chikayan / control-lora-v2

ControlLoRA Version 2: A Lightweight Neural Network To Control Stable Diffusion Spatial Information Version 2

ControlLoRA Version 2: A Lightweight Neural Network To Control Stable Diffusion Spatial Information Version 2

ControlLoRA Version 2 is a neural network structure extended from Controlnet to control diffusion models by adding extra conditions.

ControlLoRA Version 2 uses the same structure as Controlnet. But its core weight comes from UNet, unmodified. Only hint image encoding layers, linear lora layers and conv2d lora layers used for weight offset are trained.

The main idea is from my ControlLoRA and sdxl control-lora.

The current implementation idea is basically the same as sdxl's control-lora. But I mainly extend the ControlNetModel implementation from diffusers and use the user-friendly sd v1.5 for training. (The training method of sdxl control-lora could be easily used after modification according to train_controlnet_sdxl.py. My training code is also from train_controlnet.py)

Notice: I didn't train some extra layers like the norm layers (while stabilityai did). So this repo is more appropriate to the name of control-lora.

Why Version 2

My first version of ControlLoRA directly used the spatial information after simple convolutions to perform LoRA offset on the linear layers in the attention layers. This results in a lot of prior information in UNet not being utilized.

Therefore, in version 2, I directly operate ControlNet, but do not change the weights of some UNet layers used in ControlNet, and only train the hint image encoder and the LoRA offset weights to achieve lightweighting.

Thanks to Locon's idea, we could also apply LoRA offset to the conv2d weights.

Training

Run this command:

$ accelerate launch -m train.<TASK>

<TASK> means the module in the train folder. For example, you could run this command to launch the train script train/tutorial.py:

$ accelerate launch -m train.tutorial

Run a command with --push_to_hub to release your model to huggingface hub after training:

$ accelerate launch -m train.<TASK> --push_to_hub

Available Pretrained Models (WIP)

sd-control-lora-face-landmarks

sd-control-lora-head3d

sd-latent-control-dora-rank128-head3d

Example

1. Clone ControlLoRA from Github:

$ git clone https://github.com/HighCWu/control-lora-v2

2. Enter the repo dir:

$ cd control-lora-v2

3. Run code:

$ python -m infer.<TASK>

Replace <TASK> to the task module. For example:

$ python -m infer.face_landmarks

Replace the hint image and model paths according to your needs. For example, you could apply controllora to anime by replace runwayml/stable-diffusion-v1-5 to ckpt/anything-v3-vae-swapped.

Discuss together

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Citation

@software{wu2023controllorav2,
    author = {Wu Hecong},
    month = {9},
    title = {{ControlLoRA Version 2: A Lightweight Neural Network To Control Stable Diffusion Spatial Information Version 2}},
    url = {https://github.com/HighCWu/control-lora-2},
    version = {1.0.0},
    year = {2023}
}