FashionChameleon: Towards Real-Time and Interactive Human-Garment Video Customization

<span>
    <a href="https://arxiv.org/pdf/2605.15824" target="_blank"> 
    <img src='https://img.shields.io/badge/2605.15824-FashionChameleon-red' alt='Paper PDF'></a> &emsp;  &emsp; 
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    <a href='https://quanjiansong.github.io/projects/FashionChameleon' target="_blank">
    <img src='https://img.shields.io/badge/Project_Page-FashionChameleon-green' alt='Project Page'></a>  &emsp;  &emsp;
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<span> 
    <a href='https://huggingface.co/papers/2605.15824' target="_blank"> 
    <img src='https://img.shields.io/badge/Hugging_Face-FashionChameleon-blue' alt='Hugging Face'></a> &emsp;  &emsp;
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    <a href='https://huggingface.co/datasets/QuanjianSong/HGC-Bench' target="_blank"> 
    <img src='https://img.shields.io/badge/Hugging_Face-HGC--Bench-yellow' alt='HGC-Bench'></a> &emsp;  &emsp;
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TL;DR:

We propose FashionChameleon, a real-time and interactive framework for human-garment customization in streaming autoregressive video generation.
It achieves real-time generation at 23.8 FPS on a single GPU.

📅 Todo

• [ ] Release the checkpoint.
• [ ] Release the training-free kv cache rescheduling for interactive inference.
• [x] 🔥 Release the code (Wan2.2-TI2V-5B) for gradient-reweighted dmd and the corresponding inference.
• [x] 🔥 Release the code (Wan2.2-TI2V-5B) for in-context teacher forcing and the corresponding inference.
• [x] 🔥 Release the code (Wan2.2-TI2V-5B) for in-context sft and the corresponding inference.
• [x] 🔥 Release the HGC-Bench.
• [x] 🔥 Release the Project Page.
• [x] 🔥 Release the Technical Report.

✨ Highlight

1. Interactive Customization. We train a single-garment switching teacher using tailored I2V priors and mismatched reference–garment pairs. During generation, we introduce KV-cache rescheduling to enable interactive multi-garment customization without requiring video data containing multi-garment switching.

2. Gradient-Reweighted DMD. Traditional self-forcing treats all self-rolled frames equally during DMD backpropagation. However, later frames typically suffer from larger quality degradation and thus require stronger gradient supervision. We dynamically reweight DMD gradients during self-rolling using a reward model to improve extrapolation consistency.

3. Real-Time Generation. Through streaming distillation with in-context learning, FashionChameleon achieves 23.8 FPS for 720p generation on a single H200 GPU, 30–180× faster than existing customization methods.

🎬 Overview

FashionChameleon is built upon Wan2.2-TI2V-5B, featuring: (i) Teacher Model with In-Context Learning, (ii) Streaming Distillation with In-Context Learning, and (iii) Training-Free KV Cache Rescheduling.

🔧 Step0. Setup

Prepare Environment

git clone https://github.com/QuanjianSong/FashionChameleon.git
cd FashionChameleon
# Installation with the requirement.txt
conda create -n FashionChameleon python=3.10
conda activate FashionChameleon
pip install -r requirements.txt

Download Backbones

Our FashionChameleon is built upon Wan2.2-TI2V-5B. You should first download the backbone weight by running:

export HF_ENDPOINT=https://hf-mirror.com
huggingface-cli download Wan-AI/Wan2.2-TI2V-5B --local-dir-use-symlinks False --local-dir checkpoints/wan_models/Wan2.2-TI2V-5B

In addition, you need to download the CLIP-ViT weights for aesthetic reward initialization by running:

export HF_ENDPOINT=https://hf-mirror.com
huggingface-cli download openai/clip-vit-large-patch14 --local-dir-use-symlinks False --local-dir checkpoints/clip-vit-large-patch14

As well as download the PredictionHead for aesthetic reward initialization by running:

wget -c https://github.com/christophschuhmann/improved-aesthetic-predictor/blob/main/sac%2Blogos%2Bava1-l14-linearMSE.pth -O checkpoints/sac+logos+ava1-l14-linearMSE.pth

🚀 Step1. In-Context SFT

Start Training

You can run the following command to start sft:

CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nnodes=1 --nproc_per_node=4 --master_port=1234 trainer/train.py \
    --config_path configs/sft_wan22_ic.yaml \
    --save_dir outputs/sft_wan22_ic

or simply run:

bash scripts/train/sft_wan22_ic.sh

All training configurations are recorded in configs/sft_wan22_ic.yaml, which can be freely modified according to your needs. Note that our training framework supports variable-resolution bucketing strategies, gradient accumulation, and mixed captions; you only need to adjust the corresponding ASPECT_RATIO, grad_accum_steps, and mixed_captions parameters accordingly. Our FashionChameleon keeps a fixed training resolution of 1280 × 704 while simultaneously maintaining mixed captions during the in-context sft process, with a global batch size of 64.

Start Inference

You can run the following command to start bidirectional inference:

CUDA_VISIBLE_DEVICES=1 python predictor/infer_ic.py \
    --config_path configs/sft_wan22_ic.yaml \
    --seed 42 \
    --h 1280 \
    --w 704 \
    --num_frames 81 \
    --output_path samples/sft_wan22_ic/ \
    --checkpoint XXX

or simple run:

bash scripts/infer/infer_wan22_ic.sh

The checkpoint denotes the weights after in-context sft. Our inference code by default processes data in the format of HGC-Bench. You can first download the test dataset from Hugging Face.

🧑‍🏫 Step2. In-Context Teacher Forcing

Start Training

You can run the following command to start in-context teacher forcing:

CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nnodes=1 --nproc_per_node=4 --master_port=1234 trainer/train.py \
    --config_path configs/tf_wan22_ic.yaml \
    --save_dir outputs/tf_wan22_ic

or simple run:

bash scripts/train/tf_wan22_ic.sh

All training configurations are recorded in configs/tf_wan22_ic.yaml, which can be freely modified according to your needs. Note that our training framework supports variable-resolution bucketing strategies, gradient accumulation, and mixed captions; you only need to adjust the corresponding ASPECT_RATIO, grad_accum_steps, and mixed_captions parameters accordingly. Our FashionChameleon keeps a fixed training resolution of 1280 × 704 while simultaneously maintaining long caption during the in-context teacher forcing, with a global batch size of 64.

Start Inference

You can run the following command to start causal inference:

CUDA_VISIBLE_DEVICES=1 python predictor/causal_infer_ic.py \
    --config_path configs/tf_wan22_ic.yaml \
    --seed 42 \
    --h 1280 \
    --w 704 \
    --num_frames 81 \
    --output_path samples/tf_wan22_ic/ \
    --checkpoint XXX

or simple run:

bash scripts/infer/causal_infer_wan22_ic.sh

The checkpoint denotes the weights after in-context teacher forcing. Our inference code by default processes data in the format of HGC-Bench. You can first download the test dataset from Hugging Face.

📈 Step3. Gradient-Reweighted DMD

Start Training

You can run the following command to start gradient-reweighted dmd with self-forcing:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 torchrun --nnodes=1 --nproc_per_node=8 --master_port=1234 trainer/train.py \
    --config_path configs/gr_dmd_wan22_ic.yaml \
    --save_dir outputs/gr_dmd_wan22_ic

or simple run:

bash scripts/train/sf_wan22_ic.sh

All training configurations are recorded in configs/gr_dmd_wan22_ic.yaml, which can be freely modified according to your needs. Note that our training framework supports variable-resolution bucketing strategies, gradient accumulation, and mixed captions; you only need to adjust the corresponding ASPECT_RATIO, grad_accum_steps, and mixed_captions parameters accordingly. Our FashionChameleon keeps a fixed training resolution of 1280 × 704 while simultaneously maintaining long caption during the in-context gradient-reweighted dmd, with a global batch size of 64..

Start Inference

You can run the following command to start stream inference:

CUDA_VISIBLE_DEVICES=1 python predictor/stream_infer_ic.py \
    --config_path configs/gr_dmd_wan22_ic.yaml \
    --seed 42 \
    --h 1280 \
    --w 704 \
    --num_frames 81 \
    --output_path samples/gr_dmd_wan22_ic_reward/ \
    --checkpoint XXX

or simple run:

bash scripts/infer/stream_infer_wan22_ic.sh

The checkpoint denotes the weights after gradient-reweighted dmd with self-forcing. Our inference code by default processes data in the format of HGC-Bench. You can first download the test dataset from Hugging Face.

🌈 Comparison

🌊 Application

🤝 Acknowledgements

This codebase borrows from Wan2.2 and Self-Forcing. Many thanks to them for open-source contributions.

🎓 Bibtex

🤗 If you find this code helpful for your research, please cite:

@article{song2026fashionchameleon,
  title={FashionChameleon: Towards Real-Time and Interactive Human-Garment Video Customization},
  author={Song, Quanjian and Shen, Yefeng and Chen, Mengting and Sun, Hao and Lan, Jinsong and Zhu, Xiaoyong and Zheng, Bo and Cao, Liujuan},
  journal={arXiv preprint arXiv:2605.15824},
  year={2026}
}