MochunniaN1/One-to-All-sub

One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer

This repository contains the sample training data and benchmarks associated with the paper One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer.

The paper presents a unified framework for high-fidelity character animation and image pose transfer for references with arbitrary layouts, addressing spatial misalignment and partially visible references through innovative techniques.

• 🌐 Project Page
• 💻 GitHub Repository

🌟 Highlights

We provide a complete and reproducible training and evaluation pipeline:

• ✅ Full Training Code: Three-stage progressive training from scratch
• ✅ Complete Benchmarks: Reproduction code and pre-trained checkpoints
• ✅ Flexible Training Codebase: Multi-resolution, multi-aspect-ratio, and multi-frame training codebase
• ✅ Datasets: Pre-processed open-source datasets + self-collected cartoon data

☕️ Quick Inference (Sample Usage)

To perform quick inference with the models, follow these steps from the GitHub repository:

🔧 Dependencies and Installation

1. Clone Repo

   git clone https://github.com/ssj9596/One-to-All-Animation.git
   cd One-to-All-Animation
   

2. Create Conda Environment and Install Dependencies

   # create new conda env
   conda create -n one-to-all python=3.12
   conda activate one-to-all
   
   # install pytorch
   pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124
   # or
   pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 -i https://mirrors.aliyun.com/pypi/simple/
   
   # install python dependencies
   pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
   
   
   # (Recommended) install flash attention 3 (or 2) from source:
   # https://github.com/Dao-AILab/flash-attention
   

🎬 Training from scratch

💡 Data Collection Required: We find current open-source datasets are not sufficient for training from scratch. We strongly recommend collecting at least 3,000 additional high-quality video samples for better results.

We divide the training process into several steps to help you reproduce our results from scratch (using 1.3B as an example).

1. Download Pretrained Models

   Download the base model from HuggingFace: Wan-AI/Wan2.1-T2V-1.3B-Diffusers

2. Download Training Datasets and Pose Pool

   cd datasets
   bash setup_datasets.sh
   

   This will download and prepare:

   • Training datasets (open-source + cartoon): datasets/opensource_dataset/
   • Pose pool for face enhancement: datasets/opensource_pose_pool/
   Manual Download Links

   • opensource_dataset
   • opensource_pose_pool

3. Training

   We provide three-stage training scripts:

   • Stage 1: Reference Extractor

   cd video-generation
   bash training_scripts/train1.3b_only_refextractor_2d.sh
   # Convert checkpoint to FP32
   cd outputs_wanx1.3b/train1.3b_only_refextractor_2d/checkpoint-xxx
   mkdir fp32_model_xxx
   python zero_to_fp32.py . fp32_model_xxx --safe_serialization
   # Run inference (update model path in inference_refextractor.py first)
   cd ../../../
   # Edit inference_refextractor.py and change ckpt_path to:
   # ./outputs_wanx1.3b/train1.3b_only_refextractor_2d/checkpoint-xxx/fp32_model_xxx
   python inference_refextractor.py
   

   • Stage 2: Pose Control

   bash training_scripts/train1.3b_posecontrol_prefix_2d.sh
   

   • Stage 3: Token Replace for Long video generation

   bash training_scripts/train1.3b_posecontrol_prefix_2d_tokenreplace.sh
   

   💡 Training Notes:

   • Each stage uses different training resolutions - check the scripts for specific resolution settings
   • Fine-tuning from our checkpoints: If you want to continue training from our pre-trained models, directly use the Stage 3 script and modify the checkpoint path

📊 Reproduce Paper Results

We provide scripts to reproduce the quantitative results reported in our paper.

1. Download Benchmark

   cd benchmark
   bash setup_datasets.sh
   

2. Prepare Model Input

   cd ../video-generation
   python reproduce/infer_preprocess.py 
   

3. Run Inference We provide inference scripts for different model sizes and datasets:

   # TikTok dataset
   python reproduce/inference_tiktok1.3b.py   # 1.3B model
   python reproduce/inference_tiktok14b.py    # 14B model
   
   # Cartoon dataset
   python reproduce/inference_cartoon1.3b.py  # 1.3B model
   python reproduce/inference_cartoon14b.py   # 14B model
   

4. Prepare gt/pred pairs for Judge

   cd ../benchmark
   # TikTok dataset
   python prepare_eval_frames_tiktok.py
   # Cartoon dataset
   python prepare_eval_frames_cartoon.py
   

5. Run judge

   # prepare DisCo environment and lpips fvd ckpt for judge
   cd DisCo
   # TikTok dataset
   bash eval_tiktok.sh
   python summary.py