fofr
/
cinematic-redmond
A cinematic model fine-tuned on SDXL
- Public
- 6.3K runs
-
L40S
- Paper
Prediction
fofr/cinematic-redmond:c798f0f5IDhkhurctb2uvpuoxg4u2lycarm4StatusSucceededSourceWebHardwareA40Total durationCreatedInput
- steps
- 17
- width
- 1640
- height
- 744
- prompt
- A high-speed, cinematic film still of an intergalactic bounty hunter in hot pursuit of their target, the frame alive with a kinetic, adrenaline-fueled energy as the two combatants weave through the hazardous terrain of an alien world.
- scheduler
- karras
- num_outputs
- 1
- sampler_name
- dpmpp_2m_sde_gpu
- guidance_scale
- 6
- negative_prompt
- ugly, disfigured, low quality, blurry
{ "steps": 17, "width": 1640, "height": 744, "prompt": "A high-speed, cinematic film still of an intergalactic bounty hunter in hot pursuit of their target, the frame alive with a kinetic, adrenaline-fueled energy as the two combatants weave through the hazardous terrain of an alien world.", "scheduler": "karras", "num_outputs": 1, "sampler_name": "dpmpp_2m_sde_gpu", "guidance_scale": 6, "negative_prompt": "ugly, disfigured, low quality, blurry" }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/cinematic-redmond using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/cinematic-redmond:c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18", { input: { steps: 17, width: 1640, height: 744, prompt: "A high-speed, cinematic film still of an intergalactic bounty hunter in hot pursuit of their target, the frame alive with a kinetic, adrenaline-fueled energy as the two combatants weave through the hazardous terrain of an alien world.", scheduler: "karras", num_outputs: 1, sampler_name: "dpmpp_2m_sde_gpu", guidance_scale: 6, negative_prompt: "ugly, disfigured, low quality, blurry" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/cinematic-redmond using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/cinematic-redmond:c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18", input={ "steps": 17, "width": 1640, "height": 744, "prompt": "A high-speed, cinematic film still of an intergalactic bounty hunter in hot pursuit of their target, the frame alive with a kinetic, adrenaline-fueled energy as the two combatants weave through the hazardous terrain of an alien world.", "scheduler": "karras", "num_outputs": 1, "sampler_name": "dpmpp_2m_sde_gpu", "guidance_scale": 6, "negative_prompt": "ugly, disfigured, low quality, blurry" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/cinematic-redmond 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": "c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18", "input": { "steps": 17, "width": 1640, "height": 744, "prompt": "A high-speed, cinematic film still of an intergalactic bounty hunter in hot pursuit of their target, the frame alive with a kinetic, adrenaline-fueled energy as the two combatants weave through the hazardous terrain of an alien world.", "scheduler": "karras", "num_outputs": 1, "sampler_name": "dpmpp_2m_sde_gpu", "guidance_scale": 6, "negative_prompt": "ugly, disfigured, low quality, blurry" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/fofr/cinematic-redmond@sha256:c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18 \ -i 'steps=17' \ -i 'width=1640' \ -i 'height=744' \ -i 'prompt="A high-speed, cinematic film still of an intergalactic bounty hunter in hot pursuit of their target, the frame alive with a kinetic, adrenaline-fueled energy as the two combatants weave through the hazardous terrain of an alien world."' \ -i 'scheduler="karras"' \ -i 'num_outputs=1' \ -i 'sampler_name="dpmpp_2m_sde_gpu"' \ -i 'guidance_scale=6' \ -i 'negative_prompt="ugly, disfigured, low quality, blurry"'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/fofr/cinematic-redmond@sha256:c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "steps": 17, "width": 1640, "height": 744, "prompt": "A high-speed, cinematic film still of an intergalactic bounty hunter in hot pursuit of their target, the frame alive with a kinetic, adrenaline-fueled energy as the two combatants weave through the hazardous terrain of an alien world.", "scheduler": "karras", "num_outputs": 1, "sampler_name": "dpmpp_2m_sde_gpu", "guidance_scale": 6, "negative_prompt": "ugly, disfigured, low quality, blurry" } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-03-22T21:31:41.467265Z", "created_at": "2024-03-22T21:31:36.200027Z", "data_removed": false, "error": null, "id": "hkhurctb2uvpuoxg4u2lycarm4", "input": { "steps": 17, "width": 1640, "height": 744, "prompt": "A high-speed, cinematic film still of an intergalactic bounty hunter in hot pursuit of their target, the frame alive with a kinetic, adrenaline-fueled energy as the two combatants weave through the hazardous terrain of an alien world.", "scheduler": "karras", "num_outputs": 1, "sampler_name": "dpmpp_2m_sde_gpu", "guidance_scale": 6, "negative_prompt": "ugly, disfigured, low quality, blurry" }, "logs": "Random seed set to: 2144351484\n{'3': {'inputs': {'seed': 2144351484, 'steps': 17, 'cfg': 6.0, 'sampler_name': 'dpmpp_2m_sde_gpu', 'scheduler': 'karras', 'denoise': 1, 'model': ['4', 0], 'positive': ['6', 0], 'negative': ['7', 0], 'latent_image': ['5', 0]}, 'class_type': 'KSampler', '_meta': {'title': 'KSampler'}}, '4': {'inputs': {'ckpt_name': 'CinematicRedmond.safetensors'}, 'class_type': 'CheckpointLoaderSimple', '_meta': {'title': 'Load Checkpoint'}}, '5': {'inputs': {'width': 1640, 'height': 744, 'batch_size': 1}, 'class_type': 'EmptyLatentImage', '_meta': {'title': 'Empty Latent Image'}}, '6': {'inputs': {'text': 'A high-speed, cinematic film still of an intergalactic bounty hunter in hot pursuit of their target, the frame alive with a kinetic, adrenaline-fueled energy as the two combatants weave through the hazardous terrain of an alien world.', 'clip': ['4', 1]}, 'class_type': 'CLIPTextEncode', '_meta': {'title': 'CLIP Text Encode (Prompt)'}}, '7': {'inputs': {'text': 'ugly, disfigured, low quality, blurry', 'clip': ['4', 1]}, 'class_type': 'CLIPTextEncode', '_meta': {'title': 'CLIP Text Encode (Prompt)'}}, '8': {'inputs': {'samples': ['3', 0], 'vae': ['4', 2]}, 'class_type': 'VAEDecode', '_meta': {'title': 'VAE Decode'}}, '9': {'inputs': {'filename_prefix': 'ComfyUI', 'images': ['8', 0]}, 'class_type': 'SaveImage', '_meta': {'title': 'Save Image'}}}\nRunning workflow\ngot prompt\nExecuting node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nExecuting node 3, title: KSampler, class type: KSampler\n 0%| | 0/17 [00:00<?, ?it/s]\n 6%|▌ | 1/17 [00:00<00:03, 4.35it/s]\n 12%|█▏ | 2/17 [00:00<00:03, 4.31it/s]\n 18%|█▊ | 3/17 [00:00<00:03, 4.29it/s]\n 24%|██▎ | 4/17 [00:00<00:03, 4.28it/s]\n 29%|██▉ | 5/17 [00:01<00:02, 4.27it/s]\n 35%|███▌ | 6/17 [00:01<00:02, 4.26it/s]\n 41%|████ | 7/17 [00:01<00:02, 4.27it/s]\n 47%|████▋ | 8/17 [00:01<00:02, 4.27it/s]\n 53%|█████▎ | 9/17 [00:02<00:01, 4.27it/s]\n 59%|█████▉ | 10/17 [00:02<00:01, 4.27it/s]\n 65%|██████▍ | 11/17 [00:02<00:01, 4.27it/s]\n 71%|███████ | 12/17 [00:02<00:01, 4.28it/s]\n 76%|███████▋ | 13/17 [00:03<00:00, 4.27it/s]\n 82%|████████▏ | 14/17 [00:03<00:00, 4.27it/s]\n 88%|████████▊ | 15/17 [00:03<00:00, 4.28it/s]\n 94%|█████████▍| 16/17 [00:03<00:00, 4.29it/s]\n100%|██████████| 17/17 [00:03<00:00, 4.31it/s]\n100%|██████████| 17/17 [00:03<00:00, 4.28it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 4.47 seconds\noutputs: {'9': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nContents of /tmp/outputs:\nComfyUI_00001_.png", "metrics": { "predict_time": 5.255788, "total_time": 5.267238 }, "output": [ "https://replicate.delivery/pbxt/uh3CSDEp0T6mClyNAeXi0QeBXknxAQwsWzxCIM4e8BN4V1FlA/ComfyUI_00001_.png" ], "started_at": "2024-03-22T21:31:36.211477Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hkhurctb2uvpuoxg4u2lycarm4", "cancel": "https://api.replicate.com/v1/predictions/hkhurctb2uvpuoxg4u2lycarm4/cancel" }, "version": "c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18" }Generated inRandom seed set to: 2144351484 {'3': {'inputs': {'seed': 2144351484, 'steps': 17, 'cfg': 6.0, 'sampler_name': 'dpmpp_2m_sde_gpu', 'scheduler': 'karras', 'denoise': 1, 'model': ['4', 0], 'positive': ['6', 0], 'negative': ['7', 0], 'latent_image': ['5', 0]}, 'class_type': 'KSampler', '_meta': {'title': 'KSampler'}}, '4': {'inputs': {'ckpt_name': 'CinematicRedmond.safetensors'}, 'class_type': 'CheckpointLoaderSimple', '_meta': {'title': 'Load Checkpoint'}}, '5': {'inputs': {'width': 1640, 'height': 744, 'batch_size': 1}, 'class_type': 'EmptyLatentImage', '_meta': {'title': 'Empty Latent Image'}}, '6': {'inputs': {'text': 'A high-speed, cinematic film still of an intergalactic bounty hunter in hot pursuit of their target, the frame alive with a kinetic, adrenaline-fueled energy as the two combatants weave through the hazardous terrain of an alien world.', 'clip': ['4', 1]}, 'class_type': 'CLIPTextEncode', '_meta': {'title': 'CLIP Text Encode (Prompt)'}}, '7': {'inputs': {'text': 'ugly, disfigured, low quality, blurry', 'clip': ['4', 1]}, 'class_type': 'CLIPTextEncode', '_meta': {'title': 'CLIP Text Encode (Prompt)'}}, '8': {'inputs': {'samples': ['3', 0], 'vae': ['4', 2]}, 'class_type': 'VAEDecode', '_meta': {'title': 'VAE Decode'}}, '9': {'inputs': {'filename_prefix': 'ComfyUI', 'images': ['8', 0]}, 'class_type': 'SaveImage', '_meta': {'title': 'Save Image'}}} Running workflow got prompt Executing node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode Executing node 3, title: KSampler, class type: KSampler 0%| | 0/17 [00:00<?, ?it/s] 6%|▌ | 1/17 [00:00<00:03, 4.35it/s] 12%|█▏ | 2/17 [00:00<00:03, 4.31it/s] 18%|█▊ | 3/17 [00:00<00:03, 4.29it/s] 24%|██▎ | 4/17 [00:00<00:03, 4.28it/s] 29%|██▉ | 5/17 [00:01<00:02, 4.27it/s] 35%|███▌ | 6/17 [00:01<00:02, 4.26it/s] 41%|████ | 7/17 [00:01<00:02, 4.27it/s] 47%|████▋ | 8/17 [00:01<00:02, 4.27it/s] 53%|█████▎ | 9/17 [00:02<00:01, 4.27it/s] 59%|█████▉ | 10/17 [00:02<00:01, 4.27it/s] 65%|██████▍ | 11/17 [00:02<00:01, 4.27it/s] 71%|███████ | 12/17 [00:02<00:01, 4.28it/s] 76%|███████▋ | 13/17 [00:03<00:00, 4.27it/s] 82%|████████▏ | 14/17 [00:03<00:00, 4.27it/s] 88%|████████▊ | 15/17 [00:03<00:00, 4.28it/s] 94%|█████████▍| 16/17 [00:03<00:00, 4.29it/s] 100%|██████████| 17/17 [00:03<00:00, 4.31it/s] 100%|██████████| 17/17 [00:03<00:00, 4.28it/s] Executing node 8, title: VAE Decode, class type: VAEDecode Executing node 9, title: Save Image, class type: SaveImage Prompt executed in 4.47 seconds outputs: {'9': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== Contents of /tmp/outputs: ComfyUI_00001_.pngPrediction
fofr/cinematic-redmond:c798f0f5IDmw3mijlbojaeps6bh5vc3mliduStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- steps
- 17
- width
- 1640
- height
- 744
- prompt
- A film still showcasing a serene forest clearing, dappled sunlight filtering through the trees, as a solitary figure stands in quiet contemplation, immersed in the beauty and tranquility of nature
- scheduler
- karras
- num_outputs
- 1
- sampler_name
- dpmpp_2m_sde_gpu
- guidance_scale
- 6
- negative_prompt
- ugly, disfigured, low quality, blurry
{ "steps": 17, "width": 1640, "height": 744, "prompt": "A film still showcasing a serene forest clearing, dappled sunlight filtering through the trees, as a solitary figure stands in quiet contemplation, immersed in the beauty and tranquility of nature", "scheduler": "karras", "num_outputs": 1, "sampler_name": "dpmpp_2m_sde_gpu", "guidance_scale": 6, "negative_prompt": "ugly, disfigured, low quality, blurry" }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/cinematic-redmond using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/cinematic-redmond:c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18", { input: { steps: 17, width: 1640, height: 744, prompt: "A film still showcasing a serene forest clearing, dappled sunlight filtering through the trees, as a solitary figure stands in quiet contemplation, immersed in the beauty and tranquility of nature", scheduler: "karras", num_outputs: 1, sampler_name: "dpmpp_2m_sde_gpu", guidance_scale: 6, negative_prompt: "ugly, disfigured, low quality, blurry" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/cinematic-redmond using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/cinematic-redmond:c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18", input={ "steps": 17, "width": 1640, "height": 744, "prompt": "A film still showcasing a serene forest clearing, dappled sunlight filtering through the trees, as a solitary figure stands in quiet contemplation, immersed in the beauty and tranquility of nature", "scheduler": "karras", "num_outputs": 1, "sampler_name": "dpmpp_2m_sde_gpu", "guidance_scale": 6, "negative_prompt": "ugly, disfigured, low quality, blurry" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/cinematic-redmond 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": "c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18", "input": { "steps": 17, "width": 1640, "height": 744, "prompt": "A film still showcasing a serene forest clearing, dappled sunlight filtering through the trees, as a solitary figure stands in quiet contemplation, immersed in the beauty and tranquility of nature", "scheduler": "karras", "num_outputs": 1, "sampler_name": "dpmpp_2m_sde_gpu", "guidance_scale": 6, "negative_prompt": "ugly, disfigured, low quality, blurry" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/fofr/cinematic-redmond@sha256:c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18 \ -i 'steps=17' \ -i 'width=1640' \ -i 'height=744' \ -i 'prompt="A film still showcasing a serene forest clearing, dappled sunlight filtering through the trees, as a solitary figure stands in quiet contemplation, immersed in the beauty and tranquility of nature"' \ -i 'scheduler="karras"' \ -i 'num_outputs=1' \ -i 'sampler_name="dpmpp_2m_sde_gpu"' \ -i 'guidance_scale=6' \ -i 'negative_prompt="ugly, disfigured, low quality, blurry"'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/fofr/cinematic-redmond@sha256:c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "steps": 17, "width": 1640, "height": 744, "prompt": "A film still showcasing a serene forest clearing, dappled sunlight filtering through the trees, as a solitary figure stands in quiet contemplation, immersed in the beauty and tranquility of nature", "scheduler": "karras", "num_outputs": 1, "sampler_name": "dpmpp_2m_sde_gpu", "guidance_scale": 6, "negative_prompt": "ugly, disfigured, low quality, blurry" } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-03-22T21:32:28.445486Z", "created_at": "2024-03-22T21:32:23.168720Z", "data_removed": false, "error": null, "id": "mw3mijlbojaeps6bh5vc3mlidu", "input": { "steps": 17, "width": 1640, "height": 744, "prompt": "A film still showcasing a serene forest clearing, dappled sunlight filtering through the trees, as a solitary figure stands in quiet contemplation, immersed in the beauty and tranquility of nature", "scheduler": "karras", "num_outputs": 1, "sampler_name": "dpmpp_2m_sde_gpu", "guidance_scale": 6, "negative_prompt": "ugly, disfigured, low quality, blurry" }, "logs": "Random seed set to: 1709802865\n{'3': {'inputs': {'seed': 1709802865, 'steps': 17, 'cfg': 6.0, 'sampler_name': 'dpmpp_2m_sde_gpu', 'scheduler': 'karras', 'denoise': 1, 'model': ['4', 0], 'positive': ['6', 0], 'negative': ['7', 0], 'latent_image': ['5', 0]}, 'class_type': 'KSampler', '_meta': {'title': 'KSampler'}}, '4': {'inputs': {'ckpt_name': 'CinematicRedmond.safetensors'}, 'class_type': 'CheckpointLoaderSimple', '_meta': {'title': 'Load Checkpoint'}}, '5': {'inputs': {'width': 1640, 'height': 744, 'batch_size': 1}, 'class_type': 'EmptyLatentImage', '_meta': {'title': 'Empty Latent Image'}}, '6': {'inputs': {'text': 'A film still showcasing a serene forest clearing, dappled sunlight filtering through the trees, as a solitary figure stands in quiet contemplation, immersed in the beauty and tranquility of nature', 'clip': ['4', 1]}, 'class_type': 'CLIPTextEncode', '_meta': {'title': 'CLIP Text Encode (Prompt)'}}, '7': {'inputs': {'text': 'ugly, disfigured, low quality, blurry', 'clip': ['4', 1]}, 'class_type': 'CLIPTextEncode', '_meta': {'title': 'CLIP Text Encode (Prompt)'}}, '8': {'inputs': {'samples': ['3', 0], 'vae': ['4', 2]}, 'class_type': 'VAEDecode', '_meta': {'title': 'VAE Decode'}}, '9': {'inputs': {'filename_prefix': 'ComfyUI', 'images': ['8', 0]}, 'class_type': 'SaveImage', '_meta': {'title': 'Save Image'}}}\nRunning workflow\ngot prompt\nExecuting node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nExecuting node 3, title: KSampler, class type: KSampler\n 0%| | 0/17 [00:00<?, ?it/s]\n 6%|▌ | 1/17 [00:00<00:03, 4.33it/s]\n 12%|█▏ | 2/17 [00:00<00:03, 4.31it/s]\n 18%|█▊ | 3/17 [00:00<00:03, 4.29it/s]\n 24%|██▎ | 4/17 [00:00<00:03, 4.28it/s]\n 29%|██▉ | 5/17 [00:01<00:02, 4.27it/s]\n 35%|███▌ | 6/17 [00:01<00:02, 4.27it/s]\n 41%|████ | 7/17 [00:01<00:02, 4.27it/s]\n 47%|████▋ | 8/17 [00:01<00:02, 4.27it/s]\n 53%|█████▎ | 9/17 [00:02<00:01, 4.27it/s]\n 59%|█████▉ | 10/17 [00:02<00:01, 4.26it/s]\n 65%|██████▍ | 11/17 [00:02<00:01, 4.27it/s]\n 71%|███████ | 12/17 [00:02<00:01, 4.27it/s]\n 76%|███████▋ | 13/17 [00:03<00:00, 4.27it/s]\n 82%|████████▏ | 14/17 [00:03<00:00, 4.27it/s]\n 88%|████████▊ | 15/17 [00:03<00:00, 4.27it/s]\n 94%|█████████▍| 16/17 [00:03<00:00, 4.29it/s]\n100%|██████████| 17/17 [00:03<00:00, 4.31it/s]\n100%|██████████| 17/17 [00:03<00:00, 4.28it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 4.49 seconds\noutputs: {'9': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nContents of /tmp/outputs:\nComfyUI_00001_.png", "metrics": { "predict_time": 5.233397, "total_time": 5.276766 }, "output": [ "https://replicate.delivery/pbxt/oC6mXeLMx4zeepNPRAGCUymmZkYjjcoIOnc4SzTm202WX1FlA/ComfyUI_00001_.png" ], "started_at": "2024-03-22T21:32:23.212089Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/mw3mijlbojaeps6bh5vc3mlidu", "cancel": "https://api.replicate.com/v1/predictions/mw3mijlbojaeps6bh5vc3mlidu/cancel" }, "version": "c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18" }Generated inRandom seed set to: 1709802865 {'3': {'inputs': {'seed': 1709802865, 'steps': 17, 'cfg': 6.0, 'sampler_name': 'dpmpp_2m_sde_gpu', 'scheduler': 'karras', 'denoise': 1, 'model': ['4', 0], 'positive': ['6', 0], 'negative': ['7', 0], 'latent_image': ['5', 0]}, 'class_type': 'KSampler', '_meta': {'title': 'KSampler'}}, '4': {'inputs': {'ckpt_name': 'CinematicRedmond.safetensors'}, 'class_type': 'CheckpointLoaderSimple', '_meta': {'title': 'Load Checkpoint'}}, '5': {'inputs': {'width': 1640, 'height': 744, 'batch_size': 1}, 'class_type': 'EmptyLatentImage', '_meta': {'title': 'Empty Latent Image'}}, '6': {'inputs': {'text': 'A film still showcasing a serene forest clearing, dappled sunlight filtering through the trees, as a solitary figure stands in quiet contemplation, immersed in the beauty and tranquility of nature', 'clip': ['4', 1]}, 'class_type': 'CLIPTextEncode', '_meta': {'title': 'CLIP Text Encode (Prompt)'}}, '7': {'inputs': {'text': 'ugly, disfigured, low quality, blurry', 'clip': ['4', 1]}, 'class_type': 'CLIPTextEncode', '_meta': {'title': 'CLIP Text Encode (Prompt)'}}, '8': {'inputs': {'samples': ['3', 0], 'vae': ['4', 2]}, 'class_type': 'VAEDecode', '_meta': {'title': 'VAE Decode'}}, '9': {'inputs': {'filename_prefix': 'ComfyUI', 'images': ['8', 0]}, 'class_type': 'SaveImage', '_meta': {'title': 'Save Image'}}} Running workflow got prompt Executing node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode Executing node 3, title: KSampler, class type: KSampler 0%| | 0/17 [00:00<?, ?it/s] 6%|▌ | 1/17 [00:00<00:03, 4.33it/s] 12%|█▏ | 2/17 [00:00<00:03, 4.31it/s] 18%|█▊ | 3/17 [00:00<00:03, 4.29it/s] 24%|██▎ | 4/17 [00:00<00:03, 4.28it/s] 29%|██▉ | 5/17 [00:01<00:02, 4.27it/s] 35%|███▌ | 6/17 [00:01<00:02, 4.27it/s] 41%|████ | 7/17 [00:01<00:02, 4.27it/s] 47%|████▋ | 8/17 [00:01<00:02, 4.27it/s] 53%|█████▎ | 9/17 [00:02<00:01, 4.27it/s] 59%|█████▉ | 10/17 [00:02<00:01, 4.26it/s] 65%|██████▍ | 11/17 [00:02<00:01, 4.27it/s] 71%|███████ | 12/17 [00:02<00:01, 4.27it/s] 76%|███████▋ | 13/17 [00:03<00:00, 4.27it/s] 82%|████████▏ | 14/17 [00:03<00:00, 4.27it/s] 88%|████████▊ | 15/17 [00:03<00:00, 4.27it/s] 94%|█████████▍| 16/17 [00:03<00:00, 4.29it/s] 100%|██████████| 17/17 [00:03<00:00, 4.31it/s] 100%|██████████| 17/17 [00:03<00:00, 4.28it/s] Executing node 8, title: VAE Decode, class type: VAEDecode Executing node 9, title: Save Image, class type: SaveImage Prompt executed in 4.49 seconds outputs: {'9': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== Contents of /tmp/outputs: ComfyUI_00001_.pngPrediction
fofr/cinematic-redmond:c798f0f5ID4q2tghlbxhxpggxul23sucuk6qStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- steps
- 17
- width
- 1640
- height
- 744
- prompt
- A film still portrait face focused of a reclusive artist lost in thought, abstract, moody, art-house, portrait
- scheduler
- karras
- num_outputs
- 1
- sampler_name
- dpmpp_2m_sde_gpu
- guidance_scale
- 6
- negative_prompt
- ugly, disfigured, low quality, blurry
{ "steps": 17, "width": 1640, "height": 744, "prompt": "A film still portrait face focused of a reclusive artist lost in thought, abstract, moody, art-house, portrait", "scheduler": "karras", "num_outputs": 1, "sampler_name": "dpmpp_2m_sde_gpu", "guidance_scale": 6, "negative_prompt": "ugly, disfigured, low quality, blurry" }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/cinematic-redmond using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/cinematic-redmond:c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18", { input: { steps: 17, width: 1640, height: 744, prompt: "A film still portrait face focused of a reclusive artist lost in thought, abstract, moody, art-house, portrait", scheduler: "karras", num_outputs: 1, sampler_name: "dpmpp_2m_sde_gpu", guidance_scale: 6, negative_prompt: "ugly, disfigured, low quality, blurry" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/cinematic-redmond using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/cinematic-redmond:c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18", input={ "steps": 17, "width": 1640, "height": 744, "prompt": "A film still portrait face focused of a reclusive artist lost in thought, abstract, moody, art-house, portrait", "scheduler": "karras", "num_outputs": 1, "sampler_name": "dpmpp_2m_sde_gpu", "guidance_scale": 6, "negative_prompt": "ugly, disfigured, low quality, blurry" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/cinematic-redmond 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": "c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18", "input": { "steps": 17, "width": 1640, "height": 744, "prompt": "A film still portrait face focused of a reclusive artist lost in thought, abstract, moody, art-house, portrait", "scheduler": "karras", "num_outputs": 1, "sampler_name": "dpmpp_2m_sde_gpu", "guidance_scale": 6, "negative_prompt": "ugly, disfigured, low quality, blurry" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/fofr/cinematic-redmond@sha256:c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18 \ -i 'steps=17' \ -i 'width=1640' \ -i 'height=744' \ -i 'prompt="A film still portrait face focused of a reclusive artist lost in thought, abstract, moody, art-house, portrait"' \ -i 'scheduler="karras"' \ -i 'num_outputs=1' \ -i 'sampler_name="dpmpp_2m_sde_gpu"' \ -i 'guidance_scale=6' \ -i 'negative_prompt="ugly, disfigured, low quality, blurry"'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/fofr/cinematic-redmond@sha256:c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "steps": 17, "width": 1640, "height": 744, "prompt": "A film still portrait face focused of a reclusive artist lost in thought, abstract, moody, art-house, portrait", "scheduler": "karras", "num_outputs": 1, "sampler_name": "dpmpp_2m_sde_gpu", "guidance_scale": 6, "negative_prompt": "ugly, disfigured, low quality, blurry" } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-03-22T21:33:07.753039Z", "created_at": "2024-03-22T21:33:02.529268Z", "data_removed": false, "error": null, "id": "4q2tghlbxhxpggxul23sucuk6q", "input": { "steps": 17, "width": 1640, "height": 744, "prompt": "A film still portrait face focused of a reclusive artist lost in thought, abstract, moody, art-house, portrait", "scheduler": "karras", "num_outputs": 1, "sampler_name": "dpmpp_2m_sde_gpu", "guidance_scale": 6, "negative_prompt": "ugly, disfigured, low quality, blurry" }, "logs": "Random seed set to: 1338869919\n{'3': {'inputs': {'seed': 1338869919, 'steps': 17, 'cfg': 6.0, 'sampler_name': 'dpmpp_2m_sde_gpu', 'scheduler': 'karras', 'denoise': 1, 'model': ['4', 0], 'positive': ['6', 0], 'negative': ['7', 0], 'latent_image': ['5', 0]}, 'class_type': 'KSampler', '_meta': {'title': 'KSampler'}}, '4': {'inputs': {'ckpt_name': 'CinematicRedmond.safetensors'}, 'class_type': 'CheckpointLoaderSimple', '_meta': {'title': 'Load Checkpoint'}}, '5': {'inputs': {'width': 1640, 'height': 744, 'batch_size': 1}, 'class_type': 'EmptyLatentImage', '_meta': {'title': 'Empty Latent Image'}}, '6': {'inputs': {'text': 'A film still portrait face focused of a reclusive artist lost in thought, abstract, moody, art-house, portrait', 'clip': ['4', 1]}, 'class_type': 'CLIPTextEncode', '_meta': {'title': 'CLIP Text Encode (Prompt)'}}, '7': {'inputs': {'text': 'ugly, disfigured, low quality, blurry', 'clip': ['4', 1]}, 'class_type': 'CLIPTextEncode', '_meta': {'title': 'CLIP Text Encode (Prompt)'}}, '8': {'inputs': {'samples': ['3', 0], 'vae': ['4', 2]}, 'class_type': 'VAEDecode', '_meta': {'title': 'VAE Decode'}}, '9': {'inputs': {'filename_prefix': 'ComfyUI', 'images': ['8', 0]}, 'class_type': 'SaveImage', '_meta': {'title': 'Save Image'}}}\nRunning workflow\ngot prompt\nExecuting node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nExecuting node 3, title: KSampler, class type: KSampler\n 0%| | 0/17 [00:00<?, ?it/s]\n 6%|▌ | 1/17 [00:00<00:03, 4.32it/s]\n 12%|█▏ | 2/17 [00:00<00:03, 4.29it/s]\n 18%|█▊ | 3/17 [00:00<00:03, 4.26it/s]\n 24%|██▎ | 4/17 [00:00<00:03, 4.25it/s]\n 29%|██▉ | 5/17 [00:01<00:02, 4.25it/s]\n 35%|███▌ | 6/17 [00:01<00:02, 4.24it/s]\n 41%|████ | 7/17 [00:01<00:02, 4.24it/s]\n 47%|████▋ | 8/17 [00:01<00:02, 4.24it/s]\n 53%|█████▎ | 9/17 [00:02<00:01, 4.24it/s]\n 59%|█████▉ | 10/17 [00:02<00:01, 4.24it/s]\n 65%|██████▍ | 11/17 [00:02<00:01, 4.24it/s]\n 71%|███████ | 12/17 [00:02<00:01, 4.25it/s]\n 76%|███████▋ | 13/17 [00:03<00:00, 4.25it/s]\n 82%|████████▏ | 14/17 [00:03<00:00, 4.25it/s]\n 88%|████████▊ | 15/17 [00:03<00:00, 4.25it/s]\n 94%|█████████▍| 16/17 [00:03<00:00, 4.26it/s]\n100%|██████████| 17/17 [00:03<00:00, 4.28it/s]\n100%|██████████| 17/17 [00:03<00:00, 4.26it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 4.48 seconds\noutputs: {'9': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nContents of /tmp/outputs:\nComfyUI_00001_.png", "metrics": { "predict_time": 5.213386, "total_time": 5.223771 }, "output": [ "https://replicate.delivery/pbxt/egCLfxweDMN1PIlel1Smjpp4iE6CP5eQ5iehuIea4tCmJWdRJA/ComfyUI_00001_.png" ], "started_at": "2024-03-22T21:33:02.539653Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4q2tghlbxhxpggxul23sucuk6q", "cancel": "https://api.replicate.com/v1/predictions/4q2tghlbxhxpggxul23sucuk6q/cancel" }, "version": "c798f0f5600aa127d0b38a17c6d156fefecbb55e11e061c515e4b52cfdd43c18" }Generated inRandom seed set to: 1338869919 {'3': {'inputs': {'seed': 1338869919, 'steps': 17, 'cfg': 6.0, 'sampler_name': 'dpmpp_2m_sde_gpu', 'scheduler': 'karras', 'denoise': 1, 'model': ['4', 0], 'positive': ['6', 0], 'negative': ['7', 0], 'latent_image': ['5', 0]}, 'class_type': 'KSampler', '_meta': {'title': 'KSampler'}}, '4': {'inputs': {'ckpt_name': 'CinematicRedmond.safetensors'}, 'class_type': 'CheckpointLoaderSimple', '_meta': {'title': 'Load Checkpoint'}}, '5': {'inputs': {'width': 1640, 'height': 744, 'batch_size': 1}, 'class_type': 'EmptyLatentImage', '_meta': {'title': 'Empty Latent Image'}}, '6': {'inputs': {'text': 'A film still portrait face focused of a reclusive artist lost in thought, abstract, moody, art-house, portrait', 'clip': ['4', 1]}, 'class_type': 'CLIPTextEncode', '_meta': {'title': 'CLIP Text Encode (Prompt)'}}, '7': {'inputs': {'text': 'ugly, disfigured, low quality, blurry', 'clip': ['4', 1]}, 'class_type': 'CLIPTextEncode', '_meta': {'title': 'CLIP Text Encode (Prompt)'}}, '8': {'inputs': {'samples': ['3', 0], 'vae': ['4', 2]}, 'class_type': 'VAEDecode', '_meta': {'title': 'VAE Decode'}}, '9': {'inputs': {'filename_prefix': 'ComfyUI', 'images': ['8', 0]}, 'class_type': 'SaveImage', '_meta': {'title': 'Save Image'}}} Running workflow got prompt Executing node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode Executing node 3, title: KSampler, class type: KSampler 0%| | 0/17 [00:00<?, ?it/s] 6%|▌ | 1/17 [00:00<00:03, 4.32it/s] 12%|█▏ | 2/17 [00:00<00:03, 4.29it/s] 18%|█▊ | 3/17 [00:00<00:03, 4.26it/s] 24%|██▎ | 4/17 [00:00<00:03, 4.25it/s] 29%|██▉ | 5/17 [00:01<00:02, 4.25it/s] 35%|███▌ | 6/17 [00:01<00:02, 4.24it/s] 41%|████ | 7/17 [00:01<00:02, 4.24it/s] 47%|████▋ | 8/17 [00:01<00:02, 4.24it/s] 53%|█████▎ | 9/17 [00:02<00:01, 4.24it/s] 59%|█████▉ | 10/17 [00:02<00:01, 4.24it/s] 65%|██████▍ | 11/17 [00:02<00:01, 4.24it/s] 71%|███████ | 12/17 [00:02<00:01, 4.25it/s] 76%|███████▋ | 13/17 [00:03<00:00, 4.25it/s] 82%|████████▏ | 14/17 [00:03<00:00, 4.25it/s] 88%|████████▊ | 15/17 [00:03<00:00, 4.25it/s] 94%|█████████▍| 16/17 [00:03<00:00, 4.26it/s] 100%|██████████| 17/17 [00:03<00:00, 4.28it/s] 100%|██████████| 17/17 [00:03<00:00, 4.26it/s] Executing node 8, title: VAE Decode, class type: VAEDecode Executing node 9, title: Save Image, class type: SaveImage Prompt executed in 4.48 seconds outputs: {'9': {'images': [{'filename': 'ComfyUI_00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== Contents of /tmp/outputs: ComfyUI_00001_.png
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