greeneryscenery/sheeps-control-v4
sheeps-control-v4
Sketch2Image
Prediction
greeneryscenery/sheeps-control-v4:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1IDkvlyljmw3zd77djrhwuexgygf4StatusSucceededSourceWebHardware–Total durationCreatedInput
- image
- steps
- 20
- prompt
- Red bicycle on grass
{ "image": "https://replicate.delivery/pbxt/Ie1husXoROGhwQFFaDv5zaDDbnFuXrxorLTyyHOUu8rJLJ6Y/bicycle.png", "steps": 20, "prompt": "Red bicycle on grass" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run greeneryscenery/sheeps-control-v4 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "greeneryscenery/sheeps-control-v4:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1", { input: { image: "https://replicate.delivery/pbxt/Ie1husXoROGhwQFFaDv5zaDDbnFuXrxorLTyyHOUu8rJLJ6Y/bicycle.png", steps: 20, prompt: "Red bicycle on grass" } } ); // 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 replicateImport the client:import replicateRun greeneryscenery/sheeps-control-v4 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "greeneryscenery/sheeps-control-v4:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1", input={ "image": "https://replicate.delivery/pbxt/Ie1husXoROGhwQFFaDv5zaDDbnFuXrxorLTyyHOUu8rJLJ6Y/bicycle.png", "steps": 20, "prompt": "Red bicycle on grass" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run greeneryscenery/sheeps-control-v4 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": "greeneryscenery/sheeps-control-v4:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1", "input": { "image": "https://replicate.delivery/pbxt/Ie1husXoROGhwQFFaDv5zaDDbnFuXrxorLTyyHOUu8rJLJ6Y/bicycle.png", "steps": 20, "prompt": "Red bicycle on grass" } }' \ https://api.replicate.com/v1/predictionsTo 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 cogIf 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/greeneryscenery/sheeps-control-v4@sha256:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1 \ -i 'image="https://replicate.delivery/pbxt/Ie1husXoROGhwQFFaDv5zaDDbnFuXrxorLTyyHOUu8rJLJ6Y/bicycle.png"' \ -i 'steps=20' \ -i 'prompt="Red bicycle on grass"'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/greeneryscenery/sheeps-control-v4@sha256:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/Ie1husXoROGhwQFFaDv5zaDDbnFuXrxorLTyyHOUu8rJLJ6Y/bicycle.png", "steps": 20, "prompt": "Red bicycle on grass" } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-04-13T12:28:58.580916Z", "created_at": "2023-04-13T12:28:54.131157Z", "data_removed": false, "error": null, "id": "kvlyljmw3zd77djrhwuexgygf4", "input": { "image": "https://replicate.delivery/pbxt/Ie1husXoROGhwQFFaDv5zaDDbnFuXrxorLTyyHOUu8rJLJ6Y/bicycle.png", "steps": 20, "prompt": "Red bicycle on grass" }, "logs": "0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:18, 1.03it/s]\n 10%|█ | 2/20 [00:01<00:08, 2.14it/s]\n 20%|██ | 4/20 [00:01<00:03, 4.24it/s]\n 25%|██▌ | 5/20 [00:01<00:02, 5.05it/s]\n 30%|███ | 6/20 [00:01<00:02, 5.93it/s]\n 35%|███▌ | 7/20 [00:01<00:01, 6.73it/s]\n 45%|████▌ | 9/20 [00:01<00:01, 8.06it/s]\n 55%|█████▌ | 11/20 [00:01<00:01, 8.89it/s]\n 65%|██████▌ | 13/20 [00:02<00:00, 9.39it/s]\n 75%|███████▌ | 15/20 [00:02<00:00, 9.66it/s]\n 85%|████████▌ | 17/20 [00:02<00:00, 9.55it/s]\n 95%|█████████▌| 19/20 [00:02<00:00, 9.84it/s]\n100%|██████████| 20/20 [00:02<00:00, 6.96it/s]", "metrics": { "predict_time": 4.335477, "total_time": 4.449759 }, "output": "https://replicate.delivery/pbxt/Z8m0U6G5I5Kfa6YVtjsuULFW80JfetNzXePTe4kGOtcSxTLGC/image.png", "started_at": "2023-04-13T12:28:54.245439Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kvlyljmw3zd77djrhwuexgygf4", "cancel": "https://api.replicate.com/v1/predictions/kvlyljmw3zd77djrhwuexgygf4/cancel" }, "version": "19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1" }Generated in0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:18, 1.03it/s] 10%|█ | 2/20 [00:01<00:08, 2.14it/s] 20%|██ | 4/20 [00:01<00:03, 4.24it/s] 25%|██▌ | 5/20 [00:01<00:02, 5.05it/s] 30%|███ | 6/20 [00:01<00:02, 5.93it/s] 35%|███▌ | 7/20 [00:01<00:01, 6.73it/s] 45%|████▌ | 9/20 [00:01<00:01, 8.06it/s] 55%|█████▌ | 11/20 [00:01<00:01, 8.89it/s] 65%|██████▌ | 13/20 [00:02<00:00, 9.39it/s] 75%|███████▌ | 15/20 [00:02<00:00, 9.66it/s] 85%|████████▌ | 17/20 [00:02<00:00, 9.55it/s] 95%|█████████▌| 19/20 [00:02<00:00, 9.84it/s] 100%|██████████| 20/20 [00:02<00:00, 6.96it/s]Prediction
greeneryscenery/sheeps-control-v4:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1ID2uv7unz7jfhbpkblqu6rwfm2seStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 1
- image
- steps
- 20
- prompt
- Cute turtle
{ "seed": 1, "image": "https://replicate.delivery/pbxt/Ie1kGdCYbdUwm0ql8bL1BT61MtmbYPwWazKBS0ooCqqWq1Wd/turtle.png", "steps": 20, "prompt": "Cute turtle" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run greeneryscenery/sheeps-control-v4 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "greeneryscenery/sheeps-control-v4:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1", { input: { seed: 1, image: "https://replicate.delivery/pbxt/Ie1kGdCYbdUwm0ql8bL1BT61MtmbYPwWazKBS0ooCqqWq1Wd/turtle.png", steps: 20, prompt: "Cute turtle" } } ); // 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 replicateImport the client:import replicateRun greeneryscenery/sheeps-control-v4 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "greeneryscenery/sheeps-control-v4:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1", input={ "seed": 1, "image": "https://replicate.delivery/pbxt/Ie1kGdCYbdUwm0ql8bL1BT61MtmbYPwWazKBS0ooCqqWq1Wd/turtle.png", "steps": 20, "prompt": "Cute turtle" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run greeneryscenery/sheeps-control-v4 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": "greeneryscenery/sheeps-control-v4:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1", "input": { "seed": 1, "image": "https://replicate.delivery/pbxt/Ie1kGdCYbdUwm0ql8bL1BT61MtmbYPwWazKBS0ooCqqWq1Wd/turtle.png", "steps": 20, "prompt": "Cute turtle" } }' \ https://api.replicate.com/v1/predictionsTo 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 cogIf 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/greeneryscenery/sheeps-control-v4@sha256:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1 \ -i 'seed=1' \ -i 'image="https://replicate.delivery/pbxt/Ie1kGdCYbdUwm0ql8bL1BT61MtmbYPwWazKBS0ooCqqWq1Wd/turtle.png"' \ -i 'steps=20' \ -i 'prompt="Cute turtle"'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/greeneryscenery/sheeps-control-v4@sha256:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 1, "image": "https://replicate.delivery/pbxt/Ie1kGdCYbdUwm0ql8bL1BT61MtmbYPwWazKBS0ooCqqWq1Wd/turtle.png", "steps": 20, "prompt": "Cute turtle" } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-04-13T12:31:28.240740Z", "created_at": "2023-04-13T12:31:23.846152Z", "data_removed": false, "error": null, "id": "2uv7unz7jfhbpkblqu6rwfm2se", "input": { "seed": 1, "image": "https://replicate.delivery/pbxt/Ie1kGdCYbdUwm0ql8bL1BT61MtmbYPwWazKBS0ooCqqWq1Wd/turtle.png", "steps": 20, "prompt": "Cute turtle" }, "logs": "0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:18, 1.03it/s]\n 10%|█ | 2/20 [00:01<00:08, 2.15it/s]\n 20%|██ | 4/20 [00:01<00:03, 4.27it/s]\n 30%|███ | 6/20 [00:01<00:02, 5.94it/s]\n 40%|████ | 8/20 [00:01<00:01, 7.23it/s]\n 50%|█████ | 10/20 [00:01<00:01, 8.08it/s]\n 60%|██████ | 12/20 [00:02<00:00, 8.78it/s]\n 70%|███████ | 14/20 [00:02<00:00, 9.31it/s]\n 80%|████████ | 16/20 [00:02<00:00, 9.56it/s]\n 90%|█████████ | 18/20 [00:02<00:00, 9.63it/s]\n100%|██████████| 20/20 [00:02<00:00, 9.68it/s]\n100%|██████████| 20/20 [00:02<00:00, 7.05it/s]", "metrics": { "predict_time": 4.28348, "total_time": 4.394588 }, "output": "https://replicate.delivery/pbxt/hRAWKtAtuQovChhbFwkMpOIheoYV5a3p4GsGyyI4WNHQQtYIA/image.png", "started_at": "2023-04-13T12:31:23.957260Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2uv7unz7jfhbpkblqu6rwfm2se", "cancel": "https://api.replicate.com/v1/predictions/2uv7unz7jfhbpkblqu6rwfm2se/cancel" }, "version": "19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1" }Generated in0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:18, 1.03it/s] 10%|█ | 2/20 [00:01<00:08, 2.15it/s] 20%|██ | 4/20 [00:01<00:03, 4.27it/s] 30%|███ | 6/20 [00:01<00:02, 5.94it/s] 40%|████ | 8/20 [00:01<00:01, 7.23it/s] 50%|█████ | 10/20 [00:01<00:01, 8.08it/s] 60%|██████ | 12/20 [00:02<00:00, 8.78it/s] 70%|███████ | 14/20 [00:02<00:00, 9.31it/s] 80%|████████ | 16/20 [00:02<00:00, 9.56it/s] 90%|█████████ | 18/20 [00:02<00:00, 9.63it/s] 100%|██████████| 20/20 [00:02<00:00, 9.68it/s] 100%|██████████| 20/20 [00:02<00:00, 7.05it/s]Prediction
greeneryscenery/sheeps-control-v4:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1IDkgiydqtmv5eados36gcaq7ipqaStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 0
- image
- steps
- 20
- prompt
- A sheep on a green field
{ "seed": 0, "image": "https://replicate.delivery/pbxt/Ie1pKQF15VkTsEZSoK7PT7lBnsl9fWofa0AwPzvZqXoFWcea/sheep.png", "steps": 20, "prompt": "A sheep on a green field" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run greeneryscenery/sheeps-control-v4 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "greeneryscenery/sheeps-control-v4:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1", { input: { seed: 0, image: "https://replicate.delivery/pbxt/Ie1pKQF15VkTsEZSoK7PT7lBnsl9fWofa0AwPzvZqXoFWcea/sheep.png", steps: 20, prompt: "A sheep on a green field" } } ); // 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 replicateImport the client:import replicateRun greeneryscenery/sheeps-control-v4 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "greeneryscenery/sheeps-control-v4:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1", input={ "seed": 0, "image": "https://replicate.delivery/pbxt/Ie1pKQF15VkTsEZSoK7PT7lBnsl9fWofa0AwPzvZqXoFWcea/sheep.png", "steps": 20, "prompt": "A sheep on a green field" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run greeneryscenery/sheeps-control-v4 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": "greeneryscenery/sheeps-control-v4:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1", "input": { "seed": 0, "image": "https://replicate.delivery/pbxt/Ie1pKQF15VkTsEZSoK7PT7lBnsl9fWofa0AwPzvZqXoFWcea/sheep.png", "steps": 20, "prompt": "A sheep on a green field" } }' \ https://api.replicate.com/v1/predictionsTo 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 cogIf 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/greeneryscenery/sheeps-control-v4@sha256:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1 \ -i 'seed=0' \ -i 'image="https://replicate.delivery/pbxt/Ie1pKQF15VkTsEZSoK7PT7lBnsl9fWofa0AwPzvZqXoFWcea/sheep.png"' \ -i 'steps=20' \ -i 'prompt="A sheep on a green field"'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/greeneryscenery/sheeps-control-v4@sha256:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 0, "image": "https://replicate.delivery/pbxt/Ie1pKQF15VkTsEZSoK7PT7lBnsl9fWofa0AwPzvZqXoFWcea/sheep.png", "steps": 20, "prompt": "A sheep on a green field" } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-04-13T12:36:50.183776Z", "created_at": "2023-04-13T12:36:45.419467Z", "data_removed": false, "error": null, "id": "kgiydqtmv5eados36gcaq7ipqa", "input": { "seed": 0, "image": "https://replicate.delivery/pbxt/Ie1pKQF15VkTsEZSoK7PT7lBnsl9fWofa0AwPzvZqXoFWcea/sheep.png", "steps": 20, "prompt": "A sheep on a green field" }, "logs": "0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:01<00:20, 1.09s/it]\n 10%|█ | 2/20 [00:01<00:09, 1.95it/s]\n 15%|█▌ | 3/20 [00:01<00:05, 3.05it/s]\n 25%|██▌ | 5/20 [00:01<00:02, 5.07it/s]\n 35%|███▌ | 7/20 [00:01<00:01, 6.62it/s]\n 45%|████▌ | 9/20 [00:01<00:01, 7.72it/s]\n 55%|█████▌ | 11/20 [00:02<00:01, 8.54it/s]\n 65%|██████▌ | 13/20 [00:02<00:00, 9.00it/s]\n 75%|███████▌ | 15/20 [00:02<00:00, 9.40it/s]\n 85%|████████▌ | 17/20 [00:02<00:00, 9.74it/s]\n 95%|█████████▌| 19/20 [00:02<00:00, 9.96it/s]\n100%|██████████| 20/20 [00:02<00:00, 6.80it/s]", "metrics": { "predict_time": 4.639628, "total_time": 4.764309 }, "output": "https://replicate.delivery/pbxt/Ctb1xCE8BeROQqL5gWaZoZ3SbP3HVOTHTvgRdb0qZqkwStYIA/image.png", "started_at": "2023-04-13T12:36:45.544148Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kgiydqtmv5eados36gcaq7ipqa", "cancel": "https://api.replicate.com/v1/predictions/kgiydqtmv5eados36gcaq7ipqa/cancel" }, "version": "19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1" }Generated in0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:01<00:20, 1.09s/it] 10%|█ | 2/20 [00:01<00:09, 1.95it/s] 15%|█▌ | 3/20 [00:01<00:05, 3.05it/s] 25%|██▌ | 5/20 [00:01<00:02, 5.07it/s] 35%|███▌ | 7/20 [00:01<00:01, 6.62it/s] 45%|████▌ | 9/20 [00:01<00:01, 7.72it/s] 55%|█████▌ | 11/20 [00:02<00:01, 8.54it/s] 65%|██████▌ | 13/20 [00:02<00:00, 9.00it/s] 75%|███████▌ | 15/20 [00:02<00:00, 9.40it/s] 85%|████████▌ | 17/20 [00:02<00:00, 9.74it/s] 95%|█████████▌| 19/20 [00:02<00:00, 9.96it/s] 100%|██████████| 20/20 [00:02<00:00, 6.80it/s]Prediction
greeneryscenery/sheeps-control-v4:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1ID3pjqjdkssfevnmqn7wybdrcafyStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 1
- image
- steps
- 20
- prompt
- A dog sitting down
{ "seed": 1, "image": "https://replicate.delivery/pbxt/Ie2drS6pIaDR7MkuAUzLrwyXUyxvdZKp2Mhgnzoi3KKblT4X/dog.png", "steps": 20, "prompt": "A dog sitting down" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run greeneryscenery/sheeps-control-v4 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "greeneryscenery/sheeps-control-v4:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1", { input: { seed: 1, image: "https://replicate.delivery/pbxt/Ie2drS6pIaDR7MkuAUzLrwyXUyxvdZKp2Mhgnzoi3KKblT4X/dog.png", steps: 20, prompt: "A dog sitting down" } } ); // 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 replicateImport the client:import replicateRun greeneryscenery/sheeps-control-v4 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "greeneryscenery/sheeps-control-v4:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1", input={ "seed": 1, "image": "https://replicate.delivery/pbxt/Ie2drS6pIaDR7MkuAUzLrwyXUyxvdZKp2Mhgnzoi3KKblT4X/dog.png", "steps": 20, "prompt": "A dog sitting down" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run greeneryscenery/sheeps-control-v4 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": "greeneryscenery/sheeps-control-v4:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1", "input": { "seed": 1, "image": "https://replicate.delivery/pbxt/Ie2drS6pIaDR7MkuAUzLrwyXUyxvdZKp2Mhgnzoi3KKblT4X/dog.png", "steps": 20, "prompt": "A dog sitting down" } }' \ https://api.replicate.com/v1/predictionsTo 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 cogIf 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/greeneryscenery/sheeps-control-v4@sha256:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1 \ -i 'seed=1' \ -i 'image="https://replicate.delivery/pbxt/Ie2drS6pIaDR7MkuAUzLrwyXUyxvdZKp2Mhgnzoi3KKblT4X/dog.png"' \ -i 'steps=20' \ -i 'prompt="A dog sitting down"'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/greeneryscenery/sheeps-control-v4@sha256:19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 1, "image": "https://replicate.delivery/pbxt/Ie2drS6pIaDR7MkuAUzLrwyXUyxvdZKp2Mhgnzoi3KKblT4X/dog.png", "steps": 20, "prompt": "A dog sitting down" } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-04-13T13:32:42.339784Z", "created_at": "2023-04-13T13:30:05.233774Z", "data_removed": false, "error": null, "id": "3pjqjdkssfevnmqn7wybdrcafy", "input": { "seed": 1, "image": "https://replicate.delivery/pbxt/Ie2drS6pIaDR7MkuAUzLrwyXUyxvdZKp2Mhgnzoi3KKblT4X/dog.png", "steps": 20, "prompt": "A dog sitting down" }, "logs": "0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:02<00:46, 2.44s/it]\n 10%|█ | 2/20 [00:02<00:19, 1.07s/it]\n 15%|█▌ | 3/20 [00:02<00:12, 1.41it/s]\n 20%|██ | 4/20 [00:02<00:07, 2.14it/s]\n 25%|██▌ | 5/20 [00:03<00:05, 2.96it/s]\n 35%|███▌ | 7/20 [00:03<00:02, 4.64it/s]\n 40%|████ | 8/20 [00:03<00:02, 5.36it/s]\n 50%|█████ | 10/20 [00:03<00:01, 6.76it/s]\n 55%|█████▌ | 11/20 [00:03<00:01, 7.32it/s]\n 65%|██████▌ | 13/20 [00:03<00:00, 8.28it/s]\n 75%|███████▌ | 15/20 [00:04<00:00, 8.83it/s]\n 85%|████████▌ | 17/20 [00:04<00:00, 9.27it/s]\n 90%|█████████ | 18/20 [00:04<00:00, 9.37it/s]\n100%|██████████| 20/20 [00:04<00:00, 9.64it/s]\n100%|██████████| 20/20 [00:04<00:00, 4.42it/s]", "metrics": { "predict_time": 9.248708, "total_time": 157.10601 }, "output": "https://replicate.delivery/pbxt/vWZeNyTJq5RdfkbEJpiAdYGzLZJQa2TU8Dnoehtm8ZW1z2ihA/image.png", "started_at": "2023-04-13T13:32:33.091076Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3pjqjdkssfevnmqn7wybdrcafy", "cancel": "https://api.replicate.com/v1/predictions/3pjqjdkssfevnmqn7wybdrcafy/cancel" }, "version": "19632a95f72bc6d1112e9b70303aad7d3651cbcec5b8c3539ad9131227a75fd1" }Generated in0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:02<00:46, 2.44s/it] 10%|█ | 2/20 [00:02<00:19, 1.07s/it] 15%|█▌ | 3/20 [00:02<00:12, 1.41it/s] 20%|██ | 4/20 [00:02<00:07, 2.14it/s] 25%|██▌ | 5/20 [00:03<00:05, 2.96it/s] 35%|███▌ | 7/20 [00:03<00:02, 4.64it/s] 40%|████ | 8/20 [00:03<00:02, 5.36it/s] 50%|█████ | 10/20 [00:03<00:01, 6.76it/s] 55%|█████▌ | 11/20 [00:03<00:01, 7.32it/s] 65%|██████▌ | 13/20 [00:03<00:00, 8.28it/s] 75%|███████▌ | 15/20 [00:04<00:00, 8.83it/s] 85%|████████▌ | 17/20 [00:04<00:00, 9.27it/s] 90%|█████████ | 18/20 [00:04<00:00, 9.37it/s] 100%|██████████| 20/20 [00:04<00:00, 9.64it/s] 100%|██████████| 20/20 [00:04<00:00, 4.42it/s]
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