Instructions to use WMSD/World-Model-Self-Distillation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WMSD/World-Model-Self-Distillation with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WMSD/World-Model-Self-Distillation", 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
| license: other | |
| library_name: diffusers | |
| tags: | |
| - world-models | |
| - self-distillation | |
| - video-generation | |
| - task-conditioned-video-generation | |
| # World Model Self-Distillation | |
| This repository will host the model weights for **World Model Self-Distillation: Training World Models to Solve General Tasks**. | |
| Weights, loading instructions, and model details will be added later. | |