Instructions to use Gjm1234/Wan2.2-I2V-A14B-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Gjm1234/Wan2.2-I2V-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("Gjm1234/Wan2.2-I2V-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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Check out the documentation for more information.
WAN 2.2 β Image to Video (I2V) β Diffusers Conversion
This repository contains a Diffusers-compatible custom pipeline for running WAN 2.2 Image-to-Video models inside a HuggingFace Inference Endpoint.
β Works with:
DiffusionPipeline.from_pretrained(...)- Custom pipeline loading via
custom_pipeline="pipeline_wan_i2v" - GPU HF Inference Endpoints
- Automatic model component loading defined in
model_index.json
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