Instructions to use bytedance-research/ATI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bytedance-research/ATI 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("bytedance-research/ATI", 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
- Xet hash:
- cb119f532691ea16c2d1b3df2f8965f6dd63160ee0c0c59d4f6d998e9cd6d9de
- Size of remote file:
- 1.93 MB
- SHA256:
- 3f01392f632dc5ad7289ee9995ca8d0e6e601d1f841719fbb1850991158eec5d
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