lucataco / sdxl-controlnet-openpose
SDXL ControlNet - OpenPose
Prediction
lucataco/sdxl-controlnet-openpose:d63e0b238b2d963d90348e2dad19830fbe372a7a43d90d234b2b63cae76d4397IDv2doo4dbv2foj6ngr26wnjy7cmStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 25403
- image
- prompt
- a latina ballerina, romantic sunset, 4k photo
- guidance_scale
- 7.5
- high_noise_frac
- 0.8
- negative_prompt
- low quality, bad quality
- num_inference_steps
- 50
{ "seed": 25403, "image": "https://replicate.delivery/pbxt/JMf0SV6w1yp8ZcrtrYungReCNQzUUhXJHciDFi5nylAdqmCo/demo.jpg", "prompt": "a latina ballerina, romantic sunset, 4k photo", "guidance_scale": 7.5, "high_noise_frac": 0.8, "negative_prompt": "low quality, bad quality", "num_inference_steps": 50 }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 lucataco/sdxl-controlnet-openpose using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/sdxl-controlnet-openpose:d63e0b238b2d963d90348e2dad19830fbe372a7a43d90d234b2b63cae76d4397", { input: { seed: 25403, image: "https://replicate.delivery/pbxt/JMf0SV6w1yp8ZcrtrYungReCNQzUUhXJHciDFi5nylAdqmCo/demo.jpg", prompt: "a latina ballerina, romantic sunset, 4k photo", guidance_scale: 7.5, high_noise_frac: 0.8, negative_prompt: "low quality, bad quality", num_inference_steps: 50 } } ); // 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 lucataco/sdxl-controlnet-openpose using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/sdxl-controlnet-openpose:d63e0b238b2d963d90348e2dad19830fbe372a7a43d90d234b2b63cae76d4397", input={ "seed": 25403, "image": "https://replicate.delivery/pbxt/JMf0SV6w1yp8ZcrtrYungReCNQzUUhXJHciDFi5nylAdqmCo/demo.jpg", "prompt": "a latina ballerina, romantic sunset, 4k photo", "guidance_scale": 7.5, "high_noise_frac": 0.8, "negative_prompt": "low quality, bad quality", "num_inference_steps": 50 } ) # 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 lucataco/sdxl-controlnet-openpose 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": "lucataco/sdxl-controlnet-openpose:d63e0b238b2d963d90348e2dad19830fbe372a7a43d90d234b2b63cae76d4397", "input": { "seed": 25403, "image": "https://replicate.delivery/pbxt/JMf0SV6w1yp8ZcrtrYungReCNQzUUhXJHciDFi5nylAdqmCo/demo.jpg", "prompt": "a latina ballerina, romantic sunset, 4k photo", "guidance_scale": 7.5, "high_noise_frac": 0.8, "negative_prompt": "low quality, bad quality", "num_inference_steps": 50 } }' \ 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/lucataco/sdxl-controlnet-openpose@sha256:d63e0b238b2d963d90348e2dad19830fbe372a7a43d90d234b2b63cae76d4397 \ -i 'seed=25403' \ -i 'image="https://replicate.delivery/pbxt/JMf0SV6w1yp8ZcrtrYungReCNQzUUhXJHciDFi5nylAdqmCo/demo.jpg"' \ -i 'prompt="a latina ballerina, romantic sunset, 4k photo"' \ -i 'guidance_scale=7.5' \ -i 'high_noise_frac=0.8' \ -i 'negative_prompt="low quality, bad quality"' \ -i 'num_inference_steps=50'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/lucataco/sdxl-controlnet-openpose@sha256:d63e0b238b2d963d90348e2dad19830fbe372a7a43d90d234b2b63cae76d4397curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 25403, "image": "https://replicate.delivery/pbxt/JMf0SV6w1yp8ZcrtrYungReCNQzUUhXJHciDFi5nylAdqmCo/demo.jpg", "prompt": "a latina ballerina, romantic sunset, 4k photo", "guidance_scale": 7.5, "high_noise_frac": 0.8, "negative_prompt": "low quality, bad quality", "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-08-17T06:24:28.501864Z", "created_at": "2023-08-17T06:21:10.994936Z", "data_removed": false, "error": null, "id": "v2doo4dbv2foj6ngr26wnjy7cm", "input": { "seed": 25403, "image": "https://replicate.delivery/pbxt/JMf0SV6w1yp8ZcrtrYungReCNQzUUhXJHciDFi5nylAdqmCo/demo.jpg", "prompt": "a latina ballerina, romantic sunset, 4k photo", "guidance_scale": 7.5, "high_noise_frac": 0.8, "negative_prompt": "low quality, bad quality", "num_inference_steps": 50 }, "logs": "Using seed: 25403\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:36, 1.35it/s]\n 4%|▍ | 2/50 [00:01<00:22, 2.09it/s]\n 6%|▌ | 3/50 [00:01<00:18, 2.54it/s]\n 8%|▊ | 4/50 [00:01<00:16, 2.82it/s]\n 10%|█ | 5/50 [00:01<00:14, 3.00it/s]\n 12%|█▏ | 6/50 [00:02<00:14, 3.13it/s]\n 14%|█▍ | 7/50 [00:02<00:13, 3.21it/s]\n 16%|█▌ | 8/50 [00:02<00:12, 3.26it/s]\n 18%|█▊ | 9/50 [00:03<00:12, 3.30it/s]\n 20%|██ | 10/50 [00:03<00:12, 3.33it/s]\n 22%|██▏ | 11/50 [00:03<00:11, 3.35it/s]\n 24%|██▍ | 12/50 [00:03<00:11, 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5/10 [00:01<00:01, 4.32it/s]\n 60%|██████ | 6/10 [00:01<00:00, 4.33it/s]\n 70%|███████ | 7/10 [00:01<00:00, 4.33it/s]\n 80%|████████ | 8/10 [00:01<00:00, 4.33it/s]\n 90%|█████████ | 9/10 [00:02<00:00, 4.33it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.33it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.32it/s]", "metrics": { "predict_time": 21.248046, "total_time": 197.506928 }, "output": "https://replicate.delivery/pbxt/6qIkIhoz7EosJVfFfrQUnPYSsXfy2WIcTk9u27TM3LP44t1iA/output.png", "started_at": "2023-08-17T06:24:07.253818Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/v2doo4dbv2foj6ngr26wnjy7cm", "cancel": "https://api.replicate.com/v1/predictions/v2doo4dbv2foj6ngr26wnjy7cm/cancel" }, "version": "d63e0b238b2d963d90348e2dad19830fbe372a7a43d90d234b2b63cae76d4397" }Generated inUsing seed: 25403 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:36, 1.35it/s] 4%|▍ | 2/50 [00:01<00:22, 2.09it/s] 6%|▌ | 3/50 [00:01<00:18, 2.54it/s] 8%|▊ | 4/50 [00:01<00:16, 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[00:15<00:00, 3.28it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:02, 4.22it/s] 20%|██ | 2/10 [00:00<00:01, 4.29it/s] 30%|███ | 3/10 [00:00<00:01, 4.32it/s] 40%|████ | 4/10 [00:00<00:01, 4.31it/s] 50%|█████ | 5/10 [00:01<00:01, 4.32it/s] 60%|██████ | 6/10 [00:01<00:00, 4.33it/s] 70%|███████ | 7/10 [00:01<00:00, 4.33it/s] 80%|████████ | 8/10 [00:01<00:00, 4.33it/s] 90%|█████████ | 9/10 [00:02<00:00, 4.33it/s] 100%|██████████| 10/10 [00:02<00:00, 4.33it/s] 100%|██████████| 10/10 [00:02<00:00, 4.32it/s]Prediction
lucataco/sdxl-controlnet-openpose:d63e0b238b2d963d90348e2dad19830fbe372a7a43d90d234b2b63cae76d4397ID32i3fudbh5o4dchmn4s6lghw5uStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 64525
- image
- prompt
- iron man, 4k photo
- guidance_scale
- 7.5
- high_noise_frac
- 0.8
- negative_prompt
- low quality, bad quality
- num_inference_steps
- 50
{ "seed": 64525, "image": "https://replicate.delivery/pbxt/JMf3p9mz2uYkHRVOeBjYZZEN7bY40qn1INor1sCIrY2TJrMW/demo2.png", "prompt": "iron man, 4k photo", "guidance_scale": 7.5, "high_noise_frac": 0.8, "negative_prompt": "low quality, bad quality", "num_inference_steps": 50 }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 lucataco/sdxl-controlnet-openpose using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lucataco/sdxl-controlnet-openpose:d63e0b238b2d963d90348e2dad19830fbe372a7a43d90d234b2b63cae76d4397", { input: { seed: 64525, image: "https://replicate.delivery/pbxt/JMf3p9mz2uYkHRVOeBjYZZEN7bY40qn1INor1sCIrY2TJrMW/demo2.png", prompt: "iron man, 4k photo", guidance_scale: 7.5, high_noise_frac: 0.8, negative_prompt: "low quality, bad quality", num_inference_steps: 50 } } ); // 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 lucataco/sdxl-controlnet-openpose using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lucataco/sdxl-controlnet-openpose:d63e0b238b2d963d90348e2dad19830fbe372a7a43d90d234b2b63cae76d4397", input={ "seed": 64525, "image": "https://replicate.delivery/pbxt/JMf3p9mz2uYkHRVOeBjYZZEN7bY40qn1INor1sCIrY2TJrMW/demo2.png", "prompt": "iron man, 4k photo", "guidance_scale": 7.5, "high_noise_frac": 0.8, "negative_prompt": "low quality, bad quality", "num_inference_steps": 50 } ) # 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 lucataco/sdxl-controlnet-openpose 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": "lucataco/sdxl-controlnet-openpose:d63e0b238b2d963d90348e2dad19830fbe372a7a43d90d234b2b63cae76d4397", "input": { "seed": 64525, "image": "https://replicate.delivery/pbxt/JMf3p9mz2uYkHRVOeBjYZZEN7bY40qn1INor1sCIrY2TJrMW/demo2.png", "prompt": "iron man, 4k photo", "guidance_scale": 7.5, "high_noise_frac": 0.8, "negative_prompt": "low quality, bad quality", "num_inference_steps": 50 } }' \ 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/lucataco/sdxl-controlnet-openpose@sha256:d63e0b238b2d963d90348e2dad19830fbe372a7a43d90d234b2b63cae76d4397 \ -i 'seed=64525' \ -i 'image="https://replicate.delivery/pbxt/JMf3p9mz2uYkHRVOeBjYZZEN7bY40qn1INor1sCIrY2TJrMW/demo2.png"' \ -i 'prompt="iron man, 4k photo"' \ -i 'guidance_scale=7.5' \ -i 'high_noise_frac=0.8' \ -i 'negative_prompt="low quality, bad quality"' \ -i 'num_inference_steps=50'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/lucataco/sdxl-controlnet-openpose@sha256:d63e0b238b2d963d90348e2dad19830fbe372a7a43d90d234b2b63cae76d4397curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 64525, "image": "https://replicate.delivery/pbxt/JMf3p9mz2uYkHRVOeBjYZZEN7bY40qn1INor1sCIrY2TJrMW/demo2.png", "prompt": "iron man, 4k photo", "guidance_scale": 7.5, "high_noise_frac": 0.8, "negative_prompt": "low quality, bad quality", "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-08-17T06:25:04.368658Z", "created_at": "2023-08-17T06:24:44.274142Z", "data_removed": false, "error": null, "id": "32i3fudbh5o4dchmn4s6lghw5u", "input": { "seed": 64525, "image": "https://replicate.delivery/pbxt/JMf3p9mz2uYkHRVOeBjYZZEN7bY40qn1INor1sCIrY2TJrMW/demo2.png", "prompt": "iron man, 4k photo", "guidance_scale": 7.5, "high_noise_frac": 0.8, "negative_prompt": "low quality, bad quality", "num_inference_steps": 50 }, "logs": "Using seed: 64525\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:14, 3.38it/s]\n 4%|▍ | 2/50 [00:00<00:14, 3.40it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.40it/s]\n 8%|▊ | 4/50 [00:01<00:13, 3.40it/s]\n 10%|█ | 5/50 [00:01<00:13, 3.40it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.40it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.40it/s]\n 16%|█▌ | 8/50 [00:02<00:12, 3.40it/s]\n 18%|█▊ | 9/50 [00:02<00:12, 3.40it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.40it/s]\n 22%|██▏ | 11/50 [00:03<00:11, 3.40it/s]\n 24%|██▍ | 12/50 [00:03<00:11, 3.40it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.39it/s]\n 28%|██▊ | 14/50 [00:04<00:10, 3.39it/s]\n 30%|███ | 15/50 [00:04<00:10, 3.39it/s]\n 32%|███▏ | 16/50 [00:04<00:10, 3.39it/s]\n 34%|███▍ | 17/50 [00:05<00:09, 3.39it/s]\n 36%|███▌ | 18/50 [00:05<00:09, 3.39it/s]\n 38%|███▊ | 19/50 [00:05<00:09, 3.39it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.38it/s]\n 42%|████▏ | 21/50 [00:06<00:08, 3.38it/s]\n 44%|████▍ | 22/50 [00:06<00:08, 3.39it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.38it/s]\n 48%|████▊ | 24/50 [00:07<00:07, 3.38it/s]\n 50%|█████ | 25/50 [00:07<00:07, 3.38it/s]\n 52%|█████▏ | 26/50 [00:07<00:07, 3.38it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.38it/s]\n 56%|█████▌ | 28/50 [00:08<00:06, 3.38it/s]\n 58%|█████▊ | 29/50 [00:08<00:06, 3.38it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.38it/s]\n 62%|██████▏ | 31/50 [00:09<00:05, 3.38it/s]\n 64%|██████▍ | 32/50 [00:09<00:05, 3.38it/s]\n 66%|██████▌ | 33/50 [00:09<00:05, 3.38it/s]\n 68%|██████▊ | 34/50 [00:10<00:04, 3.38it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.38it/s]\n 72%|███████▏ | 36/50 [00:10<00:04, 3.38it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.38it/s]\n 76%|███████▌ | 38/50 [00:11<00:03, 3.37it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.37it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.37it/s]\n 82%|████████▏ | 41/50 [00:12<00:02, 3.37it/s]\n 84%|████████▍ | 42/50 [00:12<00:02, 3.37it/s]\n 86%|████████▌ | 43/50 [00:12<00:02, 3.38it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.37it/s]\n 90%|█████████ | 45/50 [00:13<00:01, 3.37it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.37it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.37it/s]\n 96%|█████████▌| 48/50 [00:14<00:00, 3.37it/s]\n 98%|█████████▊| 49/50 [00:14<00:00, 3.37it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.37it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.38it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:02, 4.33it/s]\n 20%|██ | 2/10 [00:00<00:01, 4.33it/s]\n 30%|███ | 3/10 [00:00<00:01, 4.33it/s]\n 40%|████ | 4/10 [00:00<00:01, 4.31it/s]\n 50%|█████ | 5/10 [00:01<00:01, 4.31it/s]\n 60%|██████ | 6/10 [00:01<00:00, 4.31it/s]\n 70%|███████ | 7/10 [00:01<00:00, 4.32it/s]\n 80%|████████ | 8/10 [00:01<00:00, 4.32it/s]\n 90%|█████████ | 9/10 [00:02<00:00, 4.31it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.32it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.32it/s]", "metrics": { "predict_time": 20.117294, "total_time": 20.094516 }, "output": "https://replicate.delivery/pbxt/s9qfG3Ab3fkKKUu9WhZk2LvD6UHfQeN3XTGpV9r7wqG8zbrFB/output.png", "started_at": "2023-08-17T06:24:44.251364Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/32i3fudbh5o4dchmn4s6lghw5u", "cancel": "https://api.replicate.com/v1/predictions/32i3fudbh5o4dchmn4s6lghw5u/cancel" }, "version": "d63e0b238b2d963d90348e2dad19830fbe372a7a43d90d234b2b63cae76d4397" }Generated inUsing seed: 64525 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:14, 3.38it/s] 4%|▍ | 2/50 [00:00<00:14, 3.40it/s] 6%|▌ | 3/50 [00:00<00:13, 3.40it/s] 8%|▊ | 4/50 [00:01<00:13, 3.40it/s] 10%|█ | 5/50 [00:01<00:13, 3.40it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.40it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.40it/s] 16%|█▌ | 8/50 [00:02<00:12, 3.40it/s] 18%|█▊ | 9/50 [00:02<00:12, 3.40it/s] 20%|██ | 10/50 [00:02<00:11, 3.40it/s] 22%|██▏ | 11/50 [00:03<00:11, 3.40it/s] 24%|██▍ | 12/50 [00:03<00:11, 3.40it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.39it/s] 28%|██▊ | 14/50 [00:04<00:10, 3.39it/s] 30%|███ | 15/50 [00:04<00:10, 3.39it/s] 32%|███▏ | 16/50 [00:04<00:10, 3.39it/s] 34%|███▍ | 17/50 [00:05<00:09, 3.39it/s] 36%|███▌ | 18/50 [00:05<00:09, 3.39it/s] 38%|███▊ | 19/50 [00:05<00:09, 3.39it/s] 40%|████ | 20/50 [00:05<00:08, 3.38it/s] 42%|████▏ | 21/50 [00:06<00:08, 3.38it/s] 44%|████▍ | 22/50 [00:06<00:08, 3.39it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.38it/s] 48%|████▊ | 24/50 [00:07<00:07, 3.38it/s] 50%|█████ | 25/50 [00:07<00:07, 3.38it/s] 52%|█████▏ | 26/50 [00:07<00:07, 3.38it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.38it/s] 56%|█████▌ | 28/50 [00:08<00:06, 3.38it/s] 58%|█████▊ | 29/50 [00:08<00:06, 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