AnonymizedGithub/DisCo

DisCo: Referring Human Dance Generation in Real World

DisCo: Disentangled Control for Referring Human Dance Generation in Real World

Tan Wang*, Linjie Li*, Kevin Lin*, Chung-Ching Lin, Zhengyuan Yang, Hanwang Zhang, Zicheng Liu, Lijuan Wang

Nanyang Technological University | Microsoft Azure AI

🔥 News

• [2023.07.03] Provide the local demo deployment example code. Now you can try our demo on you own dev machine!
• [2023.07.03] We update the Pre-training tsv data.
• [2023.06.28] We have released DisCo Human Attribute Pre-training Code.
• [2023.06.21] DisCo Human Image Editing Demo is released! Have a try!
• [2023.06.21] We release the human-specific fine-tuning code for reference. Come and build your own specific dance model!
• [2023.06.21] Release the code for general fine-tuning.
• [2023.06.21] We release the human attribute pre-trained checkpoint and the fine-tuning checkpoint.

🎨 DEMO

Launch Demo Locally (Video dance generation demo is on the way!)

1. Download the checkpoint model or use your own model.

2. Run the jupyter notebook file (remember to revise the checkpoint path and args).

[Online Gradio Demo]

📝 Introduction

In this project, we introduce DisCo as a generalized referring human dance generation toolkit, which supports both human image & video generation with multiple usage cases (pre-training, fine-tuning, and human-specific fine-tuning), especially good in real-world scenarios.

✨Compared to existing works, DisCo achieves:

• Generalizability to a large-scale real-world human without human-specific fine-tuning (We also support human-specific fine-tuning). Previous methods only support generation for a specific domain of human, e.g., DreamPose only generate fashion model with easy catwalk pose.

• Current SOTA results for referring human dance generation.

• Extensive usage cases and applications (see project page for more details).

• An easy-to-follow framework, supporting efficient training (x-formers, FP16 training, deepspeed, wandb) and a wide range of possible research directions (pre-training -> fine-tuning -> human-specific fine-tuning).

🌟With this project, you can get:

• [User]: Just try our online demo! Or deploy the model inference locally.
• [Researcher]: An easy-to-use codebase for re-implementation and devleplment.
• [Researcher]: A large amount of research directions for further improvement.

🚀 Getting Started

Installation

pip install --user torch==1.12.1+cu113 torchvision==0.13.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html
pip install --user progressbar psutil pymongo simplejson yacs boto3 pyyaml ete3 easydict deprecated future django orderedset python-magic datasets h5py omegaconf einops ipdb
pip install --user --exists-action w -r requirements.txt
pip install git+https://github.com/microsoft/azfuse.git


## for acceleration
pip install --user deepspeed==0.6.3
pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers

Data Preparation

1. Human Attribute Pre-training

We create a human image subset (700K Images) filtered from existing image corpus for human attribute pre-training:

Dataset	COCO (Single Person)	TikTok Style	DeepFashion2	SHHQ-1.0	LAION-Human
Size	20K	124K	276K	40K	240K

The pre-processed pre-training data with the efficient TSV data format can be downloaded here (Google Cloud) [within Human_Attribute_Pretrain folder].

Data Root
└── composite/
    ├── train_xxx.yaml  # The path need to be then specified in the training args
    └── val_xxx.yaml
    ...
└── TikTokDance/
    ├── xxx_images.tsv
    └── xxx_poses.tsv
    ...
└── coco/  
    ├── xxx_images.tsv
    └── xxx_poses.tsv

2. Fine-tuning with Disentangled Control

We use the TikTok dataset for the fine-tuning.

We have already pre-processed the tiktok data with the efficient TSV format which can be downloaded here (Google Cloud). (Note that we only use the 1st frame of each TikTok video as the reference image.)

The data folder structure should be like:

Data Root
└── composite_offset/
    ├── train_xxx.yaml  # The path need to be then specified in the training args
    └── val_xxx.yaml
    ...
└── TikTokDance/
    ├── xxx_images.tsv
    └── xxx_poses.tsv
    ...

Human Attribute Pre-training

Training:

AZFUSE_USE_FUSE=0 QD_USE_LINEIDX_8B=0 NCCL_ASYNC_ERROR_HANDLING=0 python finetune_sdm_yaml.py --cf config/ref_attn_clip_combine_controlnet_attr_pretraining/coco_S256_xformers_tsv_strongrand.py --do_train --root_dir /home1/wangtan/code/ms_internship2/github_repo/run_test \
--local_train_batch_size 64 --local_eval_batch_size 64 --log_dir exp/tiktok_pretrain \
--epochs 40 --deepspeed --eval_step 2000 --save_step 2000 --gradient_accumulate_steps 1 \
--learning_rate 1e-3 --fix_dist_seed --loss_target "noise" \
--train_yaml ./blob_dir/debug_output/video_sythesis/dataset/composite/train_TiktokDance-coco-single_person-Lindsey_0411_youtube-SHHQ-1.0-deepfashion2-laion_human-masks-single_cap.yaml --val_yaml ./blob_dir/debug_output/video_sythesis/dataset/composite/val_TiktokDance-coco-single_person-SHHQ-1.0-masks-single_cap.yaml \
--unet_unfreeze_type "transblocks" --refer_sdvae --ref_null_caption False --combine_clip_local --combine_use_mask \
--conds "masks" --max_eval_samples 2000 --strong_aug_stage1 --node_split_sampler 0

Pre-trained Model Checkpoint: Google Cloud

Fine-tuning with Disentangled Control

1. Modify the config file

Download the sd-image-variations-diffusers from official diffusers repo and put it according to the config file pretrained_model_path. Or you can also choose to modify the pretrained_model_path.

2. w/o Classifier-Free Guidance (CFG)

Training:

[*To enable WANDB, set up the wandb key in utils/lib.py]

AZFUSE_USE_FUSE=0 NCCL_ASYNC_ERROR_HANDLING=0 python finetune_sdm_yaml.py --cf config/ref_attn_clip_combine_controlnet/tiktok_S256L16_xformers_tsv.py \
--do_train --root_dir /home1/wangtan/code/ms_internship2/github_repo/run_test \ 
--local_train_batch_size 32 \
--local_eval_batch_size 32 \
--log_dir exp/tiktok_ft \ 
--epochs 20 --deepspeed \
--eval_step 500 --save_step 500 \
--gradient_accumulate_steps 1 \
--learning_rate 2e-4 --fix_dist_seed --loss_target "noise" \
--train_yaml /home/wangtan/data/disco/yaml_file/train_TiktokDance-poses-masks.yaml \
--val_yaml /home/wangtan/data/disco/yaml_file/new10val_TiktokDance-poses-masks.yaml \
--unet_unfreeze_type "all" \
--refer_sdvae \
--ref_null_caption False \
--combine_clip_local --combine_use_mask \
--conds "poses" "masks" \
--stage1_pretrain_path /path/to/pretrained_model_checkpoint/mp_rank_00_model_states.pt 

Evaluation:

We use gen_eval.sh to one-stop get the evaluation metrics for {exp_dir_path}/{exp_folder_name})

bash gen_eval.sh {exp_dir_path} {exp_folder_name}

Visualization:

To run the visualization, just change --do_train to --eval_visu . You can also specify the visualization folder name with '--eval_save_filename' xxx.

Model Checkpoint (Google Cloud): TikTok Training Data (FID-FVD: 20.2) | More TikTok-Style Training Data (FID-FVD: 18.7)

3. w/ Classifier-Free Guidance (CFG) [CFG can bring a slightly better results]

Training (add the following args into the training script of w/o CFG):

--drop_ref 0.05 # probability to dropout the reference image during training
--guidance_scale 1.5 # the scale of the CFG

Evaluation:

We use gen_eval.sh to one-stop get the evaluation metrics for {exp_dir_path}/{exp_folder_name})

bash gen_eval.sh {exp_dir_path} {exp_folder_name}

Visualization:

To run the visualization, just change --do_train to --eval_visu . You can also specify the visualization folder name with '--eval_save_filename' xxx. (Remember to also specify the --guidance_scale)

Model Checkpoint (Google Cloud): TikTok Training Data (FID-FVD: 18.8) | More TikTok-Style Training Data (FID-FVD: 15.7)

Human-Specific Fine-tuning

1. Prepare dataset that you want to use for training

• Prepare a human-specific video or a set of human images

• Use Grounded-SAM and OpenPose to obtain human mask and human skeleton for each training image (See PREPRO.MD for more details)

2. Run the following script for human-specific fine-tuning:

For parameter tuning, recommend to first tune the learning-rate and unet_unfreeze_type.

AZFUSE_USE_FUSE=0 NCCL_ASYNC_ERROR_HANDLING=0 python finetune_sdm_yaml.py \
--cf config/ref_attn_clip_combine_controlnet_imgspecific_ft/webtan_S256L16_xformers_upsquare.py --do_train --root_dir /path/of/saving/root \
--local_train_batch_size 32 --local_eval_batch_size 32 --log_dir exp/human_specific_ft/ \
--epochs 20 --deepspeed --eval_step 500 --save_step 500 --gradient_accumulate_steps 1 \
--learning_rate 1e-3  --fix_dist_seed  --loss_target "noise" \
--unet_unfreeze_type "crossattn" \
--refer_sdvae --ref_null_caption False --combine_clip_local --combine_use_mask --conds "poses" "masks" \
--freeze_pose True --freeze_background False \
--pretrained_model /path/to/the/ft_model_checkpoint \
--ft_iters 500 --ft_one_ref_image False --ft_idx dataset/folder/name --strong_aug_stage1 True --strong_rand_stage2 True

Release Plan

• Code for "Fine-tuning with Disentangled Control"
• Code for "Human-Specific Fine-tuning"
• Model Checkpoints for Pre-training and Fine-tuning
• HuggingFace Demo
• Code for "Human Attribute Pre-training"

Citation

If you use our work in your research, please cite:

@article{disco,
title={DisCo: Disentangled Control for Referring Human Dance Generation in Real World},
author={Wang, Tan and Li, Linjie and Lin, Kevin and Lin, Chung-Ching and Yang, Zhengyuan and Liu, Zicheng and Wang, Lijuan},
website={https://disco-dance.github.io/},
year={2023}
}