You can deploy this model and start earning money today!

DiffSynth-Studio/
AttriCtrl-FLUX.1-Dev

2508.02151

apache-2.0

Views

No views yet

AttriCtrl Numerical Image Generation Control Model

Introduction

AttriCtrl enables fine-grained control of numerical image attributes during image generation.

For more details, please refer to our paper: AttriCtrl: Fine-Grained Control of Aesthetic Attribute Intensity in Diffusion Models

Result Demonstration

Brightness

scale = 0.1	scale = 0.3	scale = 0.5	scale = 0.7	scale = 0.9

Detail

scale = 0.1	scale = 0.3	scale = 0.5	scale = 0.7	scale = 0.9

Realism

scale = 0.1	scale = 0.3	scale = 0.5	scale = 0.7	scale = 0.9

Inference Code

shell

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

python

import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig

python

pipe = FluxImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-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"),
        ModelConfig(model_id="DiffSynth-Studio/AttriCtrl-FLUX.1-Dev", origin_file_pattern="models/detail.safetensors")
    ],
)

for i in [0.1, 0.3, 0.5, 0.7, 0.9]:
    image = pipe(prompt="a cat on the beach", seed=2, value_controller_inputs=[i])
    image.save(f"value_control_{i}.jpg")