Instructions to use quantum-whisper/edl-relight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use quantum-whisper/edl-relight with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("quantum-whisper/edl-relight", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
jayhsu0627 commited on
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README.md
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- stable-diffusion
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license: apache-2.0
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library_name: diffusers
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pipeline_tag:
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---
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# My Video Relight Model
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- stable-diffusion
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license: apache-2.0
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library_name: diffusers
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pipeline_tag: image-to-video
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---
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# My Video Relight Model
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