Update README.md

README.md

@@ -4,8 +4,53 @@ tags:
   - diffusion-single-file
   - distillation
   - video genration
+  - video-vae
+  - video-generation
+  - wan2.1
+  - wan2.2
+  - minimax-h3
 pipeline_tags: 
   - image-to-video
   - text-to-video
-library_name: diffusers
----
+---
+
+# LightVAE: Towards Compact and Efficient Video Autoencoders
+
+LightVAE accelerates pretrained video autoencoders using temporal low-rank compression and block pruning. It provides efficient checkpoints for Wan2.1, Wan2.2, and MiniMax-H3 while preserving their latent interfaces.
+
+**Paper:** *LightVAE: Towards Compact and Efficient Video Autoencoders*  
+**Code and examples:** [ModelTC/LightVAE](https://github.com/ModelTC/LightVAE)
+
+## Checkpoints
+
+| Backbone | Available checkpoints |
+| --- | --- |
+| Wan2.1 | `lightvae-pro-wan21-decoder.safetensors`, `lightvae-lite-wan21-decoder.safetensors` |
+| Wan2.2 | `lightvae-pro-wan22-decoder.safetensors`, `lightvae-lite-wan22-decoder.safetensors` |
+| MiniMax-H3 | `lightvae-lite-h3-decoder.safetensors`, `lightvae-lite-h3-encoder.safetensors` |
+
+**Pro** retains the original decoder depth for higher reconstruction fidelity. **Lite** applies additional pruning for faster decoding. For Wan2.1 and Wan2.2, use the original VAE encoder with a LightVAE decoder. MiniMax-H3 can use either its original encoder or the provided Lite encoder.
+
+## Quick start
+
+```bash
+git clone https://github.com/ModelTC/LightVAE.git
+cd LightVAE
+pip install -r requirements.txt
+hf download lightx2v/LightVAE --local-dir weights/LightVAE
+```
+
+For example, reconstruct a video with the Wan2.1 Lite decoder:
+
+```bash
+hf download Wan-AI/Wan2.1-T2V-1.3B Wan2.1_VAE.pth --local-dir weights/Wan2.1
+
+python infer.py \
+  --model wan21 --variant lite \
+  --encoder weights/Wan2.1/Wan2.1_VAE.pth \
+  --decoder weights/LightVAE/lightvae-lite-wan21-decoder.safetensors \
+  --input demo.mp4 --frames 81 --height 480 --width 832 \
+  --output outputs/wan21_lite_recon.mp4
+```
+
+See the [GitHub repository](https://github.com/ModelTC/LightVAE) for Wan2.2 and MiniMax-H3 examples, benchmarks, and visual comparisons.