Leffa: High-fidelity model virtual fitting and character pose adjustment, Meta open source controllable character image generation model

General Introduction

Leffa is a unified framework for generating controlled character images that accurately manipulate the appearance (e.g., virtual fitting) and pose (e.g., pose transfer) of characters. The framework significantly reduces distortion of fine-grained details while maintaining high image quality by directing the target query to focus on the correct reference key in the attention layer.Leffa's loss function is model-independent and can be used to improve the performance of other diffusion models. This project was developed by franciszzj and published on the Hugging Face platform.

As an excellent model for changing clothes: Leffa. Previous approaches to this type of modeling often let the details "run away", for example, making the texture of the clothes blurry. To solve this problem, Meta has invented a "navigation system" (Leffa) that allows the generation process to be more "on target", finding the exact location of details in the reference image, so that the clothing maintains its fine texture! Moreover, this approach enhances the performance of other generative models.

Leffa Model Dressup

Leffa Model Posture Adjustment

Function List

• Controlled Character Image Generation: Generate a controlled character image based on a reference image.
• Virtual Try-On: precisely manipulate the appearance of the character to realize the virtual try-on effect.
• Pose Shift: precisely manipulate the character's pose to realize the pose shift effect.
• High-quality image generation: Maintains high image quality and reduces detail distortion.
• Model-independent loss function: can be used to improve the performance of other diffusion models.

Using Help

Installation process

1. Create the conda environment and install the required dependencies:

   conda create -n leffa python==3.10
   conda activate leffa
   cd Leffa
   pip install -r requirements.txt
   

2. Run the Gradio application:

   python app.py
   

Functional operation flow

1. Controlled character image generation::
   • Upload the reference and target images.
   • Generating Controlled Character Images Using the Leffa Model.
   • Adjust the parameters to achieve the desired look and pose effect.
2. virtual try-on::
   • Upload character images and costume images.
   • Use Leffa models to generate virtual fitting results.
   • Adjust garment position and size for best results.
3. postural shift::
   • Upload a character image and a target pose image.
   • Generate pose transfer effects using the Leffa model.
   • Adjust pose parameters for natural pose transfer.
4. High quality image generation::
   • Ensure that the uploaded reference and target images are of high quality.
   • Generate high quality images using the Leffa model.
   • Check the details of the generated image to make sure there is no distortion.
5. Model-independent loss function::
   • Applying Leffa's loss function to other diffusion models.
   • Perform model training and evaluation to observe the performance improvement effect.

With the above steps, users can easily get started with Leffa for controllable character image generation, virtual try-on and pose transfer, and enjoy the fun of high-quality image generation.

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