Instructions to use Rin247/Wan2.2-A14B-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Rin247/Wan2.2-A14B-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Rin247/Wan2.2-A14B-Diffusers", 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
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Download README.md from Rin247/Wan2.2-A14B-Diffusers: direct link, hf CLI and curl.
- Browser
- Download file 2.18 kB
-
https://huggingface.co/Rin247/Wan2.2-A14B-Diffusers/resolve/main/README.md
- Command line
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hf download hf://Rin247/Wan2.2-A14B-Diffusers/README.md
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curl -L -o README.md https://huggingface.co/Rin247/Wan2.2-A14B-Diffusers/resolve/main/README.md
2.18 kB
| tags: | |
| - quantized | |
| - safetensors | |
| - diffusers | |
| - mecha | |
| - weight-only | |
| library_name: diffusers | |
| # Wan2.2-A14B-Diffusers | |
| Quantised media packages of [Wan-AI/Wan2.2-A14B](https://huggingface.co/Wan-AI/Wan2.2-A14B). | |
| ## MECHA Forge | |
| MECHA — Model Engineering & Contextual Hearth Assembly | |
| These media packages have been forged and optimized in the MECHA Forge. | |
| ## Available models | |
| - Wan2.2-I2V-A14B (image-to-video) | |
| - Wan2.2-T2V-A14B (text-to-video) | |
| ## Description | |
| Wan 2.2 A14B: high/low noise MoE video model. 40 steps, CFG 5.0. Text encoder (umt5-xxl) shared. | |
| ## Available quantizations | |
| | Quant | Denoiser | Text encoder | VAE | | |
| |---|---|---|---| | |
| | FP8 | FP8 | FP8 | FP8 | | |
| | INT8 | INT8 | INT8 | INT8 | | |
| | FP4 | FP4 | FP4 | FP8 | | |
| | INT4 | INT4 | INT4 | INT8 | | |
| The VAE is shared between formats: FP4 reuses the FP8 file, INT4 reuses the INT8 file. | |
| Text encoder (umt5-xxl) is shared across I2V and T2V. | |
| ## Components | |
| In addition to the denoiser, text encoder, and VAE, the package includes: | |
| - transformer (high_noise + low_noise for I2V/T2V) | |
| - connectors, scheduler, tokenizer, processor | |
| ## Recommended settings | |
| From the source model card (adjust to taste): | |
| | Parameter | Value | | |
| |---|---| | |
| | Steps | 40 | | |
| | CFG / Guidance scale | 5.0 | | |
| | Resolution | 480P or 720P | | |
| | Notes | High/low noise MoE; text encoder (umt5-xxl) shared | | |
| ## Example prompts | |
| Caught in the moment (candid, slice of life): | |
| ``` | |
| masterpiece, best quality, highres, absurdres, very aesthetic, safe, 1man, solo, | |
| candid moment, natural lighting. A rooftop bar in the city at night, skyline | |
| lights, a glass on the table, cool breeze. A 34-year-old man with a strong jaw | |
| and grey eyes. He has a lean build and a relaxed expression. He wears a linen | |
| shirt and chinos, holding a drink, mid-conversation, candid and relaxed. | |
| ``` | |
| Fashion photography (magazine cover style): | |
| ``` | |
| Fashion video still: a 34-year-old man with a strong jaw, grey eyes, and salt- | |
| and-pepper hair; wearing a tailored navy suit, pocket square; city rooftop, | |
| golden hour, cinematic, 24fps, 8K. | |
| ``` | |
| Scenery: | |
| ``` | |
| Scenery: a city skyline at night, lights reflecting on water, skyscrapers, | |
| bridges, cinematic wide shot, 8K. | |
| ``` | |