Instructions to use DeepBeepMeep/Wan2.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DeepBeepMeep/Wan2.2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("DeepBeepMeep/Wan2.2", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Diffusion Single File
How to use DeepBeepMeep/Wan2.2 with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
| { | |
| "_class_name": "MLLMEncoder", | |
| "mllm_model_path": ".", | |
| "dit_dim": 3072, | |
| "hidden_size": 2048, | |
| "num_image_queries": 256, | |
| "num_video_queries": 512, | |
| "num_ref_queries": 768, | |
| "max_object_token": 768, | |
| "max_frames": 16, | |
| "max_pixels_per_frame": 262144 | |
| } |