metadata
frameworks:
- Pytorch
license: Apache License 2.0
tasks:
- text-to-image-synthesis
AttriCtrl 数值型生图控制模型
简介
AttriCtrl 可以实现数值型图像指标的生图控制。
更多细节请参考我们的论文: AttriCtrl: Fine-Grained Control of Aesthetic Attribute Intensity in Diffusion Models
效果展示
亮度（Brightness）
| scale = 0.1 | scale = 0.3 | scale = 0.5 | scale = 0.7 | scale = 0.9 |
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细节（Detail）
| scale = 0.1 | scale = 0.3 | scale = 0.5 | scale = 0.7 | scale = 0.9 |
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摄影感（Realism）
| scale = 0.1 | scale = 0.3 | scale = 0.5 | scale = 0.7 | scale = 0.9 |
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推理代码
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
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")