Instructions to use TeslaYang123/TC-Light with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use TeslaYang123/TC-Light with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("TeslaYang123/TC-Light", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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Improve model card: Update pipeline tag, add library name, and detailed content
Verified signatureThis PR significantly enhances the model card for TC-Light by: - Updating the `pipeline_tag` from `image-to-image` to `video-to-video` to accurately reflect its core functionality in video processing and ensure better discoverability on the Hub (https://huggingface.co/models?pipeline_tag=video-to-video). - Adding `library_name: diffusers` as the model's underlying components and dependencies align with the Diffusers library, which enables better integration and a "how to use" widget on the model page. - Expanding the model card content using the comprehensive information from the official GitHub repository, including detailed explanations of the method, usage instructions, examples, behavioral insights, and a proper citation. This provides a more informative and user-friendly experience for anyone visiting the model page.8273596
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Add pipeline tag and project page link (#1)
Verified signature- Add pipeline tag and project page link (592b21dc6d326ce6ff4989a5bdf6928db8f00989) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>03b9f03
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