Kolors (Kotaku): Efficient Virtual Fitting/Model Dressing, Text-to-Image Generation Models

General Introduction

Kolors is a large-scale text-to-image generation model developed by the Racer team, based on potential diffusion techniques. The model is trained on billions of text-image pairs of data, supports bilingualism in English and Chinese, and is capable of generating high-quality, complex semantically-accurate images.Kolors has significant advantages in visual quality, complex semantic accuracy, and text rendering, and is suitable for both academic research and commercial use.

Online Experience:

https://huggingface.co/spaces/Kwai-Kolors/Kolors-Virtual-Try-On

https://modelscope.cn/studios/Kwai-Kolors/Kolors-Virtual-Try-On

https://virtual-try-on.online/zh/playground

Function List

• Text-to-Image Generation: Input text descriptions to generate corresponding high-quality images.
• Virtual Try-On: Upload portraits and clothing pictures to generate realistic virtual try-on effects.
• Multiple model support: including ControlNet, IP-Adapter, LoRA, etc.
• Open source code: complete training and inference code is provided to support academic research and commercial applications.
• Multi-Platform Compatibility: Supports Diffusers, ComfyUI and other platforms.

Using Help

Installation process

1. environmental preparation: Ensure that Python 3.8 and above is installed and that the necessary dependency libraries are installed.

   pip install -r requirements.txt
   

2. Download model: fromGitHubmaybeHugging FaceDownload Kolors model weights.
3. Configuration environment: Configure environment variables and paths according to the guidelines in the README file.

Guidelines for use

1. Text-to-Image Generation::
   • Run the following command and enter a text description to generate an image:

     python generate.py --text "A dog running on grass."
     

   • The generated image will be saved in the specified directory.
2. virtual try-on::
   • Upload portrait pictures and costume pictures:

     python virtual_try_on.py --person_image "person.jpg" --clothing_image "clothing.jpg"
     

   • The generated virtual fitting results will be saved in the specified directory.
3. model training::
   • Use the following command to start training:

     python train.py --config "config.yaml"
     

   • Adjust the parameters in the configuration file, such as learning rate, batch size, etc., as required.
4. Multi-Platform Compatibility::
   • Use Kolors in Diffusers:

     from diffusers import DiffusionPipeline
     pipeline = DiffusionPipeline.from_pretrained("Kwai-Kolors/Kolors")
     image = pipeline("A dog running on grass.").images[0]
     image.save("output.png")
     

   • Using Kolors in ComfyUI:

     from comfyui import ComfyUI
     ui = ComfyUI(model="Kwai-Kolors/Kolors")
     ui.generate("A dog running on grass.", output="output.png")
     

common problems

1. How to improve the quality of generated images?
   • Adjusting model parameters such as increasing the amount of training data, optimizing the learning rate, etc.
2. What if the virtual fitting doesn't work?
   • Ensure that the uploaded images are clear and well lit, and adjust the angles and proportions of the clothing images.
3. Model training taking too long?
   • Train with high-performance GPUs or choose a cloud computing platform to accelerate the training process.

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