Image-to-Video
Diffusers
Safetensors
English
Chinese
WanVACEPipeline
video generation
video-to-video editing
refernce-to-video
Instructions to use Wan-AI/Wan2.1-VACE-1.3B-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Wan-AI/Wan2.1-VACE-1.3B-diffusers 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("Wan-AI/Wan2.1-VACE-1.3B-diffusers", 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") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -556,7 +556,7 @@ def prepare_video_and_mask(first_img: PIL.Image.Image, last_img: PIL.Image.Image
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return frames, mask
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model_id = "/
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vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
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pipe = WanVACEPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
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flow_shift = 5.0 # 5.0 for 720P, 3.0 for 480P
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return frames, mask
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model_id = "Wan-AI/Wan2.1-VACE-1.3B-diffusers"
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vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
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pipe = WanVACEPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
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flow_shift = 5.0 # 5.0 for 720P, 3.0 for 480P
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