Improve model card and add metadata

README.md

@@ -1,16 +1,47 @@
 ---
 license: apache-2.0
+library_name: diffusers
+pipeline_tag: image-to-video
+tags:
+- video-frame-interpolation
+- vfi
+- diffusion-transformer
 ---
 
-**Towards Holistic Modeling for Video Frame Interpolation with Auto-regressive Diffusion Transformers**
+# LDF-VFI: Towards Holistic Modeling for Video Frame Interpolation with Auto-regressive Diffusion Transformers
 
-Xinyu Peng*, Han Li*, Yuyang Huang, Ziyang Zheng, Yaoming Wang, 
-Xin Chen, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
+This repository contains the weights for **LDF-VFI** (Local Diffusion Forcing for Video Frame Interpolation), as introduced in the paper [Towards Holistic Modeling for Video Frame Interpolation with Auto-regressive Diffusion Transformers](https://huggingface.co/papers/2601.14959).
 
-\* Equal contribution  
+[[Paper](https://arxiv.org/abs/2601.14959)] [[Project Page](https://xypeng9903.github.io/ldf-vfi-web/)] [[GitHub](https://github.com/xypeng9903/LDF-VFI)]
 
-- [Project Page](https://xypeng9903.github.io/ldf-vfi-web/)  
-- [Github](https://github.com/xypeng9903/LDF-VFI) 
+## Introduction
 
-**Abstract:**
-Existing video frame interpolation (VFI) methods often adopt a frame-centric approach, processing videos as independent short segments (e.g., triplets), which leads to temporal inconsistencies and motion artifacts. To overcome this, we propose a holistic, video-centric paradigm named **L**ocal **D**iffusion **F**orcing for **V**ideo **F**rame **I**nterpolation (LDF-VFI). Our framework is built upon an auto-regressive diffusion transformer that models the entire video sequence to ensure long-range temporal coherence. To mitigate error accumulation inherent in auto-regressive generation, we introduce a novel skip-concatenate sampling strategy that effectively maintains temporal stability. Furthermore, LDF-VFI incorporates sparse, local attention and tiled VAE encoding, a combination that not only enables efficient processing of long sequences but also allows generalization to arbitrary spatial resolutions (e.g., 4K) at inference without retraining. An enhanced conditional VAE decoder, which leverages multi-scale features from the input video, further improves reconstruction fidelity. Empirically, LDF-VFI achieves state-of-the-art performance on challenging long-sequence benchmarks, demonstrating superior per-frame quality and temporal consistency, especially in scenes with large motion.
+Existing video frame interpolation (VFI) methods often adopt a frame-centric approach, processing videos as independent short segments (e.g., triplets), which leads to temporal inconsistencies and motion artifacts. To overcome this, we propose a holistic, video-centric paradigm named **L**ocal **D**iffusion **F**orcing for **V**ideo **F**rame **I**nterpolation (LDF-VFI). 
+
+Our framework is built upon an auto-regressive diffusion transformer that models the entire video sequence to ensure long-range temporal coherence. LDF-VFI incorporates sparse, local attention and tiled VAE encoding, enabling efficient processing of long sequences and generalization to arbitrary spatial resolutions (e.g., 4K) at inference without retraining.
+
+## Key Features
+
+- **Auto-regressive Diffusion Transformer**: Models the entire video sequence for long-range temporal coherence.
+- **Skip-concatenate Sampling**: A novel strategy to maintain temporal stability and mitigate error accumulation.
+- **Resolution Generalization**: Supports arbitrary spatial resolutions (including 4K) at inference time.
+- **Enhanced Conditional VAE**: Leverages multi-scale features from input videos to improve reconstruction fidelity.
+
+## Usage
+
+For installation and usage instructions, please refer to the [official GitHub repository](https://github.com/xypeng9903/LDF-VFI).
+
+## Citation
+
+If you find this work helpful, please cite:
+```bibtex
+@misc{peng2026holisticmodelingvideoframe,
+      title={Towards Holistic Modeling for Video Frame Interpolation with Auto-regressive Diffusion Transformers}, 
+      author={Xinyu Peng and Han Li and Yuyang Huang and Ziyang Zheng and Yaoming Wang and Xin Chen and Wenrui Dai and Chenglin Li and Junni Zou and Hongkai Xiong},
+      year={2026},
+      eprint={2601.14959},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2601.14959}, 
+}
+```