A W Portrait C N

Introduction

AWPortraitCN is a model based on the FLUX.1-dev framework, specifically designed for generating realistic images that align with the aesthetics commonly associated with Chinese portraits. It excels in rendering various types of portraits, including indoor, outdoor, fashion, and studio photos, with a focus on producing delicate and realistic skin textures. The model is compatible with the AWPortraitSR workflow for enhanced raw image effects.

Architecture

AWPortraitCN utilizes the diffusers library to implement stable diffusion techniques for text-to-image conversion. The model has been tailored to capture the unique visual aesthetics of Chinese portraiture, achieving high-quality results in terms of detail and realism.

Training

The model has been trained on a curated dataset of images that emphasize the cultural and aesthetic nuances of Chinese portraiture. The training process focused on improving image quality and generalization while maintaining a realistic portrayal of subjects. The expertise of DynamicWang, a copyrighted user, contributed to the model's development under specific permissions.

Guide: Running Locally

1. Installation: Clone the repository from Hugging Face and install necessary dependencies.

   git clone https://huggingface.co/Shakker-Labs/AWPortraitCN
   cd AWPortraitCN
   pip install -r requirements.txt
   

2. Model Setup: Load the model using the diffusers library.

   from diffusers import StableDiffusionPipeline
   model = StableDiffusionPipeline.from_pretrained("Shakker-Labs/AWPortraitCN")
   

3. Inference: Use the model to generate images.

   image = model("Enter your text prompt here")
   image.save("output.png")
   

4. Hardware Recommendations: For optimal performance, using cloud GPUs such as NVIDIA Tesla V100 or A100 on platforms like AWS, Google Cloud, or Azure is recommended.

License

AWPortraitCN is released under the flux-1-dev-non-commercial-license. For more details, refer to the license document.