Update README.md

README.md

@@ -4,12 +4,55 @@ license: apache-2.0
 
 # ContentV: Efficient Training of Video Generation Models with Limited Compute
 
-This project presents ContentV, a novel framework that accelerates DiT-based video generation through three key innovations:
-- A minimalist model design that enables effective reuse of pre-trained image generation models for video synthesis
-- A comprehensive exploration of a multi-stage, efficient training strategy based on Flow Matching
-- A low-cost Reinforcement Learning with Human Feedback (RLHF) approach that further enhances generation quality without the need for additional human annotations.
+<div align="center">
+<p align="center">
+  <a href="https://contentv.github.io">
+    <img
+      src="https://img.shields.io/badge/Demo-Project Page-0A66C2?logo=googlechrome&logoColor=blue"
+      alt="Project Page"
+    />
+  </a>
+  <!-- <a>
+    <img
+      src="https://img.shields.io/badge/Tech Report-ArXiv-red?logo=arxiv&logoColor=red"
+      alt="Tech Report"
+    />
+  </a> -->
+  <a href="https://huggingface.co/ByteDance/ContentV-8B">
+    <img 
+        src="https://img.shields.io/badge/HuggingFace-Model-yellow?logo=huggingface&logoColor=yellow" 
+        alt="Model"
+    />
+  </a>
+  <a href="https://github.com/bytedance/ContentV">
+    <img 
+        src="https://img.shields.io/badge/Code-GitHub-orange?logo=github&logoColor=white" 
+        alt="Code"
+    />
+  </a>
+  <a href="https://www.apache.org/licenses/LICENSE-2.0">
+    <img
+      src="https://img.shields.io/badge/License-Apache 2.0-5865F2?logo=apache&logoColor=purple"
+      alt="License"
+    />
+  </a>
+</p>
+</div>
 
-## Quickstart
+This project presents *ContentV*, an efficient framework for accelerating the training of DiT-based video generation models through three key innovations:
+
+- A minimalist architecture that maximizes reuse of pre-trained image generation models for video synthesis
+- A systematic multi-stage training strategy leveraging flow matching for enhanced efficiency
+- A cost-effective reinforcement learning with human feedback framework that improves generation quality without requiring additional human annotations
+
+Our 8B model achieves state-of-the-art result (85.14 on VBench) in only 4 weeks of training with 256×64GB NPUs.
+
+<div align="center">
+    <img src="https://raw.githubusercontent.com/bytedance/ContentV/refs/heads/main/assets/demo.jpg" width="100%">
+    <img src="https://raw.githubusercontent.com/bytedance/ContentV/refs/heads/main/assets/arch.png" width="100%">
+</div>
+
+## ⚡ Quickstart
 
 #### Recommended PyTorch Version
 
@@ -18,32 +61,61 @@ This project presents ContentV, a novel framework that accelerates DiT-based vid
 
 #### Installation
 
-```sh
+```bash
 git clone https://github.com/bytedance/ContentV.git
-pip3 install -r ContentV/requirements.txt
+cd ContentV
+pip3 install -r requirements.txt
 ```
 
 #### T2V Generation
 
-```sh
-cd ContentV
+```bash
 ## For GPU
 python3 demo.py
 ## For NPU
 USE_ASCEND_NPU=1 python3 demo.py
 ```
 
-## Todo List
+## 📊 VBench
+
+| Model | Total Score | Quality Score | Semantic Score | Human Action | Scene | Dynamic Degree | Multiple Objects  | Appear. Style |
+|----------------------|--------|-------|-------|-------|-------|-------|-------|-------|
+| Wan2.1-14B           | 86.22  | 86.67 | 84.44 | 99.20 | 61.24 | 94.26 | 86.59 | 21.59 |
+| **ContentV (Long)**  | 85.14  | 86.64 | 79.12 | 96.80 | 57.38 | 83.05 | 71.41 | 23.02 |
+| Goku†                | 84.85  | 85.60 | 81.87 | 97.60 | 57.08 | 76.11 | 79.48 | 23.08 |
+| Open-Sora 2.0        | 84.34  | 85.40 | 80.12 | 95.40 | 52.71 | 71.39 | 77.72 | 22.98 |
+| Sora†                | 84.28  | 85.51 | 79.35 | 98.20 | 56.95 | 79.91 | 70.85 | 24.76 |
+| **ContentV (Short)** | 84.11  | 86.23 | 75.61 | 89.60 | 44.02 | 79.26 | 74.58 | 21.21 |
+| EasyAnimate 5.1      | 83.42  | 85.03 | 77.01 | 95.60 | 54.31 | 57.15 | 66.85 | 23.06 |
+| Kling 1.6†           | 83.40  | 85.00 | 76.99 | 96.20 | 55.57 | 62.22 | 63.99 | 20.75 |
+| HunyuanVideo         | 83.24  | 85.09 | 75.82 | 94.40 | 53.88 | 70.83 | 68.55 | 19.80 |
+| CogVideoX-5B         | 81.61  | 82.75 | 77.04 | 99.40 | 53.20 | 70.97 | 62.11 | 24.91 |
+| Pika-1.0†            | 80.69  | 82.92 | 71.77 | 86.20 | 49.83 | 47.50 | 43.08 | 22.26 |
+| VideoCrafter-2.0     | 80.44  | 82.20 | 73.42 | 95.00 | 55.29 | 42.50 | 40.66 | 25.13 |
+| AnimateDiff-V2       | 80.27  | 82.90 | 69.75 | 92.60 | 50.19 | 40.83 | 36.88 | 22.42 |
+| OpenSora 1.2         | 79.23  | 80.71 | 73.30 | 85.80 | 42.47 | 47.22 | 58.41 | 23.89 |
+
+## ✅ Todo List
 - [x] Inference code and checkpoints
 - [ ] Training code of RLHF
 
-## License
+## 🧾 License
 This code repository and part of the model weights are licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). Please note that:
 - MM DiT are derived from [Stable Diffusion 3.5 Large](https://huggingface.co/stabilityai/stable-diffusion-3.5-large) and trained with video samples. This Stability AI Model is licensed under the [Stability AI Community License](https://stability.ai/community-license-agreement), Copyright ©  Stability AI Ltd. All Rights Reserved
 - Video VAE from [Wan2.1](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B) is licensed under [Apache 2.0 License](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B/blob/main/LICENSE.txt)
 
-## Acknowledgement
+## ❤️ Acknowledgement
 * [Stable Diffusion 3.5 Large](https://huggingface.co/stabilityai/stable-diffusion-3.5-large)
 * [Wan2.1](https://github.com/Wan-Video/Wan2.1)
 * [Diffusers](https://github.com/huggingface/diffusers)
 * [HuggingFace](https://huggingface.co)
+
+## 🔗 Citation
+
+```bibtex
+@article{contentv2025,
+  title     = {ContentV: Efficient Training of Video Generation Models with Limited Compute},
+  author    = {Bytedance Douyin Content Team},
+  year      = {2025},
+  }
+```