prompthero
/
poolsuite-diffusion
Reproduce Poolsuite vibes with a fine tuned Dreambooth model
- Public
- 6.1K runs
-
A100 (80GB)
- Paper
Prediction
prompthero/poolsuite-diffusion:e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814IDwy2cqo6o55gtdcn6jghti3tbfiStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 2281662610
- width
- 512
- height
- 512
- prompt
- clothing design collection, poolsuite style
- num_outputs
- 1
- guidance_scale
- "7"
- num_inference_steps
- "75"
{ "seed": 2281662610, "width": 512, "height": 512, "prompt": "clothing design collection, poolsuite style", "num_outputs": 1, "guidance_scale": "7", "num_inference_steps": "75" }
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 prompthero/poolsuite-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "prompthero/poolsuite-diffusion:e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814", { input: { seed: 2281662610, width: 512, height: 512, prompt: "clothing design collection, poolsuite style", num_outputs: 1, guidance_scale: "7", num_inference_steps: "75" } } ); // 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 prompthero/poolsuite-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "prompthero/poolsuite-diffusion:e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814", input={ "seed": 2281662610, "width": 512, "height": 512, "prompt": "clothing design collection, poolsuite style", "num_outputs": 1, "guidance_scale": "7", "num_inference_steps": "75" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run prompthero/poolsuite-diffusion 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": "prompthero/poolsuite-diffusion:e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814", "input": { "seed": 2281662610, "width": 512, "height": 512, "prompt": "clothing design collection, poolsuite style", "num_outputs": 1, "guidance_scale": "7", "num_inference_steps": "75" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-11-15T22:22:02.114221Z", "created_at": "2022-11-15T22:21:55.712172Z", "data_removed": false, "error": null, "id": "wy2cqo6o55gtdcn6jghti3tbfi", "input": { "seed": 2281662610, "width": 512, "height": 512, "prompt": "clothing design collection, poolsuite style", "num_outputs": 1, "guidance_scale": "7", "num_inference_steps": "75" }, "logs": "Using seed: 2281662610\nGlobal seed set to 2281662610\n 0%| | 0/75 [00:00<?, ?it/s]\n 3%|▎ | 2/75 [00:00<00:05, 12.66it/s]\n 5%|▌ | 4/75 [00:00<00:05, 12.91it/s]\n 8%|▊ | 6/75 [00:00<00:05, 13.43it/s]\n 11%|█ | 8/75 [00:00<00:04, 13.81it/s]\n 13%|█▎ | 10/75 [00:00<00:04, 13.54it/s]\n 16%|█▌ | 12/75 [00:00<00:04, 13.68it/s]\n 19%|█▊ | 14/75 [00:01<00:04, 13.77it/s]\n 21%|██▏ | 16/75 [00:01<00:04, 13.84it/s]\n 24%|██▍ | 18/75 [00:01<00:04, 13.82it/s]\n 27%|██▋ | 20/75 [00:01<00:03, 13.92it/s]\n 29%|██▉ | 22/75 [00:01<00:03, 13.99it/s]\n 32%|███▏ | 24/75 [00:01<00:03, 13.99it/s]\n 35%|███▍ | 26/75 [00:01<00:03, 13.89it/s]\n 37%|███▋ | 28/75 [00:02<00:03, 13.94it/s]\n 40%|████ | 30/75 [00:02<00:03, 13.99it/s]\n 43%|████▎ | 32/75 [00:02<00:03, 13.97it/s]\n 45%|████▌ | 34/75 [00:02<00:02, 14.00it/s]\n 48%|████▊ | 36/75 [00:02<00:02, 14.03it/s]\n 51%|█████ | 38/75 [00:02<00:02, 14.02it/s]\n 53%|█████▎ | 40/75 [00:02<00:02, 14.07it/s]\n 56%|█████▌ | 42/75 [00:03<00:02, 14.07it/s]\n 59%|█████▊ | 44/75 [00:03<00:02, 14.11it/s]\n 61%|██████▏ | 46/75 [00:03<00:02, 13.92it/s]\n 64%|██████▍ | 48/75 [00:03<00:01, 13.96it/s]\n 67%|██████▋ | 50/75 [00:03<00:01, 14.00it/s]\n 69%|██████▉ | 52/75 [00:03<00:01, 14.08it/s]\n 72%|███████▏ | 54/75 [00:03<00:01, 14.09it/s]\n 75%|███████▍ | 56/75 [00:04<00:01, 14.08it/s]\n 77%|███████▋ | 58/75 [00:04<00:01, 14.13it/s]\n 80%|████████ | 60/75 [00:04<00:01, 13.99it/s]\n 83%|████████▎ | 62/75 [00:04<00:00, 13.96it/s]\n 85%|████████▌ | 64/75 [00:04<00:00, 13.99it/s]\n 88%|████████▊ | 66/75 [00:04<00:00, 14.03it/s]\n 91%|█████████ | 68/75 [00:04<00:00, 14.03it/s]\n 93%|█████████▎| 70/75 [00:05<00:00, 14.05it/s]\n 96%|█████████▌| 72/75 [00:05<00:00, 14.12it/s]\n 99%|█████████▊| 74/75 [00:05<00:00, 14.07it/s]\n100%|██████████| 75/75 [00:05<00:00, 13.94it/s]", "metrics": { "predict_time": 6.365168, "total_time": 6.402049 }, "output": [ "https://replicate.delivery/pbxt/aypnzLWTIMIjLpDxzXVNA3inzrrvvA83A8TFh1Yk0ReEGOAIA/out-0.png" ], "started_at": "2022-11-15T22:21:55.749053Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wy2cqo6o55gtdcn6jghti3tbfi", "cancel": "https://api.replicate.com/v1/predictions/wy2cqo6o55gtdcn6jghti3tbfi/cancel" }, "version": "e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814" }
Generated inUsing seed: 2281662610 Global seed set to 2281662610 0%| | 0/75 [00:00<?, ?it/s] 3%|▎ | 2/75 [00:00<00:05, 12.66it/s] 5%|▌ | 4/75 [00:00<00:05, 12.91it/s] 8%|▊ | 6/75 [00:00<00:05, 13.43it/s] 11%|█ | 8/75 [00:00<00:04, 13.81it/s] 13%|█▎ | 10/75 [00:00<00:04, 13.54it/s] 16%|█▌ | 12/75 [00:00<00:04, 13.68it/s] 19%|█▊ | 14/75 [00:01<00:04, 13.77it/s] 21%|██▏ | 16/75 [00:01<00:04, 13.84it/s] 24%|██▍ | 18/75 [00:01<00:04, 13.82it/s] 27%|██▋ | 20/75 [00:01<00:03, 13.92it/s] 29%|██▉ | 22/75 [00:01<00:03, 13.99it/s] 32%|███▏ | 24/75 [00:01<00:03, 13.99it/s] 35%|███▍ | 26/75 [00:01<00:03, 13.89it/s] 37%|███▋ | 28/75 [00:02<00:03, 13.94it/s] 40%|████ | 30/75 [00:02<00:03, 13.99it/s] 43%|████▎ | 32/75 [00:02<00:03, 13.97it/s] 45%|████▌ | 34/75 [00:02<00:02, 14.00it/s] 48%|████▊ | 36/75 [00:02<00:02, 14.03it/s] 51%|█████ | 38/75 [00:02<00:02, 14.02it/s] 53%|█████▎ | 40/75 [00:02<00:02, 14.07it/s] 56%|█████▌ | 42/75 [00:03<00:02, 14.07it/s] 59%|█████▊ | 44/75 [00:03<00:02, 14.11it/s] 61%|██████▏ | 46/75 [00:03<00:02, 13.92it/s] 64%|██████▍ | 48/75 [00:03<00:01, 13.96it/s] 67%|██████▋ | 50/75 [00:03<00:01, 14.00it/s] 69%|██████▉ | 52/75 [00:03<00:01, 14.08it/s] 72%|███████▏ | 54/75 [00:03<00:01, 14.09it/s] 75%|███████▍ | 56/75 [00:04<00:01, 14.08it/s] 77%|███████▋ | 58/75 [00:04<00:01, 14.13it/s] 80%|████████ | 60/75 [00:04<00:01, 13.99it/s] 83%|████████▎ | 62/75 [00:04<00:00, 13.96it/s] 85%|████████▌ | 64/75 [00:04<00:00, 13.99it/s] 88%|████████▊ | 66/75 [00:04<00:00, 14.03it/s] 91%|█████████ | 68/75 [00:04<00:00, 14.03it/s] 93%|█████████▎| 70/75 [00:05<00:00, 14.05it/s] 96%|█████████▌| 72/75 [00:05<00:00, 14.12it/s] 99%|█████████▊| 74/75 [00:05<00:00, 14.07it/s] 100%|██████████| 75/75 [00:05<00:00, 13.94it/s]
Prediction
prompthero/poolsuite-diffusion:e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814IDc3le2hs3hba6xokpuatw7o2cw4StatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- photography of an Italian house in Tuscany, poolsuite style
- num_outputs
- "1"
- guidance_scale
- "7"
- num_inference_steps
- "50"
{ "width": 512, "height": 512, "prompt": "photography of an Italian house in Tuscany, poolsuite style", "num_outputs": "1", "guidance_scale": "7", "num_inference_steps": "50" }
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 prompthero/poolsuite-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "prompthero/poolsuite-diffusion:e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814", { input: { width: 512, height: 512, prompt: "photography of an Italian house in Tuscany, poolsuite style", num_outputs: "1", guidance_scale: "7", num_inference_steps: "50" } } ); // 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 prompthero/poolsuite-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "prompthero/poolsuite-diffusion:e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814", input={ "width": 512, "height": 512, "prompt": "photography of an Italian house in Tuscany, poolsuite style", "num_outputs": "1", "guidance_scale": "7", "num_inference_steps": "50" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run prompthero/poolsuite-diffusion 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": "prompthero/poolsuite-diffusion:e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814", "input": { "width": 512, "height": 512, "prompt": "photography of an Italian house in Tuscany, poolsuite style", "num_outputs": "1", "guidance_scale": "7", "num_inference_steps": "50" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-11-15T22:26:08.027714Z", "created_at": "2022-11-15T22:26:03.519184Z", "data_removed": false, "error": null, "id": "c3le2hs3hba6xokpuatw7o2cw4", "input": { "width": 512, "height": 512, "prompt": "photography of an Italian house in Tuscany, poolsuite style", "num_outputs": "1", "guidance_scale": "7", "num_inference_steps": "50" }, "logs": "Using seed: 46675\nGlobal seed set to 46675\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 13.55it/s]\n 8%|▊ | 4/50 [00:00<00:03, 13.80it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 14.06it/s]\n 16%|█▌ | 8/50 [00:00<00:02, 14.19it/s]\n 20%|██ | 10/50 [00:00<00:02, 14.26it/s]\n 24%|██▍ | 12/50 [00:00<00:02, 14.31it/s]\n 28%|██▊ | 14/50 [00:00<00:02, 14.39it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 14.39it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 14.37it/s]\n 40%|████ | 20/50 [00:01<00:02, 14.34it/s]\n 44%|████▍ | 22/50 [00:01<00:01, 14.35it/s]\n 48%|████▊ | 24/50 [00:01<00:01, 14.36it/s]\n 52%|█████▏ | 26/50 [00:01<00:01, 14.32it/s]\n 56%|█████▌ | 28/50 [00:01<00:01, 14.29it/s]\n 60%|██████ | 30/50 [00:02<00:01, 14.32it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 14.32it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 14.36it/s]\n 72%|███████▏ | 36/50 [00:02<00:00, 14.38it/s]\n 76%|███████▌ | 38/50 [00:02<00:00, 14.38it/s]\n 80%|████████ | 40/50 [00:02<00:00, 14.40it/s]\n 84%|████████▍ | 42/50 [00:02<00:00, 14.37it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 13.82it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 13.98it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 14.15it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.22it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.25it/s]", "metrics": { "predict_time": 4.469253, "total_time": 4.50853 }, "output": [ "https://replicate.delivery/pbxt/1TdeYTzEOPWqYSGTf7tYJFoWDUqftU4nNcVqLhyPQwcffhDAC/out-0.png" ], "started_at": "2022-11-15T22:26:03.558461Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/c3le2hs3hba6xokpuatw7o2cw4", "cancel": "https://api.replicate.com/v1/predictions/c3le2hs3hba6xokpuatw7o2cw4/cancel" }, "version": "e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814" }
Generated inUsing seed: 46675 Global seed set to 46675 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 13.55it/s] 8%|▊ | 4/50 [00:00<00:03, 13.80it/s] 12%|█▏ | 6/50 [00:00<00:03, 14.06it/s] 16%|█▌ | 8/50 [00:00<00:02, 14.19it/s] 20%|██ | 10/50 [00:00<00:02, 14.26it/s] 24%|██▍ | 12/50 [00:00<00:02, 14.31it/s] 28%|██▊ | 14/50 [00:00<00:02, 14.39it/s] 32%|███▏ | 16/50 [00:01<00:02, 14.39it/s] 36%|███▌ | 18/50 [00:01<00:02, 14.37it/s] 40%|████ | 20/50 [00:01<00:02, 14.34it/s] 44%|████▍ | 22/50 [00:01<00:01, 14.35it/s] 48%|████▊ | 24/50 [00:01<00:01, 14.36it/s] 52%|█████▏ | 26/50 [00:01<00:01, 14.32it/s] 56%|█████▌ | 28/50 [00:01<00:01, 14.29it/s] 60%|██████ | 30/50 [00:02<00:01, 14.32it/s] 64%|██████▍ | 32/50 [00:02<00:01, 14.32it/s] 68%|██████▊ | 34/50 [00:02<00:01, 14.36it/s] 72%|███████▏ | 36/50 [00:02<00:00, 14.38it/s] 76%|███████▌ | 38/50 [00:02<00:00, 14.38it/s] 80%|████████ | 40/50 [00:02<00:00, 14.40it/s] 84%|████████▍ | 42/50 [00:02<00:00, 14.37it/s] 88%|████████▊ | 44/50 [00:03<00:00, 13.82it/s] 92%|█████████▏| 46/50 [00:03<00:00, 13.98it/s] 96%|█████████▌| 48/50 [00:03<00:00, 14.15it/s] 100%|██████████| 50/50 [00:03<00:00, 14.22it/s] 100%|██████████| 50/50 [00:03<00:00, 14.25it/s]
Prediction
prompthero/poolsuite-diffusion:e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814ID6nq6j7fwvfec5lmqmu3okfqjy4StatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- photography of a manor, poolsuite style
- num_outputs
- "1"
- guidance_scale
- "7"
- num_inference_steps
- "50"
{ "width": 512, "height": 512, "prompt": "photography of a manor, poolsuite style", "num_outputs": "1", "guidance_scale": "7", "num_inference_steps": "50" }
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 prompthero/poolsuite-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "prompthero/poolsuite-diffusion:e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814", { input: { width: 512, height: 512, prompt: "photography of a manor, poolsuite style", num_outputs: "1", guidance_scale: "7", num_inference_steps: "50" } } ); // 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 prompthero/poolsuite-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "prompthero/poolsuite-diffusion:e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814", input={ "width": 512, "height": 512, "prompt": "photography of a manor, poolsuite style", "num_outputs": "1", "guidance_scale": "7", "num_inference_steps": "50" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run prompthero/poolsuite-diffusion 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": "prompthero/poolsuite-diffusion:e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814", "input": { "width": 512, "height": 512, "prompt": "photography of a manor, poolsuite style", "num_outputs": "1", "guidance_scale": "7", "num_inference_steps": "50" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-11-15T22:26:24.832543Z", "created_at": "2022-11-15T22:26:20.306629Z", "data_removed": false, "error": null, "id": "6nq6j7fwvfec5lmqmu3okfqjy4", "input": { "width": 512, "height": 512, "prompt": "photography of a manor, poolsuite style", "num_outputs": "1", "guidance_scale": "7", "num_inference_steps": "50" }, "logs": "Using seed: 48115\nGlobal seed set to 48115\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 13.45it/s]\n 8%|▊ | 4/50 [00:00<00:03, 13.62it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 13.91it/s]\n 16%|█▌ | 8/50 [00:00<00:02, 14.03it/s]\n 20%|██ | 10/50 [00:00<00:02, 13.98it/s]\n 24%|██▍ | 12/50 [00:00<00:02, 13.97it/s]\n 28%|██▊ | 14/50 [00:01<00:02, 13.88it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 13.91it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 14.00it/s]\n 40%|████ | 20/50 [00:01<00:02, 14.13it/s]\n 44%|████▍ | 22/50 [00:01<00:01, 14.16it/s]\n 48%|████▊ | 24/50 [00:01<00:01, 14.17it/s]\n 52%|█████▏ | 26/50 [00:01<00:01, 14.26it/s]\n 56%|█████▌ | 28/50 [00:01<00:01, 14.33it/s]\n 60%|██████ | 30/50 [00:02<00:01, 14.26it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 14.23it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 14.17it/s]\n 72%|███████▏ | 36/50 [00:02<00:00, 14.13it/s]\n 76%|███████▌ | 38/50 [00:02<00:00, 14.13it/s]\n 80%|████████ | 40/50 [00:02<00:00, 14.16it/s]\n 84%|████████▍ | 42/50 [00:02<00:00, 14.21it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 14.17it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 14.18it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 14.15it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.23it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.11it/s]", "metrics": { "predict_time": 4.488099, "total_time": 4.525914 }, "output": [ "https://replicate.delivery/pbxt/LdgmPCwFM6rfTavhrMgiSlIqal8fxU7QFELo7NLdMfDgg4AgA/out-0.png" ], "started_at": "2022-11-15T22:26:20.344444Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6nq6j7fwvfec5lmqmu3okfqjy4", "cancel": "https://api.replicate.com/v1/predictions/6nq6j7fwvfec5lmqmu3okfqjy4/cancel" }, "version": "e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814" }
Generated inUsing seed: 48115 Global seed set to 48115 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 13.45it/s] 8%|▊ | 4/50 [00:00<00:03, 13.62it/s] 12%|█▏ | 6/50 [00:00<00:03, 13.91it/s] 16%|█▌ | 8/50 [00:00<00:02, 14.03it/s] 20%|██ | 10/50 [00:00<00:02, 13.98it/s] 24%|██▍ | 12/50 [00:00<00:02, 13.97it/s] 28%|██▊ | 14/50 [00:01<00:02, 13.88it/s] 32%|███▏ | 16/50 [00:01<00:02, 13.91it/s] 36%|███▌ | 18/50 [00:01<00:02, 14.00it/s] 40%|████ | 20/50 [00:01<00:02, 14.13it/s] 44%|████▍ | 22/50 [00:01<00:01, 14.16it/s] 48%|████▊ | 24/50 [00:01<00:01, 14.17it/s] 52%|█████▏ | 26/50 [00:01<00:01, 14.26it/s] 56%|█████▌ | 28/50 [00:01<00:01, 14.33it/s] 60%|██████ | 30/50 [00:02<00:01, 14.26it/s] 64%|██████▍ | 32/50 [00:02<00:01, 14.23it/s] 68%|██████▊ | 34/50 [00:02<00:01, 14.17it/s] 72%|███████▏ | 36/50 [00:02<00:00, 14.13it/s] 76%|███████▌ | 38/50 [00:02<00:00, 14.13it/s] 80%|████████ | 40/50 [00:02<00:00, 14.16it/s] 84%|████████▍ | 42/50 [00:02<00:00, 14.21it/s] 88%|████████▊ | 44/50 [00:03<00:00, 14.17it/s] 92%|█████████▏| 46/50 [00:03<00:00, 14.18it/s] 96%|█████████▌| 48/50 [00:03<00:00, 14.15it/s] 100%|██████████| 50/50 [00:03<00:00, 14.23it/s] 100%|██████████| 50/50 [00:03<00:00, 14.11it/s]
Prediction
prompthero/poolsuite-diffusion:e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814IDzcs6cpl4xzgahnxrxflaayqgr4StatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- photography of a manor, poolsuite style
- num_outputs
- "1"
- guidance_scale
- "7"
- num_inference_steps
- "50"
{ "width": 512, "height": 512, "prompt": "photography of a manor, poolsuite style", "num_outputs": "1", "guidance_scale": "7", "num_inference_steps": "50" }
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 prompthero/poolsuite-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "prompthero/poolsuite-diffusion:e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814", { input: { width: 512, height: 512, prompt: "photography of a manor, poolsuite style", num_outputs: "1", guidance_scale: "7", num_inference_steps: "50" } } ); // 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 prompthero/poolsuite-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "prompthero/poolsuite-diffusion:e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814", input={ "width": 512, "height": 512, "prompt": "photography of a manor, poolsuite style", "num_outputs": "1", "guidance_scale": "7", "num_inference_steps": "50" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run prompthero/poolsuite-diffusion 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": "prompthero/poolsuite-diffusion:e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814", "input": { "width": 512, "height": 512, "prompt": "photography of a manor, poolsuite style", "num_outputs": "1", "guidance_scale": "7", "num_inference_steps": "50" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-11-15T22:26:35.549130Z", "created_at": "2022-11-15T22:26:31.536167Z", "data_removed": false, "error": null, "id": "zcs6cpl4xzgahnxrxflaayqgr4", "input": { "width": 512, "height": 512, "prompt": "photography of a manor, poolsuite style", "num_outputs": "1", "guidance_scale": "7", "num_inference_steps": "50" }, "logs": "Using seed: 59959\nGlobal seed set to 59959\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 12.78it/s]\n 8%|▊ | 4/50 [00:00<00:03, 13.58it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 13.94it/s]\n 16%|█▌ | 8/50 [00:00<00:02, 14.13it/s]\n 20%|██ | 10/50 [00:00<00:02, 14.22it/s]\n 24%|██▍ | 12/50 [00:00<00:02, 14.26it/s]\n 28%|██▊ | 14/50 [00:00<00:02, 14.30it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 14.27it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 14.25it/s]\n 40%|████ | 20/50 [00:01<00:02, 14.28it/s]\n 44%|████▍ | 22/50 [00:01<00:01, 14.29it/s]\n 48%|████▊ | 24/50 [00:01<00:01, 14.07it/s]\n 52%|█████▏ | 26/50 [00:01<00:01, 14.14it/s]\n 56%|█████▌ | 28/50 [00:01<00:01, 14.16it/s]\n 60%|██████ | 30/50 [00:02<00:01, 14.20it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 14.21it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 14.25it/s]\n 72%|███████▏ | 36/50 [00:02<00:00, 14.27it/s]\n 76%|███████▌ | 38/50 [00:02<00:00, 14.30it/s]\n 80%|████████ | 40/50 [00:02<00:00, 14.33it/s]\n 84%|████████▍ | 42/50 [00:02<00:00, 14.33it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 14.24it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 14.22it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 14.23it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.22it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.19it/s]", "metrics": { "predict_time": 3.979455, "total_time": 4.012963 }, "output": [ "https://replicate.delivery/pbxt/E6hztf2wfEq1DUaIzTnLHl0bS5SJpdFoTlY64wnX6TlbQcAQA/out-0.png" ], "started_at": "2022-11-15T22:26:31.569675Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zcs6cpl4xzgahnxrxflaayqgr4", "cancel": "https://api.replicate.com/v1/predictions/zcs6cpl4xzgahnxrxflaayqgr4/cancel" }, "version": "e1450d9d6feddf74dd03f61ad1be0a002c1a4bef4075d447452f774d7cd79814" }
Generated inUsing seed: 59959 Global seed set to 59959 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 12.78it/s] 8%|▊ | 4/50 [00:00<00:03, 13.58it/s] 12%|█▏ | 6/50 [00:00<00:03, 13.94it/s] 16%|█▌ | 8/50 [00:00<00:02, 14.13it/s] 20%|██ | 10/50 [00:00<00:02, 14.22it/s] 24%|██▍ | 12/50 [00:00<00:02, 14.26it/s] 28%|██▊ | 14/50 [00:00<00:02, 14.30it/s] 32%|███▏ | 16/50 [00:01<00:02, 14.27it/s] 36%|███▌ | 18/50 [00:01<00:02, 14.25it/s] 40%|████ | 20/50 [00:01<00:02, 14.28it/s] 44%|████▍ | 22/50 [00:01<00:01, 14.29it/s] 48%|████▊ | 24/50 [00:01<00:01, 14.07it/s] 52%|█████▏ | 26/50 [00:01<00:01, 14.14it/s] 56%|█████▌ | 28/50 [00:01<00:01, 14.16it/s] 60%|██████ | 30/50 [00:02<00:01, 14.20it/s] 64%|██████▍ | 32/50 [00:02<00:01, 14.21it/s] 68%|██████▊ | 34/50 [00:02<00:01, 14.25it/s] 72%|███████▏ | 36/50 [00:02<00:00, 14.27it/s] 76%|███████▌ | 38/50 [00:02<00:00, 14.30it/s] 80%|████████ | 40/50 [00:02<00:00, 14.33it/s] 84%|████████▍ | 42/50 [00:02<00:00, 14.33it/s] 88%|████████▊ | 44/50 [00:03<00:00, 14.24it/s] 92%|█████████▏| 46/50 [00:03<00:00, 14.22it/s] 96%|█████████▌| 48/50 [00:03<00:00, 14.23it/s] 100%|██████████| 50/50 [00:03<00:00, 14.22it/s] 100%|██████████| 50/50 [00:03<00:00, 14.19it/s]
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