Instructions to use callgg/wan2-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use callgg/wan2-encoder with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("callgg/wan2-encoder", 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
Update model_index.json
Browse files- model_index.json +0 -8
model_index.json
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"_class_name": "WanImageToVideoPipeline",
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"_diffusers_version": "0.35.0.dev0",
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"boundary_ratio": 0.9,
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"image_encoder": [
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],
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"image_processor": [
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],
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"scheduler": [
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"diffusers",
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"UniPCMultistepScheduler"
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"_class_name": "WanImageToVideoPipeline",
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"_diffusers_version": "0.35.0.dev0",
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"boundary_ratio": 0.9,
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"scheduler": [
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"diffusers",
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"UniPCMultistepScheduler"
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