anotherjesse / multi-control
All the original Controlnets & QR
- Public
- 60.6K runs
-
A100 (80GB)
- GitHub
Prediction
anotherjesse/multi-control:e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aaIDt5dbu3rbywbcasp3jgsoo24c3aStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- steps
- 50
- prompt
- whippet, flemish baroque, el yunque rainforest, 35mm film, quadtone color grading, chromakey
- qr_image
- scheduler
- K_EULER
- num_samples
- 2
- low_threshold
- 100
- guidance_scale
- 9
- high_threshold
- 200
- negative_prompt
- image_resolution
- 512
- qr_conditioning_scale
- 1.47
- hed_conditioning_scale
- 1
- seg_conditioning_scale
- 1
- pose_conditioning_scale
- 1
- canny_conditioning_scale
- 1
- depth_conditioning_scale
- 1
- hough_conditioning_scale
- 1
- normal_conditioning_scale
- 1
- scribble_conditioning_scale
- 1
{ "steps": 50, "prompt": "whippet, flemish baroque, el yunque rainforest, 35mm film, quadtone color grading, chromakey\n", "qr_image": "https://replicate.delivery/pbxt/J0ZYvssNVIBI906LgbXe5kpjkMrugsb5gDklk6erALej1efO/replicate-qr.png", "scheduler": "K_EULER", "num_samples": 2, "low_threshold": 100, "guidance_scale": 9, "high_threshold": 200, "negative_prompt": "", "image_resolution": 512, "qr_conditioning_scale": 1.47, "hed_conditioning_scale": 1, "seg_conditioning_scale": 1, "pose_conditioning_scale": 1, "canny_conditioning_scale": 1, "depth_conditioning_scale": 1, "hough_conditioning_scale": 1, "normal_conditioning_scale": 1, "scribble_conditioning_scale": 1 }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 anotherjesse/multi-control using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anotherjesse/multi-control:e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa", { input: { steps: 50, prompt: "whippet, flemish baroque, el yunque rainforest, 35mm film, quadtone color grading, chromakey\n", qr_image: "https://replicate.delivery/pbxt/J0ZYvssNVIBI906LgbXe5kpjkMrugsb5gDklk6erALej1efO/replicate-qr.png", scheduler: "K_EULER", num_samples: 2, low_threshold: 100, guidance_scale: 9, high_threshold: 200, negative_prompt: "", image_resolution: 512, qr_conditioning_scale: 1.47, hed_conditioning_scale: 1, seg_conditioning_scale: 1, pose_conditioning_scale: 1, canny_conditioning_scale: 1, depth_conditioning_scale: 1, hough_conditioning_scale: 1, normal_conditioning_scale: 1, scribble_conditioning_scale: 1 } } ); // 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 replicateImport the client:import replicateRun anotherjesse/multi-control using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anotherjesse/multi-control:e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa", input={ "steps": 50, "prompt": "whippet, flemish baroque, el yunque rainforest, 35mm film, quadtone color grading, chromakey\n", "qr_image": "https://replicate.delivery/pbxt/J0ZYvssNVIBI906LgbXe5kpjkMrugsb5gDklk6erALej1efO/replicate-qr.png", "scheduler": "K_EULER", "num_samples": 2, "low_threshold": 100, "guidance_scale": 9, "high_threshold": 200, "negative_prompt": "", "image_resolution": 512, "qr_conditioning_scale": 1.47, "hed_conditioning_scale": 1, "seg_conditioning_scale": 1, "pose_conditioning_scale": 1, "canny_conditioning_scale": 1, "depth_conditioning_scale": 1, "hough_conditioning_scale": 1, "normal_conditioning_scale": 1, "scribble_conditioning_scale": 1 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run anotherjesse/multi-control 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": "anotherjesse/multi-control:e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa", "input": { "steps": 50, "prompt": "whippet, flemish baroque, el yunque rainforest, 35mm film, quadtone color grading, chromakey\\n", "qr_image": "https://replicate.delivery/pbxt/J0ZYvssNVIBI906LgbXe5kpjkMrugsb5gDklk6erALej1efO/replicate-qr.png", "scheduler": "K_EULER", "num_samples": 2, "low_threshold": 100, "guidance_scale": 9, "high_threshold": 200, "negative_prompt": "", "image_resolution": 512, "qr_conditioning_scale": 1.47, "hed_conditioning_scale": 1, "seg_conditioning_scale": 1, "pose_conditioning_scale": 1, "canny_conditioning_scale": 1, "depth_conditioning_scale": 1, "hough_conditioning_scale": 1, "normal_conditioning_scale": 1, "scribble_conditioning_scale": 1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-06-16T00:59:07.884533Z", "created_at": "2023-06-16T00:59:00.910729Z", "data_removed": false, "error": null, "id": "t5dbu3rbywbcasp3jgsoo24c3a", "input": { "steps": 50, "prompt": "whippet, flemish baroque, el yunque rainforest, 35mm film, quadtone color grading, chromakey\n", "qr_image": "https://replicate.delivery/pbxt/J0ZYvssNVIBI906LgbXe5kpjkMrugsb5gDklk6erALej1efO/replicate-qr.png", "scheduler": "K_EULER", "num_samples": 2, "low_threshold": 100, "guidance_scale": 9, "high_threshold": 200, "negative_prompt": "", "image_resolution": 512, "qr_conditioning_scale": 1.47, "hed_conditioning_scale": 1, "seg_conditioning_scale": 1, "pose_conditioning_scale": 1, "canny_conditioning_scale": 1, "depth_conditioning_scale": 1, "hough_conditioning_scale": 1, "normal_conditioning_scale": 1, "scribble_conditioning_scale": 1 }, "logs": "You have disabled the safety checker for <class 'diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline'> by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\nUsing seed: 291\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:05, 9.30it/s]\n 4%|▍ | 2/50 [00:00<00:05, 9.37it/s]\n 8%|▊ | 4/50 [00:00<00:04, 10.22it/s]\n 12%|█▏ | 6/50 [00:00<00:04, 10.57it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 9.93it/s]\n 18%|█▊ | 9/50 [00:00<00:04, 9.81it/s]\n 20%|██ | 10/50 [00:01<00:04, 9.59it/s]\n 24%|██▍ | 12/50 [00:01<00:03, 10.09it/s]\n 26%|██▌ | 13/50 [00:01<00:03, 9.97it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 9.78it/s]\n 30%|███ | 15/50 [00:01<00:03, 9.52it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 9.60it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 9.48it/s]\n 36%|███▌ | 18/50 [00:01<00:03, 9.40it/s]\n 40%|████ | 20/50 [00:02<00:03, 9.75it/s]\n 42%|████▏ | 21/50 [00:02<00:03, 9.56it/s]\n 44%|████▍ | 22/50 [00:02<00:02, 9.60it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 10.22it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 9.95it/s]\n 54%|█████▍ | 27/50 [00:02<00:02, 9.79it/s]\n 58%|█████▊ | 29/50 [00:02<00:02, 10.21it/s]\n 62%|██████▏ | 31/50 [00:03<00:01, 10.39it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 10.16it/s]\n 70%|███████ | 35/50 [00:03<00:01, 10.24it/s]\n 74%|███████▍ | 37/50 [00:03<00:01, 9.94it/s]\n 78%|███████▊ | 39/50 [00:03<00:01, 10.15it/s]\n 82%|████████▏ | 41/50 [00:04<00:00, 10.14it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 10.44it/s]\n 90%|█████████ | 45/50 [00:04<00:00, 10.25it/s]\n 94%|█████████▍| 47/50 [00:04<00:00, 10.08it/s]\n 98%|█████████▊| 49/50 [00:04<00:00, 9.97it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.73it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.95it/s]", "metrics": { "predict_time": 7.056236, "total_time": 6.973804 }, "output": [ "https://replicate.delivery/pbxt/1efdEUS3f7cB0pjAQm64rxzAmfZLUtnKN6f1UCLh5YmR7yyIC/out-0.png", "https://replicate.delivery/pbxt/xEGKbZufLuzySSL8lmZ2d77JA461sf1T4K8XZe34UuI3usMiA/out-1.png" ], "started_at": "2023-06-16T00:59:00.828297Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/t5dbu3rbywbcasp3jgsoo24c3a", "cancel": "https://api.replicate.com/v1/predictions/t5dbu3rbywbcasp3jgsoo24c3a/cancel" }, "version": "e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa" }Generated inYou have disabled the safety checker for <class 'diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline'> by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 . Using seed: 291 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:05, 9.30it/s] 4%|▍ | 2/50 [00:00<00:05, 9.37it/s] 8%|▊ | 4/50 [00:00<00:04, 10.22it/s] 12%|█▏ | 6/50 [00:00<00:04, 10.57it/s] 16%|█▌ | 8/50 [00:00<00:04, 9.93it/s] 18%|█▊ | 9/50 [00:00<00:04, 9.81it/s] 20%|██ | 10/50 [00:01<00:04, 9.59it/s] 24%|██▍ | 12/50 [00:01<00:03, 10.09it/s] 26%|██▌ | 13/50 [00:01<00:03, 9.97it/s] 28%|██▊ | 14/50 [00:01<00:03, 9.78it/s] 30%|███ | 15/50 [00:01<00:03, 9.52it/s] 32%|███▏ | 16/50 [00:01<00:03, 9.60it/s] 34%|███▍ | 17/50 [00:01<00:03, 9.48it/s] 36%|███▌ | 18/50 [00:01<00:03, 9.40it/s] 40%|████ | 20/50 [00:02<00:03, 9.75it/s] 42%|████▏ | 21/50 [00:02<00:03, 9.56it/s] 44%|████▍ | 22/50 [00:02<00:02, 9.60it/s] 48%|████▊ | 24/50 [00:02<00:02, 10.22it/s] 52%|█████▏ | 26/50 [00:02<00:02, 9.95it/s] 54%|█████▍ | 27/50 [00:02<00:02, 9.79it/s] 58%|█████▊ | 29/50 [00:02<00:02, 10.21it/s] 62%|██████▏ | 31/50 [00:03<00:01, 10.39it/s] 66%|██████▌ | 33/50 [00:03<00:01, 10.16it/s] 70%|███████ | 35/50 [00:03<00:01, 10.24it/s] 74%|███████▍ | 37/50 [00:03<00:01, 9.94it/s] 78%|███████▊ | 39/50 [00:03<00:01, 10.15it/s] 82%|████████▏ | 41/50 [00:04<00:00, 10.14it/s] 86%|████████▌ | 43/50 [00:04<00:00, 10.44it/s] 90%|█████████ | 45/50 [00:04<00:00, 10.25it/s] 94%|█████████▍| 47/50 [00:04<00:00, 10.08it/s] 98%|█████████▊| 49/50 [00:04<00:00, 9.97it/s] 100%|██████████| 50/50 [00:05<00:00, 9.73it/s] 100%|██████████| 50/50 [00:05<00:00, 9.95it/s]Prediction
anotherjesse/multi-control:e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aaIDpcpr6xzbu4agtep3jihhxaycuiStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- steps
- 50
- prompt
- whippet, low-poly, the mont saint michel, 9.5mm film, warm color grading, visual effects
- qr_image
- scheduler
- K_EULER
- num_samples
- 2
- low_threshold
- 100
- guidance_scale
- 9
- high_threshold
- 200
- negative_prompt
- image_resolution
- 512
- qr_conditioning_scale
- 1.47
- hed_conditioning_scale
- 1
- seg_conditioning_scale
- 1
- pose_conditioning_scale
- 1
- canny_conditioning_scale
- 1
- depth_conditioning_scale
- 1
- hough_conditioning_scale
- 1
- normal_conditioning_scale
- 1
- scribble_conditioning_scale
- 1
{ "steps": 50, "prompt": "whippet, low-poly, the mont saint michel, 9.5mm film, warm color grading, visual effects", "qr_image": "https://replicate.delivery/pbxt/J0ZYvssNVIBI906LgbXe5kpjkMrugsb5gDklk6erALej1efO/replicate-qr.png", "scheduler": "K_EULER", "num_samples": 2, "low_threshold": 100, "guidance_scale": 9, "high_threshold": 200, "negative_prompt": "", "image_resolution": 512, "qr_conditioning_scale": 1.47, "hed_conditioning_scale": 1, "seg_conditioning_scale": 1, "pose_conditioning_scale": 1, "canny_conditioning_scale": 1, "depth_conditioning_scale": 1, "hough_conditioning_scale": 1, "normal_conditioning_scale": 1, "scribble_conditioning_scale": 1 }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 anotherjesse/multi-control using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anotherjesse/multi-control:e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa", { input: { steps: 50, prompt: "whippet, low-poly, the mont saint michel, 9.5mm film, warm color grading, visual effects", qr_image: "https://replicate.delivery/pbxt/J0ZYvssNVIBI906LgbXe5kpjkMrugsb5gDklk6erALej1efO/replicate-qr.png", scheduler: "K_EULER", num_samples: 2, low_threshold: 100, guidance_scale: 9, high_threshold: 200, negative_prompt: "", image_resolution: 512, qr_conditioning_scale: 1.47, hed_conditioning_scale: 1, seg_conditioning_scale: 1, pose_conditioning_scale: 1, canny_conditioning_scale: 1, depth_conditioning_scale: 1, hough_conditioning_scale: 1, normal_conditioning_scale: 1, scribble_conditioning_scale: 1 } } ); // 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 replicateImport the client:import replicateRun anotherjesse/multi-control using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anotherjesse/multi-control:e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa", input={ "steps": 50, "prompt": "whippet, low-poly, the mont saint michel, 9.5mm film, warm color grading, visual effects", "qr_image": "https://replicate.delivery/pbxt/J0ZYvssNVIBI906LgbXe5kpjkMrugsb5gDklk6erALej1efO/replicate-qr.png", "scheduler": "K_EULER", "num_samples": 2, "low_threshold": 100, "guidance_scale": 9, "high_threshold": 200, "negative_prompt": "", "image_resolution": 512, "qr_conditioning_scale": 1.47, "hed_conditioning_scale": 1, "seg_conditioning_scale": 1, "pose_conditioning_scale": 1, "canny_conditioning_scale": 1, "depth_conditioning_scale": 1, "hough_conditioning_scale": 1, "normal_conditioning_scale": 1, "scribble_conditioning_scale": 1 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run anotherjesse/multi-control 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": "anotherjesse/multi-control:e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa", "input": { "steps": 50, "prompt": "whippet, low-poly, the mont saint michel, 9.5mm film, warm color grading, visual effects", "qr_image": "https://replicate.delivery/pbxt/J0ZYvssNVIBI906LgbXe5kpjkMrugsb5gDklk6erALej1efO/replicate-qr.png", "scheduler": "K_EULER", "num_samples": 2, "low_threshold": 100, "guidance_scale": 9, "high_threshold": 200, "negative_prompt": "", "image_resolution": 512, "qr_conditioning_scale": 1.47, "hed_conditioning_scale": 1, "seg_conditioning_scale": 1, "pose_conditioning_scale": 1, "canny_conditioning_scale": 1, "depth_conditioning_scale": 1, "hough_conditioning_scale": 1, "normal_conditioning_scale": 1, "scribble_conditioning_scale": 1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-06-16T00:59:56.560818Z", "created_at": "2023-06-16T00:59:49.853490Z", "data_removed": false, "error": null, "id": "pcpr6xzbu4agtep3jihhxaycui", "input": { "steps": 50, "prompt": "whippet, low-poly, the mont saint michel, 9.5mm film, warm color grading, visual effects", "qr_image": "https://replicate.delivery/pbxt/J0ZYvssNVIBI906LgbXe5kpjkMrugsb5gDklk6erALej1efO/replicate-qr.png", "scheduler": "K_EULER", "num_samples": 2, "low_threshold": 100, "guidance_scale": 9, "high_threshold": 200, "negative_prompt": "", "image_resolution": 512, "qr_conditioning_scale": 1.47, "hed_conditioning_scale": 1, "seg_conditioning_scale": 1, "pose_conditioning_scale": 1, "canny_conditioning_scale": 1, "depth_conditioning_scale": 1, "hough_conditioning_scale": 1, "normal_conditioning_scale": 1, "scribble_conditioning_scale": 1 }, "logs": "You have disabled the safety checker for <class 'diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline'> by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\nUsing seed: 57273\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:04, 10.35it/s]\n 8%|▊ | 4/50 [00:00<00:04, 10.59it/s]\n 12%|█▏ | 6/50 [00:00<00:04, 10.22it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 10.06it/s]\n 20%|██ | 10/50 [00:00<00:03, 10.19it/s]\n 24%|██▍ | 12/50 [00:01<00:03, 10.19it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 10.25it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 10.37it/s]\n 36%|███▌ | 18/50 [00:01<00:03, 10.30it/s]\n 40%|████ | 20/50 [00:01<00:02, 10.38it/s]\n 44%|████▍ | 22/50 [00:02<00:02, 10.31it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 10.03it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 10.11it/s]\n 56%|█████▌ | 28/50 [00:02<00:02, 10.27it/s]\n 60%|██████ | 30/50 [00:02<00:01, 10.27it/s]\n 64%|██████▍ | 32/50 [00:03<00:01, 10.35it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 10.15it/s]\n 72%|███████▏ | 36/50 [00:03<00:01, 9.94it/s]\n 74%|███████▍ | 37/50 [00:03<00:01, 9.84it/s]\n 76%|███████▌ | 38/50 [00:03<00:01, 9.42it/s]\n 78%|███████▊ | 39/50 [00:03<00:01, 9.49it/s]\n 80%|████████ | 40/50 [00:03<00:01, 9.57it/s]\n 82%|████████▏ | 41/50 [00:04<00:00, 9.65it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 9.72it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 9.76it/s]\n 90%|█████████ | 45/50 [00:04<00:00, 10.08it/s]\n 94%|█████████▍| 47/50 [00:04<00:00, 10.40it/s]\n 98%|█████████▊| 49/50 [00:04<00:00, 10.67it/s]\n100%|██████████| 50/50 [00:04<00:00, 10.18it/s]", "metrics": { "predict_time": 6.855983, "total_time": 6.707328 }, "output": [ "https://replicate.delivery/pbxt/59c71Xhqie1xQ63MuMuiU3QyG0TWJmS6TmBg7wKBfnqLYWGRA/out-0.png", "https://replicate.delivery/pbxt/QFakXe4KCxzHDay23eMqI0kWUCPwDYl6j8ffvQtQrlnugZZEB/out-1.png" ], "started_at": "2023-06-16T00:59:49.704835Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pcpr6xzbu4agtep3jihhxaycui", "cancel": "https://api.replicate.com/v1/predictions/pcpr6xzbu4agtep3jihhxaycui/cancel" }, "version": "e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa" }Generated inYou have disabled the safety checker for <class 'diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline'> by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 . Using seed: 57273 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:04, 10.35it/s] 8%|▊ | 4/50 [00:00<00:04, 10.59it/s] 12%|█▏ | 6/50 [00:00<00:04, 10.22it/s] 16%|█▌ | 8/50 [00:00<00:04, 10.06it/s] 20%|██ | 10/50 [00:00<00:03, 10.19it/s] 24%|██▍ | 12/50 [00:01<00:03, 10.19it/s] 28%|██▊ | 14/50 [00:01<00:03, 10.25it/s] 32%|███▏ | 16/50 [00:01<00:03, 10.37it/s] 36%|███▌ | 18/50 [00:01<00:03, 10.30it/s] 40%|████ | 20/50 [00:01<00:02, 10.38it/s] 44%|████▍ | 22/50 [00:02<00:02, 10.31it/s] 48%|████▊ | 24/50 [00:02<00:02, 10.03it/s] 52%|█████▏ | 26/50 [00:02<00:02, 10.11it/s] 56%|█████▌ | 28/50 [00:02<00:02, 10.27it/s] 60%|██████ | 30/50 [00:02<00:01, 10.27it/s] 64%|██████▍ | 32/50 [00:03<00:01, 10.35it/s] 68%|██████▊ | 34/50 [00:03<00:01, 10.15it/s] 72%|███████▏ | 36/50 [00:03<00:01, 9.94it/s] 74%|███████▍ | 37/50 [00:03<00:01, 9.84it/s] 76%|███████▌ | 38/50 [00:03<00:01, 9.42it/s] 78%|███████▊ | 39/50 [00:03<00:01, 9.49it/s] 80%|████████ | 40/50 [00:03<00:01, 9.57it/s] 82%|████████▏ | 41/50 [00:04<00:00, 9.65it/s] 84%|████████▍ | 42/50 [00:04<00:00, 9.72it/s] 86%|████████▌ | 43/50 [00:04<00:00, 9.76it/s] 90%|█████████ | 45/50 [00:04<00:00, 10.08it/s] 94%|█████████▍| 47/50 [00:04<00:00, 10.40it/s] 98%|█████████▊| 49/50 [00:04<00:00, 10.67it/s] 100%|██████████| 50/50 [00:04<00:00, 10.18it/s]Prediction
anotherjesse/multi-control:e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aaIDccfw5tzbi754c5c2ihfm2pjnfeStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- steps
- 50
- prompt
- A film still of a harpy, sharing wisdom, cinematic, bold color grading, special effects, set in 1892
- qr_image
- scheduler
- K_EULER
- num_samples
- 2
- low_threshold
- 100
- guidance_scale
- 9
- high_threshold
- 200
- negative_prompt
- image_resolution
- 512
- qr_conditioning_scale
- 1.47
- hed_conditioning_scale
- 1
- seg_conditioning_scale
- 1
- pose_conditioning_scale
- 1
- canny_conditioning_scale
- 1
- depth_conditioning_scale
- 1
- hough_conditioning_scale
- 1
- normal_conditioning_scale
- 1
- scribble_conditioning_scale
- 1
{ "steps": 50, "prompt": "A film still of a harpy, sharing wisdom, cinematic, bold color grading, special effects, set in 1892", "qr_image": "https://replicate.delivery/pbxt/J0ZYvssNVIBI906LgbXe5kpjkMrugsb5gDklk6erALej1efO/replicate-qr.png", "scheduler": "K_EULER", "num_samples": 2, "low_threshold": 100, "guidance_scale": 9, "high_threshold": 200, "negative_prompt": "", "image_resolution": 512, "qr_conditioning_scale": 1.47, "hed_conditioning_scale": 1, "seg_conditioning_scale": 1, "pose_conditioning_scale": 1, "canny_conditioning_scale": 1, "depth_conditioning_scale": 1, "hough_conditioning_scale": 1, "normal_conditioning_scale": 1, "scribble_conditioning_scale": 1 }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 anotherjesse/multi-control using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "anotherjesse/multi-control:e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa", { input: { steps: 50, prompt: "A film still of a harpy, sharing wisdom, cinematic, bold color grading, special effects, set in 1892", qr_image: "https://replicate.delivery/pbxt/J0ZYvssNVIBI906LgbXe5kpjkMrugsb5gDklk6erALej1efO/replicate-qr.png", scheduler: "K_EULER", num_samples: 2, low_threshold: 100, guidance_scale: 9, high_threshold: 200, negative_prompt: "", image_resolution: 512, qr_conditioning_scale: 1.47, hed_conditioning_scale: 1, seg_conditioning_scale: 1, pose_conditioning_scale: 1, canny_conditioning_scale: 1, depth_conditioning_scale: 1, hough_conditioning_scale: 1, normal_conditioning_scale: 1, scribble_conditioning_scale: 1 } } ); // 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 replicateImport the client:import replicateRun anotherjesse/multi-control using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "anotherjesse/multi-control:e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa", input={ "steps": 50, "prompt": "A film still of a harpy, sharing wisdom, cinematic, bold color grading, special effects, set in 1892", "qr_image": "https://replicate.delivery/pbxt/J0ZYvssNVIBI906LgbXe5kpjkMrugsb5gDklk6erALej1efO/replicate-qr.png", "scheduler": "K_EULER", "num_samples": 2, "low_threshold": 100, "guidance_scale": 9, "high_threshold": 200, "negative_prompt": "", "image_resolution": 512, "qr_conditioning_scale": 1.47, "hed_conditioning_scale": 1, "seg_conditioning_scale": 1, "pose_conditioning_scale": 1, "canny_conditioning_scale": 1, "depth_conditioning_scale": 1, "hough_conditioning_scale": 1, "normal_conditioning_scale": 1, "scribble_conditioning_scale": 1 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run anotherjesse/multi-control 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": "anotherjesse/multi-control:e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa", "input": { "steps": 50, "prompt": "A film still of a harpy, sharing wisdom, cinematic, bold color grading, special effects, set in 1892", "qr_image": "https://replicate.delivery/pbxt/J0ZYvssNVIBI906LgbXe5kpjkMrugsb5gDklk6erALej1efO/replicate-qr.png", "scheduler": "K_EULER", "num_samples": 2, "low_threshold": 100, "guidance_scale": 9, "high_threshold": 200, "negative_prompt": "", "image_resolution": 512, "qr_conditioning_scale": 1.47, "hed_conditioning_scale": 1, "seg_conditioning_scale": 1, "pose_conditioning_scale": 1, "canny_conditioning_scale": 1, "depth_conditioning_scale": 1, "hough_conditioning_scale": 1, "normal_conditioning_scale": 1, "scribble_conditioning_scale": 1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-06-16T01:01:58.154103Z", "created_at": "2023-06-16T01:01:51.941064Z", "data_removed": false, "error": null, "id": "ccfw5tzbi754c5c2ihfm2pjnfe", "input": { "steps": 50, "prompt": "A film still of a harpy, sharing wisdom, cinematic, bold color grading, special effects, set in 1892", "qr_image": "https://replicate.delivery/pbxt/J0ZYvssNVIBI906LgbXe5kpjkMrugsb5gDklk6erALej1efO/replicate-qr.png", "scheduler": "K_EULER", "num_samples": 2, "low_threshold": 100, "guidance_scale": 9, "high_threshold": 200, "negative_prompt": "", "image_resolution": 512, "qr_conditioning_scale": 1.47, "hed_conditioning_scale": 1, "seg_conditioning_scale": 1, "pose_conditioning_scale": 1, "canny_conditioning_scale": 1, "depth_conditioning_scale": 1, "hough_conditioning_scale": 1, "normal_conditioning_scale": 1, "scribble_conditioning_scale": 1 }, "logs": "You have disabled the safety checker for <class 'diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline'> by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\nUsing seed: 32305\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:04, 10.81it/s]\n 8%|▊ | 4/50 [00:00<00:04, 11.19it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 11.28it/s]\n 16%|█▌ | 8/50 [00:00<00:03, 11.35it/s]\n 20%|██ | 10/50 [00:00<00:03, 11.42it/s]\n 24%|██▍ | 12/50 [00:01<00:03, 11.43it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 11.43it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 11.48it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 11.49it/s]\n 40%|████ | 20/50 [00:01<00:02, 11.44it/s]\n 44%|████▍ | 22/50 [00:01<00:02, 11.45it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 11.34it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 11.34it/s]\n 56%|█████▌ | 28/50 [00:02<00:01, 11.36it/s]\n 60%|██████ | 30/50 [00:02<00:01, 11.39it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 11.37it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 11.36it/s]\n 72%|███████▏ | 36/50 [00:03<00:01, 11.35it/s]\n 76%|███████▌ | 38/50 [00:03<00:01, 11.43it/s]\n 80%|████████ | 40/50 [00:03<00:00, 11.48it/s]\n 84%|████████▍ | 42/50 [00:03<00:00, 11.47it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 11.43it/s]\n 92%|█████████▏| 46/50 [00:04<00:00, 11.42it/s]\n 96%|█████████▌| 48/50 [00:04<00:00, 11.27it/s]\n100%|██████████| 50/50 [00:04<00:00, 11.27it/s]\n100%|██████████| 50/50 [00:04<00:00, 11.37it/s]", "metrics": { "predict_time": 6.343817, "total_time": 6.213039 }, "output": [ "https://replicate.delivery/pbxt/ziUqhTzkwJZ3DNlCuzhfB8IypYVj9CfBh7AYcIn0WtfI0sMiA/out-0.png", "https://replicate.delivery/pbxt/2ZTqeBOdfZuRYkn0JsgwPhhcNDEb11LUfXXgRpigaeuUoZZEB/out-1.png" ], "started_at": "2023-06-16T01:01:51.810286Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ccfw5tzbi754c5c2ihfm2pjnfe", "cancel": "https://api.replicate.com/v1/predictions/ccfw5tzbi754c5c2ihfm2pjnfe/cancel" }, "version": "e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa" }Generated inYou have disabled the safety checker for <class 'diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline'> by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 . Using seed: 32305 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:04, 10.81it/s] 8%|▊ | 4/50 [00:00<00:04, 11.19it/s] 12%|█▏ | 6/50 [00:00<00:03, 11.28it/s] 16%|█▌ | 8/50 [00:00<00:03, 11.35it/s] 20%|██ | 10/50 [00:00<00:03, 11.42it/s] 24%|██▍ | 12/50 [00:01<00:03, 11.43it/s] 28%|██▊ | 14/50 [00:01<00:03, 11.43it/s] 32%|███▏ | 16/50 [00:01<00:02, 11.48it/s] 36%|███▌ | 18/50 [00:01<00:02, 11.49it/s] 40%|████ | 20/50 [00:01<00:02, 11.44it/s] 44%|████▍ | 22/50 [00:01<00:02, 11.45it/s] 48%|████▊ | 24/50 [00:02<00:02, 11.34it/s] 52%|█████▏ | 26/50 [00:02<00:02, 11.34it/s] 56%|█████▌ | 28/50 [00:02<00:01, 11.36it/s] 60%|██████ | 30/50 [00:02<00:01, 11.39it/s] 64%|██████▍ | 32/50 [00:02<00:01, 11.37it/s] 68%|██████▊ | 34/50 [00:02<00:01, 11.36it/s] 72%|███████▏ | 36/50 [00:03<00:01, 11.35it/s] 76%|███████▌ | 38/50 [00:03<00:01, 11.43it/s] 80%|████████ | 40/50 [00:03<00:00, 11.48it/s] 84%|████████▍ | 42/50 [00:03<00:00, 11.47it/s] 88%|████████▊ | 44/50 [00:03<00:00, 11.43it/s] 92%|█████████▏| 46/50 [00:04<00:00, 11.42it/s] 96%|█████████▌| 48/50 [00:04<00:00, 11.27it/s] 100%|██████████| 50/50 [00:04<00:00, 11.27it/s] 100%|██████████| 50/50 [00:04<00:00, 11.37it/s]
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