Update README.md

README.md

@@ -23,6 +23,20 @@ FastVideo currently offers: (with more to come)
 - Memory efficient finetuning with LoRA, precomputed latent, and precomputed text embeddings.
 
 Dev in progress and highly experimental.
+
+## 🎥 More Demos
+
+Fast-Hunyuan comparision with original Hunyuan, achieving an 8X diffusion speed boost with the FastVideo framework.
+
+https://github.com/user-attachments/assets/064ac1d2-11ed-4a0c-955b-4d412a96ef30
+
+Comparsion between Openai Sora, original Hunyuan and FastHunyuan
+
+https://github.com/user-attachments/assets/d323b712-3f68-42b2-952b-94f6a49c4836
+
+
+
+
 ## Change Log
 
 - ```2024/12/17```: `FastVideo` v0.1 is released.
@@ -55,82 +69,12 @@ sh scripts/inference/inference_hunyuan.sh
 ```
 You can also inference FastHunyuan in the [official Hunyuan github](https://github.com/Tencent/HunyuanVideo).
 
-## 🎥 More Demos
-
-https://github.com/user-attachments/assets/064ac1d2-11ed-4a0c-955b-4d412a96ef30
-
-
-https://github.com/user-attachments/assets/122cfa1a-e2a3-47a5-80c8-b8852d347d9a
-
-
-## 🎯 Distill
-
-Our distillation recipe is based on [Phased Consistency Model](https://github.com/G-U-N/Phased-Consistency-Model). We did not find significant improvement using multi-phase distillation, so we keep the one phase setup similar to the original latent consistency model's recipe.
-
-We use the [MixKit](https://huggingface.co/datasets/LanguageBind/Open-Sora-Plan-v1.1.0/tree/main/all_mixkit) dataset for distillation. To avoid running the text encoder and VAE during training, we preprocess all data to generate text embeddings and VAE latents.
-
-Preprocessing instructions can be found [data_preprocess.md](docs/data_preprocess.md). For convenience, we also provide preprocessed data that can be downloaded directly using the following command:
-
-```bash
-python scripts/huggingface/download_hf.py --repo_id=FastVideo/HD-Mixkit-Finetune-Hunyuan --local_dir=data/HD-Mixkit-Finetune-Hunyuan --repo_type=dataset
-```
-Next, download the original model weights with:
-
-```bash
-python scripts/huggingface/download_hf.py --repo_id=FastVideo/hunyuan --local_dir=data/hunyuan --repo_type=model
-```
-To launch the distillation process, use the following commands:
-
-```
-bash scripts/distill/distill_mochi.sh # for mochi
-bash scripts/distill/distill_hunyuan.sh # for hunyuan
-```
-We also provide an optional script for distillation with adversarial loss, located at `fastvideo/distill_adv.py`. Although we tried adversarial loss, we did not observe significant improvements.
-
-
-## Finetune
-
-### ⚡ Full Finetune
-
-Ensure your data is prepared and preprocessed in the format specified in [data_preprocess.md](docs/data_preprocess.md). For convenience, we also provide a mochi preprocessed Black Myth Wukong data that can be downloaded directly:
-```bash
-python scripts/huggingface/download_hf.py --repo_id=FastVideo/Mochi-Black-Myth --local_dir=data/Mochi-Black-Myth --repo_type=dataset
-```
-Download the original model weights with:
-```bash
-python scripts/huggingface/download_hf.py --repo_id=genmo/mochi-1-preview --local_dir=data/mochi --repo_type=model
-python scripts/huggingface/download_hf.py --repo_id=FastVideo/hunyuan --local_dir=data/hunyuan --repo_type=model
-```
-
-Then you can run the finetune with:
-```
-bash scripts/finetune/finetune_mochi.sh # for mochi
-```
-**Note that for finetuning, we did not tune the hyperparameters in the provided script**
-
-### ⚡ Lora Finetune
-
-Currently, we only provide Lora Finetune for Mochi model, the command for Lora Finetune is
-```
-bash scripts/finetune/finetune_mochi_lora.sh
-```
-### Minimum Hardware Requirement
-- 40 GB GPU memory each for 2 GPUs with lora
-- 30 GB GPU memory each for 2 GPUs with CPU offload and lora.
-
-### Finetune with Both Image and Video
-Our codebase support finetuning with both image and video. 
-
-```bash
-bash scripts/finetune/finetune_hunyuan.sh
-bash scripts/finetune/finetune_mochi_lora_mix.sh
-```
-For Image-Video Mixture Fine-tuning, make sure to enable the --group_frame option in your script.
-
-
-
 
+## Distillation
+Please refer to the [distillation guide](docs/distillation.md).
 
+## Finetuning
+Please refer to the [finetuning guide](docs/finetuning.md).
 
 ## Acknowledgement
 We learned and reused code from the following projects: [PCM](https://github.com/G-U-N/Phased-Consistency-Model), [diffusers](https://github.com/huggingface/diffusers), [OpenSoraPlan](https://github.com/PKU-YuanGroup/Open-Sora-Plan), and [xDiT](https://github.com/xdit-project/xDiT).