Add pipeline tag to model card

This PR adds the `pipeline_tag: image-to-image` to the model card metadata. This makes the model discoverable through the model hub interface.

README.md

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----
-license: mit
----
+---
+license: mit
+pipeline_tag: image-to-image
+---
+
+<h2 align="center"> <a href="https://arxiv.org/abs/2503.14325">LeanVAE: An Ultra-Efficient Reconstruction VAE for Video Diffusion Models</a></h2>
+
+https://github.com/user-attachments/assets/a2a4814a-192b-4cc4-b1a3-d612caa1d872
+
+We present **LeanVAE**, a lightweight Video VAE designed for ultra-efficient video compression and scalable generation in Latent Video Diffusion Models (LVDMs).
+
+- **Lightweight & Efficient**: Only **40M parameters**, significantly reducing computational overhead 📉  
+- **Optimized for High-Resolution Videos**: Encodes and decodes a **17-frame 1080p video** in **3 seconds** using only **15GB of GPU memory** *(without tiling inference)* 🎯  
+- **State-of-the-Art Video Reconstruction**: Competes with leading Video VAEs 🏆  
+- **Versatile**: Supports both **images and videos**, preserving **causality in latent space** 📽️  
+- **Evidenced by Diffusion Model**: Enhances visual quality in video generation ✨  
+
+---
+## 🛠️ **Installation**
+Clone the repository and install dependencies:
+```
+git clone https://github.com/westlake-repl/LeanVAE
+cd LeanVAE
+pip install -r requirements.txt
+```
+---
+## 🎯 **Quick Start** 
+**Train LeanVAE**
+```bash
+bash scripts/train.sh
+```
+
+**Run Video Reconstruction**
+```bash
+bash scripts/inference.sh
+```
+
+**Evaluate Reconstruction Quality**
+```bash
+bash scripts/eval.sh
+```
+---
+
+## 📜 **Pretrained Models**
+### Video VAE Model:
+| Model            | PSNR ⬆️ | LPIPS ⬇️ | Params 📦 | TFLOPs ⚡ | Checkpoint 📥                        |
+| ---------------- | ------ | ------- | -------- | -------- | ----------------------------------- |
+| **LeanVAE-4ch**  | 26.04  | 0.0899  | 39.8M    | 0.203    | [LeanVAE-chn4.ckpt](https://huggingface.co/Yumic/LeanVAE/resolve/main/LeanVAE-dim4.ckpt?download=true) |
+| **LeanVAE-16ch** | 30.15  | 0.0461  | 39.8M    | 0.203    | [LeanVAE-chn16.ckpt](https://huggingface.co/Yumic/LeanVAE/resolve/main/LeanVAE-dim16.ckpt?download=true) |
+
+ 
+### Latte Model:
+The code and pretrained weights for video generation will be released soon. Stay tuned!
+| Model                    | Dataset      | FVD ⬇️  | Checkpoint 📥                        |
+| ---------- | ---------- | ---------- | ----------- |
+| Latte + LeanVAE-chn4 | SkyTimelapse |49.59 | sky-chn4.ckpt | 
+| Latte + LeanVAE-chn4 | UCF101 |164.45 | ucf-chn4.ckpt |
+| Latte + LeanVAE-chn16 | SkyTimelapse |95.15 | sky-chn16.ckpt |
+| Latte + LeanVAE-chn16 | UCF101 |175.33 | ucf-chn16.ckpt |
+
+---
+## 🔧 **Using LeanVAE in Your Project**
+
+```python
+from LeanVAE import LeanVAE
+
+# Load pretrained model
+model = LeanVAE.load_from_checkpoint("path/to/ckpt", strict=False)
+
+# 🔄 Encode & Decode an Image
+image, image_rec = model.inference(image)
+
+# 🖼️ Encode an image → Get latent :  
+latent = model.encode(image) # (B, C, H, W) → (B, d, 1, H/8, W/8), where d=4 or 16
+
+# 🖼️ Decode latent representation → Reconstruct image 
+image = model.decode(latent, is_image=True) # (B, d, 1, H/8, W/8) → (B, C, H, W)  
+
+
+# 🔄 Encode & Decode a Video
+video, video_rec = model.inference(video) ## Frame count must be 4n+1 (e.g., 5, 9, 13, 17...)
+
+# 🎞️ Encode Video → Get Latent Space
+latent = model.encode(video)  # (B, C, T+1, H, W) → (B, d, T/4+1, H/8, W/8), where d=4 or 16 
+
+# 🎞️ Decode Latent → Reconstruct Video
+video = model.decode(latent) # (B, d, T/4+1, H/8, W/8) → (B, C, T+1, H, W)  
+
+# ⚡ Enable **Temporal Tiling Inference** for Long Videos
+model.set_tile_inference(True)
+model.chunksize_enc = 5
+model.chunksize_dec = 5
+```
+---
+
+## 📂 **Preparing Data for Training**
+To train LeanVAE, you need to create metadata files listing the video paths, grouped by resolution. Each file contains paths to videos of the same resolution.
+```
+📂 data_list
+ ├── 📄 96x128.txt  📜  # Contains paths to all 96x128 videos
+ │   ├── /path/to/video_1.mp4
+ │   ├── /path/to/video_2.mp4
+ │   ├── ...
+ ├── 📄 256x256.txt  📜  # Contains paths to all 256×256 videos
+ │   ├── /path/to/video_3.mp4
+ │   ├── /path/to/video_4.mp4
+ │   ├── ...
+ ├── 📄 352x288.txt  📜  # Contains paths to all 352x288 videos
+ │   ├── /path/to/video_5.mp4
+ │   ├── /path/to/video_6.mp4
+ │   ├── ...
+```
+📌 Each text file lists video paths corresponding to a specific resolution. Set `args.train_datalist` to the folder containing these files.
+
+
+---
+## 📜 **License**
+
+This project is released under the **MIT License**. See the `LICENSE` file for details.
+
+
+## 🔥 **Why Choose LeanVAE?**  
+LeanVAE is **fast, lightweight and powerful**, enabling high-quality video compression and generation with minimal computational cost.  
+
+If you find this work useful, consider **starring ⭐ the repository** and citing our paper!  
+
+---
+
+## 📝 **Cite Us**  
+```bibtex
+@misc{cheng2025leanvaeultraefficientreconstructionvae,
+      title={LeanVAE: An Ultra-Efficient Reconstruction VAE for Video Diffusion Models}, 
+      author={Yu Cheng and Fajie Yuan},
+      year={2025},
+      eprint={2503.14325},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2503.14325}, 
+}
+```
+---
+
+## 👍 **Acknowledgement**
+Our work benefits from the contributions of several open-source projects, including [OmniTokenizer](https://github.com/FoundationVision/OmniTokenizer), [Open-Sora-Plan](https://github.com/PKU-YuanGroup/Open-Sora-Plan), [VidTok](https://github.com/microsoft/VidTok), and [Latte](https://github.com/Vchitect/Latte). We sincerely appreciate their efforts in advancing research and open-source collaboration!