Pruna VAE for Modular Diffusers
This repository provides a Modular Diffusers implementation of PrunaVAED. It packages the optimized video decoder implementation together with the custom loading mechanism required to use the model as a reusable Modular Diffusers component.
The repository exposes a custom LoadPrunaVAE block that downloads and instantiates PrunaAutoencoderKLLTX2Video, making it available as components.vae within a Modular Diffusers pipeline.
Installation
Be sure to install pruna, accelerate and diffusers before trying out the model, you can do so with:
pip install pruna accelerate diffusers
Features
- Custom
PrunaAutoencoderKLLTX2Videoimplementation - Compatible with
trust_remote_code=True - Self-contained repository containing both the implementation and model weights
- Intended for composition inside Modular Diffusers pipelines
Intended Use
This repository is designed to be consumed by Modular Diffusers pipelines and reusable pipeline blocks. It is particularly useful for:
- Video inference
- Modular Diffusers experimentation
- Custom video generation pipelines
- Research on pruned video VAEs
Usage
from diffusers.modular_pipelines import ModularPipelineBlocks
blocks = ModularPipelineBlocks.from_pretrained(
"AINovice2005/pruna-vaed-modular-diffusers",
trust_remote_code=True,
)
pipeline = blocks.init_pipeline()
pipeline.load_components()
vae = pipeline.vae
The loaded component is an instance of:
PrunaAutoencoderKLLTX2Video
and can be used anywhere a compatible LTX-2 Video VAE is expected.
[!WARNING] The VAE decoder reconstructs frames from compressed latent representations. Depending on the downstream workflow, applying a dedicated image or video upscaler after decoding can noticeably improve perceived sharpness and fine detail, particularly for outputs intended for display at higher resolutions.
Acknowledgements
This work builds upon the Pruna VAE implementation released by PrunaAI and adapts it for use as a reusable Modular Diffusers component.