Image-to-Video
Diffusers
Safetensors
Wan2.2
WanAnimatePipeline
wan-animate
character-animation
mxfp4
auto-round
8-bit precision
Instructions to use intel-ai/Wan2.2-Animate-14B-Diffusers-MXFP4-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use intel-ai/Wan2.2-Animate-14B-Diffusers-MXFP4-AutoRound with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("intel-ai/Wan2.2-Animate-14B-Diffusers-MXFP4-AutoRound", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Wan2.2
How to use intel-ai/Wan2.2-Animate-14B-Diffusers-MXFP4-AutoRound with Wan2.2:
# 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
File size: 498 Bytes
368274e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"architectures": [
"CLIPVisionModel"
],
"attention_dropout": 0.0,
"dropout": 0.0,
"dtype": "bfloat16",
"hidden_act": "gelu",
"hidden_size": 1280,
"image_size": 224,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"intermediate_size": 5120,
"layer_norm_eps": 1e-05,
"model_type": "clip_vision_model",
"num_attention_heads": 16,
"num_channels": 3,
"num_hidden_layers": 32,
"patch_size": 14,
"projection_dim": 1024,
"transformers_version": "5.14.1"
}
|