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
Download optimizer.bin from quantum-whisper/edl-relight: direct link, hf CLI and curl.
- Browser
- Download file 3.18 GB
-
https://huggingface.co/quantum-whisper/edl-relight/resolve/main/optimizer.bin
- Command line
-
hf download hf://quantum-whisper/edl-relight/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/quantum-whisper/edl-relight/resolve/main/optimizer.bin
3.18 GB
- Xet hash:
- 306a12673cdd76e09aef3e5af6e16b155bc54d5197efed018bf7fbf5e178307a
- Size of remote file:
- 3.18 GB
- SHA256:
- d0dd60cf7516c82d92fad56e9a2dc281ede800e5aa1376631f98fda8430e3c5e
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