DiffSynth-Studio/FLUX.1-Kontext-dev-lora-ArtAug

Aesthetic Enhancement - Kontext Image Editing LoRA

Model Introduction

This LoRA model is trained based on the Kontext model and DiffSynth-Studio. After using this model, you can input the instruction Enhance the aesthetic quality of this image. to enhance the aesthetic quality of an image. The model automatically analyzes aspects such as lighting and composition based on the image content and adjusts them to make the image more visually appealing. This model can be applied repeatedly, meaning you can perform aesthetic enhancement multiple times on an already enhanced image.

Model Results

	Example 1	Example 2	Example 3
Original
1st Enhancement
2nd Enhancement
3rd Enhancement

	Example 4	Example 5	Example 6
Original
1st Enhancement
2nd Enhancement
3rd Enhancement

Usage Instructions

This model is built on the DiffSynth-Studio framework. Please install it first:

git clone https://github.com/modelscope/DiffSynth-Studio.git  
cd DiffSynth-Studio
pip install -e .

import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
from PIL import Image
from modelscope import snapshot_download

snapshot_download("DiffSynth-Studio/FLUX.1-Kontext-dev-lora-ArtAug", cache_dir="./models")
pipe = FluxImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="flux1-kontext-dev.safetensors"),
        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
    ],
)
pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/FLUX.1-Kontext-dev-lora-ArtAug/model.safetensors", alpha=1)

image = Image.open("your_image.jpg")
image = pipe(
    prompt="Enhance the aesthetic quality of this image.",
    kontext_images=image,
    embedded_guidance=2.5,
    seed=0,
)
image.save("output.jpg")