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By combining the ideas of lllyasviel/ControlNet and cloneofsimo/lora, we can easily fine-tune stable diffusion to achieve the purpose of controlling its spatial information, with ControlLoRA, a simple and small (~7M parameters, ~25M storage space) network.

ControlNet is large and it's not easy to send to your friends. With the idea of LoRA, we don't even need to transfer the entire stable diffusion model. Use the 25M ControlLoRA to save your time.

You could use gradio apps in the apps directory to try the pretrained models. More dataset types of models and their supporting gradio apps wanted. The annotator directory is borrowed from ControlNet.

You could download some pretrained models from huggingface. Note that I only used 100 MPII pictures for the training of the openpose, so the model effect is not good. So I suggest you train your own ControlLoRA.

Refer to the script in the tasks directory. I highly refer to the training code from diffusers.

You could add or modify config file in the configs directory to custom the ControlLoRA model architecture. To enhance the effect of the model, you could change some blocks to other residual block types of diffusers and you could increase the number of layers of blocks by modify the config files.

• More type tasks mentioned in ControlNet.

• Experiment of mixing LoRA and ControlLoRA.

  We could inject pretrained LoRA models before the ControlLoRA. See mix_lora_and_control_lora.py for more details.

  portrait of male HighCWu

sd-diffusiondb-canny-model-control-lora, on 100 openpose pictures, 30k training steps

Stable Diffusion 1.5 + ControlLoRA (using simple Canny edge detection)

python apps/gradio_canny2image.py

Highly refered to the ControlNet codes.

The Gradio app also allows you to change the Canny edge thresholds. Just try it for more details.

Prompt: "bird"

Prompt: "cute dog"

sd-mpii-pose-model-control-lora, on 100 openpose pictures, 30k training steps

Stable Diffusion 1.5 + ControlLoRA (using human pose)

python apps/gradio_pose2image.py

Highly refered to the ControlNet codes.

Apparently, this model deserves a better UI to directly manipulate pose skeleton. However, again, Gradio is somewhat difficult to customize. Right now you need to input an image and then the Openpose will detect the pose for you.

Note that I only used 100 MPII pictures for the training of the openpose, so the model effect is not good. So I suggest you train your own ControlLoRA.

Prompt: "Chief in the kitchen"

Prompt: "An astronaut on the moon"

PS: I don't know why my gallery didn't show the full images and I should click an output to show the full result of one of the outputs, like this:

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@software{wu2023controllora,
    author = {Wu Hecong},
    month = {2},
    title = {{ControlLoRA: A Light Neural Network To Control Stable Diffusion Spatial Information}},
    url = {https://github.com/HighCWu/ControlLoRA},
    version = {1.0.0},
    year = {2023}
}