Instructions to use lightx2v/Autoencoders with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lightx2v/Autoencoders with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lightx2v/Autoencoders", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Diffusion Single File
How to use lightx2v/Autoencoders with Diffusion Single File:
# 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
Add LightTAE Wan2.1 and Wan2.2 v2 EMA
Browse files
lighttaew2_1_v2.safetensors
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lighttaew2_2_v2.safetensors
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oid sha256:8a85b34fcf31c639c8f9d66dc09ce5d63ead7c57910f54bc4089549306aa8c32
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size 45684280
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