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
Download kiwi_mllm_encoder_instruct_reference/tokenizer.json from DeepBeepMeep/Wan2.2: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/DeepBeepMeep/Wan2.2/resolve/main/kiwi_mllm_encoder_instruct_reference/tokenizer.json
- Command line
-
hf download hf://DeepBeepMeep/Wan2.2/kiwi_mllm_encoder_instruct_reference/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/DeepBeepMeep/Wan2.2/resolve/main/kiwi_mllm_encoder_instruct_reference/tokenizer.json
11.4 MB
- Xet hash:
- 4bd2f092eeec244c0448f13e31edd4b25e625568713e4155993a3dea90c7437a
- Size of remote file:
- 11.4 MB
- SHA256:
- 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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