fofr
/
face-to-many
Turn a face into 3D, emoji, pixel art, video game, claymation or toy
Prediction
fofr/face-to-many:a07f252aIDyjceix3bx22hjtlv6gwwykckkiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- style
- 3D
- prompt
- a zombie in a fire, burning flames behind him
- negative_prompt
- boring
- prompt_strength
- 4.5
- denoising_strength
- 1
- instant_id_strength
- 0.8
{ "image": "https://replicate.delivery/pbxt/KW7CdCusXMkkOs9bbCGYsInC8EUxlj3yBLxvfW9Fs9FFMZUL/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "3D", "prompt": "a zombie in a fire, burning flames behind him", "negative_prompt": "boring", "prompt_strength": 4.5, "denoising_strength": 1, "instant_id_strength": 0.8 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", { input: { image: "https://replicate.delivery/pbxt/KW7CdCusXMkkOs9bbCGYsInC8EUxlj3yBLxvfW9Fs9FFMZUL/MTk4MTczMTkzNzI1Mjg5NjYy.webp", style: "3D", prompt: "a zombie in a fire, burning flames behind him", negative_prompt: "boring", prompt_strength: 4.5, denoising_strength: 1, instant_id_strength: 0.8 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", input={ "image": "https://replicate.delivery/pbxt/KW7CdCusXMkkOs9bbCGYsInC8EUxlj3yBLxvfW9Fs9FFMZUL/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "3D", "prompt": "a zombie in a fire, burning flames behind him", "negative_prompt": "boring", "prompt_strength": 4.5, "denoising_strength": 1, "instant_id_strength": 0.8 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/face-to-many 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": "a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", "input": { "image": "https://replicate.delivery/pbxt/KW7CdCusXMkkOs9bbCGYsInC8EUxlj3yBLxvfW9Fs9FFMZUL/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "3D", "prompt": "a zombie in a fire, burning flames behind him", "negative_prompt": "boring", "prompt_strength": 4.5, "denoising_strength": 1, "instant_id_strength": 0.8 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cog
If you don’t have Homebrew, there are other installation options available.
Pull and run fofr/face-to-many using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf \ -i 'image="https://replicate.delivery/pbxt/KW7CdCusXMkkOs9bbCGYsInC8EUxlj3yBLxvfW9Fs9FFMZUL/MTk4MTczMTkzNzI1Mjg5NjYy.webp"' \ -i 'style="3D"' \ -i 'prompt="a zombie in a fire, burning flames behind him"' \ -i 'negative_prompt="boring"' \ -i 'prompt_strength=4.5' \ -i 'denoising_strength=1' \ -i 'instant_id_strength=0.8'
To learn more, take a look at the Cog documentation.
Pull and run fofr/face-to-many using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/KW7CdCusXMkkOs9bbCGYsInC8EUxlj3yBLxvfW9Fs9FFMZUL/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "3D", "prompt": "a zombie in a fire, burning flames behind him", "negative_prompt": "boring", "prompt_strength": 4.5, "denoising_strength": 1, "instant_id_strength": 0.8 } }' \ http://localhost:5000/predictions
Output
{ "completed_at": "2024-03-05T15:03:14.262722Z", "created_at": "2024-03-05T15:02:45.844975Z", "data_removed": false, "error": null, "id": "yjceix3bx22hjtlv6gwwykckki", "input": { "image": "https://replicate.delivery/pbxt/KW7CdCusXMkkOs9bbCGYsInC8EUxlj3yBLxvfW9Fs9FFMZUL/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "3D", "prompt": "a zombie in a fire, burning flames behind him", "negative_prompt": "boring", "prompt_strength": 4.5, "denoising_strength": 1, "instant_id_strength": 0.8 }, "logs": "Random seed set to: 3386550706\nChecking inputs\n✅ /tmp/inputs/input.webp\n====================================\nRunning workflow\ngot prompt\nExecuting node 22, title: Load Image, class type: LoadImage\nExecuting node 67, title: 🔧 Image Resize, class type: ImageResize+\nExecuting node 3, title: LoRA Stacker, class type: LoRA Stacker\nExecuting node 49, title: AIO Aux Preprocessor, class type: AIO_Preprocessor\nExecuting node 28, title: Control Net Stacker, class type: Control Net Stacker\nExecuting node 2, title: Efficient Loader, class type: Efficient Loader\nRequested to load SDXLClipModel\nLoading 1 new model\n----------------------------------------\n\u001b[36mEfficient Loader Models Cache:\u001b[0m\nCkpt:\n[1] albedobaseXL_v13\nLora:\n[1] base_ckpt: albedobaseXL_v13\nlora(mod,clip): 3DRedmond-3DRenderStyle-3DRenderAF(1,1)\nExecuting node 41, title: Apply InstantID, class type: ApplyInstantID\nExecuting node 51, title: VAE Encode, class type: VAEEncode\nExecuting node 4, title: KSampler (Efficient), class type: KSampler (Efficient)\nRequested to load SDXL\nRequested to load ControlNet\nRequested to load ControlNet\nLoading 3 new models\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:05, 3.34it/s]\n 10%|█ | 2/20 [00:00<00:05, 3.28it/s]\n 15%|█▌ | 3/20 [00:00<00:05, 3.28it/s]\n 20%|██ | 4/20 [00:01<00:04, 3.28it/s]\n 25%|██▌ | 5/20 [00:01<00:04, 3.29it/s]\n 30%|███ | 6/20 [00:01<00:04, 3.29it/s]\n 35%|███▌ | 7/20 [00:02<00:03, 3.30it/s]\n 40%|████ | 8/20 [00:02<00:03, 3.30it/s]\n 45%|████▌ | 9/20 [00:02<00:03, 3.30it/s]\n 50%|█████ | 10/20 [00:03<00:03, 3.29it/s]\n 55%|█████▌ | 11/20 [00:03<00:02, 3.30it/s]\n 60%|██████ | 12/20 [00:03<00:02, 3.30it/s]\n 65%|██████▌ | 13/20 [00:03<00:02, 3.30it/s]\n 70%|███████ | 14/20 [00:04<00:01, 3.31it/s]\n 75%|███████▌ | 15/20 [00:04<00:01, 3.30it/s]\n 80%|████████ | 16/20 [00:04<00:01, 3.31it/s]\n 85%|████████▌ | 17/20 [00:05<00:00, 3.31it/s]\n 90%|█████████ | 18/20 [00:05<00:00, 3.32it/s]\n 95%|█████████▌| 19/20 [00:05<00:00, 3.37it/s]\n100%|██████████| 20/20 [00:06<00:00, 3.42it/s]\n100%|██████████| 20/20 [00:06<00:00, 3.32it/s]\nExecuting node 5, title: Save Image, class type: SaveImage\nPrompt executed in 26.70 seconds\noutputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nContents of /tmp/outputs:\nComfyUI_00001_.png", "metrics": { "predict_time": 28.367332, "total_time": 28.417747 }, "output": [ "https://replicate.delivery/pbxt/fP9XisyvE60KY6mKBRyDHHhoC2eVfoGehEJ0fCSUHi3PGzpTC/ComfyUI_00001_.png" ], "started_at": "2024-03-05T15:02:45.895390Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yjceix3bx22hjtlv6gwwykckki", "cancel": "https://api.replicate.com/v1/predictions/yjceix3bx22hjtlv6gwwykckki/cancel" }, "version": "0acc93d551a9de22261d9c4cb3c117e2f1e7337d7f3dc1f162cf2c43a7ad6dfe" }
Generated inRandom seed set to: 3386550706 Checking inputs ✅ /tmp/inputs/input.webp ==================================== Running workflow got prompt Executing node 22, title: Load Image, class type: LoadImage Executing node 67, title: 🔧 Image Resize, class type: ImageResize+ Executing node 3, title: LoRA Stacker, class type: LoRA Stacker Executing node 49, title: AIO Aux Preprocessor, class type: AIO_Preprocessor Executing node 28, title: Control Net Stacker, class type: Control Net Stacker Executing node 2, title: Efficient Loader, class type: Efficient Loader Requested to load SDXLClipModel Loading 1 new model ---------------------------------------- Efficient Loader Models Cache: Ckpt: [1] albedobaseXL_v13 Lora: [1] base_ckpt: albedobaseXL_v13 lora(mod,clip): 3DRedmond-3DRenderStyle-3DRenderAF(1,1) Executing node 41, title: Apply InstantID, class type: ApplyInstantID Executing node 51, title: VAE Encode, class type: VAEEncode Executing node 4, title: KSampler (Efficient), class type: KSampler (Efficient) Requested to load SDXL Requested to load ControlNet Requested to load ControlNet Loading 3 new models 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:05, 3.34it/s] 10%|█ | 2/20 [00:00<00:05, 3.28it/s] 15%|█▌ | 3/20 [00:00<00:05, 3.28it/s] 20%|██ | 4/20 [00:01<00:04, 3.28it/s] 25%|██▌ | 5/20 [00:01<00:04, 3.29it/s] 30%|███ | 6/20 [00:01<00:04, 3.29it/s] 35%|███▌ | 7/20 [00:02<00:03, 3.30it/s] 40%|████ | 8/20 [00:02<00:03, 3.30it/s] 45%|████▌ | 9/20 [00:02<00:03, 3.30it/s] 50%|█████ | 10/20 [00:03<00:03, 3.29it/s] 55%|█████▌ | 11/20 [00:03<00:02, 3.30it/s] 60%|██████ | 12/20 [00:03<00:02, 3.30it/s] 65%|██████▌ | 13/20 [00:03<00:02, 3.30it/s] 70%|███████ | 14/20 [00:04<00:01, 3.31it/s] 75%|███████▌ | 15/20 [00:04<00:01, 3.30it/s] 80%|████████ | 16/20 [00:04<00:01, 3.31it/s] 85%|████████▌ | 17/20 [00:05<00:00, 3.31it/s] 90%|█████████ | 18/20 [00:05<00:00, 3.32it/s] 95%|█████████▌| 19/20 [00:05<00:00, 3.37it/s] 100%|██████████| 20/20 [00:06<00:00, 3.42it/s] 100%|██████████| 20/20 [00:06<00:00, 3.32it/s] Executing node 5, title: Save Image, class type: SaveImage Prompt executed in 26.70 seconds outputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== Contents of /tmp/outputs: ComfyUI_00001_.png
Prediction
fofr/face-to-many:a07f252aIDfcvuiulb6vdyqeemqdgzqcy37eStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- style
- Clay
- prompt
- winston churchill
- negative_prompt
- black and white
- prompt_strength
- 4.5
- denoising_strength
- 0.66
- instant_id_strength
- 0.8
{ "image": "https://replicate.delivery/pbxt/KWDyrcpmbu3DD1MlB85NKy226V6u5uybwi1O9aya1QWw6ozT/famous-photographers-Yousuf-Karsh-1941-churchill-750x954.jpg", "style": "Clay", "prompt": "winston churchill", "negative_prompt": "black and white", "prompt_strength": 4.5, "denoising_strength": 0.66, "instant_id_strength": 0.8 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", { input: { image: "https://replicate.delivery/pbxt/KWDyrcpmbu3DD1MlB85NKy226V6u5uybwi1O9aya1QWw6ozT/famous-photographers-Yousuf-Karsh-1941-churchill-750x954.jpg", style: "Clay", prompt: "winston churchill", negative_prompt: "black and white", prompt_strength: 4.5, denoising_strength: 0.66, instant_id_strength: 0.8 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", input={ "image": "https://replicate.delivery/pbxt/KWDyrcpmbu3DD1MlB85NKy226V6u5uybwi1O9aya1QWw6ozT/famous-photographers-Yousuf-Karsh-1941-churchill-750x954.jpg", "style": "Clay", "prompt": "winston churchill", "negative_prompt": "black and white", "prompt_strength": 4.5, "denoising_strength": 0.66, "instant_id_strength": 0.8 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/face-to-many 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": "a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", "input": { "image": "https://replicate.delivery/pbxt/KWDyrcpmbu3DD1MlB85NKy226V6u5uybwi1O9aya1QWw6ozT/famous-photographers-Yousuf-Karsh-1941-churchill-750x954.jpg", "style": "Clay", "prompt": "winston churchill", "negative_prompt": "black and white", "prompt_strength": 4.5, "denoising_strength": 0.66, "instant_id_strength": 0.8 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cog
If you don’t have Homebrew, there are other installation options available.
Pull and run fofr/face-to-many using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf \ -i 'image="https://replicate.delivery/pbxt/KWDyrcpmbu3DD1MlB85NKy226V6u5uybwi1O9aya1QWw6ozT/famous-photographers-Yousuf-Karsh-1941-churchill-750x954.jpg"' \ -i 'style="Clay"' \ -i 'prompt="winston churchill"' \ -i 'negative_prompt="black and white"' \ -i 'prompt_strength=4.5' \ -i 'denoising_strength=0.66' \ -i 'instant_id_strength=0.8'
To learn more, take a look at the Cog documentation.
Pull and run fofr/face-to-many using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/KWDyrcpmbu3DD1MlB85NKy226V6u5uybwi1O9aya1QWw6ozT/famous-photographers-Yousuf-Karsh-1941-churchill-750x954.jpg", "style": "Clay", "prompt": "winston churchill", "negative_prompt": "black and white", "prompt_strength": 4.5, "denoising_strength": 0.66, "instant_id_strength": 0.8 } }' \ http://localhost:5000/predictions
Output
{ "completed_at": "2024-03-05T21:05:43.192398Z", "created_at": "2024-03-05T21:05:05.568984Z", "data_removed": false, "error": null, "id": "fcvuiulb6vdyqeemqdgzqcy37e", "input": { "image": "https://replicate.delivery/pbxt/KWDyrcpmbu3DD1MlB85NKy226V6u5uybwi1O9aya1QWw6ozT/famous-photographers-Yousuf-Karsh-1941-churchill-750x954.jpg", "style": "Clay", "prompt": "winston churchill", "negative_prompt": "black and white", "prompt_strength": 4.5, "denoising_strength": 0.66, "instant_id_strength": 0.8 }, "logs": "Random seed set to: 3933738569\nChecking inputs\n✅ /tmp/inputs/input.jpg\n====================================\nRunning workflow\ngot prompt\nExecuting node 22, title: Load Image, class type: LoadImage\nExecuting node 67, title: 🔧 Image Resize, class type: ImageResize+\nExecuting node 3, title: LoRA Stacker, class type: LoRA Stacker\nExecuting node 49, title: AIO Aux Preprocessor, class type: AIO_Preprocessor\nExecuting node 28, title: Control Net Stacker, class type: Control Net Stacker\nExecuting node 2, title: Efficient Loader, class type: Efficient Loader\nRequested to load SDXLClipModel\nLoading 1 new model\n----------------------------------------\n\u001b[36mEfficient Loader Models Cache:\u001b[0m\nCkpt:\n[1] albedobaseXL_v13\nLora:\n[1] base_ckpt: albedobaseXL_v13\nlora(mod,clip): ClayAnimationRedm(1,1)\nExecuting node 41, title: Apply InstantID, class type: ApplyInstantID\nExecuting node 51, title: VAE Encode, class type: VAEEncode\nExecuting node 4, title: KSampler (Efficient), class type: KSampler (Efficient)\nRequested to load SDXL\nRequested to load ControlNet\nRequested to load ControlNet\nLoading 3 new models\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:06, 2.81it/s]\n 10%|█ | 2/20 [00:00<00:06, 2.77it/s]\n 15%|█▌ | 3/20 [00:01<00:06, 2.77it/s]\n 20%|██ | 4/20 [00:01<00:05, 2.79it/s]\n 25%|██▌ | 5/20 [00:01<00:05, 2.78it/s]\n 30%|███ | 6/20 [00:02<00:05, 2.78it/s]\n 35%|███▌ | 7/20 [00:02<00:04, 2.77it/s]\n 40%|████ | 8/20 [00:02<00:04, 2.80it/s]\n 45%|████▌ | 9/20 [00:03<00:03, 2.76it/s]\n 50%|█████ | 10/20 [00:03<00:03, 2.63it/s]\n 55%|█████▌ | 11/20 [00:04<00:03, 2.70it/s]\n 60%|██████ | 12/20 [00:04<00:02, 2.75it/s]\n 65%|██████▌ | 13/20 [00:04<00:02, 2.77it/s]\n 70%|███████ | 14/20 [00:05<00:02, 2.53it/s]\n 75%|███████▌ | 15/20 [00:05<00:01, 2.59it/s]\n 80%|████████ | 16/20 [00:05<00:01, 2.68it/s]\n 85%|████████▌ | 17/20 [00:06<00:01, 2.72it/s]\n 90%|█████████ | 18/20 [00:06<00:00, 2.76it/s]\n 95%|█████████▌| 19/20 [00:06<00:00, 2.84it/s]\n100%|██████████| 20/20 [00:07<00:00, 2.90it/s]\n100%|██████████| 20/20 [00:07<00:00, 2.75it/s]\nExecuting node 5, title: Save Image, class type: SaveImage\nPrompt executed in 35.32 seconds\noutputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nContents of /tmp/outputs:\nComfyUI_00001_.png", "metrics": { "predict_time": 37.564283, "total_time": 37.623414 }, "output": [ "https://replicate.delivery/pbxt/XDe5oQQKRg0bdyAHjo2BOLAUtFDee1lfHQnL5Z4hANzWyO1JB/ComfyUI_00001_.png" ], "started_at": "2024-03-05T21:05:05.628115Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fcvuiulb6vdyqeemqdgzqcy37e", "cancel": "https://api.replicate.com/v1/predictions/fcvuiulb6vdyqeemqdgzqcy37e/cancel" }, "version": "0acc93d551a9de22261d9c4cb3c117e2f1e7337d7f3dc1f162cf2c43a7ad6dfe" }
Generated inRandom seed set to: 3933738569 Checking inputs ✅ /tmp/inputs/input.jpg ==================================== Running workflow got prompt Executing node 22, title: Load Image, class type: LoadImage Executing node 67, title: 🔧 Image Resize, class type: ImageResize+ Executing node 3, title: LoRA Stacker, class type: LoRA Stacker Executing node 49, title: AIO Aux Preprocessor, class type: AIO_Preprocessor Executing node 28, title: Control Net Stacker, class type: Control Net Stacker Executing node 2, title: Efficient Loader, class type: Efficient Loader Requested to load SDXLClipModel Loading 1 new model ---------------------------------------- Efficient Loader Models Cache: Ckpt: [1] albedobaseXL_v13 Lora: [1] base_ckpt: albedobaseXL_v13 lora(mod,clip): ClayAnimationRedm(1,1) Executing node 41, title: Apply InstantID, class type: ApplyInstantID Executing node 51, title: VAE Encode, class type: VAEEncode Executing node 4, title: KSampler (Efficient), class type: KSampler (Efficient) Requested to load SDXL Requested to load ControlNet Requested to load ControlNet Loading 3 new models 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:06, 2.81it/s] 10%|█ | 2/20 [00:00<00:06, 2.77it/s] 15%|█▌ | 3/20 [00:01<00:06, 2.77it/s] 20%|██ | 4/20 [00:01<00:05, 2.79it/s] 25%|██▌ | 5/20 [00:01<00:05, 2.78it/s] 30%|███ | 6/20 [00:02<00:05, 2.78it/s] 35%|███▌ | 7/20 [00:02<00:04, 2.77it/s] 40%|████ | 8/20 [00:02<00:04, 2.80it/s] 45%|████▌ | 9/20 [00:03<00:03, 2.76it/s] 50%|█████ | 10/20 [00:03<00:03, 2.63it/s] 55%|█████▌ | 11/20 [00:04<00:03, 2.70it/s] 60%|██████ | 12/20 [00:04<00:02, 2.75it/s] 65%|██████▌ | 13/20 [00:04<00:02, 2.77it/s] 70%|███████ | 14/20 [00:05<00:02, 2.53it/s] 75%|███████▌ | 15/20 [00:05<00:01, 2.59it/s] 80%|████████ | 16/20 [00:05<00:01, 2.68it/s] 85%|████████▌ | 17/20 [00:06<00:01, 2.72it/s] 90%|█████████ | 18/20 [00:06<00:00, 2.76it/s] 95%|█████████▌| 19/20 [00:06<00:00, 2.84it/s] 100%|██████████| 20/20 [00:07<00:00, 2.90it/s] 100%|██████████| 20/20 [00:07<00:00, 2.75it/s] Executing node 5, title: Save Image, class type: SaveImage Prompt executed in 35.32 seconds outputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== Contents of /tmp/outputs: ComfyUI_00001_.png
Prediction
fofr/face-to-many:a07f252aIDhx2dtilb4zygugq5nljawuavseStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- style
- 3D
- prompt
- a person
- negative_prompt
- prompt_strength
- 4.5
- denoising_strength
- 0.5
- instant_id_strength
- 0.8
{ "image": "https://replicate.delivery/pbxt/KWDkejqLfER3jrroDTUsSvBWFaHtapPxfg4xxZIqYmfh3zXm/Screenshot%202024-02-28%20at%2022.14.00.png", "style": "3D", "prompt": "a person", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.5, "instant_id_strength": 0.8 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", { input: { image: "https://replicate.delivery/pbxt/KWDkejqLfER3jrroDTUsSvBWFaHtapPxfg4xxZIqYmfh3zXm/Screenshot%202024-02-28%20at%2022.14.00.png", style: "3D", prompt: "a person", negative_prompt: "", prompt_strength: 4.5, denoising_strength: 0.5, instant_id_strength: 0.8 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", input={ "image": "https://replicate.delivery/pbxt/KWDkejqLfER3jrroDTUsSvBWFaHtapPxfg4xxZIqYmfh3zXm/Screenshot%202024-02-28%20at%2022.14.00.png", "style": "3D", "prompt": "a person", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.5, "instant_id_strength": 0.8 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/face-to-many 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": "a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", "input": { "image": "https://replicate.delivery/pbxt/KWDkejqLfER3jrroDTUsSvBWFaHtapPxfg4xxZIqYmfh3zXm/Screenshot%202024-02-28%20at%2022.14.00.png", "style": "3D", "prompt": "a person", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.5, "instant_id_strength": 0.8 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cog
If you don’t have Homebrew, there are other installation options available.
Pull and run fofr/face-to-many using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf \ -i 'image="https://replicate.delivery/pbxt/KWDkejqLfER3jrroDTUsSvBWFaHtapPxfg4xxZIqYmfh3zXm/Screenshot%202024-02-28%20at%2022.14.00.png"' \ -i 'style="3D"' \ -i 'prompt="a person"' \ -i 'negative_prompt=""' \ -i 'prompt_strength=4.5' \ -i 'denoising_strength=0.5' \ -i 'instant_id_strength=0.8'
To learn more, take a look at the Cog documentation.
Pull and run fofr/face-to-many using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/KWDkejqLfER3jrroDTUsSvBWFaHtapPxfg4xxZIqYmfh3zXm/Screenshot%202024-02-28%20at%2022.14.00.png", "style": "3D", "prompt": "a person", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.5, "instant_id_strength": 0.8 } }' \ http://localhost:5000/predictions
Output
{ "completed_at": "2024-03-05T20:50:51.217782Z", "created_at": "2024-03-05T20:50:05.520052Z", "data_removed": false, "error": null, "id": "hx2dtilb4zygugq5nljawuavse", "input": { "image": "https://replicate.delivery/pbxt/KWDkejqLfER3jrroDTUsSvBWFaHtapPxfg4xxZIqYmfh3zXm/Screenshot%202024-02-28%20at%2022.14.00.png", "style": "3D", "prompt": "a person", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.5, "instant_id_strength": 0.8 }, "logs": "Random seed set to: 860309018\nChecking inputs\n✅ /tmp/inputs/input.png\n====================================\nRunning workflow\ngot prompt\nExecuting node 22, title: Load Image, class type: LoadImage\nExecuting node 67, title: 🔧 Image Resize, class type: ImageResize+\nExecuting node 3, title: LoRA Stacker, class type: LoRA Stacker\nExecuting node 49, title: AIO Aux Preprocessor, class type: AIO_Preprocessor\nExecuting node 28, title: Control Net Stacker, class type: Control Net Stacker\nExecuting node 2, title: Efficient Loader, class type: Efficient Loader\nRequested to load SDXLClipModel\nLoading 1 new model\n----------------------------------------\n\u001b[36mEfficient Loader Models Cache:\u001b[0m\nCkpt:\n[1] albedobaseXL_v13\nLora:\n[1] base_ckpt: albedobaseXL_v13\nlora(mod,clip): 3DRedmond-3DRenderStyle-3DRenderAF(1,1)\nExecuting node 41, title: Apply InstantID, class type: ApplyInstantID\nExecuting node 51, title: VAE Encode, class type: VAEEncode\nExecuting node 4, title: KSampler (Efficient), class type: KSampler (Efficient)\nRequested to load SDXL\nRequested to load ControlNet\nRequested to load ControlNet\nLoading 3 new models\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:08, 2.25it/s]\n 10%|█ | 2/20 [00:00<00:07, 2.50it/s]\n 15%|█▌ | 3/20 [00:01<00:06, 2.61it/s]\n 20%|██ | 4/20 [00:01<00:06, 2.66it/s]\n 25%|██▌ | 5/20 [00:01<00:05, 2.69it/s]\n 30%|███ | 6/20 [00:02<00:05, 2.70it/s]\n 35%|███▌ | 7/20 [00:02<00:04, 2.71it/s]\n 40%|████ | 8/20 [00:03<00:04, 2.72it/s]\n 45%|████▌ | 9/20 [00:03<00:04, 2.73it/s]\n 50%|█████ | 10/20 [00:03<00:03, 2.73it/s]\n 55%|█████▌ | 11/20 [00:04<00:03, 2.72it/s]\n 60%|██████ | 12/20 [00:04<00:02, 2.74it/s]\n 65%|██████▌ | 13/20 [00:04<00:02, 2.75it/s]\n 70%|███████ | 14/20 [00:05<00:02, 2.76it/s]\n 75%|███████▌ | 15/20 [00:05<00:01, 2.77it/s]\n 80%|████████ | 16/20 [00:05<00:01, 2.77it/s]\n 85%|████████▌ | 17/20 [00:06<00:01, 2.77it/s]\n 90%|█████████ | 18/20 [00:06<00:00, 2.79it/s]\n 95%|█████████▌| 19/20 [00:06<00:00, 2.82it/s]\n100%|██████████| 20/20 [00:07<00:00, 2.86it/s]\n100%|██████████| 20/20 [00:07<00:00, 2.74it/s]\nExecuting node 5, title: Save Image, class type: SaveImage\nPrompt executed in 28.32 seconds\noutputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nContents of /tmp/outputs:\nComfyUI_00001_.png", "metrics": { "predict_time": 30.627804, "total_time": 45.69773 }, "output": [ "https://replicate.delivery/pbxt/GIE7f6hJv8WmaiZJZWDfI3wVnu2BHomyuJikzCjKL9Bpem6kA/ComfyUI_00001_.png" ], "started_at": "2024-03-05T20:50:20.589978Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hx2dtilb4zygugq5nljawuavse", "cancel": "https://api.replicate.com/v1/predictions/hx2dtilb4zygugq5nljawuavse/cancel" }, "version": "0acc93d551a9de22261d9c4cb3c117e2f1e7337d7f3dc1f162cf2c43a7ad6dfe" }
Generated inRandom seed set to: 860309018 Checking inputs ✅ /tmp/inputs/input.png ==================================== Running workflow got prompt Executing node 22, title: Load Image, class type: LoadImage Executing node 67, title: 🔧 Image Resize, class type: ImageResize+ Executing node 3, title: LoRA Stacker, class type: LoRA Stacker Executing node 49, title: AIO Aux Preprocessor, class type: AIO_Preprocessor Executing node 28, title: Control Net Stacker, class type: Control Net Stacker Executing node 2, title: Efficient Loader, class type: Efficient Loader Requested to load SDXLClipModel Loading 1 new model ---------------------------------------- Efficient Loader Models Cache: Ckpt: [1] albedobaseXL_v13 Lora: [1] base_ckpt: albedobaseXL_v13 lora(mod,clip): 3DRedmond-3DRenderStyle-3DRenderAF(1,1) Executing node 41, title: Apply InstantID, class type: ApplyInstantID Executing node 51, title: VAE Encode, class type: VAEEncode Executing node 4, title: KSampler (Efficient), class type: KSampler (Efficient) Requested to load SDXL Requested to load ControlNet Requested to load ControlNet Loading 3 new models 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:08, 2.25it/s] 10%|█ | 2/20 [00:00<00:07, 2.50it/s] 15%|█▌ | 3/20 [00:01<00:06, 2.61it/s] 20%|██ | 4/20 [00:01<00:06, 2.66it/s] 25%|██▌ | 5/20 [00:01<00:05, 2.69it/s] 30%|███ | 6/20 [00:02<00:05, 2.70it/s] 35%|███▌ | 7/20 [00:02<00:04, 2.71it/s] 40%|████ | 8/20 [00:03<00:04, 2.72it/s] 45%|████▌ | 9/20 [00:03<00:04, 2.73it/s] 50%|█████ | 10/20 [00:03<00:03, 2.73it/s] 55%|█████▌ | 11/20 [00:04<00:03, 2.72it/s] 60%|██████ | 12/20 [00:04<00:02, 2.74it/s] 65%|██████▌ | 13/20 [00:04<00:02, 2.75it/s] 70%|███████ | 14/20 [00:05<00:02, 2.76it/s] 75%|███████▌ | 15/20 [00:05<00:01, 2.77it/s] 80%|████████ | 16/20 [00:05<00:01, 2.77it/s] 85%|████████▌ | 17/20 [00:06<00:01, 2.77it/s] 90%|█████████ | 18/20 [00:06<00:00, 2.79it/s] 95%|█████████▌| 19/20 [00:06<00:00, 2.82it/s] 100%|██████████| 20/20 [00:07<00:00, 2.86it/s] 100%|██████████| 20/20 [00:07<00:00, 2.74it/s] Executing node 5, title: Save Image, class type: SaveImage Prompt executed in 28.32 seconds outputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== Contents of /tmp/outputs: ComfyUI_00001_.png
Prediction
fofr/face-to-many:a07f252aIDf4usd7dbkvvfsj5boyz2mu25ouStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- style
- Emoji
- prompt
- a woman
- negative_prompt
- prompt_strength
- 4.5
- denoising_strength
- 0.55
- instant_id_strength
- 0.8
{ "image": "https://replicate.delivery/pbxt/KWQFvR76pjbow6oz6wYHK6QLxN9yGnneN3WMAwtPHnc5KGc4/Screenshot%202024-02-28%20at%2022.14.00.png", "style": "Emoji", "prompt": "a woman", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.55, "instant_id_strength": 0.8 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", { input: { image: "https://replicate.delivery/pbxt/KWQFvR76pjbow6oz6wYHK6QLxN9yGnneN3WMAwtPHnc5KGc4/Screenshot%202024-02-28%20at%2022.14.00.png", style: "Emoji", prompt: "a woman", negative_prompt: "", prompt_strength: 4.5, denoising_strength: 0.55, instant_id_strength: 0.8 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", input={ "image": "https://replicate.delivery/pbxt/KWQFvR76pjbow6oz6wYHK6QLxN9yGnneN3WMAwtPHnc5KGc4/Screenshot%202024-02-28%20at%2022.14.00.png", "style": "Emoji", "prompt": "a woman", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.55, "instant_id_strength": 0.8 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/face-to-many 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": "a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", "input": { "image": "https://replicate.delivery/pbxt/KWQFvR76pjbow6oz6wYHK6QLxN9yGnneN3WMAwtPHnc5KGc4/Screenshot%202024-02-28%20at%2022.14.00.png", "style": "Emoji", "prompt": "a woman", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.55, "instant_id_strength": 0.8 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cog
If you don’t have Homebrew, there are other installation options available.
Pull and run fofr/face-to-many using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf \ -i 'image="https://replicate.delivery/pbxt/KWQFvR76pjbow6oz6wYHK6QLxN9yGnneN3WMAwtPHnc5KGc4/Screenshot%202024-02-28%20at%2022.14.00.png"' \ -i 'style="Emoji"' \ -i 'prompt="a woman"' \ -i 'negative_prompt=""' \ -i 'prompt_strength=4.5' \ -i 'denoising_strength=0.55' \ -i 'instant_id_strength=0.8'
To learn more, take a look at the Cog documentation.
Pull and run fofr/face-to-many using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/KWQFvR76pjbow6oz6wYHK6QLxN9yGnneN3WMAwtPHnc5KGc4/Screenshot%202024-02-28%20at%2022.14.00.png", "style": "Emoji", "prompt": "a woman", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.55, "instant_id_strength": 0.8 } }' \ http://localhost:5000/predictions
Output
{ "completed_at": "2024-03-06T10:28:50.359196Z", "created_at": "2024-03-06T10:28:18.222650Z", "data_removed": false, "error": null, "id": "f4usd7dbkvvfsj5boyz2mu25ou", "input": { "image": "https://replicate.delivery/pbxt/KWQFvR76pjbow6oz6wYHK6QLxN9yGnneN3WMAwtPHnc5KGc4/Screenshot%202024-02-28%20at%2022.14.00.png", "style": "Emoji", "prompt": "a woman", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.55, "instant_id_strength": 0.8 }, "logs": "Random seed set to: 714045300\nChecking inputs\n✅ /tmp/inputs/input.png\n====================================\nRunning workflow\ngot prompt\nExecuting node 22, title: Load Image, class type: LoadImage\nExecuting node 67, title: 🔧 Image Resize, class type: ImageResize+\nExecuting node 3, title: LoRA Stacker, class type: LoRA Stacker\nExecuting node 49, title: AIO Aux Preprocessor, class type: AIO_Preprocessor\nExecuting node 28, title: Control Net Stacker, class type: Control Net Stacker\nExecuting node 2, title: Efficient Loader, class type: Efficient Loader\nRequested to load SDXLClipModel\nLoading 1 new model\n----------------------------------------\n\u001b[36mEfficient Loader Models Cache:\u001b[0m\nCkpt:\n[1] albedobaseXL_v13\nLora:\n[1] base_ckpt: albedobaseXL_v13\nlora(mod,clip): emoji(1,1)\nExecuting node 41, title: Apply InstantID, class type: ApplyInstantID\nExecuting node 51, title: VAE Encode, class type: VAEEncode\nExecuting node 4, title: KSampler (Efficient), class type: KSampler (Efficient)\nRequested to load SDXL\nRequested to load ControlNet\nRequested to load ControlNet\nLoading 3 new models\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:08, 2.18it/s]\n 10%|█ | 2/20 [00:00<00:07, 2.46it/s]\n 15%|█▌ | 3/20 [00:01<00:06, 2.58it/s]\n 20%|██ | 4/20 [00:01<00:06, 2.63it/s]\n 25%|██▌ | 5/20 [00:01<00:05, 2.66it/s]\n 30%|███ | 6/20 [00:02<00:05, 2.69it/s]\n 35%|███▌ | 7/20 [00:02<00:04, 2.70it/s]\n 40%|████ | 8/20 [00:03<00:04, 2.71it/s]\n 45%|████▌ | 9/20 [00:03<00:04, 2.72it/s]\n 50%|█████ | 10/20 [00:03<00:03, 2.73it/s]\n 55%|█████▌ | 11/20 [00:04<00:03, 2.74it/s]\n 60%|██████ | 12/20 [00:04<00:02, 2.75it/s]\n 65%|██████▌ | 13/20 [00:04<00:02, 2.75it/s]\n 70%|███████ | 14/20 [00:05<00:02, 2.74it/s]\n 75%|███████▌ | 15/20 [00:05<00:01, 2.75it/s]\n 80%|████████ | 16/20 [00:05<00:01, 2.75it/s]\n 85%|████████▌ | 17/20 [00:06<00:01, 2.75it/s]\n 90%|█████████ | 18/20 [00:06<00:00, 2.76it/s]\n 95%|█████████▌| 19/20 [00:06<00:00, 2.81it/s]\n100%|██████████| 20/20 [00:07<00:00, 2.85it/s]\n100%|██████████| 20/20 [00:07<00:00, 2.73it/s]\nExecuting node 5, title: Save Image, class type: SaveImage\nPrompt executed in 31.09 seconds\noutputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nContents of /tmp/outputs:\nComfyUI_00001_.png", "metrics": { "predict_time": 32.096187, "total_time": 32.136546 }, "output": [ "https://replicate.delivery/pbxt/wG9k7DqRNv4BPJBJFn37DVdEiXsXmskHMGIcwPE64gVY3XnE/ComfyUI_00001_.png" ], "started_at": "2024-03-06T10:28:18.263009Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/f4usd7dbkvvfsj5boyz2mu25ou", "cancel": "https://api.replicate.com/v1/predictions/f4usd7dbkvvfsj5boyz2mu25ou/cancel" }, "version": "19deaef633fd44776c82edf39fd60e95a7250b8ececf11a725229dc75a81f9ca" }
Generated inRandom seed set to: 714045300 Checking inputs ✅ /tmp/inputs/input.png ==================================== Running workflow got prompt Executing node 22, title: Load Image, class type: LoadImage Executing node 67, title: 🔧 Image Resize, class type: ImageResize+ Executing node 3, title: LoRA Stacker, class type: LoRA Stacker Executing node 49, title: AIO Aux Preprocessor, class type: AIO_Preprocessor Executing node 28, title: Control Net Stacker, class type: Control Net Stacker Executing node 2, title: Efficient Loader, class type: Efficient Loader Requested to load SDXLClipModel Loading 1 new model ---------------------------------------- Efficient Loader Models Cache: Ckpt: [1] albedobaseXL_v13 Lora: [1] base_ckpt: albedobaseXL_v13 lora(mod,clip): emoji(1,1) Executing node 41, title: Apply InstantID, class type: ApplyInstantID Executing node 51, title: VAE Encode, class type: VAEEncode Executing node 4, title: KSampler (Efficient), class type: KSampler (Efficient) Requested to load SDXL Requested to load ControlNet Requested to load ControlNet Loading 3 new models 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:08, 2.18it/s] 10%|█ | 2/20 [00:00<00:07, 2.46it/s] 15%|█▌ | 3/20 [00:01<00:06, 2.58it/s] 20%|██ | 4/20 [00:01<00:06, 2.63it/s] 25%|██▌ | 5/20 [00:01<00:05, 2.66it/s] 30%|███ | 6/20 [00:02<00:05, 2.69it/s] 35%|███▌ | 7/20 [00:02<00:04, 2.70it/s] 40%|████ | 8/20 [00:03<00:04, 2.71it/s] 45%|████▌ | 9/20 [00:03<00:04, 2.72it/s] 50%|█████ | 10/20 [00:03<00:03, 2.73it/s] 55%|█████▌ | 11/20 [00:04<00:03, 2.74it/s] 60%|██████ | 12/20 [00:04<00:02, 2.75it/s] 65%|██████▌ | 13/20 [00:04<00:02, 2.75it/s] 70%|███████ | 14/20 [00:05<00:02, 2.74it/s] 75%|███████▌ | 15/20 [00:05<00:01, 2.75it/s] 80%|████████ | 16/20 [00:05<00:01, 2.75it/s] 85%|████████▌ | 17/20 [00:06<00:01, 2.75it/s] 90%|█████████ | 18/20 [00:06<00:00, 2.76it/s] 95%|█████████▌| 19/20 [00:06<00:00, 2.81it/s] 100%|██████████| 20/20 [00:07<00:00, 2.85it/s] 100%|██████████| 20/20 [00:07<00:00, 2.73it/s] Executing node 5, title: Save Image, class type: SaveImage Prompt executed in 31.09 seconds outputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== Contents of /tmp/outputs: ComfyUI_00001_.png
Prediction
fofr/face-to-many:a07f252aID5avp3blbzkhampuugxxx652ooaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @fofrInput
- style
- Video game
- prompt
- a person in a post apocalyptic war game
- negative_prompt
- prompt_strength
- 4.5
- denoising_strength
- 0.65
- instant_id_strength
- 0.8
{ "image": "https://replicate.delivery/pbxt/KW7EbmXHc5gY6V2HSaxP8nrZxN9OZM8554J6URtdtbg0vZYV/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Video game", "prompt": "a person in a post apocalyptic war game", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", { input: { image: "https://replicate.delivery/pbxt/KW7EbmXHc5gY6V2HSaxP8nrZxN9OZM8554J6URtdtbg0vZYV/MTk4MTczMTkzNzI1Mjg5NjYy.webp", style: "Video game", prompt: "a person in a post apocalyptic war game", negative_prompt: "", prompt_strength: 4.5, denoising_strength: 0.65, instant_id_strength: 0.8 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", input={ "image": "https://replicate.delivery/pbxt/KW7EbmXHc5gY6V2HSaxP8nrZxN9OZM8554J6URtdtbg0vZYV/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Video game", "prompt": "a person in a post apocalyptic war game", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/face-to-many 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": "a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", "input": { "image": "https://replicate.delivery/pbxt/KW7EbmXHc5gY6V2HSaxP8nrZxN9OZM8554J6URtdtbg0vZYV/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Video game", "prompt": "a person in a post apocalyptic war game", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cog
If you don’t have Homebrew, there are other installation options available.
Pull and run fofr/face-to-many using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf \ -i 'image="https://replicate.delivery/pbxt/KW7EbmXHc5gY6V2HSaxP8nrZxN9OZM8554J6URtdtbg0vZYV/MTk4MTczMTkzNzI1Mjg5NjYy.webp"' \ -i 'style="Video game"' \ -i 'prompt="a person in a post apocalyptic war game"' \ -i 'negative_prompt=""' \ -i 'prompt_strength=4.5' \ -i 'denoising_strength=0.65' \ -i 'instant_id_strength=0.8'
To learn more, take a look at the Cog documentation.
Pull and run fofr/face-to-many using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/KW7EbmXHc5gY6V2HSaxP8nrZxN9OZM8554J6URtdtbg0vZYV/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Video game", "prompt": "a person in a post apocalyptic war game", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 } }' \ http://localhost:5000/predictions
Output
{ "completed_at": "2024-03-05T13:44:04.619143Z", "created_at": "2024-03-05T13:43:40.473161Z", "data_removed": false, "error": null, "id": "5avp3blbzkhampuugxxx652ooa", "input": { "image": "https://replicate.delivery/pbxt/KW7EbmXHc5gY6V2HSaxP8nrZxN9OZM8554J6URtdtbg0vZYV/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Video game", "prompt": "a person in a post apocalyptic war game", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 }, "logs": "Random seed set to: 3870084333\nChecking inputs\n✅ /tmp/inputs/input.webp\n====================================\nRunning workflow\ngot prompt\nExecuting node 2, title: Efficient Loader, class type: Efficient Loader\nRequested to load SDXLClipModel\nLoading 1 new model\n----------------------------------------\n\u001b[36mEfficient Loader Models Cache:\u001b[0m\nCkpt:\n[1] albedobaseXL_v13\nLora:\n[1] base_ckpt: albedobaseXL_v13\nlora(mod,clip): PS1Redmond-PS1Game-Playstation1Graphics(1,1)\nExecuting node 41, title: Apply InstantID, class type: ApplyInstantID\nExecuting node 51, title: VAE Encode, class type: VAEEncode\nExecuting node 4, title: KSampler (Efficient), class type: KSampler (Efficient)\nRequested to load SDXL\nRequested to load ControlNet\nRequested to load ControlNet\nLoading 3 new models\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:05, 3.28it/s]\n 10%|█ | 2/20 [00:00<00:05, 3.25it/s]\n 15%|█▌ | 3/20 [00:00<00:05, 3.27it/s]\n 20%|██ | 4/20 [00:01<00:04, 3.27it/s]\n 25%|██▌ | 5/20 [00:01<00:04, 3.27it/s]\n 30%|███ | 6/20 [00:01<00:04, 3.26it/s]\n 35%|███▌ | 7/20 [00:02<00:03, 3.25it/s]\n 40%|████ | 8/20 [00:02<00:03, 3.04it/s]\n 45%|████▌ | 9/20 [00:02<00:03, 3.12it/s]\n 50%|█████ | 10/20 [00:03<00:03, 3.17it/s]\n 55%|█████▌ | 11/20 [00:03<00:02, 3.21it/s]\n 60%|██████ | 12/20 [00:03<00:02, 3.25it/s]\n 65%|██████▌ | 13/20 [00:04<00:02, 3.25it/s]\n 70%|███████ | 14/20 [00:04<00:01, 3.25it/s]\n 75%|███████▌ | 15/20 [00:04<00:01, 3.26it/s]\n 80%|████████ | 16/20 [00:04<00:01, 3.28it/s]\n 85%|████████▌ | 17/20 [00:05<00:00, 3.30it/s]\n 90%|█████████ | 18/20 [00:05<00:00, 3.31it/s]\n 95%|█████████▌| 19/20 [00:05<00:00, 3.35it/s]\n100%|██████████| 20/20 [00:06<00:00, 3.39it/s]\n100%|██████████| 20/20 [00:06<00:00, 3.27it/s]\nExecuting node 5, title: Save Image, class type: SaveImage\nPrompt executed in 21.79 seconds\noutputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nContents of /tmp/outputs:\nComfyUI_00001_.png", "metrics": { "predict_time": 24.107241, "total_time": 24.145982 }, "output": [ "https://replicate.delivery/pbxt/zL6BTWfJOoVpQqxCt6sBmPFY9EBNSAQJ2dM8eLP1wiajONdSA/ComfyUI_00001_.png" ], "started_at": "2024-03-05T13:43:40.511902Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5avp3blbzkhampuugxxx652ooa", "cancel": "https://api.replicate.com/v1/predictions/5avp3blbzkhampuugxxx652ooa/cancel" }, "version": "edc6439ac55af138defbca7c472b38bcdd62c61797e8e0c2fae88696cd8afb25" }
Generated inRandom seed set to: 3870084333 Checking inputs ✅ /tmp/inputs/input.webp ==================================== Running workflow got prompt Executing node 2, title: Efficient Loader, class type: Efficient Loader Requested to load SDXLClipModel Loading 1 new model ---------------------------------------- Efficient Loader Models Cache: Ckpt: [1] albedobaseXL_v13 Lora: [1] base_ckpt: albedobaseXL_v13 lora(mod,clip): PS1Redmond-PS1Game-Playstation1Graphics(1,1) Executing node 41, title: Apply InstantID, class type: ApplyInstantID Executing node 51, title: VAE Encode, class type: VAEEncode Executing node 4, title: KSampler (Efficient), class type: KSampler (Efficient) Requested to load SDXL Requested to load ControlNet Requested to load ControlNet Loading 3 new models 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:05, 3.28it/s] 10%|█ | 2/20 [00:00<00:05, 3.25it/s] 15%|█▌ | 3/20 [00:00<00:05, 3.27it/s] 20%|██ | 4/20 [00:01<00:04, 3.27it/s] 25%|██▌ | 5/20 [00:01<00:04, 3.27it/s] 30%|███ | 6/20 [00:01<00:04, 3.26it/s] 35%|███▌ | 7/20 [00:02<00:03, 3.25it/s] 40%|████ | 8/20 [00:02<00:03, 3.04it/s] 45%|████▌ | 9/20 [00:02<00:03, 3.12it/s] 50%|█████ | 10/20 [00:03<00:03, 3.17it/s] 55%|█████▌ | 11/20 [00:03<00:02, 3.21it/s] 60%|██████ | 12/20 [00:03<00:02, 3.25it/s] 65%|██████▌ | 13/20 [00:04<00:02, 3.25it/s] 70%|███████ | 14/20 [00:04<00:01, 3.25it/s] 75%|███████▌ | 15/20 [00:04<00:01, 3.26it/s] 80%|████████ | 16/20 [00:04<00:01, 3.28it/s] 85%|████████▌ | 17/20 [00:05<00:00, 3.30it/s] 90%|█████████ | 18/20 [00:05<00:00, 3.31it/s] 95%|█████████▌| 19/20 [00:05<00:00, 3.35it/s] 100%|██████████| 20/20 [00:06<00:00, 3.39it/s] 100%|██████████| 20/20 [00:06<00:00, 3.27it/s] Executing node 5, title: Save Image, class type: SaveImage Prompt executed in 21.79 seconds outputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== Contents of /tmp/outputs: ComfyUI_00001_.png
Prediction
fofr/face-to-many:a07f252aID7utmyd3bolce23ss3f7jzlerziStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- style
- Pixels
- prompt
- a person in a post apocalyptic war game
- negative_prompt
- prompt_strength
- 4.5
- denoising_strength
- 0.65
- instant_id_strength
- 0.8
{ "image": "https://replicate.delivery/pbxt/KW7Fjr2bqEGmU0Jd6bCCuuCLTg5Wf1mQNq7yicw9IihsGYGF/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Pixels", "prompt": "a person in a post apocalyptic war game", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", { input: { image: "https://replicate.delivery/pbxt/KW7Fjr2bqEGmU0Jd6bCCuuCLTg5Wf1mQNq7yicw9IihsGYGF/MTk4MTczMTkzNzI1Mjg5NjYy.webp", style: "Pixels", prompt: "a person in a post apocalyptic war game", negative_prompt: "", prompt_strength: 4.5, denoising_strength: 0.65, instant_id_strength: 0.8 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", input={ "image": "https://replicate.delivery/pbxt/KW7Fjr2bqEGmU0Jd6bCCuuCLTg5Wf1mQNq7yicw9IihsGYGF/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Pixels", "prompt": "a person in a post apocalyptic war game", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/face-to-many 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": "a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", "input": { "image": "https://replicate.delivery/pbxt/KW7Fjr2bqEGmU0Jd6bCCuuCLTg5Wf1mQNq7yicw9IihsGYGF/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Pixels", "prompt": "a person in a post apocalyptic war game", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cog
If you don’t have Homebrew, there are other installation options available.
Pull and run fofr/face-to-many using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf \ -i 'image="https://replicate.delivery/pbxt/KW7Fjr2bqEGmU0Jd6bCCuuCLTg5Wf1mQNq7yicw9IihsGYGF/MTk4MTczMTkzNzI1Mjg5NjYy.webp"' \ -i 'style="Pixels"' \ -i 'prompt="a person in a post apocalyptic war game"' \ -i 'negative_prompt=""' \ -i 'prompt_strength=4.5' \ -i 'denoising_strength=0.65' \ -i 'instant_id_strength=0.8'
To learn more, take a look at the Cog documentation.
Pull and run fofr/face-to-many using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/KW7Fjr2bqEGmU0Jd6bCCuuCLTg5Wf1mQNq7yicw9IihsGYGF/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Pixels", "prompt": "a person in a post apocalyptic war game", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 } }' \ http://localhost:5000/predictions
Output
{ "completed_at": "2024-03-05T13:45:16.818653Z", "created_at": "2024-03-05T13:44:52.340070Z", "data_removed": false, "error": null, "id": "7utmyd3bolce23ss3f7jzlerzi", "input": { "image": "https://replicate.delivery/pbxt/KW7Fjr2bqEGmU0Jd6bCCuuCLTg5Wf1mQNq7yicw9IihsGYGF/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Pixels", "prompt": "a person in a post apocalyptic war game", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 }, "logs": "Random seed set to: 2304384884\nChecking inputs\n✅ /tmp/inputs/input.webp\n====================================\nRunning workflow\ngot prompt\nExecuting node 3, title: LoRA Stacker, class type: LoRA Stacker\nExecuting node 2, title: Efficient Loader, class type: Efficient Loader\nRequested to load SDXLClipModel\nLoading 1 new model\n----------------------------------------\n\u001b[36mEfficient Loader Models Cache:\u001b[0m\nCkpt:\n[1] albedobaseXL_v13\nLora:\n[1] base_ckpt: albedobaseXL_v13\nlora(mod,clip): PixelArtRedmond-Lite64(1,1)\nExecuting node 41, title: Apply InstantID, class type: ApplyInstantID\nExecuting node 51, title: VAE Encode, class type: VAEEncode\nExecuting node 4, title: KSampler (Efficient), class type: KSampler (Efficient)\nRequested to load SDXL\nRequested to load ControlNet\nRequested to load ControlNet\nLoading 3 new models\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:05, 3.32it/s]\n 10%|█ | 2/20 [00:00<00:05, 3.28it/s]\n 15%|█▌ | 3/20 [00:00<00:05, 3.29it/s]\n 20%|██ | 4/20 [00:01<00:04, 3.29it/s]\n 25%|██▌ | 5/20 [00:01<00:04, 3.28it/s]\n 30%|███ | 6/20 [00:01<00:04, 3.27it/s]\n 35%|███▌ | 7/20 [00:02<00:03, 3.27it/s]\n 40%|████ | 8/20 [00:02<00:03, 3.22it/s]\n 45%|████▌ | 9/20 [00:02<00:03, 3.25it/s]\n 50%|█████ | 10/20 [00:03<00:03, 3.27it/s]\n 55%|█████▌ | 11/20 [00:03<00:02, 3.27it/s]\n 60%|██████ | 12/20 [00:03<00:02, 3.28it/s]\n 65%|██████▌ | 13/20 [00:03<00:02, 3.28it/s]\n 70%|███████ | 14/20 [00:04<00:01, 3.28it/s]\n 75%|███████▌ | 15/20 [00:04<00:01, 3.29it/s]\n 80%|████████ | 16/20 [00:04<00:01, 3.31it/s]\n 85%|████████▌ | 17/20 [00:05<00:00, 3.32it/s]\n 90%|█████████ | 18/20 [00:05<00:00, 3.32it/s]\n 95%|█████████▌| 19/20 [00:05<00:00, 3.37it/s]\n100%|██████████| 20/20 [00:06<00:00, 3.41it/s]\n100%|██████████| 20/20 [00:06<00:00, 3.31it/s]\nExecuting node 5, title: Save Image, class type: SaveImage\nPrompt executed in 22.24 seconds\noutputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nContents of /tmp/outputs:\nComfyUI_00001_.png", "metrics": { "predict_time": 24.417597, "total_time": 24.478583 }, "output": [ "https://replicate.delivery/pbxt/WMr1CB93FU4EK5vqmTQAeWNhKfnUZ1KsAbuc3MbJeCzXf00JB/ComfyUI_00001_.png" ], "started_at": "2024-03-05T13:44:52.401056Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7utmyd3bolce23ss3f7jzlerzi", "cancel": "https://api.replicate.com/v1/predictions/7utmyd3bolce23ss3f7jzlerzi/cancel" }, "version": "edc6439ac55af138defbca7c472b38bcdd62c61797e8e0c2fae88696cd8afb25" }
Generated inRandom seed set to: 2304384884 Checking inputs ✅ /tmp/inputs/input.webp ==================================== Running workflow got prompt Executing node 3, title: LoRA Stacker, class type: LoRA Stacker Executing node 2, title: Efficient Loader, class type: Efficient Loader Requested to load SDXLClipModel Loading 1 new model ---------------------------------------- Efficient Loader Models Cache: Ckpt: [1] albedobaseXL_v13 Lora: [1] base_ckpt: albedobaseXL_v13 lora(mod,clip): PixelArtRedmond-Lite64(1,1) Executing node 41, title: Apply InstantID, class type: ApplyInstantID Executing node 51, title: VAE Encode, class type: VAEEncode Executing node 4, title: KSampler (Efficient), class type: KSampler (Efficient) Requested to load SDXL Requested to load ControlNet Requested to load ControlNet Loading 3 new models 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:05, 3.32it/s] 10%|█ | 2/20 [00:00<00:05, 3.28it/s] 15%|█▌ | 3/20 [00:00<00:05, 3.29it/s] 20%|██ | 4/20 [00:01<00:04, 3.29it/s] 25%|██▌ | 5/20 [00:01<00:04, 3.28it/s] 30%|███ | 6/20 [00:01<00:04, 3.27it/s] 35%|███▌ | 7/20 [00:02<00:03, 3.27it/s] 40%|████ | 8/20 [00:02<00:03, 3.22it/s] 45%|████▌ | 9/20 [00:02<00:03, 3.25it/s] 50%|█████ | 10/20 [00:03<00:03, 3.27it/s] 55%|█████▌ | 11/20 [00:03<00:02, 3.27it/s] 60%|██████ | 12/20 [00:03<00:02, 3.28it/s] 65%|██████▌ | 13/20 [00:03<00:02, 3.28it/s] 70%|███████ | 14/20 [00:04<00:01, 3.28it/s] 75%|███████▌ | 15/20 [00:04<00:01, 3.29it/s] 80%|████████ | 16/20 [00:04<00:01, 3.31it/s] 85%|████████▌ | 17/20 [00:05<00:00, 3.32it/s] 90%|█████████ | 18/20 [00:05<00:00, 3.32it/s] 95%|█████████▌| 19/20 [00:05<00:00, 3.37it/s] 100%|██████████| 20/20 [00:06<00:00, 3.41it/s] 100%|██████████| 20/20 [00:06<00:00, 3.31it/s] Executing node 5, title: Save Image, class type: SaveImage Prompt executed in 22.24 seconds outputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== Contents of /tmp/outputs: ComfyUI_00001_.png
Prediction
fofr/face-to-many:a07f252aIDpft2dadbcdnh25z5ktctsj3szeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- style
- Clay
- prompt
- a person in a post apocalyptic war game
- negative_prompt
- prompt_strength
- 4.5
- denoising_strength
- 0.65
- instant_id_strength
- 0.8
{ "image": "https://replicate.delivery/pbxt/KW7Getr2zD5ECxySdBZtLmPa322lNkXrpkMdKcmxeaDmq2b1/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Clay", "prompt": "a person in a post apocalyptic war game", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", { input: { image: "https://replicate.delivery/pbxt/KW7Getr2zD5ECxySdBZtLmPa322lNkXrpkMdKcmxeaDmq2b1/MTk4MTczMTkzNzI1Mjg5NjYy.webp", style: "Clay", prompt: "a person in a post apocalyptic war game", negative_prompt: "", prompt_strength: 4.5, denoising_strength: 0.65, instant_id_strength: 0.8 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", input={ "image": "https://replicate.delivery/pbxt/KW7Getr2zD5ECxySdBZtLmPa322lNkXrpkMdKcmxeaDmq2b1/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Clay", "prompt": "a person in a post apocalyptic war game", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/face-to-many 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": "a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", "input": { "image": "https://replicate.delivery/pbxt/KW7Getr2zD5ECxySdBZtLmPa322lNkXrpkMdKcmxeaDmq2b1/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Clay", "prompt": "a person in a post apocalyptic war game", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cog
If you don’t have Homebrew, there are other installation options available.
Pull and run fofr/face-to-many using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf \ -i 'image="https://replicate.delivery/pbxt/KW7Getr2zD5ECxySdBZtLmPa322lNkXrpkMdKcmxeaDmq2b1/MTk4MTczMTkzNzI1Mjg5NjYy.webp"' \ -i 'style="Clay"' \ -i 'prompt="a person in a post apocalyptic war game"' \ -i 'negative_prompt=""' \ -i 'prompt_strength=4.5' \ -i 'denoising_strength=0.65' \ -i 'instant_id_strength=0.8'
To learn more, take a look at the Cog documentation.
Pull and run fofr/face-to-many using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/KW7Getr2zD5ECxySdBZtLmPa322lNkXrpkMdKcmxeaDmq2b1/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Clay", "prompt": "a person in a post apocalyptic war game", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 } }' \ http://localhost:5000/predictions
Output
{ "completed_at": "2024-03-05T13:46:13.813133Z", "created_at": "2024-03-05T13:45:50.761895Z", "data_removed": false, "error": null, "id": "pft2dadbcdnh25z5ktctsj3sze", "input": { "image": "https://replicate.delivery/pbxt/KW7Getr2zD5ECxySdBZtLmPa322lNkXrpkMdKcmxeaDmq2b1/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Clay", "prompt": "a person in a post apocalyptic war game", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 }, "logs": "Random seed set to: 3672888193\nChecking inputs\n✅ /tmp/inputs/input.webp\n====================================\nRunning workflow\ngot prompt\nExecuting node 3, title: LoRA Stacker, class type: LoRA Stacker\nExecuting node 2, title: Efficient Loader, class type: Efficient Loader\nRequested to load SDXLClipModel\nLoading 1 new model\n----------------------------------------\n\u001b[36mEfficient Loader Models Cache:\u001b[0m\nCkpt:\n[1] albedobaseXL_v13\nLora:\n[1] base_ckpt: albedobaseXL_v13\nlora(mod,clip): ClayAnimationRedm(1,1)\nExecuting node 41, title: Apply InstantID, class type: ApplyInstantID\nExecuting node 51, title: VAE Encode, class type: VAEEncode\nExecuting node 4, title: KSampler (Efficient), class type: KSampler (Efficient)\nRequested to load SDXL\nRequested to load ControlNet\nRequested to load ControlNet\nLoading 3 new models\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:05, 3.26it/s]\n 10%|█ | 2/20 [00:00<00:05, 3.26it/s]\n 15%|█▌ | 3/20 [00:00<00:05, 3.28it/s]\n 20%|██ | 4/20 [00:01<00:04, 3.28it/s]\n 25%|██▌ | 5/20 [00:01<00:04, 3.28it/s]\n 30%|███ | 6/20 [00:01<00:04, 3.29it/s]\n 35%|███▌ | 7/20 [00:02<00:03, 3.27it/s]\n 40%|████ | 8/20 [00:02<00:03, 3.27it/s]\n 45%|████▌ | 9/20 [00:02<00:03, 3.29it/s]\n 50%|█████ | 10/20 [00:03<00:03, 3.29it/s]\n 55%|█████▌ | 11/20 [00:03<00:02, 3.29it/s]\n 60%|██████ | 12/20 [00:03<00:02, 3.30it/s]\n 65%|██████▌ | 13/20 [00:03<00:02, 3.30it/s]\n 70%|███████ | 14/20 [00:04<00:01, 3.30it/s]\n 75%|███████▌ | 15/20 [00:04<00:01, 3.30it/s]\n 80%|████████ | 16/20 [00:04<00:01, 3.32it/s]\n 85%|████████▌ | 17/20 [00:05<00:00, 3.32it/s]\n 90%|█████████ | 18/20 [00:05<00:00, 3.33it/s]\n 95%|█████████▌| 19/20 [00:05<00:00, 3.37it/s]\n100%|██████████| 20/20 [00:06<00:00, 3.41it/s]\n100%|██████████| 20/20 [00:06<00:00, 3.31it/s]\nExecuting node 5, title: Save Image, class type: SaveImage\nPrompt executed in 20.70 seconds\noutputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nContents of /tmp/outputs:\nComfyUI_00001_.png", "metrics": { "predict_time": 23.016322, "total_time": 23.051238 }, "output": [ "https://replicate.delivery/pbxt/R1ayGe5efoQbaoRzgDEJdLsIZ20lWRiprvoW1F4uKAZIha6kA/ComfyUI_00001_.png" ], "started_at": "2024-03-05T13:45:50.796811Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pft2dadbcdnh25z5ktctsj3sze", "cancel": "https://api.replicate.com/v1/predictions/pft2dadbcdnh25z5ktctsj3sze/cancel" }, "version": "edc6439ac55af138defbca7c472b38bcdd62c61797e8e0c2fae88696cd8afb25" }
Generated inRandom seed set to: 3672888193 Checking inputs ✅ /tmp/inputs/input.webp ==================================== Running workflow got prompt Executing node 3, title: LoRA Stacker, class type: LoRA Stacker Executing node 2, title: Efficient Loader, class type: Efficient Loader Requested to load SDXLClipModel Loading 1 new model ---------------------------------------- Efficient Loader Models Cache: Ckpt: [1] albedobaseXL_v13 Lora: [1] base_ckpt: albedobaseXL_v13 lora(mod,clip): ClayAnimationRedm(1,1) Executing node 41, title: Apply InstantID, class type: ApplyInstantID Executing node 51, title: VAE Encode, class type: VAEEncode Executing node 4, title: KSampler (Efficient), class type: KSampler (Efficient) Requested to load SDXL Requested to load ControlNet Requested to load ControlNet Loading 3 new models 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:05, 3.26it/s] 10%|█ | 2/20 [00:00<00:05, 3.26it/s] 15%|█▌ | 3/20 [00:00<00:05, 3.28it/s] 20%|██ | 4/20 [00:01<00:04, 3.28it/s] 25%|██▌ | 5/20 [00:01<00:04, 3.28it/s] 30%|███ | 6/20 [00:01<00:04, 3.29it/s] 35%|███▌ | 7/20 [00:02<00:03, 3.27it/s] 40%|████ | 8/20 [00:02<00:03, 3.27it/s] 45%|████▌ | 9/20 [00:02<00:03, 3.29it/s] 50%|█████ | 10/20 [00:03<00:03, 3.29it/s] 55%|█████▌ | 11/20 [00:03<00:02, 3.29it/s] 60%|██████ | 12/20 [00:03<00:02, 3.30it/s] 65%|██████▌ | 13/20 [00:03<00:02, 3.30it/s] 70%|███████ | 14/20 [00:04<00:01, 3.30it/s] 75%|███████▌ | 15/20 [00:04<00:01, 3.30it/s] 80%|████████ | 16/20 [00:04<00:01, 3.32it/s] 85%|████████▌ | 17/20 [00:05<00:00, 3.32it/s] 90%|█████████ | 18/20 [00:05<00:00, 3.33it/s] 95%|█████████▌| 19/20 [00:05<00:00, 3.37it/s] 100%|██████████| 20/20 [00:06<00:00, 3.41it/s] 100%|██████████| 20/20 [00:06<00:00, 3.31it/s] Executing node 5, title: Save Image, class type: SaveImage Prompt executed in 20.70 seconds outputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== Contents of /tmp/outputs: ComfyUI_00001_.png
Prediction
fofr/face-to-many:a07f252aIDetydjtlbt6xnadgk3ocsq6koaiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- style
- 3D
- prompt
- a person
- negative_prompt
- prompt_strength
- 4.5
- denoising_strength
- 0.65
- instant_id_strength
- 0.8
{ "image": "https://replicate.delivery/pbxt/KW7CdCusXMkkOs9bbCGYsInC8EUxlj3yBLxvfW9Fs9FFMZUL/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "3D", "prompt": "a person", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", { input: { image: "https://replicate.delivery/pbxt/KW7CdCusXMkkOs9bbCGYsInC8EUxlj3yBLxvfW9Fs9FFMZUL/MTk4MTczMTkzNzI1Mjg5NjYy.webp", style: "3D", prompt: "a person", negative_prompt: "", prompt_strength: 4.5, denoising_strength: 0.65, instant_id_strength: 0.8 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", input={ "image": "https://replicate.delivery/pbxt/KW7CdCusXMkkOs9bbCGYsInC8EUxlj3yBLxvfW9Fs9FFMZUL/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "3D", "prompt": "a person", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/face-to-many 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": "a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", "input": { "image": "https://replicate.delivery/pbxt/KW7CdCusXMkkOs9bbCGYsInC8EUxlj3yBLxvfW9Fs9FFMZUL/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "3D", "prompt": "a person", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cog
If you don’t have Homebrew, there are other installation options available.
Pull and run fofr/face-to-many using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf \ -i 'image="https://replicate.delivery/pbxt/KW7CdCusXMkkOs9bbCGYsInC8EUxlj3yBLxvfW9Fs9FFMZUL/MTk4MTczMTkzNzI1Mjg5NjYy.webp"' \ -i 'style="3D"' \ -i 'prompt="a person"' \ -i 'negative_prompt=""' \ -i 'prompt_strength=4.5' \ -i 'denoising_strength=0.65' \ -i 'instant_id_strength=0.8'
To learn more, take a look at the Cog documentation.
Pull and run fofr/face-to-many using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/KW7CdCusXMkkOs9bbCGYsInC8EUxlj3yBLxvfW9Fs9FFMZUL/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "3D", "prompt": "a person", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 } }' \ http://localhost:5000/predictions
Output
{ "completed_at": "2024-03-05T13:47:26.687345Z", "created_at": "2024-03-05T13:47:01.816081Z", "data_removed": false, "error": null, "id": "etydjtlbt6xnadgk3ocsq6koai", "input": { "image": "https://replicate.delivery/pbxt/KW7CdCusXMkkOs9bbCGYsInC8EUxlj3yBLxvfW9Fs9FFMZUL/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "3D", "prompt": "a person", "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8 }, "logs": "Random seed set to: 2816490038\nChecking inputs\n✅ /tmp/inputs/input.webp\n====================================\nRunning workflow\ngot prompt\nExecuting node 3, title: LoRA Stacker, class type: LoRA Stacker\nExecuting node 2, title: Efficient Loader, class type: Efficient Loader\nRequested to load SDXLClipModel\nLoading 1 new model\n----------------------------------------\n\u001b[36mEfficient Loader Models Cache:\u001b[0m\nCkpt:\n[1] albedobaseXL_v13\nLora:\n[1] base_ckpt: albedobaseXL_v13\nlora(mod,clip): 3DRedmond-3DRenderStyle-3DRenderAF(1,1)\nExecuting node 41, title: Apply InstantID, class type: ApplyInstantID\nExecuting node 51, title: VAE Encode, class type: VAEEncode\nExecuting node 4, title: KSampler (Efficient), class type: KSampler (Efficient)\nRequested to load SDXL\nRequested to load ControlNet\nRequested to load ControlNet\nLoading 3 new models\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:15, 1.20it/s]\n 10%|█ | 2/20 [00:01<00:09, 1.91it/s]\n 15%|█▌ | 3/20 [00:01<00:07, 2.37it/s]\n 20%|██ | 4/20 [00:01<00:06, 2.66it/s]\n 25%|██▌ | 5/20 [00:02<00:05, 2.62it/s]\n 30%|███ | 6/20 [00:02<00:04, 2.81it/s]\n 35%|███▌ | 7/20 [00:02<00:04, 2.95it/s]\n 40%|████ | 8/20 [00:03<00:03, 3.05it/s]\n 45%|████▌ | 9/20 [00:03<00:03, 3.11it/s]\n 50%|█████ | 10/20 [00:03<00:03, 3.17it/s]\n 55%|█████▌ | 11/20 [00:03<00:02, 3.22it/s]\n 60%|██████ | 12/20 [00:04<00:02, 3.25it/s]\n 65%|██████▌ | 13/20 [00:04<00:02, 3.25it/s]\n 70%|███████ | 14/20 [00:04<00:01, 3.26it/s]\n 75%|███████▌ | 15/20 [00:05<00:01, 3.27it/s]\n 80%|████████ | 16/20 [00:05<00:01, 3.29it/s]\n 85%|████████▌ | 17/20 [00:05<00:00, 3.31it/s]\n 90%|█████████ | 18/20 [00:06<00:00, 3.31it/s]\n 95%|█████████▌| 19/20 [00:06<00:00, 3.36it/s]\n100%|██████████| 20/20 [00:06<00:00, 3.40it/s]\n100%|██████████| 20/20 [00:06<00:00, 3.01it/s]\nExecuting node 5, title: Save Image, class type: SaveImage\nPrompt executed in 22.90 seconds\noutputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nContents of /tmp/outputs:\nComfyUI_00001_.png", "metrics": { "predict_time": 24.833947, "total_time": 24.871264 }, "output": [ "https://replicate.delivery/pbxt/m89okLE27sZ3Elsty6tUFHrSL3J6U5l6OqnCw4HaCAZbUTnE/ComfyUI_00001_.png" ], "started_at": "2024-03-05T13:47:01.853398Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/etydjtlbt6xnadgk3ocsq6koai", "cancel": "https://api.replicate.com/v1/predictions/etydjtlbt6xnadgk3ocsq6koai/cancel" }, "version": "edc6439ac55af138defbca7c472b38bcdd62c61797e8e0c2fae88696cd8afb25" }
Generated inRandom seed set to: 2816490038 Checking inputs ✅ /tmp/inputs/input.webp ==================================== Running workflow got prompt Executing node 3, title: LoRA Stacker, class type: LoRA Stacker Executing node 2, title: Efficient Loader, class type: Efficient Loader Requested to load SDXLClipModel Loading 1 new model ---------------------------------------- Efficient Loader Models Cache: Ckpt: [1] albedobaseXL_v13 Lora: [1] base_ckpt: albedobaseXL_v13 lora(mod,clip): 3DRedmond-3DRenderStyle-3DRenderAF(1,1) Executing node 41, title: Apply InstantID, class type: ApplyInstantID Executing node 51, title: VAE Encode, class type: VAEEncode Executing node 4, title: KSampler (Efficient), class type: KSampler (Efficient) Requested to load SDXL Requested to load ControlNet Requested to load ControlNet Loading 3 new models 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:15, 1.20it/s] 10%|█ | 2/20 [00:01<00:09, 1.91it/s] 15%|█▌ | 3/20 [00:01<00:07, 2.37it/s] 20%|██ | 4/20 [00:01<00:06, 2.66it/s] 25%|██▌ | 5/20 [00:02<00:05, 2.62it/s] 30%|███ | 6/20 [00:02<00:04, 2.81it/s] 35%|███▌ | 7/20 [00:02<00:04, 2.95it/s] 40%|████ | 8/20 [00:03<00:03, 3.05it/s] 45%|████▌ | 9/20 [00:03<00:03, 3.11it/s] 50%|█████ | 10/20 [00:03<00:03, 3.17it/s] 55%|█████▌ | 11/20 [00:03<00:02, 3.22it/s] 60%|██████ | 12/20 [00:04<00:02, 3.25it/s] 65%|██████▌ | 13/20 [00:04<00:02, 3.25it/s] 70%|███████ | 14/20 [00:04<00:01, 3.26it/s] 75%|███████▌ | 15/20 [00:05<00:01, 3.27it/s] 80%|████████ | 16/20 [00:05<00:01, 3.29it/s] 85%|████████▌ | 17/20 [00:05<00:00, 3.31it/s] 90%|█████████ | 18/20 [00:06<00:00, 3.31it/s] 95%|█████████▌| 19/20 [00:06<00:00, 3.36it/s] 100%|██████████| 20/20 [00:06<00:00, 3.40it/s] 100%|██████████| 20/20 [00:06<00:00, 3.01it/s] Executing node 5, title: Save Image, class type: SaveImage Prompt executed in 22.90 seconds outputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== Contents of /tmp/outputs: ComfyUI_00001_.png
Prediction
fofr/face-to-many:a07f252aInput
- style
- Clay
- prompt
- a person in a post apocalyptic war game
- lora_scale
- 1
- negative_prompt
- prompt_strength
- 4.5
- denoising_strength
- 0.65
- instant_id_strength
- 0.8
- control_depth_strength
- 0.8
{ "image": "https://replicate.delivery/pbxt/KW7Getr2zD5ECxySdBZtLmPa322lNkXrpkMdKcmxeaDmq2b1/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Clay", "prompt": "a person in a post apocalyptic war game", "lora_scale": 1, "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8, "control_depth_strength": 0.8 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", { input: { image: "https://replicate.delivery/pbxt/KW7Getr2zD5ECxySdBZtLmPa322lNkXrpkMdKcmxeaDmq2b1/MTk4MTczMTkzNzI1Mjg5NjYy.webp", style: "Clay", prompt: "a person in a post apocalyptic war game", lora_scale: 1, negative_prompt: "", prompt_strength: 4.5, denoising_strength: 0.65, instant_id_strength: 0.8, control_depth_strength: 0.8 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/face-to-many using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/face-to-many:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", input={ "image": "https://replicate.delivery/pbxt/KW7Getr2zD5ECxySdBZtLmPa322lNkXrpkMdKcmxeaDmq2b1/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Clay", "prompt": "a person in a post apocalyptic war game", "lora_scale": 1, "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8, "control_depth_strength": 0.8 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/face-to-many 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": "a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf", "input": { "image": "https://replicate.delivery/pbxt/KW7Getr2zD5ECxySdBZtLmPa322lNkXrpkMdKcmxeaDmq2b1/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Clay", "prompt": "a person in a post apocalyptic war game", "lora_scale": 1, "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8, "control_depth_strength": 0.8 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cog
If you don’t have Homebrew, there are other installation options available.
Pull and run fofr/face-to-many using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf \ -i 'image="https://replicate.delivery/pbxt/KW7Getr2zD5ECxySdBZtLmPa322lNkXrpkMdKcmxeaDmq2b1/MTk4MTczMTkzNzI1Mjg5NjYy.webp"' \ -i 'style="Clay"' \ -i 'prompt="a person in a post apocalyptic war game"' \ -i 'lora_scale=1' \ -i 'negative_prompt=""' \ -i 'prompt_strength=4.5' \ -i 'denoising_strength=0.65' \ -i 'instant_id_strength=0.8' \ -i 'control_depth_strength=0.8'
To learn more, take a look at the Cog documentation.
Pull and run fofr/face-to-many using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/fofr/face-to-many@sha256:a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/KW7Getr2zD5ECxySdBZtLmPa322lNkXrpkMdKcmxeaDmq2b1/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Clay", "prompt": "a person in a post apocalyptic war game", "lora_scale": 1, "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8, "control_depth_strength": 0.8 } }' \ http://localhost:5000/predictions
Output
{ "completed_at": "2024-11-28T11:16:48.998121Z", "created_at": "2024-11-28T11:16:37.648000Z", "data_removed": false, "error": null, "id": "3hvtjcxwj1rme0cke9y8vmxrd0", "input": { "image": "https://replicate.delivery/pbxt/KW7Getr2zD5ECxySdBZtLmPa322lNkXrpkMdKcmxeaDmq2b1/MTk4MTczMTkzNzI1Mjg5NjYy.webp", "style": "Clay", "prompt": "a person in a post apocalyptic war game", "lora_scale": 1, "negative_prompt": "", "prompt_strength": 4.5, "denoising_strength": 0.65, "instant_id_strength": 0.8, "control_depth_strength": 0.8 }, "logs": "Random seed set to: 2815191178\nChecking inputs\n✅ /tmp/inputs/input.webp\n====================================\nRunning workflow\ngot prompt\nExecuting node 22, title: Load Image, class type: LoadImage\nExecuting node 67, title: 🔧 Image Resize, class type: ImageResize+\nExecuting node 3, title: LoRA Stacker, class type: LoRA Stacker\nExecuting node 49, title: AIO Aux Preprocessor, class type: AIO_Preprocessor\nExecuting node 28, title: Control Net Stacker, class type: Control Net Stacker\nExecuting node 2, title: Efficient Loader, class type: Efficient Loader\nRequested to load SDXLClipModel\nLoading 1 new model\n----------------------------------------\n\u001b[36mEfficient Loader Models Cache:\u001b[0m\nCkpt:\n[1] albedobaseXL_v13\nLora:\n[1] base_ckpt: albedobaseXL_v13\nlora(mod,clip): ClayAnimationRedm(1.0,1.0)\nExecuting node 41, title: Apply InstantID, class type: ApplyInstantID\nExecuting node 51, title: VAE Encode, class type: VAEEncode\nExecuting node 4, title: KSampler (Efficient), class type: KSampler (Efficient)\nRequested to load SDXL\nRequested to load ControlNet\nRequested to load ControlNet\nLoading 3 new models\n 0%| | 0/17 [00:00<?, ?it/s]\n 6%|▌ | 1/17 [00:00<00:02, 6.37it/s]\n 12%|█▏ | 2/17 [00:00<00:02, 6.37it/s]\n 18%|█▊ | 3/17 [00:00<00:02, 6.36it/s]\n 24%|██▎ | 4/17 [00:00<00:02, 6.37it/s]\n 29%|██▉ | 5/17 [00:00<00:01, 6.36it/s]\n 35%|███▌ | 6/17 [00:00<00:01, 6.36it/s]\n 41%|████ | 7/17 [00:01<00:01, 6.36it/s]\n 47%|████▋ | 8/17 [00:01<00:01, 6.34it/s]\n 53%|█████▎ | 9/17 [00:01<00:01, 6.35it/s]\n 59%|█████▉ | 10/17 [00:01<00:01, 6.36it/s]\n 65%|██████▍ | 11/17 [00:01<00:00, 6.37it/s]\n 71%|███████ | 12/17 [00:01<00:00, 6.37it/s]\n 76%|███████▋ | 13/17 [00:02<00:00, 6.36it/s]\n 82%|████████▏ | 14/17 [00:02<00:00, 6.37it/s]\n 88%|████████▊ | 15/17 [00:02<00:00, 6.36it/s]\n 94%|█████████▍| 16/17 [00:02<00:00, 6.38it/s]\n100%|██████████| 17/17 [00:02<00:00, 6.41it/s]\n100%|██████████| 17/17 [00:02<00:00, 6.37it/s]\nExecuting node 5, title: Save Image, class type: SaveImage\nPrompt executed in 6.08 seconds\noutputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nContents of /tmp/outputs:\nComfyUI_00001_.png", "metrics": { "predict_time": 6.523334466, "total_time": 11.350121 }, "output": [ "https://replicate.delivery/xezq/QYCNMyAE6C7fd6GXc5Uy9KfqYKVTUfKpuGePXSFf2qLCkBseE/ComfyUI_00001_.png" ], "started_at": "2024-11-28T11:16:42.474787Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3hvtjcxwj1rme0cke9y8vmxrd0", "cancel": "https://api.replicate.com/v1/predictions/3hvtjcxwj1rme0cke9y8vmxrd0/cancel" }, "version": "a07f252abbbd832009640b27f063ea52d87d7a23a185ca165bec23b5adc8deaf" }
Generated inRandom seed set to: 2815191178 Checking inputs ✅ /tmp/inputs/input.webp ==================================== Running workflow got prompt Executing node 22, title: Load Image, class type: LoadImage Executing node 67, title: 🔧 Image Resize, class type: ImageResize+ Executing node 3, title: LoRA Stacker, class type: LoRA Stacker Executing node 49, title: AIO Aux Preprocessor, class type: AIO_Preprocessor Executing node 28, title: Control Net Stacker, class type: Control Net Stacker Executing node 2, title: Efficient Loader, class type: Efficient Loader Requested to load SDXLClipModel Loading 1 new model ---------------------------------------- Efficient Loader Models Cache: Ckpt: [1] albedobaseXL_v13 Lora: [1] base_ckpt: albedobaseXL_v13 lora(mod,clip): ClayAnimationRedm(1.0,1.0) Executing node 41, title: Apply InstantID, class type: ApplyInstantID Executing node 51, title: VAE Encode, class type: VAEEncode Executing node 4, title: KSampler (Efficient), class type: KSampler (Efficient) Requested to load SDXL Requested to load ControlNet Requested to load ControlNet Loading 3 new models 0%| | 0/17 [00:00<?, ?it/s] 6%|▌ | 1/17 [00:00<00:02, 6.37it/s] 12%|█▏ | 2/17 [00:00<00:02, 6.37it/s] 18%|█▊ | 3/17 [00:00<00:02, 6.36it/s] 24%|██▎ | 4/17 [00:00<00:02, 6.37it/s] 29%|██▉ | 5/17 [00:00<00:01, 6.36it/s] 35%|███▌ | 6/17 [00:00<00:01, 6.36it/s] 41%|████ | 7/17 [00:01<00:01, 6.36it/s] 47%|████▋ | 8/17 [00:01<00:01, 6.34it/s] 53%|█████▎ | 9/17 [00:01<00:01, 6.35it/s] 59%|█████▉ | 10/17 [00:01<00:01, 6.36it/s] 65%|██████▍ | 11/17 [00:01<00:00, 6.37it/s] 71%|███████ | 12/17 [00:01<00:00, 6.37it/s] 76%|███████▋ | 13/17 [00:02<00:00, 6.36it/s] 82%|████████▏ | 14/17 [00:02<00:00, 6.37it/s] 88%|████████▊ | 15/17 [00:02<00:00, 6.36it/s] 94%|█████████▍| 16/17 [00:02<00:00, 6.38it/s] 100%|██████████| 17/17 [00:02<00:00, 6.41it/s] 100%|██████████| 17/17 [00:02<00:00, 6.37it/s] Executing node 5, title: Save Image, class type: SaveImage Prompt executed in 6.08 seconds outputs: {'4': {'images': []}, '5': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== Contents of /tmp/outputs: ComfyUI_00001_.png
Want to make some of these yourself?
Run this model