Instructions to use rorge120ac/Wan2.2-Distill-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rorge120ac/Wan2.2-Distill-Models with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rorge120ac/Wan2.2-Distill-Models", 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 rorge120ac/Wan2.2-Distill-Models 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
Ctrl+K
- wan2.2_i2v_A14b_high_noise_int8_lightx2v_4step_1030_split
- wan2.2_i2v_A14b_high_noise_scaled_fp8_e4m3_lightx2v_4step_1030_split
- wan2.2_i2v_A14b_low_noise_int8_lightx2v_4step_split
- wan2.2_i2v_A14b_low_noise_scaled_fp8_e4m3_lightx2v_4step_split
- 1.52 kB
- 7.67 kB
- 249 Bytes
- 15 GB xet
- 15 GB xet
- 28.6 GB xet
- 28.6 GB xet
- 57.2 GB xet
- 15 GB xet
- 15 GB xet
- 15 GB xet
- 15 GB xet
- 15 GB xet
- 28.6 GB xet
- 57.2 GB xet
- 15 GB xet
- 15 GB xet
- 32.8 kB