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README.md

@@ -7,50 +7,32 @@ license_link: LICENSE
 <!-- ## **HunyuanVideo** -->
 
 <p align="center">
-  <img src="https://aivideo.hunyuan.tencent.com/download/HunyuanVideo/material/logo.png"  height=100>
+  <img src="https://raw.githubusercontent.com/Tencent/HunyuanVideo/main/assets/logo.png"  height=100>
 </p>
 
 # HunyuanVideo: A Systematic Framework For Large Video Generation Model Training
 
 -----
 
-This repo contains PyTorch model definitions, pre-trained weights and inference/sampling code for our paper exploring HunyuanVideo. You can find more visualizations on our [project page](https://aivideo.hunyuan.tencent.com).
+This repo contains the weights of HunyuanVideo-PromptRewrite model. You can find more visualizations on our [project page](https://aivideo.hunyuan.tencent.com).
 
 > [**HunyuanVideo: A Systematic Framework For Large Video Generation Model Training**](https://github.com/Tencent/HunyuanVideo/blob/main/assets/hunyuanvideo.pdf) <br>
 
-Due to the limitation of github page, the video is compressed. The original video can be downloaded from [here](https://aivideo.hunyuan.tencent.com/download/HunyuanVideo/material/demo.mov).
 
-## 🔥🔥🔥 News!!
-* Dec 3, 2024: 🤗 We release the inference code and model weights of HunyuanVideo.
-
-## 📑 Open-source Plan
-
-- HunyuanVideo (Text-to-Video Model)
-  - [x] Inference 
-  - [x] Checkpoints 
-  - [ ] Penguin Video Benchmark
-  - [ ] Web Demo (Gradio) 
-  - [ ] ComfyUI
-  - [ ] Diffusers 
-- HunyuanVideo (Image-to-Video Model)
-  - [ ] Inference 
-  - [ ] Checkpoints 
 
 ## Contents
 - [HunyuanVideo: A Systematic Framework For Large Video Generation Model Training](#hunyuanvideo--a-systematic-framework-for-large-video-generation-model-training)
-  - [🔥🔥🔥 News!!](#-news!!)
-  - [📑 Open-source Plan](#-open-source-plan)
   - [Contents](#contents)
   - [**Abstract**](#abstract)
-  - [**HunyuanVideo Overall Architechture**](#hunyuanvideo-overall-architechture)
-  - [🎉 **HunyuanVideo Key Features**](#hunyuanvideo-key-features)
+  - [**HunyuanVideo Overall Architechture**](#-hunyuanvideo-overall-architechture)
+  - [🎉 **HunyuanVideo Key Features**](#-hunyuanvideo-key-features)
     - [**Unified Image and Video Generative Architecture**](#unified-image-and-video-generative-architecture)
     - [**MLLM Text Encoder**](#mllm-text-encoder)
     - [**3D VAE**](#3d-vae)
     - [**Prompt Rewrite**](#prompt-rewrite)
-  - [📈 Comparisons](#comparisons)
-  - [🔗 BibTeX](#bibtex)
-  - [Acknowledgements](#acknowledgements)
+  - [📈 Comparisons](#-comparisons)
+  - [🔗 BibTeX](#-bibtex)
+  - [Acknowledgements](#-acknowledgements)
 ---
 
 ## **Abstract**
@@ -66,7 +48,7 @@ using a large language model, and used as the condition. Gaussian noise and cond
 input, our generate model generates an output latent, which is decoded to images or videos through
 the 3D VAE decoder.
 <p align="center">
-  <img src="https://aivideo.hunyuan.tencent.com/download/HunyuanVideo/material/overall.png"  height=300>
+  <img src="https://raw.githubusercontent.com/Tencent/HunyuanVideo/main/assets/overall.png"  height=300>
 </p>
 
 ## 🎉 **HunyuanVideo Key Features**
@@ -78,7 +60,7 @@ tokens and feed them into subsequent Transformer blocks for effective multimodal
 This design captures complex interactions between visual and semantic information, enhancing
 overall model performance.
 <p align="center">
-  <img src="https://aivideo.hunyuan.tencent.com/download/HunyuanVideo/material/backbone.png"  height=350>
+  <img src="https://raw.githubusercontent.com/Tencent/HunyuanVideo/main/assets/backbone.png"  height=350>
 </p>
 
 ### **MLLM Text Encoder**
@@ -86,19 +68,19 @@ Some previous text-to-video model typically use pretrainednCLIP and T5-XXL as te
 Compared with CLIP, MLLM has been demonstrated superior ability in image detail description
 and complex reasoning; (iii) MLLM can play as a zero-shot learner by following system instructions prepended to user prompts, helping text features pay more attention to key information. In addition, MLLM is based on causal attention while T5-XXL utilizes bidirectional attention that produces better text guidance for diffusion models. Therefore, we introduce an extra bidirectional token refiner for enhacing text features.
 <p align="center">
-  <img src="https://aivideo.hunyuan.tencent.com/download/HunyuanVideo/material/text_encoder.png"  height=275>
+  <img src="https://raw.githubusercontent.com/Tencent/HunyuanVideo/main/assets/text_encoder.png"  height=275>
 </p>
 
 ### **3D VAE**
 HunyuanVideo trains a 3D VAE with CausalConv3D to compress pixel-space videos and images into a compact latent space. We set the compression ratios of video length, space and channel to 4, 8 and 16 respectively. This can significantly reduce the number of tokens for the subsequent diffusion transformer model, allowing us to train videos at the original resolution and frame rate.
 <p align="center">
-  <img src="https://aivideo.hunyuan.tencent.com/download/HunyuanVideo/material/3dvae.png"  height=150>
+  <img src="https://raw.githubusercontent.com/Tencent/HunyuanVideo/main/assets/3dvae.png"  height=150>
 </p>
 
 ### **Prompt Rewrite**
 To address the variability in linguistic style and length of user-provided prompts, we fine-tune the [Hunyuan-Large model](https://github.com/Tencent/Tencent-Hunyuan-Large) as our prompt rewrite model to adapt the original user prompt to model-preferred prompt.
 
-We provide two rewrite modes: Normal mode and Master mode, which can be called using different prompts. The prompts are shown [here](hyvideo/prompt_rewrite.py). The Normal mode is designed to enhance the video generation model's comprehension of user intent, facilitating a more accurate interpretation of the instructions provided. The Master mode enhances the description of aspects such as composition, lighting, and camera movement, which leans towards generating videos with a higher visual quality. However, this emphasis may occasionally result in the loss of some semantic details. 
+We provide two rewrite modes: Normal mode and Master mode, which can be called using different prompts. The Normal mode is designed to enhance the video generation model's comprehension of user intent, facilitating a more accurate interpretation of the instructions provided. The Master mode enhances the description of aspects such as composition, lighting, and camera movement, which leans towards generating videos with a higher visual quality. However, this emphasis may occasionally result in the loss of some semantic details. 
 
 The Prompt Rewrite Model can be directly deployed and inferred using the [Hunyuan-Large original code](https://github.com/Tencent/Tencent-Hunyuan-Large). We release the weights of the Prompt Rewrite Model [here](https://huggingface.co/Tencent/HunyuanVideo-PromptRewrite).