/dt_tryon

Primary LanguagePythonMIT LicenseMIT

DT_TRYON: Using diffusion models to solve the problem of image generation in the fashion field

Introduction

  • pros
    • Minimalist project structure. Only a few files included
    • Continuously updating new features in the future, fully anticipating
    • Follow industry trends and reproduce hot papers

News

May 21st, 2024

  • Release SDXL finetune scripts & sample data
  • download datasets from Baidu
  • You can find running command in 'command' file
  • Some key points when you finetune your model
    • you need prepare Very High Quality data according your project needs
    • 2k-4k pictures will be enough
    • train your model through many epoch. I get the best result on 100 epoch
    • high resolution is very important. I use 960*1280 here
    • the quality of result from finetune model is far better than the ordinary model
  • result sample [960*1280]
  • .jpg

April 22th, 2024

  • Release animation step1 train scripts
  • download datasets from Baidu
  • place downloaded files to your directory
  • change your directory in config file
  • run following cmd
  • I only release partial data, so you need to prepare your data as i do

  • Train

    python -m accelerate.commands.launch --num_processes=2 train_script.py -c configs/train/animate_step1.yaml

April 8th, 2024

  • Release animation
  • download pretrained models and datasets from Baidu
  • place downloaded files to your directory
  • change your directory in config file
  • run following cmd
  • I use UBC fashion dataset to train step2. I release step2 training & testing scripts
  • Step1 training script will be released later

  • Inference

    python test_script.py -c configs/test/animate_step2.yaml

  • Train

    python -m accelerate.commands.launch --main_process_port=28500 --num_processes=2 train_script.py -c configs/train/animate_step2.yaml
  • 0_result.gif 1_result.gif

April 2nd, 2024

  • Release TryOn Demo 
    python -m deploy.viton.viton_demo
    • 4.png

March 26th, 2024

  • Release TryOn

    • Inference

      python test_script.py -c configs/test/viton.yaml

    • Train

      python train_script.py -c configs/train/viton.yaml
    • sample1
    • sample1
    • sample1
    • more results
    • including inference code and train code
    • provide pretrained model files
    • provide dataset
    • core ideas largely come from Animate Anyone and I made some modifications
      • reporduce model structure using huggingface diffusers
      • remove pose guider and cross attention from Unet because I find them no use
      • a different cross attention structure, with which you can input any size of image of condition
      • i do not reproduce temporal attention
      • i use HRViton dataset to train the virtual tryon model

Installation

  • install xformers at the same time pytorch will be installed
  • pip install -r requirements.txt
  • download pretrained models and datasets from Baidu
  • place model files into checkpoints
  • place dataset to your directory
  • change directory path in config files