, 4K, hyperdetailed, horror, art by Beksiński and Tim Burton, dark art, ", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", { input: { text: "a creepy possessed doll sitting on a wooden floor, 4K, hyperdetailed, horror, art by Beksiński and Tim Burton, dark art, ", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", input={ "text": "a creepy possessed doll sitting on a wooden floor, 4K, hyperdetailed, horror, art by Beksiński and Tim Burton, dark art, ", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", "input": { "text": "a creepy possessed doll sitting on a wooden floor, 4K, hyperdetailed, horror, art by Beksiński and Tim Burton, dark art, ", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7 \ -i 'text="a creepy possessed doll sitting on a wooden floor, 4K, hyperdetailed, horror, art by Beksiński and Tim Burton, dark art, "' \ -i 'grid_size="4"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor="6"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "a creepy possessed doll sitting on a wooden floor, 4K, hyperdetailed, horror, art by Beksiński and Tim Burton, dark art, ", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-06T20:18:19.532432Z", "created_at": "2022-07-06T20:17:42.100655Z", "data_removed": false, "error": null, "id": "f4rh33cwvnau5feywfxgzz5zzm", "input": { "text": "a creepy possessed doll sitting on a wooden floor, 4K, hyperdetailed, horror, art by Beksiński and Tim Burton, dark art, ", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġa']\n['Ġcreepy']\n['Ġposs', 'essed']\n['Ġdoll']\n['Ġsitting']\n['Ġon']\n['Ġa']\n['Ġwooden']\n['Ġfloor', ',']\n['Ġ4', 'k', ',']\n['Ġhyper', 'det', 'ailed', ',']\n['Ġhorror', ',']\n['Ġart']\n['Ġby']\n['Ġbe', 'ks', 'is', 'ki']\n['Ġand']\n['Ġtim']\n['Ġburton', ',']\n['Ġdark']\n['Ġart', ',']\ntext tokens [0, 58, 13705, 5046, 6466, 2225, 9782, 133, 58, 3180, 2433, 11, 252, 38, 11, 6139, 36665, 5311, 11, 5616, 11, 241, 185, 199, 366, 80, 1236, 128, 432, 14433, 11, 1892, 241, 11, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 15.980957, "total_time": 37.431777 }, "output": [ "https://replicate.delivery/mgxm/35195eed-ffa2-4cea-b1d2-e4b903b55abd/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/fd4c42c4-f83e-4c99-8876-7bf7539a3f74/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/2da8f16c-4b99-41c6-9b1d-e14c1506cbdd/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/1a922125-faf7-4af5-8835-9c8b3dac9f6e/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/870fd1ab-20c5-4e7e-9bda-228f0f0d8527/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/b4db083f-675c-445a-bb5a-26fec8626555/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/d5ecfd4a-5e8c-4623-8275-5dee894b9da6/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/60984708-d7da-4512-b3ee-39787e931ca6/min-dalle-iter-8.jpg" ], "started_at": "2022-07-06T20:18:03.551475Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/f4rh33cwvnau5feywfxgzz5zzm", "cancel": "https://api.replicate.com/v1/predictions/f4rh33cwvnau5feywfxgzz5zzm/cancel" }, "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7" }
Generated intokenizing text ['Ġa'] ['Ġcreepy'] ['Ġposs', 'essed'] ['Ġdoll'] ['Ġsitting'] ['Ġon'] ['Ġa'] ['Ġwooden'] ['Ġfloor', ','] ['Ġ4', 'k', ','] ['Ġhyper', 'det', 'ailed', ','] ['Ġhorror', ','] ['Ġart'] ['Ġby'] ['Ġbe', 'ks', 'is', 'ki'] ['Ġand'] ['Ġtim'] ['Ġburton', ','] ['Ġdark'] ['Ġart', ','] text tokens [0, 58, 13705, 5046, 6466, 2225, 9782, 133, 58, 3180, 2433, 11, 252, 38, 11, 6139, 36665, 5311, 11, 5616, 11, 241, 185, 199, 366, 80, 1236, 128, 432, 14433, 11, 1892, 241, 11, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7Input
- text
- a watercolour painting of a goose and a bunny having afternoon tea
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- 4
{ "text": "a watercolour painting of a goose and a bunny having afternoon tea", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": 4 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", { input: { text: "a watercolour painting of a goose and a bunny having afternoon tea", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: 4 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", input={ "text": "a watercolour painting of a goose and a bunny having afternoon tea", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": 4 } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", "input": { "text": "a watercolour painting of a goose and a bunny having afternoon tea", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": 4 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7 \ -i 'text="a watercolour painting of a goose and a bunny having afternoon tea"' \ -i 'grid_size="4"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor=4'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "a watercolour painting of a goose and a bunny having afternoon tea", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": 4 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-06T18:41:21.434053Z", "created_at": "2022-07-06T18:41:05.869054Z", "data_removed": false, "error": null, "id": "2g7mfi4ezfey7bmy3xc64ychpu", "input": { "text": "a watercolour painting of a goose and a bunny having afternoon tea", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": 4 }, "logs": "tokenizing text\n['Ġa']\n['Ġwatercolour']\n['Ġpainting']\n['Ġof']\n['Ġa']\n['Ġgoose']\n['Ġand']\n['Ġa']\n['Ġbunny']\n['Ġhaving']\n['Ġafternoon']\n['Ġtea']\ntext tokens [0, 58, 18581, 1545, 111, 58, 14215, 128, 58, 8684, 8345, 12598, 2705, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 15.38574, "total_time": 15.564999 }, "output": [ "https://replicate.delivery/mgxm/edd5c2f5-7e0f-41d1-bf1f-b92a4212bcfc/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/747836bd-e773-4ef5-a903-bd1aa4938639/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/ca863d2a-f4ec-4516-9777-d021f2403f56/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/7034c1a9-ccf1-4d12-bbf3-eb2d54924e1a/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/879e3e96-11b2-4d7f-8883-4ba80cfccba5/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/fd391f90-87a5-48cd-9a67-2c0d56dec89d/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/7adade5c-ba14-4460-801c-af35df62871f/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/07ce46f4-f554-47b3-bda8-47326fedf8c6/min-dalle-iter-8.jpg" ], "started_at": "2022-07-06T18:41:06.048313Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2g7mfi4ezfey7bmy3xc64ychpu", "cancel": "https://api.replicate.com/v1/predictions/2g7mfi4ezfey7bmy3xc64ychpu/cancel" }, "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7" }
Generated intokenizing text ['Ġa'] ['Ġwatercolour'] ['Ġpainting'] ['Ġof'] ['Ġa'] ['Ġgoose'] ['Ġand'] ['Ġa'] ['Ġbunny'] ['Ġhaving'] ['Ġafternoon'] ['Ġtea'] text tokens [0, 58, 18581, 1545, 111, 58, 14215, 128, 58, 8684, 8345, 12598, 2705, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:888c72d60932bca21344efcfdaecb3f0fbeb4bf40ee9ec601cc8fda806b5bfd9Input
- text
- Moai statue giving a TED talk
- top_k
- "128"
- grid_size
- "5"
- temperature
- "1"
- progressive_outputs
- supercondition_factor
- "16"
{ "text": "Moai statue giving a TED talk", "top_k": "128", "grid_size": "5", "temperature": "1", "progressive_outputs": true, "supercondition_factor": "16" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:888c72d60932bca21344efcfdaecb3f0fbeb4bf40ee9ec601cc8fda806b5bfd9", { input: { text: "Moai statue giving a TED talk", top_k: "128", grid_size: "5", temperature: "1", progressive_outputs: true, supercondition_factor: "16" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:888c72d60932bca21344efcfdaecb3f0fbeb4bf40ee9ec601cc8fda806b5bfd9", input={ "text": "Moai statue giving a TED talk", "top_k": "128", "grid_size": "5", "temperature": "1", "progressive_outputs": True, "supercondition_factor": "16" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "888c72d60932bca21344efcfdaecb3f0fbeb4bf40ee9ec601cc8fda806b5bfd9", "input": { "text": "Moai statue giving a TED talk", "top_k": "128", "grid_size": "5", "temperature": "1", "progressive_outputs": true, "supercondition_factor": "16" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:888c72d60932bca21344efcfdaecb3f0fbeb4bf40ee9ec601cc8fda806b5bfd9 \ -i 'text="Moai statue giving a TED talk"' \ -i 'top_k="128"' \ -i 'grid_size="5"' \ -i 'temperature="1"' \ -i 'progressive_outputs=true' \ -i 'supercondition_factor="16"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:888c72d60932bca21344efcfdaecb3f0fbeb4bf40ee9ec601cc8fda806b5bfd9
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "Moai statue giving a TED talk", "top_k": "128", "grid_size": "5", "temperature": "1", "progressive_outputs": true, "supercondition_factor": "16" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-25T00:37:21.840787Z", "created_at": "2022-07-25T00:37:05.490728Z", "data_removed": false, "error": null, "id": "drkg55flvjet3ooohc524kvfg4", "input": { "text": "Moai statue giving a TED talk", "top_k": "128", "grid_size": "5", "temperature": "1", "progressive_outputs": true, "supercondition_factor": "16" }, "logs": "tokenizing text\n['Ġmo', 'ai']\n['Ġstatue']\n['Ġgiving']\n['Ġa']\n['Ġted']\n['Ġtalk']\n9 text tokens [0, 924, 336, 4039, 8658, 58, 5678, 2727, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 16.147947, "total_time": 16.350059 }, "output": [ "https://replicate.delivery/mgxm/7d78edb7-c91a-4ccc-9734-125953840fac/moai-statue-giving-a-ted-talk-iter-1.jpg", "https://replicate.delivery/mgxm/8644d51a-c196-4896-89f0-9b8f04779e2f/moai-statue-giving-a-ted-talk-iter-2.jpg", "https://replicate.delivery/mgxm/9e1c9724-d8b5-429a-aaad-90288d1ac4ab/moai-statue-giving-a-ted-talk-iter-3.jpg", "https://replicate.delivery/mgxm/3ae242e9-5d5e-4784-bfc7-79c0051d902f/moai-statue-giving-a-ted-talk-iter-4.jpg", "https://replicate.delivery/mgxm/e48cd201-4260-40bb-8632-21da067246b8/moai-statue-giving-a-ted-talk-iter-5.jpg", "https://replicate.delivery/mgxm/0eb4d55e-e427-4db4-92e1-682f07563076/moai-statue-giving-a-ted-talk-iter-6.jpg", "https://replicate.delivery/mgxm/04bc6fb5-c1c4-4f3f-9ee9-f140a9f62412/moai-statue-giving-a-ted-talk-iter-7.jpg", "https://replicate.delivery/mgxm/b5ecd545-efca-46ae-8496-786ad16be61b/moai-statue-giving-a-ted-talk.jpg" ], "started_at": "2022-07-25T00:37:05.692840Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/drkg55flvjet3ooohc524kvfg4", "cancel": "https://api.replicate.com/v1/predictions/drkg55flvjet3ooohc524kvfg4/cancel" }, "version": "888c72d60932bca21344efcfdaecb3f0fbeb4bf40ee9ec601cc8fda806b5bfd9" }
Generated intokenizing text ['Ġmo', 'ai'] ['Ġstatue'] ['Ġgiving'] ['Ġa'] ['Ġted'] ['Ġtalk'] 9 text tokens [0, 924, 336, 4039, 8658, 58, 5678, 2727, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7Input
- text
- A dragon chess piece
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "A dragon chess piece", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", { input: { text: "A dragon chess piece", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", input={ "text": "A dragon chess piece", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", "input": { "text": "A dragon chess piece", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7 \ -i 'text="A dragon chess piece"' \ -i 'grid_size="4"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor="6"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "A dragon chess piece", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-06T19:33:02.496659Z", "created_at": "2022-07-06T19:32:47.460826Z", "data_removed": false, "error": null, "id": "3pfavqx7bncpvil55itfnxpv2a", "input": { "text": "A dragon chess piece", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġa']\n['Ġdragon']\n['Ġchess']\n['Ġpiece']\ntext tokens [0, 58, 2404, 7470, 2695, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 14.85798, "total_time": 15.035833 }, "output": [ "https://replicate.delivery/mgxm/41cf6a68-a205-40ab-b3b2-2413a1d71fa9/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/9cf57852-1db8-4636-968e-e2c66f5c8e1a/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/501f1309-9e4b-4033-8dbb-70d9cb4658b1/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/ebb5ab4d-b208-4bec-9a0b-c7899362a0b9/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/e9669f8b-adda-4141-9c45-47cac1b0f32a/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/a97e728c-e54a-4724-a86b-32e06439b539/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/9d27470a-063b-4caf-91a2-1a512c12d42b/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/4b3958ad-aaf0-4ccd-abe0-1713bd48535e/min-dalle-iter-8.jpg" ], "started_at": "2022-07-06T19:32:47.638679Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3pfavqx7bncpvil55itfnxpv2a", "cancel": "https://api.replicate.com/v1/predictions/3pfavqx7bncpvil55itfnxpv2a/cancel" }, "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7" }
Generated intokenizing text ['Ġa'] ['Ġdragon'] ['Ġchess'] ['Ġpiece'] text tokens [0, 58, 2404, 7470, 2695, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7Input
- text
- An air car with the aesthetics of a vintage airstream trailer, flying through a cyberpunk city. Hyperrealism, photorealism, tilt-shift, high detail, 4K
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "An air car with the aesthetics of a vintage airstream trailer, flying through a cyberpunk city. Hyperrealism, photorealism, tilt-shift, high detail, 4K", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", { input: { text: "An air car with the aesthetics of a vintage airstream trailer, flying through a cyberpunk city. Hyperrealism, photorealism, tilt-shift, high detail, 4K", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", input={ "text": "An air car with the aesthetics of a vintage airstream trailer, flying through a cyberpunk city. Hyperrealism, photorealism, tilt-shift, high detail, 4K", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", "input": { "text": "An air car with the aesthetics of a vintage airstream trailer, flying through a cyberpunk city. Hyperrealism, photorealism, tilt-shift, high detail, 4K", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7 \ -i 'text="An air car with the aesthetics of a vintage airstream trailer, flying through a cyberpunk city. Hyperrealism, photorealism, tilt-shift, high detail, 4K"' \ -i 'grid_size="4"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor="6"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "An air car with the aesthetics of a vintage airstream trailer, flying through a cyberpunk city. Hyperrealism, photorealism, tilt-shift, high detail, 4K", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-06T20:08:52.312596Z", "created_at": "2022-07-06T20:08:36.319572Z", "data_removed": false, "error": null, "id": "hww6r2wgjneqpbvjucccyu4mfu", "input": { "text": "An air car with the aesthetics of a vintage airstream trailer, flying through a cyberpunk city. Hyperrealism, photorealism, tilt-shift, high detail, 4K", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġan']\n['Ġair']\n['Ġcar']\n['Ġwith']\n['Ġthe']\n['Ġaesthetics']\n['Ġof']\n['Ġa']\n['Ġvintage']\n['Ġairstream']\n['Ġtrailer', ',']\n['Ġflying']\n['Ġthrough']\n['Ġa']\n['Ġcyberpunk']\n['Ġcity', '.']\n['Ġhyper', 'real', 'ism', ',']\n['Ġphot', 'oreal', 'ism', ',']\n['Ġtilt', '-', 'shift', ',']\n['Ġhigh']\n['Ġdetail', ',']\n['Ġ4', 'k']\ntext tokens [0, 101, 595, 262, 208, 99, 18041, 111, 58, 996, 43839, 3237, 11, 5052, 2135, 58, 18850, 645, 12, 6139, 4646, 822, 11, 234, 35495, 822, 11, 18332, 3, 30637, 11, 524, 5854, 11, 252, 38, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 15.840202, "total_time": 15.993024 }, "output": [ "https://replicate.delivery/mgxm/7cfbd0a7-44d2-4da0-a775-7b3b173ead5e/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/08406686-1b1d-4cbb-86e2-8032c4a57d36/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/2125ac7f-54ee-479f-8b3e-b603e833aa90/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/5f1aa89e-e2c5-4245-bb7d-e230913e5ea6/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/17cad1e4-4cff-4044-87eb-e9e4c5996dd4/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/bf94e181-9977-41db-bdd3-b310c7d1aa1a/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/1b6ad0fd-ee17-44b3-83d8-c3a76948455e/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/676159a4-3f2a-4a87-827a-1ec4de2fd955/min-dalle-iter-8.jpg" ], "started_at": "2022-07-06T20:08:36.472394Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hww6r2wgjneqpbvjucccyu4mfu", "cancel": "https://api.replicate.com/v1/predictions/hww6r2wgjneqpbvjucccyu4mfu/cancel" }, "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7" }
Generated intokenizing text ['Ġan'] ['Ġair'] ['Ġcar'] ['Ġwith'] ['Ġthe'] ['Ġaesthetics'] ['Ġof'] ['Ġa'] ['Ġvintage'] ['Ġairstream'] ['Ġtrailer', ','] ['Ġflying'] ['Ġthrough'] ['Ġa'] ['Ġcyberpunk'] ['Ġcity', '.'] ['Ġhyper', 'real', 'ism', ','] ['Ġphot', 'oreal', 'ism', ','] ['Ġtilt', '-', 'shift', ','] ['Ġhigh'] ['Ġdetail', ','] ['Ġ4', 'k'] text tokens [0, 101, 595, 262, 208, 99, 18041, 111, 58, 996, 43839, 3237, 11, 5052, 2135, 58, 18850, 645, 12, 6139, 4646, 822, 11, 234, 35495, 822, 11, 18332, 3, 30637, 11, 524, 5854, 11, 252, 38, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7ID7ud7xp6rqfab3nt6fzuzf4ls6qStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- glass sphere with a city refraction
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- 4
{ "text": "glass sphere with a city refraction", "grid_size": "4", "intermediate_outputs": false, "log2_supercondition_factor": 4 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", { input: { text: "glass sphere with a city refraction", grid_size: "4", intermediate_outputs: false, log2_supercondition_factor: 4 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", input={ "text": "glass sphere with a city refraction", "grid_size": "4", "intermediate_outputs": False, "log2_supercondition_factor": 4 } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", "input": { "text": "glass sphere with a city refraction", "grid_size": "4", "intermediate_outputs": false, "log2_supercondition_factor": 4 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7 \ -i 'text="glass sphere with a city refraction"' \ -i 'grid_size="4"' \ -i 'intermediate_outputs=false' \ -i 'log2_supercondition_factor=4'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "glass sphere with a city refraction", "grid_size": "4", "intermediate_outputs": false, "log2_supercondition_factor": 4 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-06T20:26:17.014821Z", "created_at": "2022-07-06T20:26:02.859074Z", "data_removed": false, "error": null, "id": "7ud7xp6rqfab3nt6fzuzf4ls6q", "input": { "text": "glass sphere with a city refraction", "grid_size": "4", "intermediate_outputs": false, "log2_supercondition_factor": 4 }, "logs": "tokenizing text\n['Ġglass']\n['Ġsphere']\n['Ġwith']\n['Ġa']\n['Ġcity']\n['Ġrefr', 'action']\ntext tokens [0, 1431, 17769, 208, 58, 645, 5639, 4159, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\nsampling row 3 of 16\nsampling row 4 of 16\nsampling row 5 of 16\nsampling row 6 of 16\nsampling row 7 of 16\nsampling row 8 of 16\nsampling row 9 of 16\nsampling row 10 of 16\nsampling row 11 of 16\nsampling row 12 of 16\nsampling row 13 of 16\nsampling row 14 of 16\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 13.949653, "total_time": 14.155747 }, "output": [ "https://replicate.delivery/mgxm/80b76f3b-5217-4afa-af0a-9e528a6ffae9/min-dalle-iter-1.jpg" ], "started_at": "2022-07-06T20:26:03.065168Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7ud7xp6rqfab3nt6fzuzf4ls6q", "cancel": "https://api.replicate.com/v1/predictions/7ud7xp6rqfab3nt6fzuzf4ls6q/cancel" }, "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7" }
Generated intokenizing text ['Ġglass'] ['Ġsphere'] ['Ġwith'] ['Ġa'] ['Ġcity'] ['Ġrefr', 'action'] text tokens [0, 1431, 17769, 208, 58, 645, 5639, 4159, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 sampling row 3 of 16 sampling row 4 of 16 sampling row 5 of 16 sampling row 6 of 16 sampling row 7 of 16 sampling row 8 of 16 sampling row 9 of 16 sampling row 10 of 16 sampling row 11 of 16 sampling row 12 of 16 sampling row 13 of 16 sampling row 14 of 16 sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7Input
- text
- Yoda splitting the ocean while standing on the shore facing the sea, digital art, trending on artstation, 4k, sci-fi art, dramatic, epic
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "Yoda splitting the ocean while standing on the shore facing the sea, digital art, trending on artstation, 4k, sci-fi art, dramatic, epic", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", { input: { text: "Yoda splitting the ocean while standing on the shore facing the sea, digital art, trending on artstation, 4k, sci-fi art, dramatic, epic", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", input={ "text": "Yoda splitting the ocean while standing on the shore facing the sea, digital art, trending on artstation, 4k, sci-fi art, dramatic, epic", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", "input": { "text": "Yoda splitting the ocean while standing on the shore facing the sea, digital art, trending on artstation, 4k, sci-fi art, dramatic, epic", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7 \ -i 'text="Yoda splitting the ocean while standing on the shore facing the sea, digital art, trending on artstation, 4k, sci-fi art, dramatic, epic"' \ -i 'grid_size="4"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor="6"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "Yoda splitting the ocean while standing on the shore facing the sea, digital art, trending on artstation, 4k, sci-fi art, dramatic, epic", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-06T20:59:14.047745Z", "created_at": "2022-07-06T20:58:59.103332Z", "data_removed": false, "error": null, "id": "uuy6ii5rbjddph5x23nxc4csrq", "input": { "text": "Yoda splitting the ocean while standing on the shore facing the sea, digital art, trending on artstation, 4k, sci-fi art, dramatic, epic", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġyoda']\n['Ġsplitting']\n['Ġthe']\n['Ġocean']\n['Ġwhile']\n['Ġstanding']\n['Ġon']\n['Ġthe']\n['Ġshore']\n['Ġfacing']\n['Ġthe']\n['Ġsea', ',']\n['Ġdigital']\n['Ġart', ',']\n['Ġtrending']\n['Ġon']\n['Ġartstation', ',']\n['Ġ4', 'k', ',']\n['Ġsci', '-', 'fi']\n['Ġart', ',']\n['Ġdramatic', ',']\n['Ġepic']\ntext tokens [0, 24509, 43992, 99, 3462, 4605, 7329, 133, 99, 7349, 11305, 99, 2074, 11, 1189, 241, 11, 10119, 133, 4640, 11, 252, 38, 11, 4066, 3, 5555, 241, 11, 14836, 11, 5572, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 14.769913, "total_time": 14.944413 }, "output": [ "https://replicate.delivery/mgxm/0a0bd4a1-acef-466d-8be3-256edd9cbbd1/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/280cea02-96dd-478a-93cf-5d574a3b5de4/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/57c448d6-122f-43b3-87d2-7e3a1597a8e6/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/a74f57a8-46d2-4860-b3a0-42855b2e3543/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/af1f7095-8ecf-4078-8174-04c5ab1611be/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/92d3f9da-efa1-46ff-bcea-f3b9ab611e51/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/6e8eb4ad-3316-4656-8378-ac340a8bed57/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/f25cc061-feab-4604-be8c-a792c7a6c48d/min-dalle-iter-8.jpg" ], "started_at": "2022-07-06T20:58:59.277832Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/uuy6ii5rbjddph5x23nxc4csrq", "cancel": "https://api.replicate.com/v1/predictions/uuy6ii5rbjddph5x23nxc4csrq/cancel" }, "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7" }
Generated intokenizing text ['Ġyoda'] ['Ġsplitting'] ['Ġthe'] ['Ġocean'] ['Ġwhile'] ['Ġstanding'] ['Ġon'] ['Ġthe'] ['Ġshore'] ['Ġfacing'] ['Ġthe'] ['Ġsea', ','] ['Ġdigital'] ['Ġart', ','] ['Ġtrending'] ['Ġon'] ['Ġartstation', ','] ['Ġ4', 'k', ','] ['Ġsci', '-', 'fi'] ['Ġart', ','] ['Ġdramatic', ','] ['Ġepic'] text tokens [0, 24509, 43992, 99, 3462, 4605, 7329, 133, 99, 7349, 11305, 99, 2074, 11, 1189, 241, 11, 10119, 133, 4640, 11, 252, 38, 11, 4066, 3, 5555, 241, 11, 14836, 11, 5572, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7IDjkz67i6bcvdrdb4by7xlfjyvcuStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- A confused panda sitting in a calculus class, cinematic image
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- 4
{ "text": "A confused panda sitting in a calculus class, cinematic image", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": 4 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", { input: { text: "A confused panda sitting in a calculus class, cinematic image", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: 4 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", input={ "text": "A confused panda sitting in a calculus class, cinematic image", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": 4 } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", "input": { "text": "A confused panda sitting in a calculus class, cinematic image", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": 4 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7 \ -i 'text="A confused panda sitting in a calculus class, cinematic image"' \ -i 'grid_size="4"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor=4'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "A confused panda sitting in a calculus class, cinematic image", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": 4 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-07T02:06:56.028399Z", "created_at": "2022-07-07T02:06:40.018693Z", "data_removed": false, "error": null, "id": "jkz67i6bcvdrdb4by7xlfjyvcu", "input": { "text": "A confused panda sitting in a calculus class, cinematic image", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": 4 }, "logs": "tokenizing text\n['Ġa']\n['Ġconfused']\n['Ġpanda']\n['Ġsitting']\n['Ġin']\n['Ġa']\n['Ġcalculus']\n['Ġclass', ',']\n['Ġcinematic']\n['Ġimage']\ntext tokens [0, 58, 22229, 8418, 9782, 91, 58, 27773, 687, 11, 19936, 867, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 14.8959, "total_time": 16.009706 }, "output": [ "https://replicate.delivery/mgxm/0fdaf646-c6dc-4c60-a2ae-6d2b0876da1c/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/0a8592b4-b598-46b9-9e7a-9393e22b7039/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/4b884780-262d-4b56-aaa4-76c26934d8f4/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/fc85eeaf-59ef-4650-963a-208159561c0e/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/51af9d33-6917-469e-917e-3ffa09f63723/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/ea02e5c2-da65-49a7-9fd0-69566fb26638/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/875017eb-5f18-4a09-91e6-e3f076f683bd/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/b5742b8d-e176-40f0-b605-8deb01545379/min-dalle-iter-8.jpg" ], "started_at": "2022-07-07T02:06:41.132499Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jkz67i6bcvdrdb4by7xlfjyvcu", "cancel": "https://api.replicate.com/v1/predictions/jkz67i6bcvdrdb4by7xlfjyvcu/cancel" }, "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7" }
Generated intokenizing text ['Ġa'] ['Ġconfused'] ['Ġpanda'] ['Ġsitting'] ['Ġin'] ['Ġa'] ['Ġcalculus'] ['Ġclass', ','] ['Ġcinematic'] ['Ġimage'] text tokens [0, 58, 22229, 8418, 9782, 91, 58, 27773, 687, 11, 19936, 867, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7IDu57grla73je55ntn4lulxphsr4StatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- A scientist monkey plays with chemicals in a science lab, award winning illustration by Lynn Chen, realistic
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- "5"
{ "text": "A scientist monkey plays with chemicals in a science lab, award winning illustration by Lynn Chen, realistic", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "5" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", { input: { text: "A scientist monkey plays with chemicals in a science lab, award winning illustration by Lynn Chen, realistic", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: "5" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", input={ "text": "A scientist monkey plays with chemicals in a science lab, award winning illustration by Lynn Chen, realistic", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": "5" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", "input": { "text": "A scientist monkey plays with chemicals in a science lab, award winning illustration by Lynn Chen, realistic", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "5" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7 \ -i 'text="A scientist monkey plays with chemicals in a science lab, award winning illustration by Lynn Chen, realistic"' \ -i 'grid_size="4"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor="5"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "A scientist monkey plays with chemicals in a science lab, award winning illustration by Lynn Chen, realistic", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "5" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-07T02:27:12.745181Z", "created_at": "2022-07-07T02:26:57.461973Z", "data_removed": false, "error": null, "id": "u57grla73je55ntn4lulxphsr4", "input": { "text": "A scientist monkey plays with chemicals in a science lab, award winning illustration by Lynn Chen, realistic", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "5" }, "logs": "tokenizing text\n['Ġa']\n['Ġscientist']\n['Ġmonkey']\n['Ġplays']\n['Ġwith']\n['Ġchemicals']\n['Ġin']\n['Ġa']\n['Ġscience']\n['Ġlab', ',']\n['Ġaward']\n['Ġwinning']\n['Ġillustration']\n['Ġby']\n['Ġlynn']\n['Ġchen', ',']\n['Ġrealistic']\ntext tokens [0, 58, 13911, 6984, 7019, 208, 18039, 91, 58, 1612, 1344, 11, 3457, 6534, 2262, 185, 11866, 8785, 11, 10573, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 15.112027, "total_time": 15.283208 }, "output": [ "https://replicate.delivery/mgxm/9c9c7cba-84b3-4658-bba9-7cbc7fd77f98/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/433f4404-8029-4482-a952-8677b0de284f/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/83f8271a-bc44-4c65-950f-2a4870fc8067/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/9cbe7504-be3e-48da-9f79-49e921f2ddb7/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/ab413086-1e54-4a9d-b3e3-51745e295ae4/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/f30f98bb-8a46-407f-9bcf-3a7a3c85ed8d/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/3fceb6cb-cf7e-4711-856d-08b7daf7a297/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/1bf7c459-46bd-49b7-9c66-110c24162274/min-dalle-iter-8.jpg" ], "started_at": "2022-07-07T02:26:57.633154Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/u57grla73je55ntn4lulxphsr4", "cancel": "https://api.replicate.com/v1/predictions/u57grla73je55ntn4lulxphsr4/cancel" }, "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7" }
Generated intokenizing text ['Ġa'] ['Ġscientist'] ['Ġmonkey'] ['Ġplays'] ['Ġwith'] ['Ġchemicals'] ['Ġin'] ['Ġa'] ['Ġscience'] ['Ġlab', ','] ['Ġaward'] ['Ġwinning'] ['Ġillustration'] ['Ġby'] ['Ġlynn'] ['Ġchen', ','] ['Ġrealistic'] text tokens [0, 58, 13911, 6984, 7019, 208, 18039, 91, 58, 1612, 1344, 11, 3457, 6534, 2262, 185, 11866, 8785, 11, 10573, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7Input
- text
- an astronaut walking on a desert planet, digital art
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "an astronaut walking on a desert planet, digital art", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", { input: { text: "an astronaut walking on a desert planet, digital art", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", input={ "text": "an astronaut walking on a desert planet, digital art", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", "input": { "text": "an astronaut walking on a desert planet, digital art", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7 \ -i 'text="an astronaut walking on a desert planet, digital art"' \ -i 'grid_size="4"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor="6"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "an astronaut walking on a desert planet, digital art", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-07T07:10:43.961226Z", "created_at": "2022-07-07T07:10:24.259796Z", "data_removed": false, "error": null, "id": "2ssspbciivddpkniq3xhmjdd24", "input": { "text": "an astronaut walking on a desert planet, digital art", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġan']\n['Ġastronaut']\n['Ġwalking']\n['Ġon']\n['Ġa']\n['Ġdesert']\n['Ġplanet', ',']\n['Ġdigital']\n['Ġart']\ntext tokens [0, 101, 14282, 4462, 133, 58, 3806, 3493, 11, 1189, 241, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 15.03035, "total_time": 19.70143 }, "output": [ "https://replicate.delivery/mgxm/7b5c66eb-06af-40db-bae2-1d4d609bd9fb/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/0755f1fc-aba8-467e-a839-0829e91e45e0/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/acc4f71b-06e0-42b5-a81a-39746757d7c7/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/e55be018-8b28-4dcc-97e4-414ae1e425e0/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/8e3312a2-3e03-4f4d-881b-96f3b8bf915a/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/a74db96a-b038-418a-b8e4-9fb222b5a137/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/9baf9637-b9ac-4e2d-8937-04785d80fe33/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/8a459d35-747d-4ed6-9800-b885322b457d/min-dalle-iter-8.jpg" ], "started_at": "2022-07-07T07:10:28.930876Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2ssspbciivddpkniq3xhmjdd24", "cancel": "https://api.replicate.com/v1/predictions/2ssspbciivddpkniq3xhmjdd24/cancel" }, "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7" }
Generated intokenizing text ['Ġan'] ['Ġastronaut'] ['Ġwalking'] ['Ġon'] ['Ġa'] ['Ġdesert'] ['Ġplanet', ','] ['Ġdigital'] ['Ġart'] text tokens [0, 101, 14282, 4462, 133, 58, 3806, 3493, 11, 1189, 241, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7Input
- text
- A ghostly silhouette of a Lovecraftian God peers down onto the Chicago city skyline, view from lake Michigan, dark and menacing clouds, ominous lighting, hyperrealism, high resolution, digital art
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "A ghostly silhouette of a Lovecraftian God peers down onto the Chicago city skyline, view from lake Michigan, dark and menacing clouds, ominous lighting, hyperrealism, high resolution, digital art", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", { input: { text: "A ghostly silhouette of a Lovecraftian God peers down onto the Chicago city skyline, view from lake Michigan, dark and menacing clouds, ominous lighting, hyperrealism, high resolution, digital art", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", input={ "text": "A ghostly silhouette of a Lovecraftian God peers down onto the Chicago city skyline, view from lake Michigan, dark and menacing clouds, ominous lighting, hyperrealism, high resolution, digital art", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", "input": { "text": "A ghostly silhouette of a Lovecraftian God peers down onto the Chicago city skyline, view from lake Michigan, dark and menacing clouds, ominous lighting, hyperrealism, high resolution, digital art", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7 \ -i 'text="A ghostly silhouette of a Lovecraftian God peers down onto the Chicago city skyline, view from lake Michigan, dark and menacing clouds, ominous lighting, hyperrealism, high resolution, digital art"' \ -i 'grid_size="4"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor="6"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "A ghostly silhouette of a Lovecraftian God peers down onto the Chicago city skyline, view from lake Michigan, dark and menacing clouds, ominous lighting, hyperrealism, high resolution, digital art", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-07T09:15:03.520878Z", "created_at": "2022-07-07T09:14:43.459474Z", "data_removed": false, "error": null, "id": "gbm2sxbq2zaprk3actdaf5kskq", "input": { "text": "A ghostly silhouette of a Lovecraftian God peers down onto the Chicago city skyline, view from lake Michigan, dark and menacing clouds, ominous lighting, hyperrealism, high resolution, digital art", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġa']\n['Ġghost', 'ly']\n['Ġsilhouette']\n['Ġof']\n['Ġa']\n['Ġlovecraft', 'ian']\n['Ġgod']\n['Ġpeers']\n['Ġdown']\n['Ġonto']\n['Ġthe']\n['Ġchicago']\n['Ġcity']\n['Ġskyline', ',']\n['Ġview']\n['Ġfrom']\n['Ġlake']\n['Ġmichigan', ',']\n['Ġdark']\n['Ġand']\n['Ġmen', 'acing']\n['Ġclouds', ',']\n['Ġom', 'inous']\n['Ġlighting', ',']\n['Ġhyper', 'real', 'ism', ',']\n['Ġhigh']\n['Ġresolution', ',']\n['Ġdigital']\n['Ġart']\ntext tokens [0, 58, 4896, 304, 5983, 111, 58, 33532, 217, 1646, 46264, 360, 20835, 99, 2377, 645, 9542, 11, 1328, 314, 1586, 4594, 11, 1892, 128, 554, 13231, 10201, 11, 2197, 32687, 4352, 11, 6139, 4646, 822, 11, 524, 7790, 11, 1189, 241, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 15.007779, "total_time": 20.061404 }, "output": [ "https://replicate.delivery/mgxm/fee316d3-281c-46f6-8c9c-6970aae35929/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/d7b2333e-5270-4fdc-98b9-b626f9144c5d/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/673a6735-cf65-41cc-80bf-6b24fd606e3a/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/95594e9a-c4c7-4c5a-8e46-c9866b61c91f/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/c82e93fd-7002-49b0-a35d-bef9204a7446/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/9f42fe89-6df2-4bfe-a914-7cf26bb888d8/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/e76a82ad-8f3a-4eb1-abd8-0b472948f8cd/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/d59ea561-ece4-4930-b82a-f63f2384383f/min-dalle-iter-8.jpg" ], "started_at": "2022-07-07T09:14:48.513099Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/gbm2sxbq2zaprk3actdaf5kskq", "cancel": "https://api.replicate.com/v1/predictions/gbm2sxbq2zaprk3actdaf5kskq/cancel" }, "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7" }
Generated intokenizing text ['Ġa'] ['Ġghost', 'ly'] ['Ġsilhouette'] ['Ġof'] ['Ġa'] ['Ġlovecraft', 'ian'] ['Ġgod'] ['Ġpeers'] ['Ġdown'] ['Ġonto'] ['Ġthe'] ['Ġchicago'] ['Ġcity'] ['Ġskyline', ','] ['Ġview'] ['Ġfrom'] ['Ġlake'] ['Ġmichigan', ','] ['Ġdark'] ['Ġand'] ['Ġmen', 'acing'] ['Ġclouds', ','] ['Ġom', 'inous'] ['Ġlighting', ','] ['Ġhyper', 'real', 'ism', ','] ['Ġhigh'] ['Ġresolution', ','] ['Ġdigital'] ['Ġart'] text tokens [0, 58, 4896, 304, 5983, 111, 58, 33532, 217, 1646, 46264, 360, 20835, 99, 2377, 645, 9542, 11, 1328, 314, 1586, 4594, 11, 1892, 128, 554, 13231, 10201, 11, 2197, 32687, 4352, 11, 6139, 4646, 822, 11, 524, 7790, 11, 1189, 241, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7Input
- text
- singularity, Hyperrealism
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "singularity, Hyperrealism", "grid_size": "4", "intermediate_outputs": false, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", { input: { text: "singularity, Hyperrealism", grid_size: "4", intermediate_outputs: false, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", input={ "text": "singularity, Hyperrealism", "grid_size": "4", "intermediate_outputs": False, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", "input": { "text": "singularity, Hyperrealism", "grid_size": "4", "intermediate_outputs": false, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7 \ -i 'text="singularity, Hyperrealism"' \ -i 'grid_size="4"' \ -i 'intermediate_outputs=false' \ -i 'log2_supercondition_factor="6"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "singularity, Hyperrealism", "grid_size": "4", "intermediate_outputs": false, "log2_supercondition_factor": "6" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-07T09:18:40.186995Z", "created_at": "2022-07-07T09:18:01.389984Z", "data_removed": false, "error": null, "id": "5ut2pxupnzdvrjdwqvggomhqqe", "input": { "text": "singularity, Hyperrealism", "grid_size": "4", "intermediate_outputs": false, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġsingular', 'ity', ',']\n['Ġhyper', 'real', 'ism']\ntext tokens [0, 30129, 223, 11, 6139, 4646, 822, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\nsampling row 3 of 16\nsampling row 4 of 16\nsampling row 5 of 16\nsampling row 6 of 16\nsampling row 7 of 16\nsampling row 8 of 16\nsampling row 9 of 16\nsampling row 10 of 16\nsampling row 11 of 16\nsampling row 12 of 16\nsampling row 13 of 16\nsampling row 14 of 16\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 14.746215, "total_time": 38.797011 }, "output": [ "https://replicate.delivery/mgxm/e5b3ea5c-255c-44f5-b2c0-135c61e3310e/min-dalle-iter-1.jpg" ], "started_at": "2022-07-07T09:18:25.440780Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5ut2pxupnzdvrjdwqvggomhqqe", "cancel": "https://api.replicate.com/v1/predictions/5ut2pxupnzdvrjdwqvggomhqqe/cancel" }, "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7" }
Generated intokenizing text ['Ġsingular', 'ity', ','] ['Ġhyper', 'real', 'ism'] text tokens [0, 30129, 223, 11, 6139, 4646, 822, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 sampling row 3 of 16 sampling row 4 of 16 sampling row 5 of 16 sampling row 6 of 16 sampling row 7 of 16 sampling row 8 of 16 sampling row 9 of 16 sampling row 10 of 16 sampling row 11 of 16 sampling row 12 of 16 sampling row 13 of 16 sampling row 14 of 16 sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7ID6o4wiamp7vgwjiu3k23i6m2gpeStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- .dalle i can't believe that this image is so realistic, award winning
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- "5"
{ "text": ".dalle i can't believe that this image is so realistic, award winning", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "5" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", { input: { text: ".dalle i can't believe that this image is so realistic, award winning", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: "5" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", input={ "text": ".dalle i can't believe that this image is so realistic, award winning", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": "5" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", "input": { "text": ".dalle i can\'t believe that this image is so realistic, award winning", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "5" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7 \ -i $'text=".dalle i can\'t believe that this image is so realistic, award winning"' \ -i 'grid_size="4"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor="5"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": ".dalle i can\'t believe that this image is so realistic, award winning", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "5" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-07T12:02:23.362003Z", "created_at": "2022-07-07T12:00:38.947076Z", "data_removed": false, "error": null, "id": "6o4wiamp7vgwjiu3k23i6m2gpe", "input": { "text": ".dalle i can't believe that this image is so realistic, award winning", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "5" }, "logs": "tokenizing text\n['Ġ', '.', 'dal', 'le']\n['Ġi']\n['Ġcan', \"'t\"]\n['Ġbelieve']\n['Ġthat']\n['Ġthis']\n['Ġimage']\n['Ġis']\n['Ġso']\n['Ġrealistic', ',']\n['Ġaward']\n['Ġwinning']\ntext tokens [0, 54, 12, 10626, 92, 295, 382, 1177, 9428, 766, 703, 867, 231, 945, 10573, 11, 3457, 6534, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 15.397021, "total_time": 104.414927 }, "output": [ "https://replicate.delivery/mgxm/d4c3be96-dfb4-4f1a-9377-b6d67a8a034d/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/2302da9d-3b53-49ff-a89d-e1379ef6010a/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/5dffc777-584c-4748-88a2-6555ec5e9a32/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/b951726b-0c51-4402-a8c3-c4329385f433/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/063cf161-b02d-49c6-9e7c-cf6c6d955b41/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/30706c67-63a3-41c1-a17b-f35c789352d8/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/629cd5e6-60af-451a-a45d-280c3670640b/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/68d6443c-7623-4971-a976-c3f199c8df17/min-dalle-iter-8.jpg" ], "started_at": "2022-07-07T12:02:07.964982Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6o4wiamp7vgwjiu3k23i6m2gpe", "cancel": "https://api.replicate.com/v1/predictions/6o4wiamp7vgwjiu3k23i6m2gpe/cancel" }, "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7" }
Generated intokenizing text ['Ġ', '.', 'dal', 'le'] ['Ġi'] ['Ġcan', "'t"] ['Ġbelieve'] ['Ġthat'] ['Ġthis'] ['Ġimage'] ['Ġis'] ['Ġso'] ['Ġrealistic', ','] ['Ġaward'] ['Ġwinning'] text tokens [0, 54, 12, 10626, 92, 295, 382, 1177, 9428, 766, 703, 867, 231, 945, 10573, 11, 3457, 6534, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37IDjkdoa2xhtfbljkib4d4xwbs3k4StatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- I can't believe that this image is so cute, award winning
- grid_size
- "8"
- intermediate_outputs
- log2_supercondition_factor
- "3"
{ "text": "I can't believe that this image is so cute, award winning", "grid_size": "8", "intermediate_outputs": true, "log2_supercondition_factor": "3" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37", { input: { text: "I can't believe that this image is so cute, award winning", grid_size: "8", intermediate_outputs: true, log2_supercondition_factor: "3" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37", input={ "text": "I can't believe that this image is so cute, award winning", "grid_size": "8", "intermediate_outputs": True, "log2_supercondition_factor": "3" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37", "input": { "text": "I can\'t believe that this image is so cute, award winning", "grid_size": "8", "intermediate_outputs": true, "log2_supercondition_factor": "3" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37 \ -i $'text="I can\'t believe that this image is so cute, award winning"' \ -i 'grid_size="8"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor="3"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "I can\'t believe that this image is so cute, award winning", "grid_size": "8", "intermediate_outputs": true, "log2_supercondition_factor": "3" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-07T14:51:25.089151Z", "created_at": "2022-07-07T14:50:48.779600Z", "data_removed": false, "error": null, "id": "jkdoa2xhtfbljkib4d4xwbs3k4", "input": { "text": "I can't believe that this image is so cute, award winning", "grid_size": "8", "intermediate_outputs": true, "log2_supercondition_factor": "3" }, "logs": "tokenizing text\n['Ġi']\n['Ġcan', \"'t\"]\n['Ġbelieve']\n['Ġthat']\n['Ġthis']\n['Ġimage']\n['Ġis']\n['Ġso']\n['Ġcute', ',']\n['Ġaward']\n['Ġwinning']\ntext tokens [0, 295, 382, 1177, 9428, 766, 703, 867, 231, 945, 2018, 11, 3457, 6534, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 36.155659, "total_time": 36.309551 }, "output": [ "https://replicate.delivery/mgxm/1121cb5b-f0ae-461b-ba21-d9b70e949582/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/12e86031-a34d-43bd-a161-5e0040852c09/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/630cb2db-eccd-484f-9e5c-a561d97a481e/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/ca577c34-253f-41db-8673-8f8cb1a1570c/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/b1b7a94a-4d1c-4bba-89be-31393fcc671f/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/c3ca91c0-dea3-4354-9079-a0798a0d6c9e/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/85a98be2-1fed-4071-ac22-b5b4b18fd6d1/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/1c1b18f4-d86c-44e2-968f-1384164893ae/min-dalle-iter-8.jpg" ], "started_at": "2022-07-07T14:50:48.933492Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jkdoa2xhtfbljkib4d4xwbs3k4", "cancel": "https://api.replicate.com/v1/predictions/jkdoa2xhtfbljkib4d4xwbs3k4/cancel" }, "version": "92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37" }
Generated intokenizing text ['Ġi'] ['Ġcan', "'t"] ['Ġbelieve'] ['Ġthat'] ['Ġthis'] ['Ġimage'] ['Ġis'] ['Ġso'] ['Ġcute', ','] ['Ġaward'] ['Ġwinning'] text tokens [0, 295, 382, 1177, 9428, 766, 703, 867, 231, 945, 2018, 11, 3457, 6534, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37Input
- text
- A black wolf howling to a red moon. There are thunderstrikes everywhere. There is a full moon and its shining bright, eerie fog, epic, dramatic lighting
- grid_size
- "8"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "A black wolf howling to a red moon. There are thunderstrikes everywhere. There is a full moon and its shining bright, eerie fog, epic, dramatic lighting", "grid_size": "8", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37", { input: { text: "A black wolf howling to a red moon. There are thunderstrikes everywhere. There is a full moon and its shining bright, eerie fog, epic, dramatic lighting", grid_size: "8", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37", input={ "text": "A black wolf howling to a red moon. There are thunderstrikes everywhere. There is a full moon and its shining bright, eerie fog, epic, dramatic lighting", "grid_size": "8", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37", "input": { "text": "A black wolf howling to a red moon. There are thunderstrikes everywhere. There is a full moon and its shining bright, eerie fog, epic, dramatic lighting", "grid_size": "8", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37 \ -i 'text="A black wolf howling to a red moon. There are thunderstrikes everywhere. There is a full moon and its shining bright, eerie fog, epic, dramatic lighting"' \ -i 'grid_size="8"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor="6"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "A black wolf howling to a red moon. There are thunderstrikes everywhere. There is a full moon and its shining bright, eerie fog, epic, dramatic lighting", "grid_size": "8", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-07T15:07:43.965945Z", "created_at": "2022-07-07T15:06:49.009433Z", "data_removed": false, "error": null, "id": "pt3pu3wlarg3vb5anx4dmtyynq", "input": { "text": "A black wolf howling to a red moon. There are thunderstrikes everywhere. There is a full moon and its shining bright, eerie fog, epic, dramatic lighting", "grid_size": "8", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġa']\n['Ġblack']\n['Ġwolf']\n['Ġhowling']\n['Ġto']\n['Ġa']\n['Ġred']\n['Ġmoon', '.']\n['Ġthere']\n['Ġare']\n['Ġth', 'unders', 'tr', 'ikes']\n['Ġeverywhere', '.']\n['Ġthere']\n['Ġis']\n['Ġa']\n['Ġfull']\n['Ġmoon']\n['Ġand']\n['Ġits']\n['Ġshining']\n['Ġbright', ',']\n['Ġeerie']\n['Ġfog', ',']\n['Ġepic', ',']\n['Ġdramatic']\n['Ġlighting']\ntext tokens [0, 58, 486, 3638, 45736, 123, 58, 454, 2781, 12, 2773, 553, 179, 22312, 210, 5406, 17899, 12, 2773, 231, 58, 1024, 2781, 128, 1850, 18264, 3929, 11, 49878, 8319, 11, 5572, 11, 14836, 4352, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 36.12379, "total_time": 54.956512 }, "output": [ "https://replicate.delivery/mgxm/b36474fa-5117-4a22-8daa-e50c389f2b86/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/957384d8-8f3e-4d42-b8cc-1f567edf8547/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/50d43880-fdc7-48c3-87b7-f24ea582b36f/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/ff43b403-6c8b-4ed6-bc04-2e2333063b63/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/c3742cf0-0d3c-49fa-85e5-054f38b681fc/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/b9400b3b-5242-45df-9dd8-8122b09e0526/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/aa0ec803-abca-40b4-b4e9-eb16d1093603/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/cca449ac-e209-4ed8-b090-36a501d2b261/min-dalle-iter-8.jpg" ], "started_at": "2022-07-07T15:07:07.842155Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pt3pu3wlarg3vb5anx4dmtyynq", "cancel": "https://api.replicate.com/v1/predictions/pt3pu3wlarg3vb5anx4dmtyynq/cancel" }, "version": "92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37" }
Generated intokenizing text ['Ġa'] ['Ġblack'] ['Ġwolf'] ['Ġhowling'] ['Ġto'] ['Ġa'] ['Ġred'] ['Ġmoon', '.'] ['Ġthere'] ['Ġare'] ['Ġth', 'unders', 'tr', 'ikes'] ['Ġeverywhere', '.'] ['Ġthere'] ['Ġis'] ['Ġa'] ['Ġfull'] ['Ġmoon'] ['Ġand'] ['Ġits'] ['Ġshining'] ['Ġbright', ','] ['Ġeerie'] ['Ġfog', ','] ['Ġepic', ','] ['Ġdramatic'] ['Ġlighting'] text tokens [0, 58, 486, 3638, 45736, 123, 58, 454, 2781, 12, 2773, 553, 179, 22312, 210, 5406, 17899, 12, 2773, 231, 58, 1024, 2781, 128, 1850, 18264, 3929, 11, 49878, 8319, 11, 5572, 11, 14836, 4352, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37Input
- text
- surreal garden
- grid_size
- "8"
- intermediate_outputs
- log2_supercondition_factor
- "3"
{ "text": "surreal garden", "grid_size": "8", "intermediate_outputs": true, "log2_supercondition_factor": "3" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37", { input: { text: "surreal garden", grid_size: "8", intermediate_outputs: true, log2_supercondition_factor: "3" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37", input={ "text": "surreal garden", "grid_size": "8", "intermediate_outputs": True, "log2_supercondition_factor": "3" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37", "input": { "text": "surreal garden", "grid_size": "8", "intermediate_outputs": true, "log2_supercondition_factor": "3" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37 \ -i 'text="surreal garden"' \ -i 'grid_size="8"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor="3"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "surreal garden", "grid_size": "8", "intermediate_outputs": true, "log2_supercondition_factor": "3" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-07T17:15:57.322435Z", "created_at": "2022-07-07T17:15:20.692670Z", "data_removed": false, "error": null, "id": "o25w5d7no5g5va5wphegbevly4", "input": { "text": "surreal garden", "grid_size": "8", "intermediate_outputs": true, "log2_supercondition_factor": "3" }, "logs": "tokenizing text\n['Ġsurreal']\n['Ġgarden']\ntext tokens [0, 15084, 1075, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 36.445378, "total_time": 36.629765 }, "output": [ "https://replicate.delivery/mgxm/ddbf5048-f759-4754-a22a-e9e5187e5aac/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/99ad3230-2484-4fb9-8f23-5fa45b081da1/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/67c702f6-b279-4bd6-a828-13a17eea2901/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/245590ed-1035-4bc5-b19c-47729d96734c/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/6d37fac7-d6dd-4c36-8b31-8c60b69825f2/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/20872929-c0f1-4732-a308-44faa7d29366/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/70f960cc-f3d6-4168-b7a7-873741786bcc/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/28f60291-8ac2-49dc-b7fc-c72971fcafc3/min-dalle-iter-8.jpg" ], "started_at": "2022-07-07T17:15:20.877057Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/o25w5d7no5g5va5wphegbevly4", "cancel": "https://api.replicate.com/v1/predictions/o25w5d7no5g5va5wphegbevly4/cancel" }, "version": "92cdac0f9a71b364b2923c23234f335c5cd56be84d4d180f4b92d5f268efae37" }
Generated intokenizing text ['Ġsurreal'] ['Ġgarden'] text tokens [0, 15084, 1075, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:110170593dc8d52492a3464e267b9b03f22d6d5433e0ef034ce440628f1b9b23Input
- text
- a penguin surfing on a big wave, movie poster
- grid_size
- "8"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "a penguin surfing on a big wave, movie poster", "grid_size": "8", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:110170593dc8d52492a3464e267b9b03f22d6d5433e0ef034ce440628f1b9b23", { input: { text: "a penguin surfing on a big wave, movie poster", grid_size: "8", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:110170593dc8d52492a3464e267b9b03f22d6d5433e0ef034ce440628f1b9b23", input={ "text": "a penguin surfing on a big wave, movie poster", "grid_size": "8", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "110170593dc8d52492a3464e267b9b03f22d6d5433e0ef034ce440628f1b9b23", "input": { "text": "a penguin surfing on a big wave, movie poster", "grid_size": "8", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:110170593dc8d52492a3464e267b9b03f22d6d5433e0ef034ce440628f1b9b23 \ -i 'text="a penguin surfing on a big wave, movie poster"' \ -i 'grid_size="8"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor="6"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:110170593dc8d52492a3464e267b9b03f22d6d5433e0ef034ce440628f1b9b23
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "a penguin surfing on a big wave, movie poster", "grid_size": "8", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-07T20:00:53.680206Z", "created_at": "2022-07-07T20:00:17.419166Z", "data_removed": false, "error": null, "id": "ohoha4tderexxddxbsyi746vli", "input": { "text": "a penguin surfing on a big wave, movie poster", "grid_size": "8", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġa']\n['Ġpenguin']\n['Ġsurfing']\n['Ġon']\n['Ġa']\n['Ġbig']\n['Ġwave', ',']\n['Ġmovie']\n['Ġposter']\ntext tokens [0, 58, 9026, 15391, 133, 58, 925, 4638, 11, 1053, 1229, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 36.091013, "total_time": 36.26104 }, "output": [ "https://replicate.delivery/mgxm/3fe457c9-ebb8-4b95-b17f-70b1da5c597e/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/2a8a8ca9-7581-42ad-a5f5-c3cbaaa8fd27/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/6118b09e-b3d9-4bc6-9b8d-f6c95605eafa/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/be476fc1-b353-42c8-9735-5ae485b71ddc/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/87b0aef4-8324-448c-9100-d6e63159ea4f/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/78934c86-86db-4e27-9abd-622f1190d6da/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/cb84dbf9-3f6a-4da4-94c1-01978ce416a1/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/88f9fc69-2ec7-4272-b30f-84606df2602f/min-dalle-iter-8.jpg" ], "started_at": "2022-07-07T20:00:17.589193Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ohoha4tderexxddxbsyi746vli", "cancel": "https://api.replicate.com/v1/predictions/ohoha4tderexxddxbsyi746vli/cancel" }, "version": "110170593dc8d52492a3464e267b9b03f22d6d5433e0ef034ce440628f1b9b23" }
Generated intokenizing text ['Ġa'] ['Ġpenguin'] ['Ġsurfing'] ['Ġon'] ['Ġa'] ['Ġbig'] ['Ġwave', ','] ['Ġmovie'] ['Ġposter'] text tokens [0, 58, 9026, 15391, 133, 58, 925, 4638, 11, 1053, 1229, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3Input
- text
- A photo of a tornado striking New York City taken in the 1950s
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- 4
{ "text": "A photo of a tornado striking New York City taken in the 1950s", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": 4 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3", { input: { text: "A photo of a tornado striking New York City taken in the 1950s", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: 4 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3", input={ "text": "A photo of a tornado striking New York City taken in the 1950s", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": 4 } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3", "input": { "text": "A photo of a tornado striking New York City taken in the 1950s", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": 4 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3 \ -i 'text="A photo of a tornado striking New York City taken in the 1950s"' \ -i 'grid_size="5"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor=4'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "A photo of a tornado striking New York City taken in the 1950s", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": 4 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-08T20:24:33.730023Z", "created_at": "2022-07-08T20:24:00.947371Z", "data_removed": false, "error": null, "id": "h5v22t76kncipfdqknp4gfdvje", "input": { "text": "A photo of a tornado striking New York City taken in the 1950s", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": 4 }, "logs": "tokenizing text\n['Ġa']\n['Ġphoto']\n['Ġof']\n['Ġa']\n['Ġtornado']\n['Ġstriking']\n['Ġnew']\n['Ġyork']\n['Ġcity']\n['Ġtaken']\n['Ġin']\n['Ġthe']\n['Ġ1950', 's']\ntext tokens [0, 58, 564, 111, 58, 13862, 23364, 173, 1194, 645, 8760, 91, 99, 7577, 46, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.373555, "total_time": 32.782652 }, "output": [ "https://replicate.delivery/mgxm/98db5d60-faf9-4ce0-9505-08d1e346de6f/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/e8e690cc-aff8-4890-86e3-e28fcf425e72/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/e3ae18a6-283d-49db-987f-b5167855f523/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/ad481ab7-846b-4c86-90b8-472b09c97b6b/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/14001f45-451b-4e52-a4ac-d4bc34525f86/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/09507ddd-baf5-4b86-882f-32c8ba0eba05/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/b335de55-9610-47cf-91bf-336e6b04c623/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/5374f951-6aa1-412e-acd7-ac3eb75ac4e6/min-dalle-iter-8.jpg" ], "started_at": "2022-07-08T20:24:17.356468Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/h5v22t76kncipfdqknp4gfdvje", "cancel": "https://api.replicate.com/v1/predictions/h5v22t76kncipfdqknp4gfdvje/cancel" }, "version": "52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3" }
Generated intokenizing text ['Ġa'] ['Ġphoto'] ['Ġof'] ['Ġa'] ['Ġtornado'] ['Ġstriking'] ['Ġnew'] ['Ġyork'] ['Ġcity'] ['Ġtaken'] ['Ġin'] ['Ġthe'] ['Ġ1950', 's'] text tokens [0, 58, 564, 111, 58, 13862, 23364, 173, 1194, 645, 8760, 91, 99, 7577, 46, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3Input
- text
- Balance between cosmic metaphysical abstractions. In the style of transcendental cosmic fantasy
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "Balance between cosmic metaphysical abstractions. In the style of transcendental cosmic fantasy", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3", { input: { text: "Balance between cosmic metaphysical abstractions. In the style of transcendental cosmic fantasy", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3", input={ "text": "Balance between cosmic metaphysical abstractions. In the style of transcendental cosmic fantasy", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3", "input": { "text": "Balance between cosmic metaphysical abstractions. In the style of transcendental cosmic fantasy", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3 \ -i 'text="Balance between cosmic metaphysical abstractions. In the style of transcendental cosmic fantasy"' \ -i 'grid_size="5"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor="6"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "Balance between cosmic metaphysical abstractions. In the style of transcendental cosmic fantasy", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-08T20:38:57.650313Z", "created_at": "2022-07-08T20:38:30.430964Z", "data_removed": false, "error": null, "id": "gnnj3texgzhurdl6kpbx6ure6m", "input": { "text": "Balance between cosmic metaphysical abstractions. In the style of transcendental cosmic fantasy", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġbalance']\n['Ġbetween']\n['Ġcosmic']\n['Ġmetaphys', 'ical']\n['Ġabstract', 'ions', '.']\n['Ġin']\n['Ġthe']\n['Ġstyle']\n['Ġof']\n['Ġtranscend', 'ental']\n['Ġcosmic']\n['Ġfantasy']\ntext tokens [0, 7303, 2725, 15673, 35561, 398, 4877, 401, 12, 91, 99, 1155, 111, 32905, 2066, 15673, 3091, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.462936, "total_time": 27.219349 }, "output": [ "https://replicate.delivery/mgxm/b131fdc1-7893-4a49-a642-0ae8b51b87d7/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/c2b5f06f-9b1f-4647-86cc-2435ad4ecf6c/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/6035e9b2-619a-48f3-9625-cb73662d75c1/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/a07a2120-3801-4b5c-8a2a-3a44b58770a0/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/3bf217e3-b81c-440f-823c-e120cb5b8720/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/e71efb9b-206e-42ed-8e05-18386f8fff0f/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/da2fb2ed-ed09-4415-9605-f0b48b3b27a1/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/91ba07c0-9858-4b77-bc98-03dd8e08e6b2/min-dalle-iter-8.jpg" ], "started_at": "2022-07-08T20:38:41.187377Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/gnnj3texgzhurdl6kpbx6ure6m", "cancel": "https://api.replicate.com/v1/predictions/gnnj3texgzhurdl6kpbx6ure6m/cancel" }, "version": "52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3" }
Generated intokenizing text ['Ġbalance'] ['Ġbetween'] ['Ġcosmic'] ['Ġmetaphys', 'ical'] ['Ġabstract', 'ions', '.'] ['Ġin'] ['Ġthe'] ['Ġstyle'] ['Ġof'] ['Ġtranscend', 'ental'] ['Ġcosmic'] ['Ġfantasy'] text tokens [0, 7303, 2725, 15673, 35561, 398, 4877, 401, 12, 91, 99, 1155, 111, 32905, 2066, 15673, 3091, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3IDdw7iwex3njfiphdn2k3sbd3lcyStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- Dali painting of WALL·E
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- 4
{ "text": "Dali painting of WALL·E", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": 4 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3", { input: { text: "Dali painting of WALL·E", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: 4 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3", input={ "text": "Dali painting of WALL·E", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": 4 } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3", "input": { "text": "Dali painting of WALL·E", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": 4 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3 \ -i 'text="Dali painting of WALL·E"' \ -i 'grid_size="5"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor=4'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "Dali painting of WALL·E", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": 4 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-09T07:53:24.118859Z", "created_at": "2022-07-09T07:53:07.381985Z", "data_removed": false, "error": null, "id": "dw7iwex3njfiphdn2k3sbd3lcy", "input": { "text": "Dali painting of WALL·E", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": 4 }, "logs": "tokenizing text\n['Ġdali']\n['Ġpainting']\n['Ġof']\n['Ġwal', 'le']\ntext tokens [0, 21853, 1545, 111, 563, 92, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.561318, "total_time": 16.736874 }, "output": [ "https://replicate.delivery/mgxm/d1256731-a3a9-474d-9add-c271083c0902/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/79c1ca0c-1cd8-4e81-b1d9-4f66efb9e63f/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/6d445ff7-c5c7-45a3-a663-8647416bbd06/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/7e57e059-12bd-4ae8-836d-f9cb6c21bf59/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/4993e140-fb92-4738-9067-ada31f89304e/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/4b44a48d-807c-41bf-8a62-9551e148808c/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/e0a931c5-da05-4bcb-90ce-0b6036b973bb/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/4f657463-d526-42b1-950a-9576348440cf/min-dalle-iter-8.jpg" ], "started_at": "2022-07-09T07:53:07.557541Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dw7iwex3njfiphdn2k3sbd3lcy", "cancel": "https://api.replicate.com/v1/predictions/dw7iwex3njfiphdn2k3sbd3lcy/cancel" }, "version": "52aa777b11880357b272aa7c793080f2626cafe44a03877e3017452528a920c3" }
Generated intokenizing text ['Ġdali'] ['Ġpainting'] ['Ġof'] ['Ġwal', 'le'] text tokens [0, 21853, 1545, 111, 563, 92, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7Input
- text
- A gorilla playing golf, early 1900s newspaper photo
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- "3"
{ "text": "A gorilla playing golf, early 1900s newspaper photo", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "3" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "A gorilla playing golf, early 1900s newspaper photo", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: "3" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "A gorilla playing golf, early 1900s newspaper photo", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": "3" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "A gorilla playing golf, early 1900s newspaper photo", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "3" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-09T12:53:38.773246Z", "created_at": "2022-07-09T12:53:22.570230Z", "data_removed": false, "error": null, "id": "gqivm6236vhcjkg4vbjpr76u7i", "input": { "text": "A gorilla playing golf, early 1900s newspaper photo", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "3" }, "logs": "tokenizing text\n['Ġa']\n['Ġgorilla']\n['Ġplaying']\n['Ġgolf', ',']\n['Ġearly']\n['Ġ1900', 's']\n['Ġnewspaper']\n['Ġphoto']\ntext tokens [0, 58, 17989, 4952, 2424, 11, 3135, 10594, 46, 6937, 564, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 15.994214, "total_time": 16.203016 }, "output": [ "https://replicate.delivery/mgxm/6eacad7c-746d-4059-bed2-e5b62f65ee4a/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/98774a0c-7dbf-40a3-8ed2-aff239aeb51e/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/f6adafeb-7217-41d3-bf19-f6df25aad72d/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/5999104c-75fd-4ded-b59b-280fd909ffec/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/ebee5882-7871-4c0d-b484-99b0818cc13c/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/81cb374c-ca48-4a69-bced-c4f549c6371c/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/9c6ea789-c59e-4b69-ad58-93810a182e3b/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/8e7d6038-3269-4f2e-a233-0bba52e76add/min-dalle-iter-8.jpg" ], "started_at": "2022-07-09T12:53:22.779032Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/gqivm6236vhcjkg4vbjpr76u7i", "cancel": "https://api.replicate.com/v1/predictions/gqivm6236vhcjkg4vbjpr76u7i/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġa'] ['Ġgorilla'] ['Ġplaying'] ['Ġgolf', ','] ['Ġearly'] ['Ġ1900', 's'] ['Ġnewspaper'] ['Ġphoto'] text tokens [0, 58, 17989, 4952, 2424, 11, 3135, 10594, 46, 6937, 564, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7IDycxmuxhvvvfbbpdbohbwmainxyStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- astronaut riding a horse hyperrealistic
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- 4
{ "text": "astronaut riding a horse hyperrealistic", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": 4 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "astronaut riding a horse hyperrealistic", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: 4 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "astronaut riding a horse hyperrealistic", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": 4 } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "astronaut riding a horse hyperrealistic", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": 4 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-09T17:22:14.501569Z", "created_at": "2022-07-09T17:21:36.148910Z", "data_removed": false, "error": null, "id": "ycxmuxhvvvfbbpdbohbwmainxy", "input": { "text": "astronaut riding a horse hyperrealistic", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": 4 }, "logs": "tokenizing text\n['Ġastronaut']\n['Ġriding']\n['Ġa']\n['Ġhorse']\n['Ġhyper', 'real', 'istic']\ntext tokens [0, 14282, 8245, 58, 2748, 6139, 4646, 3478, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.16648, "total_time": 38.352659 }, "output": [ "https://replicate.delivery/mgxm/64577293-08f3-471e-9a09-41d6b5bfdce4/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/32542db6-afb6-4878-8292-98b307d833f8/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/228150ef-1d62-4315-a1e2-56a7e57b67cd/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/cab1e4dd-dc98-49b1-91df-db5f7630b9f8/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/9e49b2fd-4cd6-47e7-b862-9aedc8777655/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/95a00bb0-e0da-444b-8ee2-ae61db2f742a/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/cde5ab36-c9fc-4f8f-8ba7-fa0af21e542a/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/20c94fbf-9da5-4e87-85a3-a19c91507ecc/min-dalle-iter-8.jpg" ], "started_at": "2022-07-09T17:21:58.335089Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ycxmuxhvvvfbbpdbohbwmainxy", "cancel": "https://api.replicate.com/v1/predictions/ycxmuxhvvvfbbpdbohbwmainxy/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġastronaut'] ['Ġriding'] ['Ġa'] ['Ġhorse'] ['Ġhyper', 'real', 'istic'] text tokens [0, 14282, 8245, 58, 2748, 6139, 4646, 3478, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7Input
- text
- Darth Vader is holding a baguette
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- "5"
{ "text": "Darth Vader is holding a baguette", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "5" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "Darth Vader is holding a baguette", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: "5" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "Darth Vader is holding a baguette", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": "5" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "Darth Vader is holding a baguette", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "5" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-09T18:44:22.622759Z", "created_at": "2022-07-09T18:43:37.999360Z", "data_removed": false, "error": null, "id": "rmclm4licfgavfuspwrnan44bi", "input": { "text": "Darth Vader is holding a baguette", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "5" }, "logs": "tokenizing text\n['Ġdarth']\n['Ġvader']\n['Ġis']\n['Ġholding']\n['Ġa']\n['Ġbaguette']\ntext tokens [0, 19109, 22981, 231, 8538, 58, 37549, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.606367, "total_time": 44.623399 }, "output": [ "https://replicate.delivery/mgxm/991c115b-8551-4e49-80ea-495bdef7c9fe/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/586271c8-b146-4022-841d-cfc8faedf891/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/ff48d694-240a-4caf-b277-024e1f7cadd7/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/8a71c721-b60a-4f98-9daf-da9629977871/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/f0be6cd9-2fad-463e-ad3e-cc0e18254a76/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/ccfede44-e47a-4467-8529-c37b9a79f84e/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/832075c1-ee3b-414d-99ea-a198c605b172/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/2eb1ade2-cb63-4497-a8a7-e595ca7f8ef5/min-dalle-iter-8.jpg" ], "started_at": "2022-07-09T18:44:06.016392Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rmclm4licfgavfuspwrnan44bi", "cancel": "https://api.replicate.com/v1/predictions/rmclm4licfgavfuspwrnan44bi/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġdarth'] ['Ġvader'] ['Ġis'] ['Ġholding'] ['Ġa'] ['Ġbaguette'] text tokens [0, 19109, 22981, 231, 8538, 58, 37549, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7Input
- text
- A fridge standing on top of a mountain
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- "5"
{ "text": "A fridge standing on top of a mountain", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "5" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "A fridge standing on top of a mountain", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: "5" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "A fridge standing on top of a mountain", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": "5" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "A fridge standing on top of a mountain", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "5" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-09T20:10:09.585912Z", "created_at": "2022-07-09T20:09:46.944768Z", "data_removed": false, "error": null, "id": "fhbvbsaivzfjtnv6ocdgp27kui", "input": { "text": "A fridge standing on top of a mountain", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "5" }, "logs": "tokenizing text\n['Ġa']\n['Ġfridge']\n['Ġstanding']\n['Ġon']\n['Ġtop']\n['Ġof']\n['Ġa']\n['Ġmountain']\ntext tokens [0, 58, 13197, 7329, 133, 479, 111, 58, 2236, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.118879, "total_time": 22.641144 }, "output": [ "https://replicate.delivery/mgxm/c36b4858-270f-4eb2-9c25-1febe33f2c5e/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/990a62f4-03b8-4c4d-a527-deea088dbfc7/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/61d6a91b-f287-4723-80f5-cf357fbd9faa/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/a50142f0-b08a-48a3-8d98-349373ddafe4/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/6078d7ad-49ed-45c0-9476-19458a0e1fc0/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/71d76f98-0441-415b-a26c-453644805517/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/e1ad792b-2063-48db-94d6-e83b6a03780c/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/d713b64a-2023-4d1a-93e3-e3691a124e5b/min-dalle-iter-8.jpg" ], "started_at": "2022-07-09T20:09:53.467033Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fhbvbsaivzfjtnv6ocdgp27kui", "cancel": "https://api.replicate.com/v1/predictions/fhbvbsaivzfjtnv6ocdgp27kui/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġa'] ['Ġfridge'] ['Ġstanding'] ['Ġon'] ['Ġtop'] ['Ġof'] ['Ġa'] ['Ġmountain'] text tokens [0, 58, 13197, 7329, 133, 479, 111, 58, 2236, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7IDlv3mlhbxtrb2noqakfabo6bjt4StatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- A portrait photo of a kangaroo wearing an orange hoodie and blue sunglasses standing on the grass in front of the Sydney Opera House holding a sign on the chest that says Welcome Friends!
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- "4"
{ "text": "A portrait photo of a kangaroo wearing an orange hoodie and blue sunglasses standing on the grass in front of the Sydney Opera House holding a sign on the chest that says Welcome Friends!", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "A portrait photo of a kangaroo wearing an orange hoodie and blue sunglasses standing on the grass in front of the Sydney Opera House holding a sign on the chest that says Welcome Friends!", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: "4" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "A portrait photo of a kangaroo wearing an orange hoodie and blue sunglasses standing on the grass in front of the Sydney Opera House holding a sign on the chest that says Welcome Friends!", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": "4" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "A portrait photo of a kangaroo wearing an orange hoodie and blue sunglasses standing on the grass in front of the Sydney Opera House holding a sign on the chest that says Welcome Friends!", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-10T10:38:27.330833Z", "created_at": "2022-07-10T10:38:10.674853Z", "data_removed": false, "error": null, "id": "lv3mlhbxtrb2noqakfabo6bjt4", "input": { "text": "A portrait photo of a kangaroo wearing an orange hoodie and blue sunglasses standing on the grass in front of the Sydney Opera House holding a sign on the chest that says Welcome Friends!", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "4" }, "logs": "tokenizing text\n['Ġa']\n['Ġportrait']\n['Ġphoto']\n['Ġof']\n['Ġa']\n['Ġkangaroo']\n['Ġwearing']\n['Ġan']\n['Ġorange']\n['Ġhoodie']\n['Ġand']\n['Ġblue']\n['Ġsunglasses']\n['Ġstanding']\n['Ġon']\n['Ġthe']\n['Ġgrass']\n['Ġin']\n['Ġfront']\n['Ġof']\n['Ġthe']\n['Ġsydney']\n['Ġopera']\n['Ġhouse']\n['Ġholding']\n['Ġa']\n['Ġsign']\n['Ġon']\n['Ġthe']\n['Ġchest']\n['Ġthat']\n['Ġsays']\n['Ġwelcome']\n['Ġfriends', '!']\ntext tokens [0, 58, 3317, 564, 111, 58, 22900, 8995, 101, 2566, 3575, 128, 789, 7134, 7329, 133, 99, 4616, 91, 2037, 111, 99, 5175, 3543, 610, 8538, 58, 830, 133, 99, 6403, 766, 2022, 3077, 3103, 3, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.464777, "total_time": 16.65598 }, "output": [ "https://replicate.delivery/mgxm/fd708904-df84-41bc-b9e4-d48923e43fba/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/4904b8e3-d64a-4c03-b19d-b96ec5654764/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/71d22260-ec6d-4204-8eaa-58cff4c0d4d9/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/9e75e0b5-8cde-4b6d-9bef-a929ad8fc583/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/14dd1eda-a054-4f56-bc0e-aaa3f529151f/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/2fb74d93-e806-4c0e-9dfe-90e7cd2c17b1/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/7cb3e55b-21d0-4ef3-9450-6077d37ef3cc/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/aefed67d-cda5-47c2-96a1-607a5f71ae40/min-dalle-iter-8.jpg" ], "started_at": "2022-07-10T10:38:10.866056Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/lv3mlhbxtrb2noqakfabo6bjt4", "cancel": "https://api.replicate.com/v1/predictions/lv3mlhbxtrb2noqakfabo6bjt4/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġa'] ['Ġportrait'] ['Ġphoto'] ['Ġof'] ['Ġa'] ['Ġkangaroo'] ['Ġwearing'] ['Ġan'] ['Ġorange'] ['Ġhoodie'] ['Ġand'] ['Ġblue'] ['Ġsunglasses'] ['Ġstanding'] ['Ġon'] ['Ġthe'] ['Ġgrass'] ['Ġin'] ['Ġfront'] ['Ġof'] ['Ġthe'] ['Ġsydney'] ['Ġopera'] ['Ġhouse'] ['Ġholding'] ['Ġa'] ['Ġsign'] ['Ġon'] ['Ġthe'] ['Ġchest'] ['Ġthat'] ['Ġsays'] ['Ġwelcome'] ['Ġfriends', '!'] text tokens [0, 58, 3317, 564, 111, 58, 22900, 8995, 101, 2566, 3575, 128, 789, 7134, 7329, 133, 99, 4616, 91, 2037, 111, 99, 5175, 3543, 610, 8538, 58, 830, 133, 99, 6403, 766, 2022, 3077, 3103, 3, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7Input
- text
- Sketch of the sentient AI in a lab. Art style influenced by the movie Prometheus.
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- "4"
{ "text": "Sketch of the sentient AI in a lab. Art style influenced by the movie Prometheus.", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "Sketch of the sentient AI in a lab. Art style influenced by the movie Prometheus.", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: "4" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "Sketch of the sentient AI in a lab. Art style influenced by the movie Prometheus.", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": "4" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "Sketch of the sentient AI in a lab. Art style influenced by the movie Prometheus.", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-10T07:12:52.378947Z", "created_at": "2022-07-10T07:12:06.820400Z", "data_removed": false, "error": null, "id": "ibrvsqnpd5ctjpgbcs57gpr754", "input": { "text": "Sketch of the sentient AI in a lab. Art style influenced by the movie Prometheus.", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "4" }, "logs": "tokenizing text\n['Ġsketch']\n['Ġof']\n['Ġthe']\n['Ġsent', 'ient']\n['Ġai']\n['Ġin']\n['Ġa']\n['Ġlab', '.']\n['Ġart']\n['Ġstyle']\n['Ġinfluenced']\n['Ġby']\n['Ġthe']\n['Ġmovie']\n['Ġprometheus', '.']\ntext tokens [0, 4189, 111, 99, 2959, 937, 2838, 91, 58, 1344, 12, 241, 1155, 40678, 185, 99, 1053, 45574, 12, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 15.419261, "total_time": 45.558547 }, "output": [ "https://replicate.delivery/mgxm/959a6f40-4097-4fe2-ad5f-4a79366e79a3/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/2cc53c14-029e-4de3-bf1d-fa46bdf4bcfe/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/e43ba646-57a6-45c4-b3a3-03b38bf1b0bb/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/fb42d826-85a0-4523-88b8-f7390cff5226/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/e3adade6-e4c9-4a3a-aaa0-0dd1af90c190/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/bb51190b-f4e7-4877-9da1-c6bd634fd5a2/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/2a5b513b-dc9b-480f-9916-4c5334bc8442/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/91abfc82-5db5-4eff-8458-affe24ab4a10/min-dalle-iter-8.jpg" ], "started_at": "2022-07-10T07:12:36.959686Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ibrvsqnpd5ctjpgbcs57gpr754", "cancel": "https://api.replicate.com/v1/predictions/ibrvsqnpd5ctjpgbcs57gpr754/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġsketch'] ['Ġof'] ['Ġthe'] ['Ġsent', 'ient'] ['Ġai'] ['Ġin'] ['Ġa'] ['Ġlab', '.'] ['Ġart'] ['Ġstyle'] ['Ġinfluenced'] ['Ġby'] ['Ġthe'] ['Ġmovie'] ['Ġprometheus', '.'] text tokens [0, 4189, 111, 99, 2959, 937, 2838, 91, 58, 1344, 12, 241, 1155, 40678, 185, 99, 1053, 45574, 12, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7Input
- text
- High definition psychedelic dream like painting of a cat in space, by Thomas Kinkade.
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- "4"
{ "text": "High definition psychedelic dream like painting of a cat in space, by Thomas Kinkade.", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "High definition psychedelic dream like painting of a cat in space, by Thomas Kinkade.", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: "4" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "High definition psychedelic dream like painting of a cat in space, by Thomas Kinkade.", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": "4" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "High definition psychedelic dream like painting of a cat in space, by Thomas Kinkade.", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-10T03:47:48.667353Z", "created_at": "2022-07-10T03:46:27.849775Z", "data_removed": false, "error": null, "id": "72mhq75fazcnvhisxuoa7du5si", "input": { "text": "High definition psychedelic dream like painting of a cat in space, by Thomas Kinkade.", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "4" }, "logs": "tokenizing text\n['Ġhigh']\n['Ġdefinition']\n['Ġpsychedelic']\n['Ġdream']\n['Ġlike']\n['Ġpainting']\n['Ġof']\n['Ġa']\n['Ġcat']\n['Ġin']\n['Ġspace', ',']\n['Ġby']\n['Ġthomas']\n['Ġkink', 'ade', '.']\ntext tokens [0, 524, 4951, 19687, 2572, 1572, 1545, 111, 58, 803, 91, 1912, 11, 185, 3058, 29947, 458, 12, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 15.501601, "total_time": 80.817578 }, "output": [ "https://replicate.delivery/mgxm/5baa2ff0-b824-4f5f-8f8f-707362d61e96/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/835e4a28-c7c5-45b9-8547-c70bb6e4974d/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/afba6e15-be14-452b-9aee-683e2e506e01/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/468d4da0-ece5-4024-b299-8b27ea5d6d90/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/8a5bbef6-4576-40dc-9ad7-60f30a3a3ddd/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/86740648-4760-4b62-b5e3-763f443afd62/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/6685f4a3-d266-487c-bb28-5b79e803bb21/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/2b6ab7b1-1f75-48ba-9001-31bb12a14dbd/min-dalle-iter-8.jpg" ], "started_at": "2022-07-10T03:47:33.165752Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/72mhq75fazcnvhisxuoa7du5si", "cancel": "https://api.replicate.com/v1/predictions/72mhq75fazcnvhisxuoa7du5si/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġhigh'] ['Ġdefinition'] ['Ġpsychedelic'] ['Ġdream'] ['Ġlike'] ['Ġpainting'] ['Ġof'] ['Ġa'] ['Ġcat'] ['Ġin'] ['Ġspace', ','] ['Ġby'] ['Ġthomas'] ['Ġkink', 'ade', '.'] text tokens [0, 524, 4951, 19687, 2572, 1572, 1545, 111, 58, 803, 91, 1912, 11, 185, 3058, 29947, 458, 12, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7IDwkjptofnwfc2hc7lplemrs3fc4StatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- 8k UHD, painting in the style of WW1 propaganda, trending on artstation: (subject = anthropomorphic fox soldier resting in a trench + subject detail = fox head, anthropomorphic, high detail, high texture detail)
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- "4"
{ "text": "8k UHD, painting in the style of WW1 propaganda, trending on artstation: (subject = anthropomorphic fox soldier resting in a trench + subject detail = fox head, anthropomorphic, high detail, high texture detail)", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "8k UHD, painting in the style of WW1 propaganda, trending on artstation: (subject = anthropomorphic fox soldier resting in a trench + subject detail = fox head, anthropomorphic, high detail, high texture detail)", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: "4" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "8k UHD, painting in the style of WW1 propaganda, trending on artstation: (subject = anthropomorphic fox soldier resting in a trench + subject detail = fox head, anthropomorphic, high detail, high texture detail)", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": "4" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "8k UHD, painting in the style of WW1 propaganda, trending on artstation: (subject = anthropomorphic fox soldier resting in a trench + subject detail = fox head, anthropomorphic, high detail, high texture detail)", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-10T13:10:19.822582Z", "created_at": "2022-07-10T13:09:54.746616Z", "data_removed": false, "error": null, "id": "wkjptofnwfc2hc7lplemrs3fc4", "input": { "text": "8k UHD, painting in the style of WW1 propaganda, trending on artstation: (subject = anthropomorphic fox soldier resting in a trench + subject detail = fox head, anthropomorphic, high detail, high texture detail)", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "4" }, "logs": "tokenizing text\n['Ġ8', 'k']\n['Ġuhd', ',']\n['Ġpainting']\n['Ġin']\n['Ġthe']\n['Ġstyle']\n['Ġof']\n['Ġww', '1']\n['Ġpropaganda', ',']\n['Ġtrending']\n['Ġon']\n['Ġartstation', ':']\n['Ġ', '(', 'sub', 'ject']\n['Ġ', '=']\n['Ġanthrop', 'omorphic']\n['Ġfox']\n['Ġsoldier']\n['Ġresting']\n['Ġin']\n['Ġa']\n['Ġtrench']\n['Ġ', '+']\n['Ġsubject']\n['Ġdetail']\n['Ġ', '=']\n['Ġfox']\n['Ġhead', ',']\n['Ġanthrop', 'omorphic', ',']\n['Ġhigh']\n['Ġdetail', ',']\n['Ġhigh']\n['Ġtexture']\n['Ġdetail', ')']\ntext tokens [0, 416, 38, 20531, 11, 1545, 91, 99, 1155, 111, 1121, 15, 17581, 11, 10119, 133, 4640, 3, 54, 3, 45878, 836, 54, 3, 13216, 47683, 2656, 7757, 28399, 91, 58, 17007, 54, 3, 11398, 5854, 54, 3, 2656, 1029, 11, 13216, 47683, 11, 524, 5854, 11, 524, 7141, 5854, 3, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 15.04624, "total_time": 25.075966 }, "output": [ "https://replicate.delivery/mgxm/6817a10a-e3c0-40d3-b9f6-4487623f2913/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/816edfc3-f68b-41d9-a7d8-cc61c2e9a311/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/af15a51b-7237-4b24-8cf1-08adc630a7f5/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/63569751-8b2b-47bb-a16d-4ee5e0dd57e1/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/cba3a5a3-66e9-491f-b7d6-6f54e546868b/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/230d138a-834a-457f-9fa7-38d6f32841b6/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/4f12c0e6-4690-4991-aa22-fa760fa43dbb/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/9b8e33ba-012f-43da-bfcc-b6b052637c06/min-dalle-iter-8.jpg" ], "started_at": "2022-07-10T13:10:04.776342Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wkjptofnwfc2hc7lplemrs3fc4", "cancel": "https://api.replicate.com/v1/predictions/wkjptofnwfc2hc7lplemrs3fc4/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġ8', 'k'] ['Ġuhd', ','] ['Ġpainting'] ['Ġin'] ['Ġthe'] ['Ġstyle'] ['Ġof'] ['Ġww', '1'] ['Ġpropaganda', ','] ['Ġtrending'] ['Ġon'] ['Ġartstation', ':'] ['Ġ', '(', 'sub', 'ject'] ['Ġ', '='] ['Ġanthrop', 'omorphic'] ['Ġfox'] ['Ġsoldier'] ['Ġresting'] ['Ġin'] ['Ġa'] ['Ġtrench'] ['Ġ', '+'] ['Ġsubject'] ['Ġdetail'] ['Ġ', '='] ['Ġfox'] ['Ġhead', ','] ['Ġanthrop', 'omorphic', ','] ['Ġhigh'] ['Ġdetail', ','] ['Ġhigh'] ['Ġtexture'] ['Ġdetail', ')'] text tokens [0, 416, 38, 20531, 11, 1545, 91, 99, 1155, 111, 1121, 15, 17581, 11, 10119, 133, 4640, 3, 54, 3, 45878, 836, 54, 3, 13216, 47683, 2656, 7757, 28399, 91, 58, 17007, 54, 3, 11398, 5854, 54, 3, 2656, 1029, 11, 13216, 47683, 11, 524, 5854, 11, 524, 7141, 5854, 3, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7Input
- text
- A realistic photo of a grim reaper is sitting in a bus stop, photorealistic, 8K, black and white
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "A realistic photo of a grim reaper is sitting in a bus stop, photorealistic, 8K, black and white", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "A realistic photo of a grim reaper is sitting in a bus stop, photorealistic, 8K, black and white", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "A realistic photo of a grim reaper is sitting in a bus stop, photorealistic, 8K, black and white", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "A realistic photo of a grim reaper is sitting in a bus stop, photorealistic, 8K, black and white", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-10T17:18:30.167809Z", "created_at": "2022-07-10T17:18:13.639996Z", "data_removed": false, "error": null, "id": "4z54ipsz45b23orufrjfpopata", "input": { "text": "A realistic photo of a grim reaper is sitting in a bus stop, photorealistic, 8K, black and white", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġa']\n['Ġrealistic']\n['Ġphoto']\n['Ġof']\n['Ġa']\n['Ġgrim']\n['Ġreaper']\n['Ġis']\n['Ġsitting']\n['Ġin']\n['Ġa']\n['Ġbus']\n['Ġstop', ',']\n['Ġphot', 'oreal', 'istic', ',']\n['Ġ8', 'k', ',']\n['Ġblack']\n['Ġand']\n['Ġwhite']\ntext tokens [0, 58, 10573, 564, 111, 58, 12446, 18809, 231, 9782, 91, 58, 662, 2865, 11, 234, 35495, 3478, 11, 416, 38, 11, 486, 128, 657, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.353974, "total_time": 16.527813 }, "output": [ "https://replicate.delivery/mgxm/a746ddea-351b-4f6a-91e6-792787061202/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/de467041-c56d-4671-87b8-835a3c90be03/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/def7c335-2e33-4c26-92f1-6204c8cdfc5f/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/3560de96-dbbe-4849-bbb1-a2d1541f0f77/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/f7c74538-555a-44b5-b4cb-25e0b6e9b86e/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/9e5e1a0c-ef1f-4b38-8fc1-856c32d26cd8/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/713d0313-f09b-4b75-9c30-95debba5b9ea/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/0d2d9dca-c13f-4252-a4c9-50a53f32d0e6/min-dalle-iter-8.jpg" ], "started_at": "2022-07-10T17:18:13.813835Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4z54ipsz45b23orufrjfpopata", "cancel": "https://api.replicate.com/v1/predictions/4z54ipsz45b23orufrjfpopata/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġa'] ['Ġrealistic'] ['Ġphoto'] ['Ġof'] ['Ġa'] ['Ġgrim'] ['Ġreaper'] ['Ġis'] ['Ġsitting'] ['Ġin'] ['Ġa'] ['Ġbus'] ['Ġstop', ','] ['Ġphot', 'oreal', 'istic', ','] ['Ġ8', 'k', ','] ['Ġblack'] ['Ġand'] ['Ġwhite'] text tokens [0, 58, 10573, 564, 111, 58, 12446, 18809, 231, 9782, 91, 58, 662, 2865, 11, 234, 35495, 3478, 11, 416, 38, 11, 486, 128, 657, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507Input
- seed
- -1
- text
- space and time fractured
- grid_size
- "4"
{ "seed": -1, "text": "space and time fractured", "grid_size": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", { input: { seed: -1, text: "space and time fractured", grid_size: "4" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", input={ "seed": -1, "text": "space and time fractured", "grid_size": "4" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", "input": { "seed": -1, "text": "space and time fractured", "grid_size": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-04T15:56:14.057399Z", "created_at": "2022-07-04T15:55:52.535801Z", "data_removed": false, "error": null, "id": "gtys4wjfvven3jqlm65a2sjoba", "input": { "seed": -1, "text": "space and time fractured", "grid_size": "4" }, "logs": "tokenizing text\n['Ġspace']\n['Ġand']\n['Ġtime']\n['Ġfract', 'ured']\ntext tokens [0, 1912, 128, 1010, 9619, 4076, 2]\nencoding text tokens\nsampling image tokens\ndetokenizing image", "metrics": { "predict_time": 15.004164, "total_time": 21.521598 }, "output": "https://replicate.delivery/mgxm/3cb7b507-7e8b-4a2a-aeca-84988b4c1e91/output.jpg", "started_at": "2022-07-04T15:55:59.053235Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/gtys4wjfvven3jqlm65a2sjoba", "cancel": "https://api.replicate.com/v1/predictions/gtys4wjfvven3jqlm65a2sjoba/cancel" }, "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507" }
Generated intokenizing text ['Ġspace'] ['Ġand'] ['Ġtime'] ['Ġfract', 'ured'] text tokens [0, 1912, 128, 1010, 9619, 4076, 2] encoding text tokens sampling image tokens detokenizing image
Prediction
kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507Input
- seed
- 2
- text
- Rusty Iron Man suit found abandoned in the woods being reclaimed by nature
- grid_size
- "3"
{ "seed": 2, "text": "Rusty Iron Man suit found abandoned in the woods being reclaimed by nature", "grid_size": "3" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", { input: { seed: 2, text: "Rusty Iron Man suit found abandoned in the woods being reclaimed by nature", grid_size: "3" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", input={ "seed": 2, "text": "Rusty Iron Man suit found abandoned in the woods being reclaimed by nature", "grid_size": "3" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", "input": { "seed": 2, "text": "Rusty Iron Man suit found abandoned in the woods being reclaimed by nature", "grid_size": "3" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-04T15:57:01.656974Z", "created_at": "2022-07-04T15:56:46.777754Z", "data_removed": false, "error": null, "id": "tavbsdamiba5rmpjkiwgwt3otq", "input": { "seed": 2, "text": "Rusty Iron Man suit found abandoned in the woods being reclaimed by nature", "grid_size": "3" }, "logs": "tokenizing text\n['Ġrusty']\n['Ġiron']\n['Ġman']\n['Ġsuit']\n['Ġfound']\n['Ġabandoned']\n['Ġin']\n['Ġthe']\n['Ġwoods']\n['Ġbeing']\n['Ġreclaimed']\n['Ġby']\n['Ġnature']\ntext tokens [0, 22648, 2711, 339, 3717, 1593, 9352, 91, 99, 7550, 3032, 22912, 185, 2102, 2]\nencoding text tokens\nsampling image tokens\ndetokenizing image", "metrics": { "predict_time": 14.698031, "total_time": 14.87922 }, "output": "https://replicate.delivery/mgxm/89151ec9-0156-49e6-a39c-04c6d1d7ab49/output.jpg", "started_at": "2022-07-04T15:56:46.958943Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tavbsdamiba5rmpjkiwgwt3otq", "cancel": "https://api.replicate.com/v1/predictions/tavbsdamiba5rmpjkiwgwt3otq/cancel" }, "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507" }
Generated intokenizing text ['Ġrusty'] ['Ġiron'] ['Ġman'] ['Ġsuit'] ['Ġfound'] ['Ġabandoned'] ['Ġin'] ['Ġthe'] ['Ġwoods'] ['Ġbeing'] ['Ġreclaimed'] ['Ġby'] ['Ġnature'] text tokens [0, 22648, 2711, 339, 3717, 1593, 9352, 91, 99, 7550, 3032, 22912, 185, 2102, 2] encoding text tokens sampling image tokens detokenizing image
Prediction
kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507IDbc3vodc3inde7n62dcjdgtu57eStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- text
- Glass statue of cat on wooden table with ambient light
- grid_size
- "4"
{ "seed": -1, "text": "Glass statue of cat on wooden table with ambient light", "grid_size": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", { input: { seed: -1, text: "Glass statue of cat on wooden table with ambient light", grid_size: "4" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", input={ "seed": -1, "text": "Glass statue of cat on wooden table with ambient light", "grid_size": "4" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", "input": { "seed": -1, "text": "Glass statue of cat on wooden table with ambient light", "grid_size": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-04T15:57:58.806293Z", "created_at": "2022-07-04T15:57:35.446104Z", "data_removed": false, "error": null, "id": "bc3vodc3inde7n62dcjdgtu57e", "input": { "seed": -1, "text": "Glass statue of cat on wooden table with ambient light", "grid_size": "4" }, "logs": "tokenizing text\n['Ġglass']\n['Ġstatue']\n['Ġof']\n['Ġcat']\n['Ġon']\n['Ġwooden']\n['Ġtable']\n['Ġwith']\n['Ġambient']\n['Ġlight']\ntext tokens [0, 1431, 4039, 111, 803, 133, 3180, 1268, 208, 18136, 895, 2]\nencoding text tokens\nsampling image tokens\ndetokenizing image", "metrics": { "predict_time": 14.631041, "total_time": 23.360189 }, "output": "https://replicate.delivery/mgxm/f531fbfb-f65c-4e12-9024-76ff8543bd63/output.jpg", "started_at": "2022-07-04T15:57:44.175252Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bc3vodc3inde7n62dcjdgtu57e", "cancel": "https://api.replicate.com/v1/predictions/bc3vodc3inde7n62dcjdgtu57e/cancel" }, "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507" }
Generated intokenizing text ['Ġglass'] ['Ġstatue'] ['Ġof'] ['Ġcat'] ['Ġon'] ['Ġwooden'] ['Ġtable'] ['Ġwith'] ['Ġambient'] ['Ġlight'] text tokens [0, 1431, 4039, 111, 803, 133, 3180, 1268, 208, 18136, 895, 2] encoding text tokens sampling image tokens detokenizing image
Prediction
kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507IDy4i532atgnagfdmatcajyoav6aStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 10
- text
- court sketch of Godzilla on trial
- grid_size
- "4"
{ "seed": 10, "text": "court sketch of Godzilla on trial", "grid_size": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", { input: { seed: 10, text: "court sketch of Godzilla on trial", grid_size: "4" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", input={ "seed": 10, "text": "court sketch of Godzilla on trial", "grid_size": "4" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", "input": { "seed": 10, "text": "court sketch of Godzilla on trial", "grid_size": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-04T16:02:01.736528Z", "created_at": "2022-07-04T16:01:46.789810Z", "data_removed": false, "error": null, "id": "y4i532atgnagfdmatcajyoav6a", "input": { "seed": 10, "text": "court sketch of Godzilla on trial", "grid_size": "4" }, "logs": "tokenizing text\n['Ġcourt']\n['Ġsketch']\n['Ġof']\n['Ġgodzilla']\n['Ġon']\n['Ġtrial']\ntext tokens [0, 2634, 4189, 111, 14450, 133, 5167, 2]\nencoding text tokens\nsampling image tokens\ndetokenizing image", "metrics": { "predict_time": 14.778754, "total_time": 14.946718 }, "output": "https://replicate.delivery/mgxm/184d6f71-6883-4912-9eb3-0955fda122d6/output.jpg", "started_at": "2022-07-04T16:01:46.957774Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/y4i532atgnagfdmatcajyoav6a", "cancel": "https://api.replicate.com/v1/predictions/y4i532atgnagfdmatcajyoav6a/cancel" }, "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507" }
Generated intokenizing text ['Ġcourt'] ['Ġsketch'] ['Ġof'] ['Ġgodzilla'] ['Ġon'] ['Ġtrial'] text tokens [0, 2634, 4189, 111, 14450, 133, 5167, 2] encoding text tokens sampling image tokens detokenizing image
Prediction
kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507IDeop4rsggirekjjesbxm6f2bz5mStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- text
- scientists trying to rhyme orange with banana
- grid_size
- "4"
{ "seed": -1, "text": "scientists trying to rhyme orange with banana", "grid_size": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", { input: { seed: -1, text: "scientists trying to rhyme orange with banana", grid_size: "4" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", input={ "seed": -1, "text": "scientists trying to rhyme orange with banana", "grid_size": "4" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", "input": { "seed": -1, "text": "scientists trying to rhyme orange with banana", "grid_size": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-04T16:02:59.392469Z", "created_at": "2022-07-04T16:02:28.191019Z", "data_removed": false, "error": null, "id": "eop4rsggirekjjesbxm6f2bz5m", "input": { "seed": -1, "text": "scientists trying to rhyme orange with banana", "grid_size": "4" }, "logs": "tokenizing text\n['Ġscientists']\n['Ġtrying']\n['Ġto']\n['Ġrhyme']\n['Ġorange']\n['Ġwith']\n['Ġbanana']\ntext tokens [0, 11563, 9806, 123, 37866, 2566, 208, 9023, 2]\nencoding text tokens\nsampling image tokens\ndetokenizing image", "metrics": { "predict_time": 14.767875, "total_time": 31.20145 }, "output": "https://replicate.delivery/mgxm/d7a818a4-88e6-4352-b2a1-500bbb5ea69f/output.jpg", "started_at": "2022-07-04T16:02:44.624594Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/eop4rsggirekjjesbxm6f2bz5m", "cancel": "https://api.replicate.com/v1/predictions/eop4rsggirekjjesbxm6f2bz5m/cancel" }, "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507" }
Generated intokenizing text ['Ġscientists'] ['Ġtrying'] ['Ġto'] ['Ġrhyme'] ['Ġorange'] ['Ġwith'] ['Ġbanana'] text tokens [0, 11563, 9806, 123, 37866, 2566, 208, 9023, 2] encoding text tokens sampling image tokens detokenizing image
Prediction
kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507IDpqez4dxu5nawhj65dgy3owleueStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- text
- Cleopatra checking her iPhone
- grid_size
- "4"
{ "seed": -1, "text": "Cleopatra checking her iPhone", "grid_size": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", { input: { seed: -1, text: "Cleopatra checking her iPhone", grid_size: "4" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", input={ "seed": -1, "text": "Cleopatra checking her iPhone", "grid_size": "4" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", "input": { "seed": -1, "text": "Cleopatra checking her iPhone", "grid_size": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-04T16:06:11.007181Z", "created_at": "2022-07-04T16:05:55.995545Z", "data_removed": false, "error": null, "id": "pqez4dxu5nawhj65dgy3owleue", "input": { "seed": -1, "text": "Cleopatra checking her iPhone", "grid_size": "4" }, "logs": "tokenizing text\n['Ġcleopatra']\n['Ġchecking']\n['Ġher']\n['Ġiphone']\ntext tokens [0, 31316, 24119, 447, 2465, 2]\nencoding text tokens\nsampling image tokens\ndetokenizing image", "metrics": { "predict_time": 14.846385, "total_time": 15.011636 }, "output": "https://replicate.delivery/mgxm/9e3a5542-fd89-45d1-985a-eaeca0b460c8/output.jpg", "started_at": "2022-07-04T16:05:56.160796Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pqez4dxu5nawhj65dgy3owleue", "cancel": "https://api.replicate.com/v1/predictions/pqez4dxu5nawhj65dgy3owleue/cancel" }, "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507" }
Generated intokenizing text ['Ġcleopatra'] ['Ġchecking'] ['Ġher'] ['Ġiphone'] text tokens [0, 31316, 24119, 447, 2465, 2] encoding text tokens sampling image tokens detokenizing image
Prediction
kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507IDyiekfkkj7fdqrbw6qvbgam6lwaStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- text
- a painting of an astronaut couple
- grid_size
- "4"
{ "seed": -1, "text": "a painting of an astronaut couple", "grid_size": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", { input: { seed: -1, text: "a painting of an astronaut couple", grid_size: "4" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", input={ "seed": -1, "text": "a painting of an astronaut couple", "grid_size": "4" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", "input": { "seed": -1, "text": "a painting of an astronaut couple", "grid_size": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-04T16:07:05.367898Z", "created_at": "2022-07-04T16:06:50.078632Z", "data_removed": false, "error": null, "id": "yiekfkkj7fdqrbw6qvbgam6lwa", "input": { "seed": -1, "text": "a painting of an astronaut couple", "grid_size": "4" }, "logs": "tokenizing text\n['Ġa']\n['Ġpainting']\n['Ġof']\n['Ġan']\n['Ġastronaut']\n['Ġcouple']\ntext tokens [0, 58, 1545, 111, 101, 14282, 4808, 2]\nencoding text tokens\nsampling image tokens\ndetokenizing image", "metrics": { "predict_time": 15.063463, "total_time": 15.289266 }, "output": "https://replicate.delivery/mgxm/51d6a24e-99a0-44e9-8aae-f15d13f8de93/output.jpg", "started_at": "2022-07-04T16:06:50.304435Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yiekfkkj7fdqrbw6qvbgam6lwa", "cancel": "https://api.replicate.com/v1/predictions/yiekfkkj7fdqrbw6qvbgam6lwa/cancel" }, "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507" }
Generated intokenizing text ['Ġa'] ['Ġpainting'] ['Ġof'] ['Ġan'] ['Ġastronaut'] ['Ġcouple'] text tokens [0, 58, 1545, 111, 101, 14282, 4808, 2] encoding text tokens sampling image tokens detokenizing image
Prediction
kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507IDkif3tf63r5hrnljjb6z7un5joyStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- text
- sally selling sea shells by the sea shore
- grid_size
- "4"
{ "seed": -1, "text": "sally selling sea shells by the sea shore", "grid_size": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", { input: { seed: -1, text: "sally selling sea shells by the sea shore", grid_size: "4" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", input={ "seed": -1, "text": "sally selling sea shells by the sea shore", "grid_size": "4" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", "input": { "seed": -1, "text": "sally selling sea shells by the sea shore", "grid_size": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-04T16:08:56.858704Z", "created_at": "2022-07-04T16:08:31.972325Z", "data_removed": false, "error": null, "id": "kif3tf63r5hrnljjb6z7un5joy", "input": { "seed": -1, "text": "sally selling sea shells by the sea shore", "grid_size": "4" }, "logs": "tokenizing text\n['Ġsally']\n['Ġselling']\n['Ġsea']\n['Ġshells']\n['Ġby']\n['Ġthe']\n['Ġsea']\n['Ġshore']\ntext tokens [0, 14159, 6382, 2074, 18052, 185, 99, 2074, 7349, 2]\nencoding text tokens\nsampling image tokens\ndetokenizing image", "metrics": { "predict_time": 14.599486, "total_time": 24.886379 }, "output": "https://replicate.delivery/mgxm/0aa2f5bb-e206-445b-a3be-5a864cb1358d/output.jpg", "started_at": "2022-07-04T16:08:42.259218Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kif3tf63r5hrnljjb6z7un5joy", "cancel": "https://api.replicate.com/v1/predictions/kif3tf63r5hrnljjb6z7un5joy/cancel" }, "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507" }
Generated intokenizing text ['Ġsally'] ['Ġselling'] ['Ġsea'] ['Ġshells'] ['Ġby'] ['Ġthe'] ['Ġsea'] ['Ġshore'] text tokens [0, 14159, 6382, 2074, 18052, 185, 99, 2074, 7349, 2] encoding text tokens sampling image tokens detokenizing image
Prediction
kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507IDlwqsrjduljdjdm5467lgr7lnxiStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- text
- trail cam footage of gollum eating watermelon
- grid_size
- "4"
{ "seed": -1, "text": "trail cam footage of gollum eating watermelon", "grid_size": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", { input: { seed: -1, text: "trail cam footage of gollum eating watermelon", grid_size: "4" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", input={ "seed": -1, "text": "trail cam footage of gollum eating watermelon", "grid_size": "4" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", "input": { "seed": -1, "text": "trail cam footage of gollum eating watermelon", "grid_size": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-04T16:13:16.010250Z", "created_at": "2022-07-04T16:13:01.484774Z", "data_removed": false, "error": null, "id": "lwqsrjduljdjdm5467lgr7lnxi", "input": { "seed": -1, "text": "trail cam footage of gollum eating watermelon", "grid_size": "4" }, "logs": "tokenizing text\n['Ġtrail']\n['Ġcam']\n['Ġfootage']\n['Ġof']\n['Ġgoll', 'um']\n['Ġeating']\n['Ġwatermelon']\ntext tokens [0, 1737, 904, 8271, 111, 39831, 140, 7077, 16927, 2]\nencoding text tokens\nsampling image tokens\ndetokenizing image", "metrics": { "predict_time": 14.350364, "total_time": 14.525476 }, "output": "https://replicate.delivery/mgxm/446ec677-7933-4d31-b767-f95e58352890/output.jpg", "started_at": "2022-07-04T16:13:01.659886Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/lwqsrjduljdjdm5467lgr7lnxi", "cancel": "https://api.replicate.com/v1/predictions/lwqsrjduljdjdm5467lgr7lnxi/cancel" }, "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507" }
Generated intokenizing text ['Ġtrail'] ['Ġcam'] ['Ġfootage'] ['Ġof'] ['Ġgoll', 'um'] ['Ġeating'] ['Ġwatermelon'] text tokens [0, 1737, 904, 8271, 111, 39831, 140, 7077, 16927, 2] encoding text tokens sampling image tokens detokenizing image
Prediction
kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507IDguvqbywe2bei5oqymgxbkwp2ryStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- text
- iPhone from early 1900s
- grid_size
- "4"
{ "seed": -1, "text": "iPhone from early 1900s", "grid_size": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", { input: { seed: -1, text: "iPhone from early 1900s", grid_size: "4" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", input={ "seed": -1, "text": "iPhone from early 1900s", "grid_size": "4" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", "input": { "seed": -1, "text": "iPhone from early 1900s", "grid_size": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-04T16:14:04.202576Z", "created_at": "2022-07-04T16:13:45.868667Z", "data_removed": false, "error": null, "id": "guvqbywe2bei5oqymgxbkwp2ry", "input": { "seed": -1, "text": "iPhone from early 1900s", "grid_size": "4" }, "logs": "tokenizing text\n['Ġiphone']\n['Ġfrom']\n['Ġearly']\n['Ġ1900', 's']\ntext tokens [0, 2465, 314, 3135, 10594, 46, 2]\nencoding text tokens\nsampling image tokens\ndetokenizing image", "metrics": { "predict_time": 14.57614, "total_time": 18.333909 }, "output": "https://replicate.delivery/mgxm/fa175257-87ba-4f9a-a1e3-dd64a1c6a605/output.jpg", "started_at": "2022-07-04T16:13:49.626436Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/guvqbywe2bei5oqymgxbkwp2ry", "cancel": "https://api.replicate.com/v1/predictions/guvqbywe2bei5oqymgxbkwp2ry/cancel" }, "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507" }
Generated intokenizing text ['Ġiphone'] ['Ġfrom'] ['Ġearly'] ['Ġ1900', 's'] text tokens [0, 2465, 314, 3135, 10594, 46, 2] encoding text tokens sampling image tokens detokenizing image
Prediction
kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507IDid24rcj7b5dgjdaiucxgdgmv2eStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 8
- text
- cctv footage of Yoda robbing a liquor store
- grid_size
- "4"
{ "seed": 8, "text": "cctv footage of Yoda robbing a liquor store", "grid_size": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", { input: { seed: 8, text: "cctv footage of Yoda robbing a liquor store", grid_size: "4" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", input={ "seed": 8, "text": "cctv footage of Yoda robbing a liquor store", "grid_size": "4" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", "input": { "seed": 8, "text": "cctv footage of Yoda robbing a liquor store", "grid_size": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-04T16:14:41.417373Z", "created_at": "2022-07-04T16:14:26.682430Z", "data_removed": false, "error": null, "id": "id24rcj7b5dgjdaiucxgdgmv2e", "input": { "seed": 8, "text": "cctv footage of Yoda robbing a liquor store", "grid_size": "4" }, "logs": "tokenizing text\n['Ġcctv']\n['Ġfootage']\n['Ġof']\n['Ġyoda']\n['Ġrob', 'bing']\n['Ġa']\n['Ġliquor']\n['Ġstore']\ntext tokens [0, 17685, 8271, 111, 24509, 976, 11811, 58, 13142, 1110, 2]\nencoding text tokens\nsampling image tokens\ndetokenizing image", "metrics": { "predict_time": 14.5809, "total_time": 14.734943 }, "output": "https://replicate.delivery/mgxm/c85acb55-8f86-41db-9b7d-99b18ba94583/output.jpg", "started_at": "2022-07-04T16:14:26.836473Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/id24rcj7b5dgjdaiucxgdgmv2e", "cancel": "https://api.replicate.com/v1/predictions/id24rcj7b5dgjdaiucxgdgmv2e/cancel" }, "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507" }
Generated intokenizing text ['Ġcctv'] ['Ġfootage'] ['Ġof'] ['Ġyoda'] ['Ġrob', 'bing'] ['Ġa'] ['Ġliquor'] ['Ġstore'] text tokens [0, 17685, 8271, 111, 24509, 976, 11811, 58, 13142, 1110, 2] encoding text tokens sampling image tokens detokenizing image
Prediction
kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507ID5idjj6jsonayncyg7pnaxtfqtqStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- text
- funeral at Whole Foods
- grid_size
- "4"
{ "seed": -1, "text": "funeral at Whole Foods", "grid_size": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", { input: { seed: -1, text: "funeral at Whole Foods", grid_size: "4" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", input={ "seed": -1, "text": "funeral at Whole Foods", "grid_size": "4" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", "input": { "seed": -1, "text": "funeral at Whole Foods", "grid_size": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-04T16:15:45.390278Z", "created_at": "2022-07-04T16:15:18.664723Z", "data_removed": false, "error": null, "id": "5idjj6jsonayncyg7pnaxtfqtq", "input": { "seed": -1, "text": "funeral at Whole Foods", "grid_size": "4" }, "logs": "tokenizing text\n['Ġfuneral']\n['Ġat']\n['Ġwhole']\n['Ġfoods']\ntext tokens [0, 4676, 202, 5510, 4898, 2]\nencoding text tokens\nsampling image tokens\ndetokenizing image", "metrics": { "predict_time": 14.385538, "total_time": 26.725555 }, "output": "https://replicate.delivery/mgxm/d6aaa347-d6da-4bf8-93a0-af08c78b9851/output.jpg", "started_at": "2022-07-04T16:15:31.004740Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5idjj6jsonayncyg7pnaxtfqtq", "cancel": "https://api.replicate.com/v1/predictions/5idjj6jsonayncyg7pnaxtfqtq/cancel" }, "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507" }
Generated intokenizing text ['Ġfuneral'] ['Ġat'] ['Ġwhole'] ['Ġfoods'] text tokens [0, 4676, 202, 5510, 4898, 2] encoding text tokens sampling image tokens detokenizing image
Prediction
kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507IDmlyag4eqjjbgplhnqqr6hs646yStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- text
- pixel art of the San Francisco skyline in the style of the Starry Night, distinct pixels, 1:1
- grid_size
- "4"
{ "seed": -1, "text": "pixel art of the San Francisco skyline in the style of the Starry Night, distinct pixels, 1:1", "grid_size": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", { input: { seed: -1, text: "pixel art of the San Francisco skyline in the style of the Starry Night, distinct pixels, 1:1", grid_size: "4" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", input={ "seed": -1, "text": "pixel art of the San Francisco skyline in the style of the Starry Night, distinct pixels, 1:1", "grid_size": "4" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507", "input": { "seed": -1, "text": "pixel art of the San Francisco skyline in the style of the Starry Night, distinct pixels, 1:1", "grid_size": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-05T09:35:40.346897Z", "created_at": "2022-07-05T09:35:20.063231Z", "data_removed": false, "error": null, "id": "mlyag4eqjjbgplhnqqr6hs646y", "input": { "seed": -1, "text": "pixel art of the San Francisco skyline in the style of the Starry Night, distinct pixels, 1:1", "grid_size": "4" }, "logs": "tokenizing text\n['Ġpixel']\n['Ġart']\n['Ġof']\n['Ġthe']\n['Ġsan']\n['Ġfrancisco']\n['Ġskyline']\n['Ġin']\n['Ġthe']\n['Ġstyle']\n['Ġof']\n['Ġthe']\n['Ġstarry']\n['Ġnight', ',']\n['Ġdistinct']\n['Ġpixels', ',']\n['Ġ1', ':', '1']\ntext tokens [0, 6423, 241, 111, 99, 897, 4535, 9542, 91, 99, 1155, 111, 99, 21483, 1413, 11, 20784, 10984, 11, 116, 3, 15, 2]\nencoding text tokens\nsampling image tokens\ndetokenizing image", "metrics": { "predict_time": 14.665834, "total_time": 20.283666 }, "output": "https://replicate.delivery/mgxm/c011e258-a77e-496c-99ce-039650eaa8b3/output.jpg", "started_at": "2022-07-05T09:35:25.681063Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/mlyag4eqjjbgplhnqqr6hs646y", "cancel": "https://api.replicate.com/v1/predictions/mlyag4eqjjbgplhnqqr6hs646y/cancel" }, "version": "2cbfe7a26259c267c9485366b8d3fb98b243cf20eadfd207bd6b760d63859507" }
Generated intokenizing text ['Ġpixel'] ['Ġart'] ['Ġof'] ['Ġthe'] ['Ġsan'] ['Ġfrancisco'] ['Ġskyline'] ['Ġin'] ['Ġthe'] ['Ġstyle'] ['Ġof'] ['Ġthe'] ['Ġstarry'] ['Ġnight', ','] ['Ġdistinct'] ['Ġpixels', ','] ['Ġ1', ':', '1'] text tokens [0, 6423, 241, 111, 99, 897, 4535, 9542, 91, 99, 1155, 111, 99, 21483, 1413, 11, 20784, 10984, 11, 116, 3, 15, 2] encoding text tokens sampling image tokens detokenizing image
Prediction
kuprel/min-dalle:f53055192b9f0422f3d3a5fe6874deb59aef64b90249372186901c9f02d4760dIDvrzrveolzzboth6b2h4ddqgr2aStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- nuclear explosion broccoli
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- "5"
{ "text": "nuclear explosion broccoli", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "5" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:f53055192b9f0422f3d3a5fe6874deb59aef64b90249372186901c9f02d4760d", { input: { text: "nuclear explosion broccoli", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: "5" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:f53055192b9f0422f3d3a5fe6874deb59aef64b90249372186901c9f02d4760d", input={ "text": "nuclear explosion broccoli", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": "5" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "f53055192b9f0422f3d3a5fe6874deb59aef64b90249372186901c9f02d4760d", "input": { "text": "nuclear explosion broccoli", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "5" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-05T17:04:05.156555Z", "created_at": "2022-07-05T17:03:49.490588Z", "data_removed": false, "error": null, "id": "vrzrveolzzboth6b2h4ddqgr2a", "input": { "text": "nuclear explosion broccoli", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "5" }, "logs": "tokenizing text\n['Ġnuclear']\n['Ġexplosion']\n['Ġbroccoli']\ntext tokens [0, 7711, 13322, 23182, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 15.525143, "total_time": 15.665967 }, "output": [ "https://replicate.delivery/mgxm/36921011-92be-4d89-b056-63497284cd40/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/3c69898a-d3d3-4c99-8b9b-6fc1ff9748df/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/81d1c766-fe4b-47fe-98f1-44fedfe5b074/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/50468997-e81e-4550-8f39-6a61c9d5d5d1/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/02ba2d9d-7848-423d-a424-4925b3a43adb/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/1bc7dc53-74bd-4c47-ae91-8cee31278395/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/2f1056f9-af9c-4113-88b9-33b26e2876cd/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/9ddad12c-473d-4980-898e-5f0007e68df5/min-dalle-iter-8.jpg" ], "started_at": "2022-07-05T17:03:49.631412Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vrzrveolzzboth6b2h4ddqgr2a", "cancel": "https://api.replicate.com/v1/predictions/vrzrveolzzboth6b2h4ddqgr2a/cancel" }, "version": "f53055192b9f0422f3d3a5fe6874deb59aef64b90249372186901c9f02d4760d" }
Generated intokenizing text ['Ġnuclear'] ['Ġexplosion'] ['Ġbroccoli'] text tokens [0, 7711, 13322, 23182, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7Input
- text
- A rusty abandoned Tesla Model 3 parked on a Boulevard lined with Palm Trees in a Cyberpunk style New York City on a sunny afternoon, 8k, Photorealistic
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "A rusty abandoned Tesla Model 3 parked on a Boulevard lined with Palm Trees in a Cyberpunk style New York City on a sunny afternoon, 8k, Photorealistic ", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "A rusty abandoned Tesla Model 3 parked on a Boulevard lined with Palm Trees in a Cyberpunk style New York City on a sunny afternoon, 8k, Photorealistic ", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "A rusty abandoned Tesla Model 3 parked on a Boulevard lined with Palm Trees in a Cyberpunk style New York City on a sunny afternoon, 8k, Photorealistic ", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "A rusty abandoned Tesla Model 3 parked on a Boulevard lined with Palm Trees in a Cyberpunk style New York City on a sunny afternoon, 8k, Photorealistic ", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-10T19:26:47.965307Z", "created_at": "2022-07-10T19:26:25.690700Z", "data_removed": false, "error": null, "id": "wvc4xri2xzefxlzelqk54w2zhi", "input": { "text": "A rusty abandoned Tesla Model 3 parked on a Boulevard lined with Palm Trees in a Cyberpunk style New York City on a sunny afternoon, 8k, Photorealistic ", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġa']\n['Ġrusty']\n['Ġabandoned']\n['Ġtesla']\n['Ġmodel']\n['Ġ3']\n['Ġparked']\n['Ġon']\n['Ġa']\n['Ġboulevard']\n['Ġlined']\n['Ġwith']\n['Ġpalm']\n['Ġtrees']\n['Ġin']\n['Ġa']\n['Ġcyberpunk']\n['Ġstyle']\n['Ġnew']\n['Ġyork']\n['Ġcity']\n['Ġon']\n['Ġa']\n['Ġsunny']\n['Ġafternoon', ',']\n['Ġ8', 'k', ',']\n['Ġphot', 'oreal', 'istic']\ntext tokens [0, 58, 22648, 9352, 10914, 1080, 204, 45282, 133, 58, 11790, 12903, 208, 4227, 4807, 91, 58, 18850, 1155, 173, 1194, 645, 133, 58, 8751, 12598, 11, 416, 38, 11, 234, 35495, 3478, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 22.094111, "total_time": 22.274607 }, "output": [ "https://replicate.delivery/mgxm/e4573f7d-c829-4e18-a6b6-b6323bda6092/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/e8596cb0-4623-46c4-8db1-15a2a180990c/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/5d891d19-a9a7-4d56-9183-5a2a62c3a4fb/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/2532cce7-3843-4a4f-b425-882aa22828df/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/8d5e3199-3b40-4499-a10a-80d2ad408b01/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/a8a15cdf-e24d-447f-a681-8c853a39fcc1/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/8eb99296-e00d-472a-9cf4-2fd9ab01e7a4/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/64a688ef-a16c-4c14-8f03-4645d76888e5/min-dalle-iter-8.jpg" ], "started_at": "2022-07-10T19:26:25.871196Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wvc4xri2xzefxlzelqk54w2zhi", "cancel": "https://api.replicate.com/v1/predictions/wvc4xri2xzefxlzelqk54w2zhi/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġa'] ['Ġrusty'] ['Ġabandoned'] ['Ġtesla'] ['Ġmodel'] ['Ġ3'] ['Ġparked'] ['Ġon'] ['Ġa'] ['Ġboulevard'] ['Ġlined'] ['Ġwith'] ['Ġpalm'] ['Ġtrees'] ['Ġin'] ['Ġa'] ['Ġcyberpunk'] ['Ġstyle'] ['Ġnew'] ['Ġyork'] ['Ġcity'] ['Ġon'] ['Ġa'] ['Ġsunny'] ['Ġafternoon', ','] ['Ġ8', 'k', ','] ['Ġphot', 'oreal', 'istic'] text tokens [0, 58, 22648, 9352, 10914, 1080, 204, 45282, 133, 58, 11790, 12903, 208, 4227, 4807, 91, 58, 18850, 1155, 173, 1194, 645, 133, 58, 8751, 12598, 11, 416, 38, 11, 234, 35495, 3478, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7Input
- text
- A gray rat cooking ratatouille in a kitchen, action shot, photograph, 4k, HD
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "A gray rat cooking ratatouille in a kitchen, action shot, photograph, 4k, HD", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "A gray rat cooking ratatouille in a kitchen, action shot, photograph, 4k, HD", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "A gray rat cooking ratatouille in a kitchen, action shot, photograph, 4k, HD", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "A gray rat cooking ratatouille in a kitchen, action shot, photograph, 4k, HD", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-10T20:25:08.470306Z", "created_at": "2022-07-10T20:24:51.968150Z", "data_removed": false, "error": null, "id": "totpf7v5hreo5arh7dhljxeuwq", "input": { "text": "A gray rat cooking ratatouille in a kitchen, action shot, photograph, 4k, HD", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġa']\n['Ġgray']\n['Ġrat']\n['Ġcooking']\n['Ġrat', 'at', 'ouille']\n['Ġin']\n['Ġa']\n['Ġkitchen', ',']\n['Ġaction']\n['Ġshot', ',']\n['Ġphotograph', ',']\n['Ġ4', 'k', ',']\n['Ġhd']\ntext tokens [0, 58, 3891, 2726, 4774, 2726, 73, 32743, 91, 58, 1449, 11, 1945, 3798, 11, 701, 11, 252, 38, 11, 831, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.312669, "total_time": 16.502156 }, "output": [ "https://replicate.delivery/mgxm/7603a91a-08a4-42bd-ac81-83e77662f60d/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/c8f35f88-2ce9-4948-95e1-85f51df074bb/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/dfbf589d-5ddb-47b0-b1c2-47a537308601/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/83ab5e8c-d290-4495-9e01-47821b30fbd6/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/4af9b6a9-1492-41ed-bea0-cc0e35ade5f5/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/fcd3e624-f327-4a69-a9ac-275250fc87fa/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/138098aa-f6ab-4a4c-bd36-ab94f0eee591/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/1ab9eae4-450d-492c-abbe-058ff8ef9f81/min-dalle-iter-8.jpg" ], "started_at": "2022-07-10T20:24:52.157637Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/totpf7v5hreo5arh7dhljxeuwq", "cancel": "https://api.replicate.com/v1/predictions/totpf7v5hreo5arh7dhljxeuwq/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġa'] ['Ġgray'] ['Ġrat'] ['Ġcooking'] ['Ġrat', 'at', 'ouille'] ['Ġin'] ['Ġa'] ['Ġkitchen', ','] ['Ġaction'] ['Ġshot', ','] ['Ġphotograph', ','] ['Ġ4', 'k', ','] ['Ġhd'] text tokens [0, 58, 3891, 2726, 4774, 2726, 73, 32743, 91, 58, 1449, 11, 1945, 3798, 11, 701, 11, 252, 38, 11, 831, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7Input
- text
- a bird's-eye view of an endlessly large futuristic synthwave-style city, at the noon, great color photo, small subtitles in Georgian language below
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "a bird's-eye view of an endlessly large futuristic synthwave-style city, at the noon, great color photo, small subtitles in Georgian language below ", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "a bird's-eye view of an endlessly large futuristic synthwave-style city, at the noon, great color photo, small subtitles in Georgian language below ", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "a bird's-eye view of an endlessly large futuristic synthwave-style city, at the noon, great color photo, small subtitles in Georgian language below ", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "a bird\'s-eye view of an endlessly large futuristic synthwave-style city, at the noon, great color photo, small subtitles in Georgian language below ", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-11T05:47:34.236263Z", "created_at": "2022-07-11T05:46:34.762683Z", "data_removed": false, "error": null, "id": "kokoo4nq7rflvh67vogorvh4cm", "input": { "text": "a bird's-eye view of an endlessly large futuristic synthwave-style city, at the noon, great color photo, small subtitles in Georgian language below ", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġa']\n['Ġbird', \"'s\", '-', 'eye']\n['Ġview']\n['Ġof']\n['Ġan']\n['Ġendless', 'ly']\n['Ġlarge']\n['Ġfuturistic']\n['Ġsynth', 'wave', '-', 'style']\n['Ġcity', ',']\n['Ġat']\n['Ġthe']\n['Ġnoon', ',']\n['Ġgreat']\n['Ġcolor']\n['Ġphoto', ',']\n['Ġsmall']\n['Ġsubtitles']\n['Ġin']\n['Ġgeorgian']\n['Ġlanguage']\n['Ġbelow']\ntext tokens [0, 58, 2460, 168, 3, 12250, 1328, 111, 101, 17055, 304, 2033, 18196, 8465, 11341, 3, 9394, 645, 11, 202, 99, 24101, 11, 1242, 833, 564, 11, 1577, 23801, 91, 18032, 3622, 9464, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.371969, "total_time": 59.47358 }, "output": [ "https://replicate.delivery/mgxm/bebc2cf1-dbb3-49eb-90ac-66a544e3e473/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/1c83e964-5a08-4ef5-9299-9b55fd20c1c1/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/789ede3f-17ec-40e6-9485-3036617cda31/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/f7f48fb8-89fe-42c7-bb2b-94138583523f/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/9db4c52f-8ff3-4e2b-80c7-cb0953d9a24e/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/b1a2c650-3c58-4eda-8042-bac529abb7dd/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/76835bff-c1ec-4e4f-9648-127fbf7cdeca/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/82a4de4d-cd28-4813-afe6-f4543677ef51/min-dalle-iter-8.jpg" ], "started_at": "2022-07-11T05:47:17.864294Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kokoo4nq7rflvh67vogorvh4cm", "cancel": "https://api.replicate.com/v1/predictions/kokoo4nq7rflvh67vogorvh4cm/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġa'] ['Ġbird', "'s", '-', 'eye'] ['Ġview'] ['Ġof'] ['Ġan'] ['Ġendless', 'ly'] ['Ġlarge'] ['Ġfuturistic'] ['Ġsynth', 'wave', '-', 'style'] ['Ġcity', ','] ['Ġat'] ['Ġthe'] ['Ġnoon', ','] ['Ġgreat'] ['Ġcolor'] ['Ġphoto', ','] ['Ġsmall'] ['Ġsubtitles'] ['Ġin'] ['Ġgeorgian'] ['Ġlanguage'] ['Ġbelow'] text tokens [0, 58, 2460, 168, 3, 12250, 1328, 111, 101, 17055, 304, 2033, 18196, 8465, 11341, 3, 9394, 645, 11, 202, 99, 24101, 11, 1242, 833, 564, 11, 1577, 23801, 91, 18032, 3622, 9464, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7Input
- text
- a cat wearing a leather jacket and sunglasses
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- "4"
{ "text": "a cat wearing a leather jacket and sunglasses", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "a cat wearing a leather jacket and sunglasses", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: "4" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "a cat wearing a leather jacket and sunglasses", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": "4" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "a cat wearing a leather jacket and sunglasses", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-11T06:05:47.567809Z", "created_at": "2022-07-11T06:05:17.352755Z", "data_removed": false, "error": null, "id": "k4d6vnu7frchhpthbo6cssaxlm", "input": { "text": "a cat wearing a leather jacket and sunglasses", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "4" }, "logs": "tokenizing text\n['Ġa']\n['Ġcat']\n['Ġwearing']\n['Ġa']\n['Ġleather']\n['Ġjacket']\n['Ġand']\n['Ġsunglasses']\ntext tokens [0, 58, 803, 8995, 58, 2264, 3326, 128, 7134, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.756429, "total_time": 30.215054 }, "output": [ "https://replicate.delivery/mgxm/16ec7fa9-b881-4647-9678-d9a61d2f8fe7/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/9a18d50d-f5ec-4f2e-8ec0-3189ee8e44b9/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/b3a0d278-27a5-417e-8dca-70dd63535d6a/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/9679b816-0891-40c9-8cd1-fde625b28c93/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/d9e6f2d4-1b6d-4147-ae68-1910411234e8/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/4513e993-7561-4e11-84e6-91387b072542/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/47fbec40-59f8-43b9-b16d-47756e28de8e/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/b3579969-17b2-43f2-b757-ee93ba3668c5/min-dalle-iter-8.jpg" ], "started_at": "2022-07-11T06:05:30.811380Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/k4d6vnu7frchhpthbo6cssaxlm", "cancel": "https://api.replicate.com/v1/predictions/k4d6vnu7frchhpthbo6cssaxlm/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġa'] ['Ġcat'] ['Ġwearing'] ['Ġa'] ['Ġleather'] ['Ġjacket'] ['Ġand'] ['Ġsunglasses'] text tokens [0, 58, 803, 8995, 58, 2264, 3326, 128, 7134, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7Input
- text
- photo of woolly mammoth far away in a field, dramatic lighting, 35mm film
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "photo of woolly mammoth far away in a field, dramatic lighting, 35mm film", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "photo of woolly mammoth far away in a field, dramatic lighting, 35mm film", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "photo of woolly mammoth far away in a field, dramatic lighting, 35mm film", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "photo of woolly mammoth far away in a field, dramatic lighting, 35mm film", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-11T06:20:20.661291Z", "created_at": "2022-07-11T06:19:54.393499Z", "data_removed": false, "error": null, "id": "aevaklbw25dtrni6v5nkdkwfbu", "input": { "text": "photo of woolly mammoth far away in a field, dramatic lighting, 35mm film", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġphoto']\n['Ġof']\n['Ġwoolly']\n['Ġmammoth']\n['Ġfar']\n['Ġaway']\n['Ġin']\n['Ġa']\n['Ġfield', ',']\n['Ġdramatic']\n['Ġlighting', ',']\n['Ġ35', 'mm']\n['Ġfilm']\ntext tokens [0, 564, 111, 43913, 24399, 1953, 3823, 91, 58, 2851, 11, 14836, 4352, 11, 2013, 1309, 1144, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.383742, "total_time": 26.267792 }, "output": [ "https://replicate.delivery/mgxm/41582ac9-09f8-4d7e-8230-3a121469eb41/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/d6ef4d0f-c57e-4798-b67a-dac190baa8f3/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/e4251eae-0b35-4d62-a723-2b75fa5d4259/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/775a67e3-6846-4568-a06b-ad3b5312d67f/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/ea341245-4439-45c2-b26a-cc471f7fe99a/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/c7073ca1-fbd2-4ff6-bfb6-84f4c079132a/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/e61c7ad4-cff7-468b-9f48-475725f28228/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/c65db64a-d353-48e3-a92a-e785a8963d3d/min-dalle-iter-8.jpg" ], "started_at": "2022-07-11T06:20:04.277549Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/aevaklbw25dtrni6v5nkdkwfbu", "cancel": "https://api.replicate.com/v1/predictions/aevaklbw25dtrni6v5nkdkwfbu/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġphoto'] ['Ġof'] ['Ġwoolly'] ['Ġmammoth'] ['Ġfar'] ['Ġaway'] ['Ġin'] ['Ġa'] ['Ġfield', ','] ['Ġdramatic'] ['Ġlighting', ','] ['Ġ35', 'mm'] ['Ġfilm'] text tokens [0, 564, 111, 43913, 24399, 1953, 3823, 91, 58, 2851, 11, 14836, 4352, 11, 2013, 1309, 1144, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7Input
- text
- futuristic castle surrounded by sprawling city on borderlands pandora, digital painting
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "futuristic castle surrounded by sprawling city on borderlands pandora, digital painting", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "futuristic castle surrounded by sprawling city on borderlands pandora, digital painting", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "futuristic castle surrounded by sprawling city on borderlands pandora, digital painting", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "futuristic castle surrounded by sprawling city on borderlands pandora, digital painting", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-11T06:25:45.769538Z", "created_at": "2022-07-11T06:25:06.394134Z", "data_removed": false, "error": null, "id": "yafnygka5vhuzkis4xljfxf63u", "input": { "text": "futuristic castle surrounded by sprawling city on borderlands pandora, digital painting", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġfuturistic']\n['Ġcastle']\n['Ġsurrounded']\n['Ġby']\n['Ġspraw', 'ling']\n['Ġcity']\n['Ġon']\n['Ġborderlands']\n['Ġpandora', ',']\n['Ġdigital']\n['Ġpainting']\ntext tokens [0, 18196, 2846, 30419, 185, 44665, 1048, 645, 133, 23637, 14567, 11, 1189, 1545, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.605728, "total_time": 39.375404 }, "output": [ "https://replicate.delivery/mgxm/9f84a278-918d-41d7-90a7-9b116598ce3f/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/5beaed08-3950-4683-aac1-757e8e1dce1c/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/38e6bc19-c8d9-4278-91f4-a4d852a7e12e/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/9a5f71df-0ac3-4380-a4c2-1b668042c695/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/9f9ec4c9-ffbd-4323-93b5-c23291a53b27/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/07e598ce-ffd6-4926-804b-8dbf83bc88b5/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/aa4079e4-448a-4388-a517-e119121ce492/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/a994f11a-de65-422f-a18c-9c73fd3c10ab/min-dalle-iter-8.jpg" ], "started_at": "2022-07-11T06:25:29.163810Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yafnygka5vhuzkis4xljfxf63u", "cancel": "https://api.replicate.com/v1/predictions/yafnygka5vhuzkis4xljfxf63u/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġfuturistic'] ['Ġcastle'] ['Ġsurrounded'] ['Ġby'] ['Ġspraw', 'ling'] ['Ġcity'] ['Ġon'] ['Ġborderlands'] ['Ġpandora', ','] ['Ġdigital'] ['Ġpainting'] text tokens [0, 18196, 2846, 30419, 185, 44665, 1048, 645, 133, 23637, 14567, 11, 1189, 1545, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7Input
- text
- futuristic castle surrounded by sprawling city on arrakis, digital painting
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "futuristic castle surrounded by sprawling city on arrakis, digital painting", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "futuristic castle surrounded by sprawling city on arrakis, digital painting", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "futuristic castle surrounded by sprawling city on arrakis, digital painting", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "futuristic castle surrounded by sprawling city on arrakis, digital painting", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-11T06:27:49.672233Z", "created_at": "2022-07-11T06:27:32.845287Z", "data_removed": false, "error": null, "id": "eikt7c2bgbai7hhjmlbgeqc6ki", "input": { "text": "futuristic castle surrounded by sprawling city on arrakis, digital painting", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġfuturistic']\n['Ġcastle']\n['Ġsurrounded']\n['Ġby']\n['Ġspraw', 'ling']\n['Ġcity']\n['Ġon']\n['Ġarr', 'akis', ',']\n['Ġdigital']\n['Ġpainting']\ntext tokens [0, 18196, 2846, 30419, 185, 44665, 1048, 645, 133, 1618, 19471, 11, 1189, 1545, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.661466, "total_time": 16.826946 }, "output": [ "https://replicate.delivery/mgxm/3a276ea7-874b-48d4-bfaa-41c72dfc42ee/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/e5af9750-d30d-479e-b2a1-20d6b4fd312b/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/07a82569-ca8e-4c41-90ff-4e7dcedf29ae/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/f16677bd-5d7d-4db1-97fd-bbb335270d47/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/29a85b02-66dc-4b00-a936-fa9f5b5046c2/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/80b242f0-32ff-4865-8ea7-c69f881406f2/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/8e334486-52a1-42d5-82b8-35960b0e7c4b/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/9ce8c431-eae8-439e-bd69-9b7e0f4818e0/min-dalle-iter-8.jpg" ], "started_at": "2022-07-11T06:27:33.010767Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/eikt7c2bgbai7hhjmlbgeqc6ki", "cancel": "https://api.replicate.com/v1/predictions/eikt7c2bgbai7hhjmlbgeqc6ki/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġfuturistic'] ['Ġcastle'] ['Ġsurrounded'] ['Ġby'] ['Ġspraw', 'ling'] ['Ġcity'] ['Ġon'] ['Ġarr', 'akis', ','] ['Ġdigital'] ['Ġpainting'] text tokens [0, 18196, 2846, 30419, 185, 44665, 1048, 645, 133, 1618, 19471, 11, 1189, 1545, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7Input
- text
- A silver express steam locomotive pulls up to an American 1940's city style train station at night, digital painting, high detail
- grid_size
- "5"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "A silver express steam locomotive pulls up to an American 1940's city style train station at night, digital painting, high detail", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", { input: { text: "A silver express steam locomotive pulls up to an American 1940's city style train station at night, digital painting, high detail", grid_size: "5", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", input={ "text": "A silver express steam locomotive pulls up to an American 1940's city style train station at night, digital painting, high detail", "grid_size": "5", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7", "input": { "text": "A silver express steam locomotive pulls up to an American 1940\'s city style train station at night, digital painting, high detail", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-11T07:02:50.562680Z", "created_at": "2022-07-11T07:02:33.982054Z", "data_removed": false, "error": null, "id": "44pqf6m55zhntihsha33tiwx54", "input": { "text": "A silver express steam locomotive pulls up to an American 1940's city style train station at night, digital painting, high detail", "grid_size": "5", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġa']\n['Ġsilver']\n['Ġexpress']\n['Ġsteam']\n['Ġlocomotive']\n['Ġpulls']\n['Ġup']\n['Ġto']\n['Ġan']\n['Ġamerican']\n['Ġ1940', \"'s\"]\n['Ġcity']\n['Ġstyle']\n['Ġtrain']\n['Ġstation']\n['Ġat']\n['Ġnight', ',']\n['Ġdigital']\n['Ġpainting', ',']\n['Ġhigh']\n['Ġdetail']\ntext tokens [0, 58, 1484, 1785, 3373, 15816, 18816, 369, 123, 101, 1206, 9758, 168, 645, 1155, 1286, 2087, 202, 1413, 11, 1189, 1545, 11, 524, 5854, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.441846, "total_time": 16.580626 }, "output": [ "https://replicate.delivery/mgxm/423d0f24-2afb-4131-bb99-6fddecff973d/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/9c81ebd8-5b48-427f-8f46-85b3e3951f03/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/921ae340-ea01-4786-adf1-5dbc664442f9/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/ff4ed46a-1a15-47d8-8411-578dbb746c8b/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/b69c1565-34b1-4f5e-a48b-cc96c3050f59/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/2be1e36a-6b61-441a-8b18-1f8bebd294e5/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/aba4b68d-fdef-49ea-9bd1-068a9f43ade3/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/4494b3a6-39c0-4961-aa58-6bdc4f32d1d1/min-dalle-iter-8.jpg" ], "started_at": "2022-07-11T07:02:34.120834Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/44pqf6m55zhntihsha33tiwx54", "cancel": "https://api.replicate.com/v1/predictions/44pqf6m55zhntihsha33tiwx54/cancel" }, "version": "fd77461b5df14ab0870ee331ecdd6c134900f6abed5525d8ad7dcd89458036c7" }
Generated intokenizing text ['Ġa'] ['Ġsilver'] ['Ġexpress'] ['Ġsteam'] ['Ġlocomotive'] ['Ġpulls'] ['Ġup'] ['Ġto'] ['Ġan'] ['Ġamerican'] ['Ġ1940', "'s"] ['Ġcity'] ['Ġstyle'] ['Ġtrain'] ['Ġstation'] ['Ġat'] ['Ġnight', ','] ['Ġdigital'] ['Ġpainting', ','] ['Ġhigh'] ['Ġdetail'] text tokens [0, 58, 1484, 1785, 3373, 15816, 18816, 369, 123, 101, 1206, 9758, 168, 645, 1155, 1286, 2087, 202, 1413, 11, 1189, 1545, 11, 524, 5854, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:ab54079a82a73c255bc5c744d330b7c6ff973f72e40ffb7898c6a664440260b0ID4lnrn3pli5gmbbv7emuko7jqzyStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- carrot in solitary confinement
- grid_size
- "5"
- temperature
- "1.5"
- intermediate_outputs
{ "text": "carrot in solitary confinement", "grid_size": "5", "temperature": "1.5", "intermediate_outputs": true }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:ab54079a82a73c255bc5c744d330b7c6ff973f72e40ffb7898c6a664440260b0", { input: { text: "carrot in solitary confinement", grid_size: "5", temperature: "1.5", intermediate_outputs: true } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:ab54079a82a73c255bc5c744d330b7c6ff973f72e40ffb7898c6a664440260b0", input={ "text": "carrot in solitary confinement", "grid_size": "5", "temperature": "1.5", "intermediate_outputs": True } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "ab54079a82a73c255bc5c744d330b7c6ff973f72e40ffb7898c6a664440260b0", "input": { "text": "carrot in solitary confinement", "grid_size": "5", "temperature": "1.5", "intermediate_outputs": true } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-11T18:25:10.128655Z", "created_at": "2022-07-11T18:24:53.863197Z", "data_removed": false, "error": null, "id": "4lnrn3pli5gmbbv7emuko7jqzy", "input": { "text": "carrot in solitary confinement", "grid_size": "5", "temperature": "1.5", "intermediate_outputs": true }, "logs": "tokenizing text\n['Ġcarrot']\n['Ġin']\n['Ġsolitary']\n['Ġconfinement']\n6 text tokens [0, 19615, 91, 44086, 43654, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.0712, "total_time": 16.265458 }, "output": [ "https://replicate.delivery/mgxm/23c0231d-a1ce-4ca7-9008-344a6dda8beb/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/840b0712-0275-4630-a853-ee171432f41a/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/eb0abae5-44a8-4a98-b5bc-25d82b97e6fd/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/ab723a56-6c8a-4877-af49-3fec3c2bfde5/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/a902e9e7-0a03-4ac8-b17d-cd4b8e2e7bce/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/cbfebdf1-1c71-4939-b12d-45d6e48bc36c/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/7093154b-7d42-47c6-bc95-cf43c592d988/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/a439aec0-4996-4aa1-b2e7-dec19a353746/min-dalle-iter-8.jpg" ], "started_at": "2022-07-11T18:24:54.057455Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4lnrn3pli5gmbbv7emuko7jqzy", "cancel": "https://api.replicate.com/v1/predictions/4lnrn3pli5gmbbv7emuko7jqzy/cancel" }, "version": "ab54079a82a73c255bc5c744d330b7c6ff973f72e40ffb7898c6a664440260b0" }
Generated intokenizing text ['Ġcarrot'] ['Ġin'] ['Ġsolitary'] ['Ġconfinement'] 6 text tokens [0, 19615, 91, 44086, 43654, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:ab54079a82a73c255bc5c744d330b7c6ff973f72e40ffb7898c6a664440260b0IDsl5afndbmjd4vpfk4upxfrkuluStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- lofi nuclear war to relax and study to
- grid_size
- "5"
- temperature
- "1.5"
- intermediate_outputs
{ "text": "lofi nuclear war to relax and study to", "grid_size": "5", "temperature": "1.5", "intermediate_outputs": true }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:ab54079a82a73c255bc5c744d330b7c6ff973f72e40ffb7898c6a664440260b0", { input: { text: "lofi nuclear war to relax and study to", grid_size: "5", temperature: "1.5", intermediate_outputs: true } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:ab54079a82a73c255bc5c744d330b7c6ff973f72e40ffb7898c6a664440260b0", input={ "text": "lofi nuclear war to relax and study to", "grid_size": "5", "temperature": "1.5", "intermediate_outputs": True } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "ab54079a82a73c255bc5c744d330b7c6ff973f72e40ffb7898c6a664440260b0", "input": { "text": "lofi nuclear war to relax and study to", "grid_size": "5", "temperature": "1.5", "intermediate_outputs": true } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-11T18:29:58.271543Z", "created_at": "2022-07-11T18:29:41.850708Z", "data_removed": false, "error": null, "id": "sl5afndbmjd4vpfk4upxfrkulu", "input": { "text": "lofi nuclear war to relax and study to", "grid_size": "5", "temperature": "1.5", "intermediate_outputs": true }, "logs": "tokenizing text\n['Ġlof', 'i']\n['Ġnuclear']\n['Ġwar']\n['Ġto']\n['Ġrelax']\n['Ġand']\n['Ġstudy']\n['Ġto']\n11 text tokens [0, 13754, 36, 7711, 574, 123, 7693, 128, 2162, 123, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.211983, "total_time": 16.420835 }, "output": [ "https://replicate.delivery/mgxm/c04b86ed-0f26-42da-a774-52023b35f764/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/983cf476-7e8a-4a0c-a7f8-3c818ecdd181/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/f9a03b69-5bf7-4067-896b-5d06bc1b45a8/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/52a854c6-e9aa-485d-a592-c5ab6c985e9b/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/b7c8ac63-0420-40af-92b5-928442ad87b1/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/c70e13f1-f10a-4f0a-ac9c-366ed84b702c/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/e68d9a18-76a1-435a-b6be-ea545c3af6e0/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/f46ccfb3-c50b-488a-abf5-a5e37d216be3/min-dalle-iter-8.jpg" ], "started_at": "2022-07-11T18:29:42.059560Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/sl5afndbmjd4vpfk4upxfrkulu", "cancel": "https://api.replicate.com/v1/predictions/sl5afndbmjd4vpfk4upxfrkulu/cancel" }, "version": "ab54079a82a73c255bc5c744d330b7c6ff973f72e40ffb7898c6a664440260b0" }
Generated intokenizing text ['Ġlof', 'i'] ['Ġnuclear'] ['Ġwar'] ['Ġto'] ['Ġrelax'] ['Ġand'] ['Ġstudy'] ['Ġto'] 11 text tokens [0, 13754, 36, 7711, 574, 123, 7693, 128, 2162, 123, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:ab54079a82a73c255bc5c744d330b7c6ff973f72e40ffb7898c6a664440260b0IDvrakyicgp5cejgpdhjd5qbiu2mStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- Jesus turning water into wine on Americas Got Talent
- grid_size
- "7"
- temperature
- "2"
- intermediate_outputs
{ "text": "Jesus turning water into wine on Americas Got Talent", "grid_size": "7", "temperature": "2", "intermediate_outputs": true }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:ab54079a82a73c255bc5c744d330b7c6ff973f72e40ffb7898c6a664440260b0", { input: { text: "Jesus turning water into wine on Americas Got Talent", grid_size: "7", temperature: "2", intermediate_outputs: true } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:ab54079a82a73c255bc5c744d330b7c6ff973f72e40ffb7898c6a664440260b0", input={ "text": "Jesus turning water into wine on Americas Got Talent", "grid_size": "7", "temperature": "2", "intermediate_outputs": True } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "ab54079a82a73c255bc5c744d330b7c6ff973f72e40ffb7898c6a664440260b0", "input": { "text": "Jesus turning water into wine on Americas Got Talent", "grid_size": "7", "temperature": "2", "intermediate_outputs": true } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-11T18:35:15.820557Z", "created_at": "2022-07-11T18:34:35.561760Z", "data_removed": false, "error": null, "id": "vrakyicgp5cejgpdhjd5qbiu2m", "input": { "text": "Jesus turning water into wine on Americas Got Talent", "grid_size": "7", "temperature": "2", "intermediate_outputs": true }, "logs": "tokenizing text\n['Ġjesus']\n['Ġturning']\n['Ġwater']\n['Ġinto']\n['Ġwine']\n['Ġon']\n['Ġamericas']\n['Ġgot']\n['Ġtalent']\n11 text tokens [0, 5318, 10987, 725, 1733, 2397, 133, 17634, 2489, 7399, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 29.21242, "total_time": 40.258797 }, "output": [ "https://replicate.delivery/mgxm/ddb875ac-8f6b-4f19-a1b7-2d6ed5d2de46/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/e3771466-332a-40cd-81a8-790df78f3ea9/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/8b66017d-a56e-4320-8533-977f1005b3ab/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/34f147ad-b014-4391-821c-b686d2206d23/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/d686f2f9-af5b-42e1-8e83-033e9dfe6d36/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/e8d488e6-5530-4ca0-98f8-a8e323fc703d/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/ffc3376e-a96a-48d7-8b93-60c192c22dea/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/d8fa8bcf-4fc8-4c93-a5c5-e6dc5d3f6563/min-dalle-iter-8.jpg" ], "started_at": "2022-07-11T18:34:46.608137Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vrakyicgp5cejgpdhjd5qbiu2m", "cancel": "https://api.replicate.com/v1/predictions/vrakyicgp5cejgpdhjd5qbiu2m/cancel" }, "version": "ab54079a82a73c255bc5c744d330b7c6ff973f72e40ffb7898c6a664440260b0" }
Generated intokenizing text ['Ġjesus'] ['Ġturning'] ['Ġwater'] ['Ġinto'] ['Ġwine'] ['Ġon'] ['Ġamericas'] ['Ġgot'] ['Ġtalent'] 11 text tokens [0, 5318, 10987, 725, 1733, 2397, 133, 17634, 2489, 7399, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:fd743fcf7c1d858f6ff5d11269a2b45b970b30d5e246505ae1f26a1eb70eef8dInput
- text
- Journey to the West Monkey King, digital painting, high detail, 4k, profile picture
- grid_size
- "5"
- temperature
- "3"
- intermediate_outputs
{ "text": "Journey to the West Monkey King, digital painting, high detail, 4k, profile picture", "grid_size": "5", "temperature": "3", "intermediate_outputs": true }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:fd743fcf7c1d858f6ff5d11269a2b45b970b30d5e246505ae1f26a1eb70eef8d", { input: { text: "Journey to the West Monkey King, digital painting, high detail, 4k, profile picture", grid_size: "5", temperature: "3", intermediate_outputs: true } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:fd743fcf7c1d858f6ff5d11269a2b45b970b30d5e246505ae1f26a1eb70eef8d", input={ "text": "Journey to the West Monkey King, digital painting, high detail, 4k, profile picture", "grid_size": "5", "temperature": "3", "intermediate_outputs": True } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "fd743fcf7c1d858f6ff5d11269a2b45b970b30d5e246505ae1f26a1eb70eef8d", "input": { "text": "Journey to the West Monkey King, digital painting, high detail, 4k, profile picture", "grid_size": "5", "temperature": "3", "intermediate_outputs": true } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-11T20:41:20.572300Z", "created_at": "2022-07-11T20:40:33.951262Z", "data_removed": false, "error": null, "id": "hzii6bz4e5dmzgboldm2l3pbwa", "input": { "text": "Journey to the West Monkey King, digital painting, high detail, 4k, profile picture", "grid_size": "5", "temperature": "3", "intermediate_outputs": true }, "logs": "tokenizing text\n['Ġjourney']\n['Ġto']\n['Ġthe']\n['Ġwest']\n['Ġmonkey']\n['Ġking', ',']\n['Ġdigital']\n['Ġpainting', ',']\n['Ġhigh']\n['Ġdetail', ',']\n['Ġ4', 'k', ',']\n['Ġprofile']\n['Ġpicture']\n20 text tokens [0, 4027, 123, 99, 1047, 6984, 1187, 11, 1189, 1545, 11, 524, 5854, 11, 252, 38, 11, 2334, 1179, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.63242, "total_time": 46.621038 }, "output": [ "https://replicate.delivery/mgxm/06e7be73-1464-4114-91e5-90755e93218e/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/feb26dd5-be52-4ddc-96ce-bfa9d45752f0/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/6e1546a5-2e61-4446-9728-5372532c1515/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/e9bca033-cdbe-4141-9f1f-70815dcd42e5/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/c7a4ecf7-c9af-48d9-be99-013cfe7d21e8/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/e9b53316-7d39-43f0-b3f8-91629b8a7df0/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/aa1423e0-ec6e-48d0-bf7c-c65972ace5ed/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/c7a2a00e-cdeb-4999-8ab7-ac01ecf65b30/min-dalle-iter-8.jpg" ], "started_at": "2022-07-11T20:41:03.939880Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hzii6bz4e5dmzgboldm2l3pbwa", "cancel": "https://api.replicate.com/v1/predictions/hzii6bz4e5dmzgboldm2l3pbwa/cancel" }, "version": "fd743fcf7c1d858f6ff5d11269a2b45b970b30d5e246505ae1f26a1eb70eef8d" }
Generated intokenizing text ['Ġjourney'] ['Ġto'] ['Ġthe'] ['Ġwest'] ['Ġmonkey'] ['Ġking', ','] ['Ġdigital'] ['Ġpainting', ','] ['Ġhigh'] ['Ġdetail', ','] ['Ġ4', 'k', ','] ['Ġprofile'] ['Ġpicture'] 20 text tokens [0, 4027, 123, 99, 1047, 6984, 1187, 11, 1189, 1545, 11, 524, 5854, 11, 252, 38, 11, 2334, 1179, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:6a52ed0649a102b137b0d7d6787ef457e6e8e870387b0a6667aacb6c654a9f43IDa3zhripw2zbvxdmxyq4jvigvaiStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- An astronaut walking on Mars next to a Starship rocket, realistic
- grid_size
- "5"
- log2_top_k
- "6"
- log2_temperature
- "2"
- intermediate_outputs
- log2_supercondition_factor
- 4
{ "text": "An astronaut walking on Mars next to a Starship rocket, realistic", "grid_size": "5", "log2_top_k": "6", "log2_temperature": "2", "intermediate_outputs": true, "log2_supercondition_factor": 4 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:6a52ed0649a102b137b0d7d6787ef457e6e8e870387b0a6667aacb6c654a9f43", { input: { text: "An astronaut walking on Mars next to a Starship rocket, realistic", grid_size: "5", log2_top_k: "6", log2_temperature: "2", intermediate_outputs: true, log2_supercondition_factor: 4 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:6a52ed0649a102b137b0d7d6787ef457e6e8e870387b0a6667aacb6c654a9f43", input={ "text": "An astronaut walking on Mars next to a Starship rocket, realistic", "grid_size": "5", "log2_top_k": "6", "log2_temperature": "2", "intermediate_outputs": True, "log2_supercondition_factor": 4 } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "6a52ed0649a102b137b0d7d6787ef457e6e8e870387b0a6667aacb6c654a9f43", "input": { "text": "An astronaut walking on Mars next to a Starship rocket, realistic", "grid_size": "5", "log2_top_k": "6", "log2_temperature": "2", "intermediate_outputs": true, "log2_supercondition_factor": 4 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:6a52ed0649a102b137b0d7d6787ef457e6e8e870387b0a6667aacb6c654a9f43 \ -i 'text="An astronaut walking on Mars next to a Starship rocket, realistic"' \ -i 'grid_size="5"' \ -i 'log2_top_k="6"' \ -i 'log2_temperature="2"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor=4'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:6a52ed0649a102b137b0d7d6787ef457e6e8e870387b0a6667aacb6c654a9f43
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "An astronaut walking on Mars next to a Starship rocket, realistic", "grid_size": "5", "log2_top_k": "6", "log2_temperature": "2", "intermediate_outputs": true, "log2_supercondition_factor": 4 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-12T17:03:14.710799Z", "created_at": "2022-07-12T17:02:50.812897Z", "data_removed": false, "error": null, "id": "a3zhripw2zbvxdmxyq4jvigvai", "input": { "text": "An astronaut walking on Mars next to a Starship rocket, realistic", "grid_size": "5", "log2_top_k": "6", "log2_temperature": "2", "intermediate_outputs": true, "log2_supercondition_factor": 4 }, "logs": "tokenizing text\n['Ġan']\n['Ġastronaut']\n['Ġwalking']\n['Ġon']\n['Ġmars']\n['Ġnext']\n['Ġto']\n['Ġa']\n['Ġstarship']\n['Ġrocket', ',']\n['Ġrealistic']\n14 text tokens [0, 101, 14282, 4462, 133, 6705, 2365, 123, 58, 32568, 6110, 11, 10573, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.174735, "total_time": 23.897902 }, "output": [ "https://replicate.delivery/mgxm/6baa6a24-4ff0-4dbf-8e42-ac53a7ef0efb/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/56d0546f-1e24-4313-8f05-ee68d687f843/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/19e6cb08-97c1-4318-bdee-db3f16723c6d/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/17cf07bc-b5bd-40bf-af14-2d8533307f7d/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/05ffbae6-689b-4ac1-a58d-4a3818af8ccf/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/5808be9b-633d-4bc9-98b2-c3140fa417f7/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/b549e8e9-abb8-4d4b-9952-2099ca679426/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/b911ca11-88d1-4b7e-bb34-0478504f878a/min-dalle-iter-8.jpg" ], "started_at": "2022-07-12T17:02:58.536064Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/a3zhripw2zbvxdmxyq4jvigvai", "cancel": "https://api.replicate.com/v1/predictions/a3zhripw2zbvxdmxyq4jvigvai/cancel" }, "version": "6a52ed0649a102b137b0d7d6787ef457e6e8e870387b0a6667aacb6c654a9f43" }
Generated intokenizing text ['Ġan'] ['Ġastronaut'] ['Ġwalking'] ['Ġon'] ['Ġmars'] ['Ġnext'] ['Ġto'] ['Ġa'] ['Ġstarship'] ['Ġrocket', ','] ['Ġrealistic'] 14 text tokens [0, 101, 14282, 4462, 133, 6705, 2365, 123, 58, 32568, 6110, 11, 10573, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:3890b8ef46141a539fd33849f1965eae34e8311c53c0d8859ecff2dad91b7900Input
- text
- a man walking on water oil painting smooth concept art 4k matte painting by greg rutkowski thomas kinkade Ted Nasmith
- top_k
- "2048"
- grid_size
- "5"
- temperature
- "2"
- progressive_outputs
- supercondition_factor
- "32"
{ "text": "a man walking on water oil painting smooth concept art 4k matte painting by greg rutkowski thomas kinkade Ted Nasmith", "top_k": "2048", "grid_size": "5", "temperature": "2", "progressive_outputs": true, "supercondition_factor": "32" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:3890b8ef46141a539fd33849f1965eae34e8311c53c0d8859ecff2dad91b7900", { input: { text: "a man walking on water oil painting smooth concept art 4k matte painting by greg rutkowski thomas kinkade Ted Nasmith", top_k: "2048", grid_size: "5", temperature: "2", progressive_outputs: true, supercondition_factor: "32" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:3890b8ef46141a539fd33849f1965eae34e8311c53c0d8859ecff2dad91b7900", input={ "text": "a man walking on water oil painting smooth concept art 4k matte painting by greg rutkowski thomas kinkade Ted Nasmith", "top_k": "2048", "grid_size": "5", "temperature": "2", "progressive_outputs": True, "supercondition_factor": "32" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "3890b8ef46141a539fd33849f1965eae34e8311c53c0d8859ecff2dad91b7900", "input": { "text": "a man walking on water oil painting smooth concept art 4k matte painting by greg rutkowski thomas kinkade Ted Nasmith", "top_k": "2048", "grid_size": "5", "temperature": "2", "progressive_outputs": true, "supercondition_factor": "32" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-12T18:51:14.509740Z", "created_at": "2022-07-12T18:50:57.873304Z", "data_removed": false, "error": null, "id": "wpuhqc7yprfzripefxbhcalqeu", "input": { "text": "a man walking on water oil painting smooth concept art 4k matte painting by greg rutkowski thomas kinkade Ted Nasmith", "top_k": "2048", "grid_size": "5", "temperature": "2", "progressive_outputs": true, "supercondition_factor": "32" }, "logs": "tokenizing text\n['Ġa']\n['Ġman']\n['Ġwalking']\n['Ġon']\n['Ġwater']\n['Ġoil']\n['Ġpainting']\n['Ġsmooth']\n['Ġconcept']\n['Ġart']\n['Ġ4', 'k']\n['Ġmatte']\n['Ġpainting']\n['Ġby']\n['Ġgreg']\n['Ġrut', 'kowski']\n['Ġthomas']\n['Ġkink', 'ade']\n['Ġted']\n['Ġnas', 'mith']\n26 text tokens [0, 58, 339, 4462, 133, 725, 1707, 1545, 7087, 3319, 241, 252, 38, 6645, 1545, 185, 7120, 8924, 24622, 3058, 29947, 458, 5678, 3463, 12117, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.445939, "total_time": 16.636436 }, "output": [ "https://replicate.delivery/mgxm/f7d01ecc-f9af-4b86-947a-1aaacfd71bc3/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/7ebc1b0f-6d60-4266-a1bb-21de6abb2177/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/464688d8-3b51-4c79-b73d-0b0f55c801ab/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/e697aa39-bc4e-4b8b-9810-695fbf3f4b1b/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/8a5105e4-d013-4a92-a765-691bfd716cf6/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/7eb93331-597b-41cb-b356-2e8fe4b3fed0/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/110b9c9a-3c58-43d9-802e-08d46d7fd8e5/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/17a360cd-9024-484e-86df-a4df6f10a230/min-dalle-iter-8.jpg" ], "started_at": "2022-07-12T18:50:58.063801Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wpuhqc7yprfzripefxbhcalqeu", "cancel": "https://api.replicate.com/v1/predictions/wpuhqc7yprfzripefxbhcalqeu/cancel" }, "version": "3890b8ef46141a539fd33849f1965eae34e8311c53c0d8859ecff2dad91b7900" }
Generated intokenizing text ['Ġa'] ['Ġman'] ['Ġwalking'] ['Ġon'] ['Ġwater'] ['Ġoil'] ['Ġpainting'] ['Ġsmooth'] ['Ġconcept'] ['Ġart'] ['Ġ4', 'k'] ['Ġmatte'] ['Ġpainting'] ['Ġby'] ['Ġgreg'] ['Ġrut', 'kowski'] ['Ġthomas'] ['Ġkink', 'ade'] ['Ġted'] ['Ġnas', 'mith'] 26 text tokens [0, 58, 339, 4462, 133, 725, 1707, 1545, 7087, 3319, 241, 252, 38, 6645, 1545, 185, 7120, 8924, 24622, 3058, 29947, 458, 5678, 3463, 12117, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:3890b8ef46141a539fd33849f1965eae34e8311c53c0d8859ecff2dad91b7900IDzav6z67tzbdiflrrtx2walpepaStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- Elmo in a street riot throwing a Molotov cocktail, hyperrealistic
- top_k
- 64
- grid_size
- "5"
- temperature
- 4
- progressive_outputs
- supercondition_factor
- 16
{ "text": "Elmo in a street riot throwing a Molotov cocktail, hyperrealistic", "top_k": 64, "grid_size": "5", "temperature": 4, "progressive_outputs": true, "supercondition_factor": 16 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:3890b8ef46141a539fd33849f1965eae34e8311c53c0d8859ecff2dad91b7900", { input: { text: "Elmo in a street riot throwing a Molotov cocktail, hyperrealistic", top_k: 64, grid_size: "5", temperature: 4, progressive_outputs: true, supercondition_factor: 16 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:3890b8ef46141a539fd33849f1965eae34e8311c53c0d8859ecff2dad91b7900", input={ "text": "Elmo in a street riot throwing a Molotov cocktail, hyperrealistic", "top_k": 64, "grid_size": "5", "temperature": 4, "progressive_outputs": True, "supercondition_factor": 16 } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "3890b8ef46141a539fd33849f1965eae34e8311c53c0d8859ecff2dad91b7900", "input": { "text": "Elmo in a street riot throwing a Molotov cocktail, hyperrealistic", "top_k": 64, "grid_size": "5", "temperature": 4, "progressive_outputs": true, "supercondition_factor": 16 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-12T20:07:06.795894Z", "created_at": "2022-07-12T20:06:36.312169Z", "data_removed": false, "error": null, "id": "zav6z67tzbdiflrrtx2walpepa", "input": { "text": "Elmo in a street riot throwing a Molotov cocktail, hyperrealistic", "top_k": 64, "grid_size": "5", "temperature": 4, "progressive_outputs": true, "supercondition_factor": 16 }, "logs": "tokenizing text\n['Ġelmo']\n['Ġin']\n['Ġa']\n['Ġstreet']\n['Ġriot']\n['Ġthrowing']\n['Ġa']\n['Ġmol', 'ot', 'ov']\n['Ġcocktail', ',']\n['Ġhyper', 'real', 'istic']\n17 text tokens [0, 33823, 91, 58, 1182, 13714, 20659, 58, 4385, 98, 165, 7798, 11, 6139, 4646, 3478, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.557723, "total_time": 30.483725 }, "output": [ "https://replicate.delivery/mgxm/b9551cac-2361-434f-82b2-93c5d9e6a4e3/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/157cc663-5cd4-45ad-b93f-957add9b2780/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/3d7909ba-ff5e-4bfe-99f7-5ec675cfa4ae/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/63e83518-8dbf-4219-87f3-e039823918c7/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/d8d8343c-7165-48bb-963f-2e124efed82d/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/c9f1bcd1-4ab7-410f-903d-ad87e3d49780/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/da212e88-9f05-4cc4-a004-d12441f4fbf5/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/36ca851d-71da-46a0-8f5c-f46e8eb1c197/min-dalle-iter-8.jpg" ], "started_at": "2022-07-12T20:06:50.238171Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zav6z67tzbdiflrrtx2walpepa", "cancel": "https://api.replicate.com/v1/predictions/zav6z67tzbdiflrrtx2walpepa/cancel" }, "version": "3890b8ef46141a539fd33849f1965eae34e8311c53c0d8859ecff2dad91b7900" }
Generated intokenizing text ['Ġelmo'] ['Ġin'] ['Ġa'] ['Ġstreet'] ['Ġriot'] ['Ġthrowing'] ['Ġa'] ['Ġmol', 'ot', 'ov'] ['Ġcocktail', ','] ['Ġhyper', 'real', 'istic'] 17 text tokens [0, 33823, 91, 58, 1182, 13714, 20659, 58, 4385, 98, 165, 7798, 11, 6139, 4646, 3478, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:001b06eaf60394bac3fd8505e90774abc87df2ca34e0252ee2698940ef49b0beIDtsuuvikppvbxnjtayxpxhz7qieStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- An astronaut walking next to a starship rocket, city on mars in the background, realistic
- top_k
- "64"
- grid_size
- "5"
- temperature
- 4
- progressive_outputs
- supercondition_factor
- 16
{ "text": "An astronaut walking next to a starship rocket, city on mars in the background, realistic", "top_k": "64", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": 16 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:001b06eaf60394bac3fd8505e90774abc87df2ca34e0252ee2698940ef49b0be", { input: { text: "An astronaut walking next to a starship rocket, city on mars in the background, realistic", top_k: "64", grid_size: "5", temperature: "4", progressive_outputs: true, supercondition_factor: 16 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:001b06eaf60394bac3fd8505e90774abc87df2ca34e0252ee2698940ef49b0be", input={ "text": "An astronaut walking next to a starship rocket, city on mars in the background, realistic", "top_k": "64", "grid_size": "5", "temperature": "4", "progressive_outputs": True, "supercondition_factor": 16 } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "001b06eaf60394bac3fd8505e90774abc87df2ca34e0252ee2698940ef49b0be", "input": { "text": "An astronaut walking next to a starship rocket, city on mars in the background, realistic", "top_k": "64", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": 16 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-13T12:23:49.742242Z", "created_at": "2022-07-13T12:23:28.595992Z", "data_removed": false, "error": null, "id": "tsuuvikppvbxnjtayxpxhz7qie", "input": { "text": "An astronaut walking next to a starship rocket, city on mars in the background, realistic", "top_k": "64", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": 16 }, "logs": "tokenizing text\n['Ġan']\n['Ġastronaut']\n['Ġwalking']\n['Ġnext']\n['Ġto']\n['Ġa']\n['Ġstarship']\n['Ġrocket', ',']\n['Ġcity']\n['Ġon']\n['Ġmars']\n['Ġin']\n['Ġthe']\n['Ġbackground', ',']\n['Ġrealistic']\n19 text tokens [0, 101, 14282, 4462, 2365, 123, 58, 32568, 6110, 11, 645, 133, 6705, 91, 99, 1396, 11, 10573, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.226286, "total_time": 21.14625 }, "output": [ "https://replicate.delivery/mgxm/67f40e68-c3b7-416f-bca4-3aee8252d4f3/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/369e1f22-5427-44fa-9cef-73e9c925728a/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/2c27cf26-d831-4043-9df4-fed0e6d18dae/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/7f39354a-1e3c-4ab2-9ffc-2d3e97228af1/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/da7bf317-a5a0-485e-a650-4ecb885151f9/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/bbdd63d1-6b94-473c-9bab-7c451491c098/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/ca4d31f3-061b-4ee8-835b-20bb2b9468e6/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/4cabcf06-523b-4854-a7d8-40d3cce3aa02/min-dalle-iter-8.jpg" ], "started_at": "2022-07-13T12:23:33.515956Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tsuuvikppvbxnjtayxpxhz7qie", "cancel": "https://api.replicate.com/v1/predictions/tsuuvikppvbxnjtayxpxhz7qie/cancel" }, "version": "001b06eaf60394bac3fd8505e90774abc87df2ca34e0252ee2698940ef49b0be" }
Generated intokenizing text ['Ġan'] ['Ġastronaut'] ['Ġwalking'] ['Ġnext'] ['Ġto'] ['Ġa'] ['Ġstarship'] ['Ġrocket', ','] ['Ġcity'] ['Ġon'] ['Ġmars'] ['Ġin'] ['Ġthe'] ['Ġbackground', ','] ['Ġrealistic'] 19 text tokens [0, 101, 14282, 4462, 2365, 123, 58, 32568, 6110, 11, 645, 133, 6705, 91, 99, 1396, 11, 10573, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:888c72d60932bca21344efcfdaecb3f0fbeb4bf40ee9ec601cc8fda806b5bfd9IDlzfo5hkzffetppfanwh6ujw6dqStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- highly detailed 3D image of cthulhu playing saxophone with a piano, double bass and drums, rendered using Maya, 8K, #artstation
- top_k
- "512"
- grid_size
- "5"
- save_as_png
- temperature
- "2.79"
- progressive_outputs
- supercondition_factor
- "64"
{ "text": "highly detailed 3D image of cthulhu playing saxophone with a piano, double bass and drums, rendered using Maya, 8K, #artstation", "top_k": "512", "grid_size": "5", "save_as_png": true, "temperature": "2.79", "progressive_outputs": true, "supercondition_factor": "64" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:888c72d60932bca21344efcfdaecb3f0fbeb4bf40ee9ec601cc8fda806b5bfd9", { input: { text: "highly detailed 3D image of cthulhu playing saxophone with a piano, double bass and drums, rendered using Maya, 8K, #artstation", top_k: "512", grid_size: "5", save_as_png: true, temperature: "2.79", progressive_outputs: true, supercondition_factor: "64" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:888c72d60932bca21344efcfdaecb3f0fbeb4bf40ee9ec601cc8fda806b5bfd9", input={ "text": "highly detailed 3D image of cthulhu playing saxophone with a piano, double bass and drums, rendered using Maya, 8K, #artstation", "top_k": "512", "grid_size": "5", "save_as_png": True, "temperature": "2.79", "progressive_outputs": True, "supercondition_factor": "64" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "888c72d60932bca21344efcfdaecb3f0fbeb4bf40ee9ec601cc8fda806b5bfd9", "input": { "text": "highly detailed 3D image of cthulhu playing saxophone with a piano, double bass and drums, rendered using Maya, 8K, #artstation", "top_k": "512", "grid_size": "5", "save_as_png": true, "temperature": "2.79", "progressive_outputs": true, "supercondition_factor": "64" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:888c72d60932bca21344efcfdaecb3f0fbeb4bf40ee9ec601cc8fda806b5bfd9 \ -i 'text="highly detailed 3D image of cthulhu playing saxophone with a piano, double bass and drums, rendered using Maya, 8K, #artstation"' \ -i 'top_k="512"' \ -i 'grid_size="5"' \ -i 'save_as_png=true' \ -i 'temperature="2.79"' \ -i 'progressive_outputs=true' \ -i 'supercondition_factor="64"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:888c72d60932bca21344efcfdaecb3f0fbeb4bf40ee9ec601cc8fda806b5bfd9
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "highly detailed 3D image of cthulhu playing saxophone with a piano, double bass and drums, rendered using Maya, 8K, #artstation", "top_k": "512", "grid_size": "5", "save_as_png": true, "temperature": "2.79", "progressive_outputs": true, "supercondition_factor": "64" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-27T09:01:58.936298Z", "created_at": "2022-07-27T09:01:41.396144Z", "data_removed": false, "error": null, "id": "lzfo5hkzffetppfanwh6ujw6dq", "input": { "text": "highly detailed 3D image of cthulhu playing saxophone with a piano, double bass and drums, rendered using Maya, 8K, #artstation", "top_k": "512", "grid_size": "5", "save_as_png": true, "temperature": "2.79", "progressive_outputs": true, "supercondition_factor": "64" }, "logs": "tokenizing text\n['Ġhighly']\n['Ġdetailed']\n['Ġ3', 'd']\n['Ġimage']\n['Ġof']\n['Ġcthulhu']\n['Ġplaying']\n['Ġsaxophone']\n['Ġwith']\n['Ġa']\n['Ġpiano', ',']\n['Ġdouble']\n['Ġbass']\n['Ġand']\n['Ġdrums', ',']\n['Ġrender', 'ed']\n['Ġusing']\n['Ġmaya', ',']\n['Ġ8', 'k', ',']\n['Ġ', '#', 'art', 'station']\n32 text tokens [0, 13169, 8461, 204, 31, 867, 111, 34353, 4952, 19871, 208, 58, 3858, 11, 2151, 4007, 128, 15939, 11, 14110, 93, 2421, 10463, 11, 416, 38, 11, 54, 3, 163, 3356, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 16.939079, "total_time": 17.540154 }, "output": [ "https://replicate.delivery/mgxm/570d5641-2d91-486a-9219-0d62c1f6f74f/highly-detailed-d-image-of-cthulhu-playing-saxophone-with-a.jpg", "https://replicate.delivery/mgxm/dcabf987-0258-48af-ab8a-2da47e8a915c/highly-detailed-d-image-of-cthulhu-playing-saxophone-with-a.jpg", "https://replicate.delivery/mgxm/afe15d8c-e3f5-4fc2-be0f-9dc018b2e761/highly-detailed-d-image-of-cthulhu-playing-saxophone-with-a.jpg", "https://replicate.delivery/mgxm/eb29d81f-1a0b-4f44-a004-4fe0616a8b12/highly-detailed-d-image-of-cthulhu-playing-saxophone-with-a.jpg", "https://replicate.delivery/mgxm/50601397-ed6f-45bf-8293-4a46138d2692/highly-detailed-d-image-of-cthulhu-playing-saxophone-with-a.jpg", "https://replicate.delivery/mgxm/ba797fe6-b522-4132-aef6-bf5984b6d426/highly-detailed-d-image-of-cthulhu-playing-saxophone-with-a.jpg", "https://replicate.delivery/mgxm/b5b081f7-3eed-40e1-a5c1-04ca22b89e26/highly-detailed-d-image-of-cthulhu-playing-saxophone-with-a.jpg", "https://replicate.delivery/mgxm/da3833ae-981b-4295-9409-dad0daaf77b0/highly-detailed-d-image-of-cthulhu-playing-saxophone-with-a.png" ], "started_at": "2022-07-27T09:01:41.997219Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/lzfo5hkzffetppfanwh6ujw6dq", "cancel": "https://api.replicate.com/v1/predictions/lzfo5hkzffetppfanwh6ujw6dq/cancel" }, "version": "888c72d60932bca21344efcfdaecb3f0fbeb4bf40ee9ec601cc8fda806b5bfd9" }
Generated intokenizing text ['Ġhighly'] ['Ġdetailed'] ['Ġ3', 'd'] ['Ġimage'] ['Ġof'] ['Ġcthulhu'] ['Ġplaying'] ['Ġsaxophone'] ['Ġwith'] ['Ġa'] ['Ġpiano', ','] ['Ġdouble'] ['Ġbass'] ['Ġand'] ['Ġdrums', ','] ['Ġrender', 'ed'] ['Ġusing'] ['Ġmaya', ','] ['Ġ8', 'k', ','] ['Ġ', '#', 'art', 'station'] 32 text tokens [0, 13169, 8461, 204, 31, 867, 111, 34353, 4952, 19871, 208, 58, 3858, 11, 2151, 4007, 128, 15939, 11, 14110, 93, 2421, 10463, 11, 416, 38, 11, 54, 3, 163, 3356, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0IDcpulckytuzdbvi4er3js6oztueStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- An astronaut is walking on Mars next to a Starship rocket, a city is in the background, realistic
- top_k
- "128"
- grid_size
- "5"
- temperature
- 2
- progressive_outputs
- supercondition_factor
- 16
{ "text": "An astronaut is walking on Mars next to a Starship rocket, a city is in the background, realistic", "top_k": "128", "grid_size": "5", "temperature": "2", "progressive_outputs": true, "supercondition_factor": 16 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0", { input: { text: "An astronaut is walking on Mars next to a Starship rocket, a city is in the background, realistic", top_k: "128", grid_size: "5", temperature: "2", progressive_outputs: true, supercondition_factor: 16 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0", input={ "text": "An astronaut is walking on Mars next to a Starship rocket, a city is in the background, realistic", "top_k": "128", "grid_size": "5", "temperature": "2", "progressive_outputs": True, "supercondition_factor": 16 } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0", "input": { "text": "An astronaut is walking on Mars next to a Starship rocket, a city is in the background, realistic", "top_k": "128", "grid_size": "5", "temperature": "2", "progressive_outputs": true, "supercondition_factor": 16 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0 \ -i 'text="An astronaut is walking on Mars next to a Starship rocket, a city is in the background, realistic"' \ -i 'top_k="128"' \ -i 'grid_size="5"' \ -i 'temperature="2"' \ -i 'progressive_outputs=true' \ -i 'supercondition_factor=16'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "An astronaut is walking on Mars next to a Starship rocket, a city is in the background, realistic", "top_k": "128", "grid_size": "5", "temperature": "2", "progressive_outputs": true, "supercondition_factor": 16 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-13T19:37:47.796384Z", "created_at": "2022-07-13T19:37:19.384223Z", "data_removed": false, "error": null, "id": "cpulckytuzdbvi4er3js6oztue", "input": { "text": "An astronaut is walking on Mars next to a Starship rocket, a city is in the background, realistic", "top_k": "128", "grid_size": "5", "temperature": "2", "progressive_outputs": true, "supercondition_factor": 16 }, "logs": "tokenizing text\n['Ġan']\n['Ġastronaut']\n['Ġis']\n['Ġwalking']\n['Ġon']\n['Ġmars']\n['Ġnext']\n['Ġto']\n['Ġa']\n['Ġstarship']\n['Ġrocket', ',']\n['Ġa']\n['Ġcity']\n['Ġis']\n['Ġin']\n['Ġthe']\n['Ġbackground', ',']\n['Ġrealistic']\n22 text tokens [0, 101, 14282, 231, 4462, 133, 6705, 2365, 123, 58, 32568, 6110, 11, 58, 645, 231, 91, 99, 1396, 11, 10573, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 17.070607, "total_time": 28.412161 }, "output": [ "https://replicate.delivery/mgxm/4e24396d-b09f-4c6d-83e9-311f79b3880d/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/05f17bfe-78b8-4fe4-bf33-832223fcd98f/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/816beb8d-91d2-483c-9f6e-99f8013b9e9d/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/ab550423-b9d8-4636-b4ed-f88542346a17/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/0790b054-b3a1-4c32-853b-80c4c99db1b2/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/3e06b1c6-1de8-48cd-a3f1-b75988cc0517/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/f815e5a1-1e8e-463e-a719-87f19c9fb6c6/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/b519a8a9-0c8c-421a-84e3-c056f7759ec3/min-dalle-iter-8.jpg" ], "started_at": "2022-07-13T19:37:30.725777Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/cpulckytuzdbvi4er3js6oztue", "cancel": "https://api.replicate.com/v1/predictions/cpulckytuzdbvi4er3js6oztue/cancel" }, "version": "ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0" }
Generated intokenizing text ['Ġan'] ['Ġastronaut'] ['Ġis'] ['Ġwalking'] ['Ġon'] ['Ġmars'] ['Ġnext'] ['Ġto'] ['Ġa'] ['Ġstarship'] ['Ġrocket', ','] ['Ġa'] ['Ġcity'] ['Ġis'] ['Ġin'] ['Ġthe'] ['Ġbackground', ','] ['Ġrealistic'] 22 text tokens [0, 101, 14282, 231, 4462, 133, 6705, 2365, 123, 58, 32568, 6110, 11, 58, 645, 231, 91, 99, 1396, 11, 10573, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0Input
- text
- A claymation fox wearing sunglasses is reading a book in a forest, claymation art
- top_k
- "16384"
- grid_size
- "5"
- temperature
- 4
- progressive_outputs
- supercondition_factor
- "64"
{ "text": "A claymation fox wearing sunglasses is reading a book in a forest, claymation art", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "64" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0", { input: { text: "A claymation fox wearing sunglasses is reading a book in a forest, claymation art", top_k: "16384", grid_size: "5", temperature: "4", progressive_outputs: true, supercondition_factor: "64" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0", input={ "text": "A claymation fox wearing sunglasses is reading a book in a forest, claymation art", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": True, "supercondition_factor": "64" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0", "input": { "text": "A claymation fox wearing sunglasses is reading a book in a forest, claymation art", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "64" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0 \ -i 'text="A claymation fox wearing sunglasses is reading a book in a forest, claymation art"' \ -i 'top_k="16384"' \ -i 'grid_size="5"' \ -i 'temperature="4"' \ -i 'progressive_outputs=true' \ -i 'supercondition_factor="64"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "A claymation fox wearing sunglasses is reading a book in a forest, claymation art", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "64" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-14T13:33:07.875157Z", "created_at": "2022-07-14T13:32:44.760741Z", "data_removed": false, "error": null, "id": "xbjttqttbrcbdapfu5fjjfm32u", "input": { "text": "A claymation fox wearing sunglasses is reading a book in a forest, claymation art", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "64" }, "logs": "tokenizing text\n['Ġa']\n['Ġclay', 'm', 'ation']\n['Ġfox']\n['Ġwearing']\n['Ġsunglasses']\n['Ġis']\n['Ġreading']\n['Ġa']\n['Ġbook']\n['Ġin']\n['Ġa']\n['Ġforest', ',']\n['Ġclay', 'm', 'ation']\n['Ġart']\n21 text tokens [0, 58, 5379, 40, 155, 2656, 8995, 7134, 231, 4132, 58, 407, 91, 58, 2599, 11, 5379, 40, 155, 241, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.416939, "total_time": 23.114416 }, "output": [ "https://replicate.delivery/mgxm/421695d4-081d-438f-a185-21be38ed1c74/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/4e57eb62-5c42-47a1-8515-fd16fe7c32e8/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/a3539fd0-6600-4706-aa4a-ae5835400895/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/e95e808b-3b39-4818-b312-0c1f12652110/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/e4da3f17-c639-47ab-a0e2-4de3cf65a03a/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/3d4f5d31-ea06-4095-bce0-0522640cf8ea/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/059b261a-dce1-42fd-8cf7-5c54f3d07149/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/51a785ba-605b-4a4e-b36c-014da4cb2e0c/min-dalle-iter-8.jpg" ], "started_at": "2022-07-14T13:32:51.458218Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xbjttqttbrcbdapfu5fjjfm32u", "cancel": "https://api.replicate.com/v1/predictions/xbjttqttbrcbdapfu5fjjfm32u/cancel" }, "version": "ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0" }
Generated intokenizing text ['Ġa'] ['Ġclay', 'm', 'ation'] ['Ġfox'] ['Ġwearing'] ['Ġsunglasses'] ['Ġis'] ['Ġreading'] ['Ġa'] ['Ġbook'] ['Ġin'] ['Ġa'] ['Ġforest', ','] ['Ġclay', 'm', 'ation'] ['Ġart'] 21 text tokens [0, 58, 5379, 40, 155, 2656, 8995, 7134, 231, 4132, 58, 407, 91, 58, 2599, 11, 5379, 40, 155, 241, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0Input
- text
- A vintage photo of a capybara wearing a tuxedo and top hat, black and white
- top_k
- "16384"
- grid_size
- "5"
- temperature
- 4
- progressive_outputs
- supercondition_factor
- "64"
{ "text": "A vintage photo of a capybara wearing a tuxedo and top hat, black and white", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "64" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0", { input: { text: "A vintage photo of a capybara wearing a tuxedo and top hat, black and white", top_k: "16384", grid_size: "5", temperature: "4", progressive_outputs: true, supercondition_factor: "64" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0", input={ "text": "A vintage photo of a capybara wearing a tuxedo and top hat, black and white", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": True, "supercondition_factor": "64" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0", "input": { "text": "A vintage photo of a capybara wearing a tuxedo and top hat, black and white", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "64" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0 \ -i 'text="A vintage photo of a capybara wearing a tuxedo and top hat, black and white"' \ -i 'top_k="16384"' \ -i 'grid_size="5"' \ -i 'temperature="4"' \ -i 'progressive_outputs=true' \ -i 'supercondition_factor="64"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "A vintage photo of a capybara wearing a tuxedo and top hat, black and white", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "64" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-15T13:25:24.592470Z", "created_at": "2022-07-15T13:25:07.052163Z", "data_removed": false, "error": null, "id": "e5ebn5qa5fhvbl5an2ner4fylq", "input": { "text": "A vintage photo of a capybara wearing a tuxedo and top hat, black and white", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "64" }, "logs": "tokenizing text\n['Ġa']\n['Ġvintage']\n['Ġphoto']\n['Ġof']\n['Ġa']\n['Ġcap', 'yb', 'ara']\n['Ġwearing']\n['Ġa']\n['Ġtuxedo']\n['Ġand']\n['Ġtop']\n['Ġhat', ',']\n['Ġblack']\n['Ġand']\n['Ġwhite']\n20 text tokens [0, 58, 996, 564, 111, 58, 712, 21854, 761, 8995, 58, 26047, 128, 479, 2583, 11, 486, 128, 657, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 16.350287, "total_time": 17.540307 }, "output": [ "https://replicate.delivery/mgxm/d4b9786a-8f3d-4881-ae7c-a984e3fbc093/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/fefee167-08e6-4770-b66c-c4866dd3d24c/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/c08656c4-99c3-4237-9a47-e49687ecd37d/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/b0c200f5-3947-465a-80d7-7a995b7cdf75/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/1c136dc6-716d-43b5-aede-f73493fbc511/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/c4333592-a326-4c50-97b4-aaad9ecee001/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/35ae8d3f-bfb3-4dbc-9822-884ece2d35bc/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/957c66ef-dec4-4e6c-bf1e-a693f444a270/min-dalle-iter-8.jpg" ], "started_at": "2022-07-15T13:25:08.242183Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/e5ebn5qa5fhvbl5an2ner4fylq", "cancel": "https://api.replicate.com/v1/predictions/e5ebn5qa5fhvbl5an2ner4fylq/cancel" }, "version": "ee5b034a2b9525bc32f84ea885ef98b4a7847264f9453aa01de58994018234e0" }
Generated intokenizing text ['Ġa'] ['Ġvintage'] ['Ġphoto'] ['Ġof'] ['Ġa'] ['Ġcap', 'yb', 'ara'] ['Ġwearing'] ['Ġa'] ['Ġtuxedo'] ['Ġand'] ['Ġtop'] ['Ġhat', ','] ['Ġblack'] ['Ġand'] ['Ġwhite'] 20 text tokens [0, 58, 996, 564, 111, 58, 712, 21854, 761, 8995, 58, 26047, 128, 479, 2583, 11, 486, 128, 657, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0Input
- text
- A giant squid sleeping in a sewer, Sculpture, Award winning
- top_k
- "16384"
- grid_size
- "5"
- temperature
- 4
- progressive_outputs
- supercondition_factor
- "64"
{ "text": "A giant squid sleeping in a sewer, Sculpture, Award winning", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "64" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0", { input: { text: "A giant squid sleeping in a sewer, Sculpture, Award winning", top_k: "16384", grid_size: "5", temperature: "4", progressive_outputs: true, supercondition_factor: "64" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0", input={ "text": "A giant squid sleeping in a sewer, Sculpture, Award winning", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": True, "supercondition_factor": "64" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0", "input": { "text": "A giant squid sleeping in a sewer, Sculpture, Award winning", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "64" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-16T11:25:32.979643Z", "created_at": "2022-07-16T11:24:51.058080Z", "data_removed": false, "error": null, "id": "h2dgxvkouretlokp2ieinl4dpe", "input": { "text": "A giant squid sleeping in a sewer, Sculpture, Award winning", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "64" }, "logs": "tokenizing text\n['Ġa']\n['Ġgiant']\n['Ġsquid']\n['Ġsleeping']\n['Ġin']\n['Ġa']\n['Ġsewer', ',']\n['Ġsculpture', ',']\n['Ġaward']\n['Ġwinning']\n14 text tokens [0, 58, 4288, 15320, 9583, 91, 58, 25816, 11, 5022, 11, 3457, 6534, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 15.943557, "total_time": 41.921563 }, "output": [ "https://replicate.delivery/mgxm/f8e6e421-ae26-41cd-9dd4-549bb9e5b564/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/11b9c211-97b7-4275-bb06-24e59233b79d/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/ac714b97-aac5-4bb1-8ee3-d08c5bbe10e5/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/9b5efbd4-ec3e-4039-852b-6b7280e8cfa7/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/f7b666e9-f21a-44b0-82ce-908637c76cd2/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/2600ff0b-3cc1-4370-91d4-5c1b7b9eabb0/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/aa6a5b9d-2a41-4f1e-aa01-e65ba05bfd3a/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/96a3f8f1-1231-4b2b-9e06-ed4f4daaa521/min-dalle-iter-8.jpg" ], "started_at": "2022-07-16T11:25:17.036086Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/h2dgxvkouretlokp2ieinl4dpe", "cancel": "https://api.replicate.com/v1/predictions/h2dgxvkouretlokp2ieinl4dpe/cancel" }, "version": "e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0" }
Generated intokenizing text ['Ġa'] ['Ġgiant'] ['Ġsquid'] ['Ġsleeping'] ['Ġin'] ['Ġa'] ['Ġsewer', ','] ['Ġsculpture', ','] ['Ġaward'] ['Ġwinning'] 14 text tokens [0, 58, 4288, 15320, 9583, 91, 58, 25816, 11, 5022, 11, 3457, 6534, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0ID72rmcm3o7jgo7cp4pi2ixicir4StatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- A carrot wearing sunglasses on top of a mountain on a sunny day, high definition, high detail
- top_k
- "16384"
- grid_size
- "4"
- save_as_png
- temperature
- 4
- progressive_outputs
- supercondition_factor
- "64"
{ "text": "A carrot wearing sunglasses on top of a mountain on a sunny day, high definition, high detail", "top_k": "16384", "grid_size": "4", "save_as_png": true, "temperature": "4", "progressive_outputs": false, "supercondition_factor": "64" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0", { input: { text: "A carrot wearing sunglasses on top of a mountain on a sunny day, high definition, high detail", top_k: "16384", grid_size: "4", save_as_png: true, temperature: "4", progressive_outputs: false, supercondition_factor: "64" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0", input={ "text": "A carrot wearing sunglasses on top of a mountain on a sunny day, high definition, high detail", "top_k": "16384", "grid_size": "4", "save_as_png": True, "temperature": "4", "progressive_outputs": False, "supercondition_factor": "64" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0", "input": { "text": "A carrot wearing sunglasses on top of a mountain on a sunny day, high definition, high detail", "top_k": "16384", "grid_size": "4", "save_as_png": true, "temperature": "4", "progressive_outputs": false, "supercondition_factor": "64" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-16T19:05:09.915994Z", "created_at": "2022-07-16T19:04:49.411196Z", "data_removed": false, "error": null, "id": "72rmcm3o7jgo7cp4pi2ixicir4", "input": { "text": "A carrot wearing sunglasses on top of a mountain on a sunny day, high definition, high detail", "top_k": "16384", "grid_size": "4", "save_as_png": true, "temperature": "4", "progressive_outputs": false, "supercondition_factor": "64" }, "logs": "tokenizing text\n['Ġa']\n['Ġcarrot']\n['Ġwearing']\n['Ġsunglasses']\n['Ġon']\n['Ġtop']\n['Ġof']\n['Ġa']\n['Ġmountain']\n['Ġon']\n['Ġa']\n['Ġsunny']\n['Ġday', ',']\n['Ġhigh']\n['Ġdefinition', ',']\n['Ġhigh']\n['Ġdetail']\n21 text tokens [0, 58, 19615, 8995, 7134, 133, 479, 111, 58, 2236, 133, 58, 8751, 615, 11, 524, 4951, 11, 524, 5854, 2]\nencoding text tokens\ndetokenizing image", "metrics": { "predict_time": 13.781749, "total_time": 20.504798 }, "output": [ "https://replicate.delivery/mgxm/b8c1ad2d-f152-4654-aa95-79ab7ca6d1c1/min-dalle-iter-1.png" ], "started_at": "2022-07-16T19:04:56.134245Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/72rmcm3o7jgo7cp4pi2ixicir4", "cancel": "https://api.replicate.com/v1/predictions/72rmcm3o7jgo7cp4pi2ixicir4/cancel" }, "version": "e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0" }
Generated intokenizing text ['Ġa'] ['Ġcarrot'] ['Ġwearing'] ['Ġsunglasses'] ['Ġon'] ['Ġtop'] ['Ġof'] ['Ġa'] ['Ġmountain'] ['Ġon'] ['Ġa'] ['Ġsunny'] ['Ġday', ','] ['Ġhigh'] ['Ġdefinition', ','] ['Ġhigh'] ['Ġdetail'] 21 text tokens [0, 58, 19615, 8995, 7134, 133, 479, 111, 58, 2236, 133, 58, 8751, 615, 11, 524, 4951, 11, 524, 5854, 2] encoding text tokens detokenizing image
Prediction
kuprel/min-dalle:e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0IDum5bunwt4fhyzjrtfearj6d3qiStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- Giant jellyfish flying over New York, calotype photo, 1800s, damaged photo
- top_k
- "16384"
- grid_size
- "4"
- save_as_png
- temperature
- 4
- progressive_outputs
- supercondition_factor
- "64"
{ "text": "Giant jellyfish flying over New York, calotype photo, 1800s, damaged photo", "top_k": "16384", "grid_size": "4", "save_as_png": true, "temperature": "4", "progressive_outputs": false, "supercondition_factor": "64" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0", { input: { text: "Giant jellyfish flying over New York, calotype photo, 1800s, damaged photo", top_k: "16384", grid_size: "4", save_as_png: true, temperature: "4", progressive_outputs: false, supercondition_factor: "64" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0", input={ "text": "Giant jellyfish flying over New York, calotype photo, 1800s, damaged photo", "top_k": "16384", "grid_size": "4", "save_as_png": True, "temperature": "4", "progressive_outputs": False, "supercondition_factor": "64" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0", "input": { "text": "Giant jellyfish flying over New York, calotype photo, 1800s, damaged photo", "top_k": "16384", "grid_size": "4", "save_as_png": true, "temperature": "4", "progressive_outputs": false, "supercondition_factor": "64" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-16T19:10:33.166477Z", "created_at": "2022-07-16T19:10:06.521075Z", "data_removed": false, "error": null, "id": "um5bunwt4fhyzjrtfearj6d3qi", "input": { "text": "Giant jellyfish flying over New York, calotype photo, 1800s, damaged photo", "top_k": "16384", "grid_size": "4", "save_as_png": true, "temperature": "4", "progressive_outputs": false, "supercondition_factor": "64" }, "logs": "tokenizing text\n['Ġgiant']\n['Ġjellyfish']\n['Ġflying']\n['Ġover']\n['Ġnew']\n['Ġyork', ',']\n['Ġcal', 'otype']\n['Ġphoto', ',']\n['Ġ1800', 's', ',']\n['Ġdamaged']\n['Ġphoto']\n18 text tokens [0, 4288, 25311, 5052, 709, 173, 1194, 11, 668, 11078, 564, 11, 13079, 46, 11, 16336, 564, 2]\nencoding text tokens\ndetokenizing image", "metrics": { "predict_time": 13.803658, "total_time": 26.645402 }, "output": [ "https://replicate.delivery/mgxm/5f15ebce-9068-43e4-8c9d-e1b7a0e9fb44/min-dalle-iter-1.png" ], "started_at": "2022-07-16T19:10:19.362819Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/um5bunwt4fhyzjrtfearj6d3qi", "cancel": "https://api.replicate.com/v1/predictions/um5bunwt4fhyzjrtfearj6d3qi/cancel" }, "version": "e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0" }
Generated intokenizing text ['Ġgiant'] ['Ġjellyfish'] ['Ġflying'] ['Ġover'] ['Ġnew'] ['Ġyork', ','] ['Ġcal', 'otype'] ['Ġphoto', ','] ['Ġ1800', 's', ','] ['Ġdamaged'] ['Ġphoto'] 18 text tokens [0, 4288, 25311, 5052, 709, 173, 1194, 11, 668, 11078, 564, 11, 13079, 46, 11, 16336, 564, 2] encoding text tokens detokenizing image
Prediction
kuprel/min-dalle:e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0Input
- text
- A 5-star jungle themed waterpark, 4k photograph, cinematic
- top_k
- "16384"
- grid_size
- "5"
- temperature
- 4
- progressive_outputs
- supercondition_factor
- "64"
{ "text": "A 5-star jungle themed waterpark, 4k photograph, cinematic", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "64" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0", { input: { text: "A 5-star jungle themed waterpark, 4k photograph, cinematic", top_k: "16384", grid_size: "5", temperature: "4", progressive_outputs: true, supercondition_factor: "64" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0", input={ "text": "A 5-star jungle themed waterpark, 4k photograph, cinematic", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": True, "supercondition_factor": "64" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0", "input": { "text": "A 5-star jungle themed waterpark, 4k photograph, cinematic", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "64" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-17T11:28:16.227456Z", "created_at": "2022-07-17T11:28:00.035435Z", "data_removed": false, "error": null, "id": "f3ibrtkcovdafk3unwngwinxem", "input": { "text": "A 5-star jungle themed waterpark, 4k photograph, cinematic", "top_k": "16384", "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "64" }, "logs": "tokenizing text\n['Ġa']\n['Ġ5', '-', 'star']\n['Ġjungle']\n['Ġthemed']\n['Ġwaterpark', ',']\n['Ġ4', 'k']\n['Ġphotograph', ',']\n['Ġcinematic']\n15 text tokens [0, 58, 264, 3, 4046, 6941, 9928, 40859, 11, 252, 38, 701, 11, 19936, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 16.050578, "total_time": 16.192021 }, "output": [ "https://replicate.delivery/mgxm/ddf1a523-3b2e-443f-ade9-900c9b73170e/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/9a5fd677-2556-4985-b6ee-d784ce7fb09c/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/c68dccd2-7cc5-413c-b87d-8477f9b20264/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/e85bed5b-e60c-4e8f-85e7-92ea78eb0afb/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/7bfd9410-0deb-40df-a87a-5d59d9de57ef/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/657f5332-1b4f-4d71-a2fb-4ddee4ffa5f3/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/4a775a8d-fbdf-4890-99fd-4b0d0912b46f/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/36418b99-8802-4a65-81f4-196edf132847/min-dalle-iter-8.jpg" ], "started_at": "2022-07-17T11:28:00.176878Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/f3ibrtkcovdafk3unwngwinxem", "cancel": "https://api.replicate.com/v1/predictions/f3ibrtkcovdafk3unwngwinxem/cancel" }, "version": "e0b50cb5eccee66c7a158e076abeecdee1716f6e58363c7d3c83fbcff21fa0d0" }
Generated intokenizing text ['Ġa'] ['Ġ5', '-', 'star'] ['Ġjungle'] ['Ġthemed'] ['Ġwaterpark', ','] ['Ġ4', 'k'] ['Ġphotograph', ','] ['Ġcinematic'] 15 text tokens [0, 58, 264, 3, 4046, 6941, 9928, 40859, 11, 252, 38, 701, 11, 19936, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:f9aa9bc4d9afc47d5d31edec6280194fa87c29f4866b6d5ad9103597c0387904IDhef5ekddkbfnzd52izpx4avs3yStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- artificial intelligence
- top_k
- 64
- seamless
- grid_size
- "7"
- temperature
- 4
- progressive_outputs
- supercondition_factor
- 16
{ "text": "artificial intelligence", "top_k": 64, "seamless": true, "grid_size": "7", "temperature": "4", "progressive_outputs": true, "supercondition_factor": 16 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:f9aa9bc4d9afc47d5d31edec6280194fa87c29f4866b6d5ad9103597c0387904", { input: { text: "artificial intelligence", top_k: 64, seamless: true, grid_size: "7", temperature: "4", progressive_outputs: true, supercondition_factor: 16 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:f9aa9bc4d9afc47d5d31edec6280194fa87c29f4866b6d5ad9103597c0387904", input={ "text": "artificial intelligence", "top_k": 64, "seamless": True, "grid_size": "7", "temperature": "4", "progressive_outputs": True, "supercondition_factor": 16 } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "f9aa9bc4d9afc47d5d31edec6280194fa87c29f4866b6d5ad9103597c0387904", "input": { "text": "artificial intelligence", "top_k": 64, "seamless": true, "grid_size": "7", "temperature": "4", "progressive_outputs": true, "supercondition_factor": 16 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-17T12:41:39.419950Z", "created_at": "2022-07-17T12:39:44.489096Z", "data_removed": false, "error": null, "id": "hef5ekddkbfnzd52izpx4avs3y", "input": { "text": "artificial intelligence", "top_k": 64, "seamless": true, "grid_size": "7", "temperature": "4", "progressive_outputs": true, "supercondition_factor": 16 }, "logs": "tokenizing text\n['Ġartificial']\n['Ġintelligence']\n4 text tokens [0, 6316, 7815, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 28.009498, "total_time": 114.930854 }, "output": [ "https://replicate.delivery/mgxm/2e79749b-60ad-4129-9b3e-1a1669eb156b/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/76a7bff2-ac4c-4e2d-b201-d2556558b585/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/2bf9f47a-d446-4191-a83a-b811bdd47505/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/8d0b3392-f6bf-4d36-9015-3c81de70227e/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/25125ea3-48cc-4972-915c-3de86240fa5f/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/2213e4e5-41b7-4a38-bd8d-cff1325ddf15/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/7101eb7a-1a4c-4709-97dc-5c5ae4e05d8e/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/03a5d4e5-7ff9-4b59-830f-f3c57a0452ea/min-dalle-iter-8.jpg" ], "started_at": "2022-07-17T12:41:11.410452Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hef5ekddkbfnzd52izpx4avs3y", "cancel": "https://api.replicate.com/v1/predictions/hef5ekddkbfnzd52izpx4avs3y/cancel" }, "version": "f9aa9bc4d9afc47d5d31edec6280194fa87c29f4866b6d5ad9103597c0387904" }
Generated intokenizing text ['Ġartificial'] ['Ġintelligence'] 4 text tokens [0, 6316, 7815, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:f9aa9bc4d9afc47d5d31edec6280194fa87c29f4866b6d5ad9103597c0387904ID6ipn7yllarbv7fyda2dwl5jr2yStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- an astronaut walking on a desert planet, digital art
- top_k
- 64
- seamless
- grid_size
- "5"
- temperature
- 4
- progressive_outputs
- supercondition_factor
- 16
{ "text": "an astronaut walking on a desert planet, digital art", "top_k": 64, "seamless": false, "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": 16 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:f9aa9bc4d9afc47d5d31edec6280194fa87c29f4866b6d5ad9103597c0387904", { input: { text: "an astronaut walking on a desert planet, digital art", top_k: 64, seamless: false, grid_size: "5", temperature: "4", progressive_outputs: true, supercondition_factor: 16 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:f9aa9bc4d9afc47d5d31edec6280194fa87c29f4866b6d5ad9103597c0387904", input={ "text": "an astronaut walking on a desert planet, digital art", "top_k": 64, "seamless": False, "grid_size": "5", "temperature": "4", "progressive_outputs": True, "supercondition_factor": 16 } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "f9aa9bc4d9afc47d5d31edec6280194fa87c29f4866b6d5ad9103597c0387904", "input": { "text": "an astronaut walking on a desert planet, digital art", "top_k": 64, "seamless": false, "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": 16 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-17T12:59:19.062803Z", "created_at": "2022-07-17T12:58:55.024801Z", "data_removed": false, "error": null, "id": "6ipn7yllarbv7fyda2dwl5jr2y", "input": { "text": "an astronaut walking on a desert planet, digital art", "top_k": 64, "seamless": false, "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": 16 }, "logs": "tokenizing text\n['Ġan']\n['Ġastronaut']\n['Ġwalking']\n['Ġon']\n['Ġa']\n['Ġdesert']\n['Ġplanet', ',']\n['Ġdigital']\n['Ġart']\n12 text tokens [0, 101, 14282, 4462, 133, 58, 3806, 3493, 11, 1189, 241, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 15.047874, "total_time": 24.038002 }, "output": [ "https://replicate.delivery/mgxm/57d0940f-fdf4-4bd6-9b64-22f6efa114ce/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/c75f2483-6386-45e6-9684-722ec4f0bbc7/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/7e0db0a8-9498-49f3-904c-0ff7f1315645/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/8a373e1c-787d-4482-a7b3-a07d02b6ae42/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/58575e6f-0a91-470b-ab95-c0361da5b308/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/817a31be-c770-44c2-b7fe-c1aa8c6fc68e/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/c36d5073-40a6-4cfa-93bb-457a90f7df86/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/77694ea3-098e-40ce-b392-354f04c440c6/min-dalle-iter-8.jpg" ], "started_at": "2022-07-17T12:59:04.014929Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6ipn7yllarbv7fyda2dwl5jr2y", "cancel": "https://api.replicate.com/v1/predictions/6ipn7yllarbv7fyda2dwl5jr2y/cancel" }, "version": "f9aa9bc4d9afc47d5d31edec6280194fa87c29f4866b6d5ad9103597c0387904" }
Generated intokenizing text ['Ġan'] ['Ġastronaut'] ['Ġwalking'] ['Ġon'] ['Ġa'] ['Ġdesert'] ['Ġplanet', ','] ['Ġdigital'] ['Ġart'] 12 text tokens [0, 101, 14282, 4462, 133, 58, 3806, 3493, 11, 1189, 241, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:7b7516e5276271dfcfdbf9bdd0c61aecd0ae4f6acdad280072b6912f5247c1e8ID5nvilpwqszeyhl2bmqu6dauloeStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- An astronaut walking on Mars next to a Starship rocket, realistic
- top_k
- 64
- seamless
- grid_size
- "5"
- temperature
- 4
- progressive_outputs
- supercondition_factor
- 16
{ "text": "An astronaut walking on Mars next to a Starship rocket, realistic", "top_k": 64, "seamless": true, "grid_size": "5", "temperature": 4, "progressive_outputs": true, "supercondition_factor": 16 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:7b7516e5276271dfcfdbf9bdd0c61aecd0ae4f6acdad280072b6912f5247c1e8", { input: { text: "An astronaut walking on Mars next to a Starship rocket, realistic", top_k: 64, seamless: true, grid_size: "5", temperature: 4, progressive_outputs: true, supercondition_factor: 16 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:7b7516e5276271dfcfdbf9bdd0c61aecd0ae4f6acdad280072b6912f5247c1e8", input={ "text": "An astronaut walking on Mars next to a Starship rocket, realistic", "top_k": 64, "seamless": True, "grid_size": "5", "temperature": 4, "progressive_outputs": True, "supercondition_factor": 16 } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "7b7516e5276271dfcfdbf9bdd0c61aecd0ae4f6acdad280072b6912f5247c1e8", "input": { "text": "An astronaut walking on Mars next to a Starship rocket, realistic", "top_k": 64, "seamless": true, "grid_size": "5", "temperature": 4, "progressive_outputs": true, "supercondition_factor": 16 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-17T15:42:45.065835Z", "created_at": "2022-07-17T15:42:16.247409Z", "data_removed": false, "error": null, "id": "5nvilpwqszeyhl2bmqu6dauloe", "input": { "text": "An astronaut walking on Mars next to a Starship rocket, realistic", "top_k": 64, "seamless": true, "grid_size": "5", "temperature": 4, "progressive_outputs": true, "supercondition_factor": 16 }, "logs": "tokenizing text\n['Ġan']\n['Ġastronaut']\n['Ġwalking']\n['Ġon']\n['Ġmars']\n['Ġnext']\n['Ġto']\n['Ġa']\n['Ġstarship']\n['Ġrocket', ',']\n['Ġrealistic']\n14 text tokens [0, 101, 14282, 4462, 133, 6705, 2365, 123, 58, 32568, 6110, 11, 10573, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 16.065732, "total_time": 28.818426 }, "output": [ "https://replicate.delivery/mgxm/737c7691-9aef-4eba-b82c-4e709229e285/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/56846561-f92a-4fd3-8295-2369ce8c3ace/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/48a7604f-554c-4c0f-9e91-ea79d4ca3368/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/58536afd-4d89-4692-a04f-31b1af3f003b/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/0cb0c0eb-1b1c-4bb0-b7ac-a8ce3a439585/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/b13c2e41-41c0-487d-91f5-1d27068d8fd8/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/cbe4d1ef-e1fd-4c41-bfbe-5883a16c617d/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/0446cc03-f835-47cb-80f6-fc8a5c5bb5b2/min-dalle-iter-8.jpg" ], "started_at": "2022-07-17T15:42:29.000103Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5nvilpwqszeyhl2bmqu6dauloe", "cancel": "https://api.replicate.com/v1/predictions/5nvilpwqszeyhl2bmqu6dauloe/cancel" }, "version": "7b7516e5276271dfcfdbf9bdd0c61aecd0ae4f6acdad280072b6912f5247c1e8" }
Generated intokenizing text ['Ġan'] ['Ġastronaut'] ['Ġwalking'] ['Ġon'] ['Ġmars'] ['Ġnext'] ['Ġto'] ['Ġa'] ['Ġstarship'] ['Ġrocket', ','] ['Ġrealistic'] 14 text tokens [0, 101, 14282, 4462, 133, 6705, 2365, 123, 58, 32568, 6110, 11, 10573, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1IDm3xm2lguo5benlijih2gbksoeuStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- singularity, hyperrealism
- top_k
- 64
- seamless
- grid_size
- "5"
- temperature
- "1"
- progressive_outputs
- supercondition_factor
- "16"
{ "text": "singularity, hyperrealism", "top_k": 64, "seamless": true, "grid_size": "5", "temperature": "1", "progressive_outputs": true, "supercondition_factor": "16" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", { input: { text: "singularity, hyperrealism", top_k: 64, seamless: true, grid_size: "5", temperature: "1", progressive_outputs: true, supercondition_factor: "16" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", input={ "text": "singularity, hyperrealism", "top_k": 64, "seamless": True, "grid_size": "5", "temperature": "1", "progressive_outputs": True, "supercondition_factor": "16" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", "input": { "text": "singularity, hyperrealism", "top_k": 64, "seamless": true, "grid_size": "5", "temperature": "1", "progressive_outputs": true, "supercondition_factor": "16" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-17T17:20:49.135146Z", "created_at": "2022-07-17T17:20:16.982050Z", "data_removed": false, "error": null, "id": "m3xm2lguo5benlijih2gbksoeu", "input": { "text": "singularity, hyperrealism", "top_k": 64, "seamless": true, "grid_size": "5", "temperature": "1", "progressive_outputs": true, "supercondition_factor": "16" }, "logs": "tokenizing text\n['Ġsingular', 'ity', ',']\n['Ġhyper', 'real', 'ism']\n8 text tokens [0, 30129, 223, 11, 6139, 4646, 822, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 15.969548, "total_time": 32.153096 }, "output": [ "https://replicate.delivery/mgxm/f4809a49-31c7-43cf-92ae-a42972718c18/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/153da01d-0055-4384-8814-9b8fc94c6cbc/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/7f3a13ef-cf4f-4cf9-893c-854b675685fe/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/4f25e5b4-b6cb-4758-b4d6-fd866aaa06dc/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/a39a43eb-d1fd-45d8-9adb-1e58f32a822e/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/75bc106c-8d99-4947-a2ac-1ed0924db72d/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/d77bf31a-bc40-4589-bd10-8e9aaf41bbd8/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/0715b9a1-f457-4329-aed4-bed16272ab88/min-dalle-iter-8.jpg" ], "started_at": "2022-07-17T17:20:33.165598Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/m3xm2lguo5benlijih2gbksoeu", "cancel": "https://api.replicate.com/v1/predictions/m3xm2lguo5benlijih2gbksoeu/cancel" }, "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1" }
Generated intokenizing text ['Ġsingular', 'ity', ','] ['Ġhyper', 'real', 'ism'] 8 text tokens [0, 30129, 223, 11, 6139, 4646, 822, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1ID5mskldqaebcadgh5brgldtzxmaStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- cavemen at a rave
- top_k
- "128"
- seamless
- grid_size
- "5"
- temperature
- "4"
- progressive_outputs
- supercondition_factor
- "16"
{ "text": "cavemen at a rave", "top_k": "128", "seamless": false, "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "16" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", { input: { text: "cavemen at a rave", top_k: "128", seamless: false, grid_size: "5", temperature: "4", progressive_outputs: true, supercondition_factor: "16" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", input={ "text": "cavemen at a rave", "top_k": "128", "seamless": False, "grid_size": "5", "temperature": "4", "progressive_outputs": True, "supercondition_factor": "16" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", "input": { "text": "cavemen at a rave", "top_k": "128", "seamless": false, "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "16" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-17T17:24:47.097793Z", "created_at": "2022-07-17T17:24:31.273343Z", "data_removed": false, "error": null, "id": "5mskldqaebcadgh5brgldtzxma", "input": { "text": "cavemen at a rave", "top_k": "128", "seamless": false, "grid_size": "5", "temperature": "4", "progressive_outputs": true, "supercondition_factor": "16" }, "logs": "tokenizing text\n['Ġcav', 'emen']\n['Ġat']\n['Ġa']\n['Ġrave']\n7 text tokens [0, 4044, 8275, 202, 58, 14391, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 15.220954, "total_time": 15.82445 }, "output": [ "https://replicate.delivery/mgxm/72b412cf-6673-4909-a196-b9d06258dd88/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/d93242d0-ae96-47a7-90e4-2321e89bf1d5/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/088690d0-8955-4688-832b-27951221e566/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/cebc7c55-076b-4c5c-9e96-a3976a170025/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/83bc6d30-b0bf-46bc-8e23-d1a76aab14e9/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/60f2d867-4b15-4beb-bfb9-ca6172f1123d/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/4c2f790b-4d66-40c1-ad9f-d0cdc1cf77b6/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/e8eee0ae-f6f7-41f4-a1db-47fcb8b0ed34/min-dalle-iter-8.jpg" ], "started_at": "2022-07-17T17:24:31.876839Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5mskldqaebcadgh5brgldtzxma", "cancel": "https://api.replicate.com/v1/predictions/5mskldqaebcadgh5brgldtzxma/cancel" }, "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1" }
Generated intokenizing text ['Ġcav', 'emen'] ['Ġat'] ['Ġa'] ['Ġrave'] 7 text tokens [0, 4044, 8275, 202, 58, 14391, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1Input
- text
- A 5-star McDonalds themed waterpark, 4k photograph, cinematic, McDonalds
- top_k
- "16384"
- grid_size
- "5"
- temperature
- "1.5"
- progressive_outputs
- supercondition_factor
- "64"
{ "text": "A 5-star McDonalds themed waterpark, 4k photograph, cinematic, McDonalds", "top_k": "16384", "grid_size": "5", "temperature": "1.5", "progressive_outputs": true, "supercondition_factor": "64" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", { input: { text: "A 5-star McDonalds themed waterpark, 4k photograph, cinematic, McDonalds", top_k: "16384", grid_size: "5", temperature: "1.5", progressive_outputs: true, supercondition_factor: "64" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", input={ "text": "A 5-star McDonalds themed waterpark, 4k photograph, cinematic, McDonalds", "top_k": "16384", "grid_size": "5", "temperature": "1.5", "progressive_outputs": True, "supercondition_factor": "64" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", "input": { "text": "A 5-star McDonalds themed waterpark, 4k photograph, cinematic, McDonalds", "top_k": "16384", "grid_size": "5", "temperature": "1.5", "progressive_outputs": true, "supercondition_factor": "64" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-17T20:39:50.488051Z", "created_at": "2022-07-17T20:39:27.037271Z", "data_removed": false, "error": null, "id": "7tbxlvwa3zc5nedgvpg4on2q4a", "input": { "text": "A 5-star McDonalds themed waterpark, 4k photograph, cinematic, McDonalds", "top_k": "16384", "grid_size": "5", "temperature": "1.5", "progressive_outputs": true, "supercondition_factor": "64" }, "logs": "tokenizing text\n['Ġa']\n['Ġ5', '-', 'star']\n['Ġmcdonalds']\n['Ġthemed']\n['Ġwaterpark', ',']\n['Ġ4', 'k']\n['Ġphotograph', ',']\n['Ġcinematic', ',']\n['Ġmcdonalds']\n17 text tokens [0, 58, 264, 3, 4046, 30250, 9928, 40859, 11, 252, 38, 701, 11, 19936, 11, 30250, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 16.380016, "total_time": 23.45078 }, "output": [ "https://replicate.delivery/mgxm/c2fb7e5c-2f2d-483f-afbc-e6b8c1b00ff9/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/38d8d83a-3424-4dd4-89e8-8d423281fb15/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/9db3d8dc-37a5-465f-a3c4-715b8fc392f6/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/da964b44-8f0d-433d-bb2a-4c26bf805b90/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/01a7bf03-7ee9-4371-aee7-159dc7bb6822/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/a784e403-52b8-4b05-85b0-06500837edd9/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/23b0cf89-10cf-4ff9-881c-854b19d9b953/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/5e4d6a6f-223b-426d-a989-38a68a8ff04b/min-dalle-iter-8.jpg" ], "started_at": "2022-07-17T20:39:34.108035Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7tbxlvwa3zc5nedgvpg4on2q4a", "cancel": "https://api.replicate.com/v1/predictions/7tbxlvwa3zc5nedgvpg4on2q4a/cancel" }, "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1" }
Generated intokenizing text ['Ġa'] ['Ġ5', '-', 'star'] ['Ġmcdonalds'] ['Ġthemed'] ['Ġwaterpark', ','] ['Ġ4', 'k'] ['Ġphotograph', ','] ['Ġcinematic', ','] ['Ġmcdonalds'] 17 text tokens [0, 58, 264, 3, 4046, 30250, 9928, 40859, 11, 252, 38, 701, 11, 19936, 11, 30250, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1Input
- text
- Face portrait of Darth Vader, by Pablo Picasso
- top_k
- "16384"
- seamless
- grid_size
- "5"
- temperature
- "1.5"
- progressive_outputs
- supercondition_factor
- "64"
{ "text": "Face portrait of Darth Vader, by Pablo Picasso", "top_k": "16384", "seamless": false, "grid_size": "5", "temperature": "1.5", "progressive_outputs": true, "supercondition_factor": "64" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", { input: { text: "Face portrait of Darth Vader, by Pablo Picasso", top_k: "16384", seamless: false, grid_size: "5", temperature: "1.5", progressive_outputs: true, supercondition_factor: "64" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", input={ "text": "Face portrait of Darth Vader, by Pablo Picasso", "top_k": "16384", "seamless": False, "grid_size": "5", "temperature": "1.5", "progressive_outputs": True, "supercondition_factor": "64" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", "input": { "text": "Face portrait of Darth Vader, by Pablo Picasso", "top_k": "16384", "seamless": false, "grid_size": "5", "temperature": "1.5", "progressive_outputs": true, "supercondition_factor": "64" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-17T20:56:39.094278Z", "created_at": "2022-07-17T20:55:59.294294Z", "data_removed": false, "error": null, "id": "c2c5t2mbt5cjjkblmqv44abvuy", "input": { "text": "Face portrait of Darth Vader, by Pablo Picasso", "top_k": "16384", "seamless": false, "grid_size": "5", "temperature": "1.5", "progressive_outputs": true, "supercondition_factor": "64" }, "logs": "tokenizing text\n['Ġface']\n['Ġportrait']\n['Ġof']\n['Ġdarth']\n['Ġvader', ',']\n['Ġby']\n['Ġpablo']\n['Ġpicasso']\n11 text tokens [0, 1775, 3317, 111, 19109, 22981, 11, 185, 11599, 18117, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 15.280702, "total_time": 39.799984 }, "output": [ "https://replicate.delivery/mgxm/cb69bf9a-f3ab-4f78-ab17-06d47e811a41/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/2f69ca2a-55ec-4ffc-8b91-0032392c8d67/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/8eefbdde-e855-4925-a8dc-20c3945b7c4c/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/d3e7d34c-364a-45a1-af45-edae7bdfef3b/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/cf0ab068-458b-48a7-b523-4244cccbc770/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/822e2f38-ce68-49e5-95f0-041fd834d2f6/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/70437e98-e4dd-4b0b-b4d7-fea75f53dde6/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/1d55c7b2-3616-48b0-b350-016c3d6a4e78/min-dalle-iter-8.jpg" ], "started_at": "2022-07-17T20:56:23.813576Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/c2c5t2mbt5cjjkblmqv44abvuy", "cancel": "https://api.replicate.com/v1/predictions/c2c5t2mbt5cjjkblmqv44abvuy/cancel" }, "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1" }
Generated intokenizing text ['Ġface'] ['Ġportrait'] ['Ġof'] ['Ġdarth'] ['Ġvader', ','] ['Ġby'] ['Ġpablo'] ['Ġpicasso'] 11 text tokens [0, 1775, 3317, 111, 19109, 22981, 11, 185, 11599, 18117, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1IDkgsbut35kfarfkjltz3xxxm6naStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- two among us astronauts by the campfire at twilight
- top_k
- "128"
- grid_size
- "5"
- temperature
- "0.4"
- progressive_outputs
- supercondition_factor
- "4"
{ "text": "two among us astronauts by the campfire at twilight", "top_k": "128", "grid_size": "5", "temperature": "0.4", "progressive_outputs": true, "supercondition_factor": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", { input: { text: "two among us astronauts by the campfire at twilight", top_k: "128", grid_size: "5", temperature: "0.4", progressive_outputs: true, supercondition_factor: "4" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", input={ "text": "two among us astronauts by the campfire at twilight", "top_k": "128", "grid_size": "5", "temperature": "0.4", "progressive_outputs": True, "supercondition_factor": "4" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", "input": { "text": "two among us astronauts by the campfire at twilight", "top_k": "128", "grid_size": "5", "temperature": "0.4", "progressive_outputs": true, "supercondition_factor": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-17T23:23:58.839211Z", "created_at": "2022-07-17T23:23:42.231772Z", "data_removed": false, "error": null, "id": "kgsbut35kfarfkjltz3xxxm6na", "input": { "text": "two among us astronauts by the campfire at twilight", "top_k": "128", "grid_size": "5", "temperature": "0.4", "progressive_outputs": true, "supercondition_factor": "4" }, "logs": "tokenizing text\n['Ġtwo']\n['Ġamong']\n['Ġus']\n['Ġastronauts']\n['Ġby']\n['Ġthe']\n['Ġcampfire']\n['Ġat']\n['Ġtwilight']\n11 text tokens [0, 1315, 5256, 326, 37302, 185, 99, 34421, 202, 12186, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 15.238127, "total_time": 16.607439 }, "output": [ "https://replicate.delivery/mgxm/04182c9c-4d3b-4438-81bb-29a3e247ea65/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/d3e87fad-e1e4-4c80-a284-f728c6a5f145/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/64e4826f-9084-4072-be76-e75236495d79/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/74ecd8a9-5da9-4211-9c36-e754b5045f5b/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/0040b04c-412f-4a3a-b3ea-ebda0a7a6b92/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/7fc6b90e-e6b1-4ff9-8a75-d1e5419662fd/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/6efa15b4-5c82-46cb-b752-546ac32b0bf8/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/e936f312-4037-4252-af13-10d802cfba11/min-dalle-iter-8.jpg" ], "started_at": "2022-07-17T23:23:43.601084Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kgsbut35kfarfkjltz3xxxm6na", "cancel": "https://api.replicate.com/v1/predictions/kgsbut35kfarfkjltz3xxxm6na/cancel" }, "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1" }
Generated intokenizing text ['Ġtwo'] ['Ġamong'] ['Ġus'] ['Ġastronauts'] ['Ġby'] ['Ġthe'] ['Ġcampfire'] ['Ġat'] ['Ġtwilight'] 11 text tokens [0, 1315, 5256, 326, 37302, 185, 99, 34421, 202, 12186, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1Input
- text
- A canyon landscape with a cowboy holding a cilinder, high quality, 4k, sci-fi
- top_k
- "16384"
- grid_size
- "5"
- temperature
- "0.5"
- progressive_outputs
- supercondition_factor
- "64"
{ "text": "A canyon landscape with a cowboy holding a cilinder, high quality, 4k, sci-fi", "top_k": "16384", "grid_size": "5", "temperature": "0.5", "progressive_outputs": true, "supercondition_factor": "64" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", { input: { text: "A canyon landscape with a cowboy holding a cilinder, high quality, 4k, sci-fi", top_k: "16384", grid_size: "5", temperature: "0.5", progressive_outputs: true, supercondition_factor: "64" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", input={ "text": "A canyon landscape with a cowboy holding a cilinder, high quality, 4k, sci-fi", "top_k": "16384", "grid_size": "5", "temperature": "0.5", "progressive_outputs": True, "supercondition_factor": "64" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", "input": { "text": "A canyon landscape with a cowboy holding a cilinder, high quality, 4k, sci-fi", "top_k": "16384", "grid_size": "5", "temperature": "0.5", "progressive_outputs": true, "supercondition_factor": "64" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-18T11:28:20.129139Z", "created_at": "2022-07-18T11:27:40.693641Z", "data_removed": false, "error": null, "id": "vdvmrmh6nnfzbj6qpwcfy47ape", "input": { "text": "A canyon landscape with a cowboy holding a cilinder, high quality, 4k, sci-fi", "top_k": "16384", "grid_size": "5", "temperature": "0.5", "progressive_outputs": true, "supercondition_factor": "64" }, "logs": "tokenizing text\n['Ġa']\n['Ġcanyon']\n['Ġlandscape']\n['Ġwith']\n['Ġa']\n['Ġcowboy']\n['Ġholding']\n['Ġa']\n['Ġcil', 'inder', ',']\n['Ġhigh']\n['Ġquality', ',']\n['Ġ4', 'k', ',']\n['Ġsci', '-', 'fi']\n22 text tokens [0, 58, 7308, 3271, 208, 58, 11270, 8538, 58, 17778, 2585, 11, 524, 1905, 11, 252, 38, 11, 4066, 3, 5555, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 14.651629, "total_time": 39.435498 }, "output": [ "https://replicate.delivery/mgxm/cd768cb0-0c1a-44b4-90b1-d45f7783b436/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/ec5d48d4-c76e-425b-8242-effef6339245/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/55a32746-ea75-4be2-b748-d7d21029b64b/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/381330be-50ec-4f89-a8ab-d83ab693eed0/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/3a652b64-bc53-4930-a3ca-119ba7d7eb2f/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/c40b5c63-58f1-40a3-87d8-1d9835f4432e/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/1aa1292e-66c7-4f15-ba21-850996f50a10/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/965bc03f-f881-4bb0-a760-9b84c3855d26/min-dalle-iter-8.jpg" ], "started_at": "2022-07-18T11:28:05.477510Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vdvmrmh6nnfzbj6qpwcfy47ape", "cancel": "https://api.replicate.com/v1/predictions/vdvmrmh6nnfzbj6qpwcfy47ape/cancel" }, "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1" }
Generated intokenizing text ['Ġa'] ['Ġcanyon'] ['Ġlandscape'] ['Ġwith'] ['Ġa'] ['Ġcowboy'] ['Ġholding'] ['Ġa'] ['Ġcil', 'inder', ','] ['Ġhigh'] ['Ġquality', ','] ['Ġ4', 'k', ','] ['Ġsci', '-', 'fi'] 22 text tokens [0, 58, 7308, 3271, 208, 58, 11270, 8538, 58, 17778, 2585, 11, 524, 1905, 11, 252, 38, 11, 4066, 3, 5555, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7Input
- text
- Capybara bathing amongst the cosmos, vibrant water, dramatic light, 8k Ultra resolution, HD, Illustration, Featured on Artstation, Professional painting, Digital art
- grid_size
- "4"
- intermediate_outputs
- log2_supercondition_factor
- "6"
{ "text": "Capybara bathing amongst the cosmos, vibrant water, dramatic light, 8k Ultra resolution, HD, Illustration, Featured on Artstation, Professional painting, Digital art", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", { input: { text: "Capybara bathing amongst the cosmos, vibrant water, dramatic light, 8k Ultra resolution, HD, Illustration, Featured on Artstation, Professional painting, Digital art", grid_size: "4", intermediate_outputs: true, log2_supercondition_factor: "6" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", input={ "text": "Capybara bathing amongst the cosmos, vibrant water, dramatic light, 8k Ultra resolution, HD, Illustration, Featured on Artstation, Professional painting, Digital art", "grid_size": "4", "intermediate_outputs": True, "log2_supercondition_factor": "6" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7", "input": { "text": "Capybara bathing amongst the cosmos, vibrant water, dramatic light, 8k Ultra resolution, HD, Illustration, Featured on Artstation, Professional painting, Digital art", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7 \ -i 'text="Capybara bathing amongst the cosmos, vibrant water, dramatic light, 8k Ultra resolution, HD, Illustration, Featured on Artstation, Professional painting, Digital art"' \ -i 'grid_size="4"' \ -i 'intermediate_outputs=true' \ -i 'log2_supercondition_factor="6"'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/kuprel/min-dalle@sha256:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "text": "Capybara bathing amongst the cosmos, vibrant water, dramatic light, 8k Ultra resolution, HD, Illustration, Featured on Artstation, Professional painting, Digital art", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-07-07T11:10:25.725746Z", "created_at": "2022-07-07T11:10:08.722894Z", "data_removed": false, "error": null, "id": "jmzajrcbozd4tb6mheonj7g4uu", "input": { "text": "Capybara bathing amongst the cosmos, vibrant water, dramatic light, 8k Ultra resolution, HD, Illustration, Featured on Artstation, Professional painting, Digital art", "grid_size": "4", "intermediate_outputs": true, "log2_supercondition_factor": "6" }, "logs": "tokenizing text\n['Ġcap', 'yb', 'ara']\n['Ġbathing']\n['Ġamongst']\n['Ġthe']\n['Ġcosmos', ',']\n['Ġvibrant']\n['Ġwater', ',']\n['Ġdramatic']\n['Ġlight', ',']\n['Ġ8', 'k']\n['Ġultra']\n['Ġresolution', ',']\n['Ġhd', ',']\n['Ġillustration', ',']\n['Ġfeatured']\n['Ġon']\n['Ġartstation', ',']\n['Ġprofessional']\n['Ġpainting', ',']\n['Ġdigital']\n['Ġart']\ntext tokens [0, 712, 21854, 761, 20839, 40382, 99, 21679, 11, 18723, 725, 11, 14836, 895, 11, 416, 38, 4139, 7790, 11, 831, 11, 2262, 11, 8116, 133, 4640, 11, 2995, 1545, 11, 1189, 241, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image", "metrics": { "predict_time": 15.868416, "total_time": 17.002852 }, "output": [ "https://replicate.delivery/mgxm/754f9839-6470-4a90-bd42-4afed7e58f62/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/cbd37d96-4eb3-4b42-a147-13126fd98a74/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/10348643-d324-4cac-a7c3-53885cb4e206/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/004625b6-33fc-4ec0-a168-7476cdfe5dcb/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/1785fc0d-8d8c-4561-ae07-219586ff49ba/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/46de1cc7-2921-41c2-9a3d-8512fcb6e7d2/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/c9433e8d-038a-474d-90f3-2f2cd81ed7bb/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/a3d4937e-9f68-46b0-b488-32c80a34370c/min-dalle-iter-8.jpg" ], "started_at": "2022-07-07T11:10:09.857330Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jmzajrcbozd4tb6mheonj7g4uu", "cancel": "https://api.replicate.com/v1/predictions/jmzajrcbozd4tb6mheonj7g4uu/cancel" }, "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7" }
Generated intokenizing text ['Ġcap', 'yb', 'ara'] ['Ġbathing'] ['Ġamongst'] ['Ġthe'] ['Ġcosmos', ','] ['Ġvibrant'] ['Ġwater', ','] ['Ġdramatic'] ['Ġlight', ','] ['Ġ8', 'k'] ['Ġultra'] ['Ġresolution', ','] ['Ġhd', ','] ['Ġillustration', ','] ['Ġfeatured'] ['Ġon'] ['Ġartstation', ','] ['Ġprofessional'] ['Ġpainting', ','] ['Ġdigital'] ['Ġart'] text tokens [0, 712, 21854, 761, 20839, 40382, 99, 21679, 11, 18723, 725, 11, 14836, 895, 11, 416, 38, 4139, 7790, 11, 831, 11, 2262, 11, 8116, 133, 4640, 11, 2995, 1545, 11, 1189, 241, 2] encoding text tokens sampling row 1 of 16 sampling row 2 of 16 detokenizing image sampling row 3 of 16 sampling row 4 of 16 detokenizing image sampling row 5 of 16 sampling row 6 of 16 detokenizing image sampling row 7 of 16 sampling row 8 of 16 detokenizing image sampling row 9 of 16 sampling row 10 of 16 detokenizing image sampling row 11 of 16 sampling row 12 of 16 detokenizing image sampling row 13 of 16 sampling row 14 of 16 detokenizing image sampling row 15 of 16 sampling row 16 of 16 detokenizing image
Prediction
kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1IDei2wyyu3uzcinbxde6w4pbrutuStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- Professional Photography of the inside of a broken, abandoned Chuck E Cheese in the middle of the night, with flash enabled
- top_k
- "16384"
- grid_size
- "5"
- temperature
- "0.5"
- progressive_outputs
- supercondition_factor
- "64"
{ "text": "Professional Photography of the inside of a broken, abandoned Chuck E Cheese in the middle of the night, with flash enabled", "top_k": "16384", "grid_size": "5", "temperature": "0.5", "progressive_outputs": true, "supercondition_factor": "64" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", { input: { text: "Professional Photography of the inside of a broken, abandoned Chuck E Cheese in the middle of the night, with flash enabled", top_k: "16384", grid_size: "5", temperature: "0.5", progressive_outputs: true, supercondition_factor: "64" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", input={ "text": "Professional Photography of the inside of a broken, abandoned Chuck E Cheese in the middle of the night, with flash enabled", "top_k": "16384", "grid_size": "5", "temperature": "0.5", "progressive_outputs": True, "supercondition_factor": "64" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", "input": { "text": "Professional Photography of the inside of a broken, abandoned Chuck E Cheese in the middle of the night, with flash enabled", "top_k": "16384", "grid_size": "5", "temperature": "0.5", "progressive_outputs": true, "supercondition_factor": "64" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-18T13:32:26.937871Z", "created_at": "2022-07-18T13:32:10.604766Z", "data_removed": false, "error": null, "id": "ei2wyyu3uzcinbxde6w4pbrutu", "input": { "text": "Professional Photography of the inside of a broken, abandoned Chuck E Cheese in the middle of the night, with flash enabled", "top_k": "16384", "grid_size": "5", "temperature": "0.5", "progressive_outputs": true, "supercondition_factor": "64" }, "logs": "tokenizing text\n['Ġprofessional']\n['Ġphotography']\n['Ġof']\n['Ġthe']\n['Ġinside']\n['Ġof']\n['Ġa']\n['Ġbroken', ',']\n['Ġabandoned']\n['Ġchuck']\n['Ġe']\n['Ġcheese']\n['Ġin']\n['Ġthe']\n['Ġmiddle']\n['Ġof']\n['Ġthe']\n['Ġnight', ',']\n['Ġwith']\n['Ġflash']\n['Ġenabled']\n25 text tokens [0, 2995, 1224, 111, 99, 2775, 111, 58, 7010, 11, 9352, 10669, 110, 4672, 91, 99, 2845, 111, 99, 1413, 11, 208, 3745, 33869, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 15.4993, "total_time": 16.333105 }, "output": [ "https://replicate.delivery/mgxm/df46559c-aa91-468a-942e-9fd054cac1c4/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/6e2cf70a-46b8-41a5-b85e-699b942618a5/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/553f2ef1-4d9c-41f3-b805-ecb095fe69e4/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/e0cb0878-8954-4554-98da-fd5b79c31a4a/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/e6f5b9f3-705d-4f14-b4bd-299a2ff3159f/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/46008754-fee2-4798-991e-cc39d5243ede/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/891675e1-9c4a-492e-bce0-12b13c467302/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/69c2dc42-274c-4201-8846-4ce1db84a628/min-dalle-iter-8.jpg" ], "started_at": "2022-07-18T13:32:11.438571Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ei2wyyu3uzcinbxde6w4pbrutu", "cancel": "https://api.replicate.com/v1/predictions/ei2wyyu3uzcinbxde6w4pbrutu/cancel" }, "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1" }
Generated intokenizing text ['Ġprofessional'] ['Ġphotography'] ['Ġof'] ['Ġthe'] ['Ġinside'] ['Ġof'] ['Ġa'] ['Ġbroken', ','] ['Ġabandoned'] ['Ġchuck'] ['Ġe'] ['Ġcheese'] ['Ġin'] ['Ġthe'] ['Ġmiddle'] ['Ġof'] ['Ġthe'] ['Ġnight', ','] ['Ġwith'] ['Ġflash'] ['Ġenabled'] 25 text tokens [0, 2995, 1224, 111, 99, 2775, 111, 58, 7010, 11, 9352, 10669, 110, 4672, 91, 99, 2845, 111, 99, 1413, 11, 208, 3745, 33869, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1IDdu4sodbxm5cd7l5qik2wfalgeqStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- a sole time traveller wandering an abandoned planet, cinematic, award winning
- top_k
- "512"
- grid_size
- "7"
- temperature
- "0.69"
- progressive_outputs
- supercondition_factor
- "64"
{ "text": "a sole time traveller wandering an abandoned planet, cinematic, award winning", "top_k": "512", "grid_size": "7", "temperature": "0.69", "progressive_outputs": false, "supercondition_factor": "64" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", { input: { text: "a sole time traveller wandering an abandoned planet, cinematic, award winning", top_k: "512", grid_size: "7", temperature: "0.69", progressive_outputs: false, supercondition_factor: "64" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", input={ "text": "a sole time traveller wandering an abandoned planet, cinematic, award winning", "top_k": "512", "grid_size": "7", "temperature": "0.69", "progressive_outputs": False, "supercondition_factor": "64" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", "input": { "text": "a sole time traveller wandering an abandoned planet, cinematic, award winning", "top_k": "512", "grid_size": "7", "temperature": "0.69", "progressive_outputs": false, "supercondition_factor": "64" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-19T10:14:14.146623Z", "created_at": "2022-07-19T10:13:42.998226Z", "data_removed": false, "error": null, "id": "du4sodbxm5cd7l5qik2wfalgeq", "input": { "text": "a sole time traveller wandering an abandoned planet, cinematic, award winning", "top_k": "512", "grid_size": "7", "temperature": "0.69", "progressive_outputs": false, "supercondition_factor": "64" }, "logs": "tokenizing text\n['Ġa']\n['Ġsole']\n['Ġtime']\n['Ġtraveller']\n['Ġwandering']\n['Ġan']\n['Ġabandoned']\n['Ġplanet', ',']\n['Ġcinematic', ',']\n['Ġaward']\n['Ġwinning']\n15 text tokens [0, 58, 9079, 1010, 12967, 18282, 101, 9352, 3493, 11, 19936, 11, 3457, 6534, 2]\nencoding text tokens\ndetokenizing image", "metrics": { "predict_time": 19.722192, "total_time": 31.148397 }, "output": [ "https://replicate.delivery/mgxm/b0bad464-56bb-4b2a-8d86-e0b989a01f95/min-dalle-iter-1.jpg" ], "started_at": "2022-07-19T10:13:54.424431Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/du4sodbxm5cd7l5qik2wfalgeq", "cancel": "https://api.replicate.com/v1/predictions/du4sodbxm5cd7l5qik2wfalgeq/cancel" }, "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1" }
Generated intokenizing text ['Ġa'] ['Ġsole'] ['Ġtime'] ['Ġtraveller'] ['Ġwandering'] ['Ġan'] ['Ġabandoned'] ['Ġplanet', ','] ['Ġcinematic', ','] ['Ġaward'] ['Ġwinning'] 15 text tokens [0, 58, 9079, 1010, 12967, 18282, 101, 9352, 3493, 11, 19936, 11, 3457, 6534, 2] encoding text tokens detokenizing image
Prediction
kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1Input
- text
- What would you do if you were given the power to travel through time?,digital art,calm,warm,exiting,beautiful art
- top_k
- "16384"
- grid_size
- "5"
- save_as_png
- temperature
- "0.5"
- progressive_outputs
- supercondition_factor
- "4"
{ "text": "What would you do if you were given the power to travel through time?,digital art,calm,warm,exiting,beautiful art", "top_k": "16384", "grid_size": "5", "save_as_png": true, "temperature": "0.5", "progressive_outputs": true, "supercondition_factor": "4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", { input: { text: "What would you do if you were given the power to travel through time?,digital art,calm,warm,exiting,beautiful art", top_k: "16384", grid_size: "5", save_as_png: true, temperature: "0.5", progressive_outputs: true, supercondition_factor: "4" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", input={ "text": "What would you do if you were given the power to travel through time?,digital art,calm,warm,exiting,beautiful art", "top_k": "16384", "grid_size": "5", "save_as_png": True, "temperature": "0.5", "progressive_outputs": True, "supercondition_factor": "4" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", "input": { "text": "What would you do if you were given the power to travel through time?,digital art,calm,warm,exiting,beautiful art", "top_k": "16384", "grid_size": "5", "save_as_png": true, "temperature": "0.5", "progressive_outputs": true, "supercondition_factor": "4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-19T11:49:19.998376Z", "created_at": "2022-07-19T11:49:03.665113Z", "data_removed": false, "error": null, "id": "4q5mpvtb3ra43bnrzsbg4thp3e", "input": { "text": "What would you do if you were given the power to travel through time?,digital art,calm,warm,exiting,beautiful art", "top_k": "16384", "grid_size": "5", "save_as_png": true, "temperature": "0.5", "progressive_outputs": true, "supercondition_factor": "4" }, "logs": "tokenizing text\n['Ġwhat']\n['Ġwould']\n['Ġyou']\n['Ġdo']\n['Ġif']\n['Ġyou']\n['Ġwere']\n['Ġgiven']\n['Ġthe']\n['Ġpower']\n['Ġto']\n['Ġtravel']\n['Ġthrough']\n['Ġtime', '?', ',', 'digital']\n['Ġart', ',', 'cal', 'm', ',', 'w', 'arm', ',', 'ex', 'iting', ',', 'beaut', 'iful']\n['Ġart']\n33 text tokens [0, 558, 3852, 452, 434, 1859, 452, 4147, 9616, 99, 954, 123, 869, 2135, 1010, 3, 11, 33732, 241, 11, 6981, 40, 11, 50, 686, 11, 1801, 2166, 11, 45809, 1634, 241, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 16.183471, "total_time": 16.333263 }, "output": [ "https://replicate.delivery/mgxm/15169338-d242-41f4-a440-8b1846efeb43/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/f9d6dee7-55e9-4908-b290-80b503c64330/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/6083089b-1620-4b6a-a3b6-bbf218305b88/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/5dc2563d-bf0f-4c76-b34e-2beb249f7910/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/38c5e274-7154-430c-9f8e-950a0ba8dbd2/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/4560b3aa-415a-490c-9f4d-ed50b506b664/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/e1c0896d-e068-44f0-b9a1-9c7129cd859b/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/4a9b6a11-c356-4572-ae93-ac6655e4b565/min-dalle-iter-8.png" ], "started_at": "2022-07-19T11:49:03.814905Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4q5mpvtb3ra43bnrzsbg4thp3e", "cancel": "https://api.replicate.com/v1/predictions/4q5mpvtb3ra43bnrzsbg4thp3e/cancel" }, "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1" }
Generated intokenizing text ['Ġwhat'] ['Ġwould'] ['Ġyou'] ['Ġdo'] ['Ġif'] ['Ġyou'] ['Ġwere'] ['Ġgiven'] ['Ġthe'] ['Ġpower'] ['Ġto'] ['Ġtravel'] ['Ġthrough'] ['Ġtime', '?', ',', 'digital'] ['Ġart', ',', 'cal', 'm', ',', 'w', 'arm', ',', 'ex', 'iting', ',', 'beaut', 'iful'] ['Ġart'] 33 text tokens [0, 558, 3852, 452, 434, 1859, 452, 4147, 9616, 99, 954, 123, 869, 2135, 1010, 3, 11, 33732, 241, 11, 6981, 40, 11, 50, 686, 11, 1801, 2166, 11, 45809, 1634, 241, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1IDnaofjz2zvza25kyih6jcv6dwi4StatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- do you think this painting of a cow in spacesuit looks real?, it does to me
- top_k
- "512"
- grid_size
- "8"
- temperature
- "0.5"
- progressive_outputs
- supercondition_factor
- "64"
{ "text": "do you think this painting of a cow in spacesuit looks real?, it does to me", "top_k": "512", "grid_size": "8", "temperature": "0.5", "progressive_outputs": false, "supercondition_factor": "64" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", { input: { text: "do you think this painting of a cow in spacesuit looks real?, it does to me", top_k: "512", grid_size: "8", temperature: "0.5", progressive_outputs: false, supercondition_factor: "64" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", input={ "text": "do you think this painting of a cow in spacesuit looks real?, it does to me", "top_k": "512", "grid_size": "8", "temperature": "0.5", "progressive_outputs": False, "supercondition_factor": "64" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", "input": { "text": "do you think this painting of a cow in spacesuit looks real?, it does to me", "top_k": "512", "grid_size": "8", "temperature": "0.5", "progressive_outputs": false, "supercondition_factor": "64" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-19T11:52:50.749323Z", "created_at": "2022-07-19T11:51:44.407958Z", "data_removed": false, "error": null, "id": "naofjz2zvza25kyih6jcv6dwi4", "input": { "text": "do you think this painting of a cow in spacesuit looks real?, it does to me", "top_k": "512", "grid_size": "8", "temperature": "0.5", "progressive_outputs": false, "supercondition_factor": "64" }, "logs": "tokenizing text\n['Ġdo']\n['Ġyou']\n['Ġthink']\n['Ġthis']\n['Ġpainting']\n['Ġof']\n['Ġa']\n['Ġcow']\n['Ġin']\n['Ġspaces', 'uit']\n['Ġlooks']\n['Ġreal', '?', ',']\n['Ġit']\n['Ġdoes']\n['Ġto']\n['Ġme']\n21 text tokens [0, 434, 452, 3266, 703, 1545, 111, 58, 3189, 91, 10348, 1262, 4126, 639, 3, 11, 353, 2156, 123, 319, 2]\nencoding text tokens\ndetokenizing image", "metrics": { "predict_time": 28.441267, "total_time": 66.341365 }, "output": [ "https://replicate.delivery/mgxm/acb4ac39-b8c1-44b1-ae3c-d98faec3017e/min-dalle-iter-1.jpg" ], "started_at": "2022-07-19T11:52:22.308056Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/naofjz2zvza25kyih6jcv6dwi4", "cancel": "https://api.replicate.com/v1/predictions/naofjz2zvza25kyih6jcv6dwi4/cancel" }, "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1" }
Generated intokenizing text ['Ġdo'] ['Ġyou'] ['Ġthink'] ['Ġthis'] ['Ġpainting'] ['Ġof'] ['Ġa'] ['Ġcow'] ['Ġin'] ['Ġspaces', 'uit'] ['Ġlooks'] ['Ġreal', '?', ','] ['Ġit'] ['Ġdoes'] ['Ġto'] ['Ġme'] 21 text tokens [0, 434, 452, 3266, 703, 1545, 111, 58, 3189, 91, 10348, 1262, 4126, 639, 3, 11, 353, 2156, 123, 319, 2] encoding text tokens detokenizing image
Prediction
kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1Input
- text
- An abandoned hospital that was build in the 1940's. The interior is old and rusty. The windows are broken. The beds are still standing.
- top_k
- "16384"
- grid_size
- "5"
- temperature
- "0.5"
- progressive_outputs
- supercondition_factor
- "64"
{ "text": "An abandoned hospital that was build in the 1940's. The interior is old and rusty. The windows are broken. The beds are still standing.", "top_k": "16384", "grid_size": "5", "temperature": "0.5", "progressive_outputs": true, "supercondition_factor": "64" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", { input: { text: "An abandoned hospital that was build in the 1940's. The interior is old and rusty. The windows are broken. The beds are still standing.", top_k: "16384", grid_size: "5", temperature: "0.5", progressive_outputs: true, supercondition_factor: "64" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", input={ "text": "An abandoned hospital that was build in the 1940's. The interior is old and rusty. The windows are broken. The beds are still standing.", "top_k": "16384", "grid_size": "5", "temperature": "0.5", "progressive_outputs": True, "supercondition_factor": "64" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1", "input": { "text": "An abandoned hospital that was build in the 1940\'s. The interior is old and rusty. The windows are broken. The beds are still standing.", "top_k": "16384", "grid_size": "5", "temperature": "0.5", "progressive_outputs": true, "supercondition_factor": "64" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-19T12:23:50.784801Z", "created_at": "2022-07-19T12:23:26.894029Z", "data_removed": false, "error": null, "id": "4lcf6mkeajc77fpd5sfqxzelaa", "input": { "text": "An abandoned hospital that was build in the 1940's. The interior is old and rusty. The windows are broken. The beds are still standing.", "top_k": "16384", "grid_size": "5", "temperature": "0.5", "progressive_outputs": true, "supercondition_factor": "64" }, "logs": "tokenizing text\n['Ġan']\n['Ġabandoned']\n['Ġhospital']\n['Ġthat']\n['Ġwas']\n['Ġbuild']\n['Ġin']\n['Ġthe']\n['Ġ1940', \"'s\", '.']\n['Ġthe']\n['Ġinterior']\n['Ġis']\n['Ġold']\n['Ġand']\n['Ġrusty', '.']\n['Ġthe']\n['Ġwindows']\n['Ġare']\n['Ġbroken', '.']\n['Ġthe']\n['Ġbeds']\n['Ġare']\n['Ġstill']\n['Ġstanding', '.']\n31 text tokens [0, 101, 9352, 2571, 766, 1207, 1072, 91, 99, 9758, 168, 12, 99, 2615, 231, 819, 128, 22648, 12, 99, 3469, 553, 7010, 12, 99, 8859, 553, 2762, 7329, 12, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 15.252075, "total_time": 23.890772 }, "output": [ "https://replicate.delivery/mgxm/6927c42f-1dc8-42cf-abc9-aab0b8ec3c66/min-dalle-iter-1.jpg", "https://replicate.delivery/mgxm/09ac8dbc-4608-406f-a5e3-1fe38707897a/min-dalle-iter-2.jpg", "https://replicate.delivery/mgxm/1931bc52-326e-4cd2-a90a-75c97fdb86ba/min-dalle-iter-3.jpg", "https://replicate.delivery/mgxm/3ea484fc-84eb-4301-ac49-c8f2c4ea37f3/min-dalle-iter-4.jpg", "https://replicate.delivery/mgxm/29068d76-ddc7-4a36-8f9d-6f85ffed4a08/min-dalle-iter-5.jpg", "https://replicate.delivery/mgxm/e75fff1c-0954-43eb-afba-107a6b324168/min-dalle-iter-6.jpg", "https://replicate.delivery/mgxm/07727235-99ba-4294-9ed5-b65e4d5a72b1/min-dalle-iter-7.jpg", "https://replicate.delivery/mgxm/84def434-2bcf-4474-81dc-b2fb3530befb/min-dalle-iter-8.jpg" ], "started_at": "2022-07-19T12:23:35.532726Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4lcf6mkeajc77fpd5sfqxzelaa", "cancel": "https://api.replicate.com/v1/predictions/4lcf6mkeajc77fpd5sfqxzelaa/cancel" }, "version": "cb2da7da16a54c78ad915be99f00c028a8d619031946ed4de46fa02c677230f1" }
Generated intokenizing text ['Ġan'] ['Ġabandoned'] ['Ġhospital'] ['Ġthat'] ['Ġwas'] ['Ġbuild'] ['Ġin'] ['Ġthe'] ['Ġ1940', "'s", '.'] ['Ġthe'] ['Ġinterior'] ['Ġis'] ['Ġold'] ['Ġand'] ['Ġrusty', '.'] ['Ġthe'] ['Ġwindows'] ['Ġare'] ['Ġbroken', '.'] ['Ġthe'] ['Ġbeds'] ['Ġare'] ['Ġstill'] ['Ġstanding', '.'] 31 text tokens [0, 101, 9352, 2571, 766, 1207, 1072, 91, 99, 9758, 168, 12, 99, 2615, 231, 819, 128, 22648, 12, 99, 3469, 553, 7010, 12, 99, 8859, 553, 2762, 7329, 12, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:c1fca4bef52afc0c3a556060df13d7d7c429a1310feeedbb5c3f92b7002822deIDmyxbvizimndrrcwm5zj2q5chumStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- A mannequin running on a street
- top_k
- "128"
- grid_size
- "5"
- temperature
- "1"
- progressive_outputs
- supercondition_factor
- "16"
{ "text": "A mannequin running on a street", "top_k": "128", "grid_size": "5", "temperature": "1", "progressive_outputs": true, "supercondition_factor": "16" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:c1fca4bef52afc0c3a556060df13d7d7c429a1310feeedbb5c3f92b7002822de", { input: { text: "A mannequin running on a street", top_k: "128", grid_size: "5", temperature: "1", progressive_outputs: true, supercondition_factor: "16" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:c1fca4bef52afc0c3a556060df13d7d7c429a1310feeedbb5c3f92b7002822de", input={ "text": "A mannequin running on a street", "top_k": "128", "grid_size": "5", "temperature": "1", "progressive_outputs": True, "supercondition_factor": "16" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "c1fca4bef52afc0c3a556060df13d7d7c429a1310feeedbb5c3f92b7002822de", "input": { "text": "A mannequin running on a street", "top_k": "128", "grid_size": "5", "temperature": "1", "progressive_outputs": true, "supercondition_factor": "16" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-19T14:11:42.032306Z", "created_at": "2022-07-19T14:11:26.800451Z", "data_removed": false, "error": null, "id": "myxbvizimndrrcwm5zj2q5chum", "input": { "text": "A mannequin running on a street", "top_k": "128", "grid_size": "5", "temperature": "1", "progressive_outputs": true, "supercondition_factor": "16" }, "logs": "tokenizing text\n['Ġa']\n['Ġmannequin']\n['Ġrunning']\n['Ġon']\n['Ġa']\n['Ġstreet']\n8 text tokens [0, 58, 29999, 4103, 133, 58, 1182, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 15.062611, "total_time": 15.231855 }, "output": [ "https://replicate.delivery/mgxm/73ebdbe3-612a-42ef-884a-8892eadef35c/a-mannequin-running-on-a-street-iter-1.jpg", "https://replicate.delivery/mgxm/23079fac-e1b2-4b9f-945b-dd88dba3b1ac/a-mannequin-running-on-a-street-iter-2.jpg", "https://replicate.delivery/mgxm/1960e0ff-db79-4d64-88a1-5f6949318aa6/a-mannequin-running-on-a-street-iter-3.jpg", "https://replicate.delivery/mgxm/e1adabe3-6053-4886-a8b8-d0d9b7286686/a-mannequin-running-on-a-street-iter-4.jpg", "https://replicate.delivery/mgxm/8359ab07-2379-411e-812f-227216adc8ca/a-mannequin-running-on-a-street-iter-5.jpg", "https://replicate.delivery/mgxm/00c7c269-2443-4590-89e2-dc3852c39eb9/a-mannequin-running-on-a-street-iter-6.jpg", "https://replicate.delivery/mgxm/26886f5f-7fa1-4126-be1e-b1cc212f8649/a-mannequin-running-on-a-street-iter-7.jpg", "https://replicate.delivery/mgxm/97422d9f-7051-445a-b903-e0512d8194e2/a-mannequin-running-on-a-street.jpg" ], "started_at": "2022-07-19T14:11:26.969695Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/myxbvizimndrrcwm5zj2q5chum", "cancel": "https://api.replicate.com/v1/predictions/myxbvizimndrrcwm5zj2q5chum/cancel" }, "version": "c1fca4bef52afc0c3a556060df13d7d7c429a1310feeedbb5c3f92b7002822de" }
Generated intokenizing text ['Ġa'] ['Ġmannequin'] ['Ġrunning'] ['Ġon'] ['Ġa'] ['Ġstreet'] 8 text tokens [0, 58, 29999, 4103, 133, 58, 1182, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:c1fca4bef52afc0c3a556060df13d7d7c429a1310feeedbb5c3f92b7002822deIDu2dfnquwbndp5prgyrpqe3dynmStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- blue coloured charmander with ice power, rate this drawing, mind blowing, realistic
- top_k
- "128"
- grid_size
- "6"
- temperature
- "1"
- progressive_outputs
- supercondition_factor
- "16"
{ "text": "blue coloured charmander with ice power, rate this drawing, mind blowing, realistic", "top_k": "128", "grid_size": "6", "temperature": "1", "progressive_outputs": true, "supercondition_factor": "16" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:c1fca4bef52afc0c3a556060df13d7d7c429a1310feeedbb5c3f92b7002822de", { input: { text: "blue coloured charmander with ice power, rate this drawing, mind blowing, realistic", top_k: "128", grid_size: "6", temperature: "1", progressive_outputs: true, supercondition_factor: "16" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:c1fca4bef52afc0c3a556060df13d7d7c429a1310feeedbb5c3f92b7002822de", input={ "text": "blue coloured charmander with ice power, rate this drawing, mind blowing, realistic", "top_k": "128", "grid_size": "6", "temperature": "1", "progressive_outputs": True, "supercondition_factor": "16" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "c1fca4bef52afc0c3a556060df13d7d7c429a1310feeedbb5c3f92b7002822de", "input": { "text": "blue coloured charmander with ice power, rate this drawing, mind blowing, realistic", "top_k": "128", "grid_size": "6", "temperature": "1", "progressive_outputs": true, "supercondition_factor": "16" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-20T14:07:46.534175Z", "created_at": "2022-07-20T14:07:26.648960Z", "data_removed": false, "error": null, "id": "u2dfnquwbndp5prgyrpqe3dynm", "input": { "text": "blue coloured charmander with ice power, rate this drawing, mind blowing, realistic", "top_k": "128", "grid_size": "6", "temperature": "1", "progressive_outputs": true, "supercondition_factor": "16" }, "logs": "tokenizing text\n['Ġblue']\n['Ġcoloured']\n['Ġcharm', 'ander']\n['Ġwith']\n['Ġice']\n['Ġpower', ',']\n['Ġrate']\n['Ġthis']\n['Ġdrawing', ',']\n['Ġmind']\n['Ġblowing', ',']\n['Ġrealistic']\n18 text tokens [0, 789, 18382, 5760, 2223, 208, 2330, 954, 11, 5125, 703, 1740, 11, 2660, 20183, 11, 10573, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 19.701218, "total_time": 19.885215 }, "output": [ "https://replicate.delivery/mgxm/f7936771-e0c6-46fc-985d-cb9352ba154a/blue-coloured-charmander-with-ice-power-rate-this-drawing-m.jpg", "https://replicate.delivery/mgxm/6754c5e1-a224-41fd-99d0-c14f8f9f289d/blue-coloured-charmander-with-ice-power-rate-this-drawing-m.jpg", "https://replicate.delivery/mgxm/1cdac865-cdbb-49dd-8d6c-af7d1f5b6d1f/blue-coloured-charmander-with-ice-power-rate-this-drawing-m.jpg", "https://replicate.delivery/mgxm/6854d383-3482-4a40-b6f2-ed4a502e0d28/blue-coloured-charmander-with-ice-power-rate-this-drawing-m.jpg", "https://replicate.delivery/mgxm/49f06227-8c53-4814-ba09-8cf3587da099/blue-coloured-charmander-with-ice-power-rate-this-drawing-m.jpg", "https://replicate.delivery/mgxm/2919a0bc-ffb1-4cc2-99e1-022ecbfc640b/blue-coloured-charmander-with-ice-power-rate-this-drawing-m.jpg", "https://replicate.delivery/mgxm/912e69e5-6b0b-4219-b4a7-32f5f8f7e1d3/blue-coloured-charmander-with-ice-power-rate-this-drawing-m.jpg", "https://replicate.delivery/mgxm/b95338ea-8210-42d5-85b4-7a86d675a569/blue-coloured-charmander-with-ice-power-rate-this-drawing-m.jpg" ], "started_at": "2022-07-20T14:07:26.832957Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/u2dfnquwbndp5prgyrpqe3dynm", "cancel": "https://api.replicate.com/v1/predictions/u2dfnquwbndp5prgyrpqe3dynm/cancel" }, "version": "c1fca4bef52afc0c3a556060df13d7d7c429a1310feeedbb5c3f92b7002822de" }
Generated intokenizing text ['Ġblue'] ['Ġcoloured'] ['Ġcharm', 'ander'] ['Ġwith'] ['Ġice'] ['Ġpower', ','] ['Ġrate'] ['Ġthis'] ['Ġdrawing', ','] ['Ġmind'] ['Ġblowing', ','] ['Ġrealistic'] 18 text tokens [0, 789, 18382, 5760, 2223, 208, 2330, 954, 11, 5125, 703, 1740, 11, 2660, 20183, 11, 10573, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
Prediction
kuprel/min-dalle:c1fca4bef52afc0c3a556060df13d7d7c429a1310feeedbb5c3f92b7002822deID65tuaoyirfak3pqxpmz2kmy75qStatusSucceededSourceWebHardware–Total durationCreatedInput
- text
- Photo realistic render, 8K UHD, trending on artstation: (subject= dieselpunk Building + subject detail= high detailed, high level texture render) + ( background = deepsea+ background detail = calm water, sunset, high detailed, large depth of field)
- top_k
- "16384"
- grid_size
- "3"
- temperature
- "0.31"
- progressive_outputs
- supercondition_factor
- "32"
{ "text": "Photo realistic render, 8K UHD, trending on artstation: (subject= dieselpunk Building + subject detail= high detailed, high level texture render) + ( background = deepsea+ background detail = calm water, sunset, high detailed, large depth of field)", "top_k": "16384", "grid_size": "3", "temperature": "0.31", "progressive_outputs": true, "supercondition_factor": "32" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "kuprel/min-dalle:c1fca4bef52afc0c3a556060df13d7d7c429a1310feeedbb5c3f92b7002822de", { input: { text: "Photo realistic render, 8K UHD, trending on artstation: (subject= dieselpunk Building + subject detail= high detailed, high level texture render) + ( background = deepsea+ background detail = calm water, sunset, high detailed, large depth of field)", top_k: "16384", grid_size: "3", temperature: "0.31", progressive_outputs: true, supercondition_factor: "32" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "kuprel/min-dalle:c1fca4bef52afc0c3a556060df13d7d7c429a1310feeedbb5c3f92b7002822de", input={ "text": "Photo realistic render, 8K UHD, trending on artstation: (subject= dieselpunk Building + subject detail= high detailed, high level texture render) + ( background = deepsea+ background detail = calm water, sunset, high detailed, large depth of field)", "top_k": "16384", "grid_size": "3", "temperature": "0.31", "progressive_outputs": True, "supercondition_factor": "32" } ) # The kuprel/min-dalle model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/kuprel/min-dalle/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run kuprel/min-dalle using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "c1fca4bef52afc0c3a556060df13d7d7c429a1310feeedbb5c3f92b7002822de", "input": { "text": "Photo realistic render, 8K UHD, trending on artstation: (subject= dieselpunk Building + subject detail= high detailed, high level texture render) + ( background = deepsea+ background detail = calm water, sunset, high detailed, large depth of field)", "top_k": "16384", "grid_size": "3", "temperature": "0.31", "progressive_outputs": true, "supercondition_factor": "32" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-20T14:48:12.070276Z", "created_at": "2022-07-20T14:47:57.505235Z", "data_removed": false, "error": null, "id": "65tuaoyirfak3pqxpmz2kmy75q", "input": { "text": "Photo realistic render, 8K UHD, trending on artstation: (subject= dieselpunk Building + subject detail= high detailed, high level texture render) + ( background = deepsea+ background detail = calm water, sunset, high detailed, large depth of field)", "top_k": "16384", "grid_size": "3", "temperature": "0.31", "progressive_outputs": true, "supercondition_factor": "32" }, "logs": "tokenizing text\n['Ġphoto']\n['Ġrealistic']\n['Ġrender', ',']\n['Ġ8', 'k']\n['Ġuhd', ',']\n['Ġtrending']\n['Ġon']\n['Ġartstation', ':']\n['Ġ', '(', 'sub', 'ject', '=']\n['Ġdiesel', 'punk']\n['Ġbuilding']\n['Ġ', '+']\n['Ġsubject']\n['Ġdetail', '=']\n['Ġhigh']\n['Ġdetailed', ',']\n['Ġhigh']\n['Ġlevel']\n['Ġtexture']\n['Ġrender', ')']\n['Ġ', '+']\n['Ġ', '(']\n['Ġbackground']\n['Ġ', '=']\n['Ġdeep', 'sea', '+']\n['Ġbackground']\n['Ġdetail']\n['Ġ', '=']\n['Ġcalm']\n['Ġwater', ',']\n['Ġsunset', ',']\n['Ġhigh']\n['Ġdetailed', ',']\n['Ġlarge']\n['Ġdepth']\n['Ġof']\n['Ġfield', ')']\n62 text tokens [0, 564, 10573, 14110, 11, 416, 38, 20531, 11, 10119, 133, 4640, 3, 54, 3, 45878, 836, 3, 6070, 14958, 1855, 54, 3, 11398, 5854, 3, 524, 8461, 11, 524, 3220, 7141, 14110, 3, 54, 3, 54, 3, 1396, 54, 3, 2720, 4506, 3, 1396, 5854, 54, 3, 6653, 725, 11, 4450, 11, 524, 8461, 11, 2033, 10162, 111, 2851, 3, 2]\nencoding text tokens\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image\ndetokenizing image", "metrics": { "predict_time": 14.406796, "total_time": 14.565041 }, "output": [ "https://replicate.delivery/mgxm/7eb02ed0-098d-4a73-b795-7cf7bd41bc42/photo-realistic-render-k-uhd-trending-on-artstation-subject.jpg", "https://replicate.delivery/mgxm/8431758a-f38f-4c0e-a1db-ed0262ee77bb/photo-realistic-render-k-uhd-trending-on-artstation-subject.jpg", "https://replicate.delivery/mgxm/b8e33e64-39ce-4f4d-9291-9a46a541407b/photo-realistic-render-k-uhd-trending-on-artstation-subject.jpg", "https://replicate.delivery/mgxm/76bc27b7-cb5c-4a93-95b7-06bc2b89d767/photo-realistic-render-k-uhd-trending-on-artstation-subject.jpg", "https://replicate.delivery/mgxm/31734fe7-3044-4d27-91b7-82c16e03802f/photo-realistic-render-k-uhd-trending-on-artstation-subject.jpg", "https://replicate.delivery/mgxm/a6e3581a-ebb7-471a-bc25-3e96f0cd403f/photo-realistic-render-k-uhd-trending-on-artstation-subject.jpg", "https://replicate.delivery/mgxm/18752def-0534-49ef-8e30-8594b4f341cc/photo-realistic-render-k-uhd-trending-on-artstation-subject.jpg", "https://replicate.delivery/mgxm/ae803d6f-54b5-4847-a07f-d938a796e4f2/photo-realistic-render-k-uhd-trending-on-artstation-subject.jpg" ], "started_at": "2022-07-20T14:47:57.663480Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/65tuaoyirfak3pqxpmz2kmy75q", "cancel": "https://api.replicate.com/v1/predictions/65tuaoyirfak3pqxpmz2kmy75q/cancel" }, "version": "c1fca4bef52afc0c3a556060df13d7d7c429a1310feeedbb5c3f92b7002822de" }
Generated intokenizing text ['Ġphoto'] ['Ġrealistic'] ['Ġrender', ','] ['Ġ8', 'k'] ['Ġuhd', ','] ['Ġtrending'] ['Ġon'] ['Ġartstation', ':'] ['Ġ', '(', 'sub', 'ject', '='] ['Ġdiesel', 'punk'] ['Ġbuilding'] ['Ġ', '+'] ['Ġsubject'] ['Ġdetail', '='] ['Ġhigh'] ['Ġdetailed', ','] ['Ġhigh'] ['Ġlevel'] ['Ġtexture'] ['Ġrender', ')'] ['Ġ', '+'] ['Ġ', '('] ['Ġbackground'] ['Ġ', '='] ['Ġdeep', 'sea', '+'] ['Ġbackground'] ['Ġdetail'] ['Ġ', '='] ['Ġcalm'] ['Ġwater', ','] ['Ġsunset', ','] ['Ġhigh'] ['Ġdetailed', ','] ['Ġlarge'] ['Ġdepth'] ['Ġof'] ['Ġfield', ')'] 62 text tokens [0, 564, 10573, 14110, 11, 416, 38, 20531, 11, 10119, 133, 4640, 3, 54, 3, 45878, 836, 3, 6070, 14958, 1855, 54, 3, 11398, 5854, 3, 524, 8461, 11, 524, 3220, 7141, 14110, 3, 54, 3, 54, 3, 1396, 54, 3, 2720, 4506, 3, 1396, 5854, 54, 3, 6653, 725, 11, 4450, 11, 524, 8461, 11, 2033, 10162, 111, 2851, 3, 2] encoding text tokens detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image detokenizing image
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