Text-to-Video
Diffusers
Safetensors
video-generation
game-rendering
game-editing
diffusion
g-buffer
relighting
wan2.1
Instructions to use Brian9999/game-editing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Brian9999/game-editing with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Brian9999/game-editing", 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
Commit History
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Improve model card: add pipeline tag, paper/code links, and sample usage
Verified signatureHi! I'm Niels from the Hugging Face community science team. I've updated the model card for the Game Editing model to make it more informative and discoverable: - Updated the `pipeline_tag` to `image-to-video` (as the model is G-buffer guided). - Added links to the [Generative World Renderer](https://huggingface.co/papers/2604.02329) paper, the [Project Page](https://alaya-studio.github.io/renderer), and the [official GitHub repository](https://github.com/ShandaAI/AlayaRenderer). - Included a "Sample Usage" section with the CLI command found in your documentation. - Added the BibTeX citation for researchers to easily cite your work.40a96c3
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Super-squash branch 'main' using huggingface_hub
8625d6a