ControlNeXt-SDXL

🌀 ControlNeXt-SDXL

This is our implementation of ControlNeXt based on Stable Diffusion XL.

Please refer to Examples for further intuitive details.
Please refer to Inference for more details regarding installation and inference.\

Our method demonstrates the advantages listed below:

• Few trainable parameters: only requiring 5~200M trainable parameters.
• Fast training speed: reduce sudden convergence.
• Efficient: no need for additional brunch; only a lightweight module is required.
• Compatibility: can serve as a plug-and-play lightweight module and can be combined with other LoRA weights.

Examples

The demo examples are generated using the ControlNeXt trained on

• (i) our vidit_depth dataset with utilizing Stable Diffusion XL 1.0 Base as the base model.
• (ii) our anime_canny dataset with utilizing Neta Art XL 2.0 as the base model.

Our method demonstrates excellent compatibility and can be applied to most other models based on SDXL1.0 architecture and LoRA. And you can retrain your own model for better performance.

BaseModel

Our model can be applied to various base models without the need for futher training as a plug-and-play module.

📌 Of course, you can retrain your owm model, especially for complex tasks and to achieve better performance.

• Stable Diffusion XL 1.0 Base

• AAM XL

• Neta XL V2

LoRA

Our model can also be directly combined with other publicly available LoRA weights.

• Glass Sculptures

Inference

Quick Start

Clone the repository:

git clone https://github.com/dvlab-research/ControlNeXt
cd ControlNeXt/ControlNeXt-SDXL

Install the required packages:

pip install -r requirements.txt

(Optional) Download the LoRA weight, such as Amiya (Arknights) Fresh Art Style. And put them under lora/.

Run the example:

bash examples/anime_canny/run.sh

Usage

Canny Condition

# examples/anime_canny/run.sh
python run_controlnext.py --pretrained_model_name_or_path "neta-art/neta-xl-2.0" \
  --unet_model_name_or_path "Eugeoter/controlnext-sdxl-anime-canny" \
  --controlnet_model_name_or_path "Eugeoter/controlnext-sdxl-anime-canny" \
  --controlnet_scale 1.0 \ # controlnet scale factor used to adjust the strength of the control condition
  --vae_model_name_or_path "madebyollin/sdxl-vae-fp16-fix" \
  --validation_prompt "3d style, photorealistic style, 1girl, arknights, amiya (arknights), solo, white background, upper body, looking at viewer, blush, closed mouth, low ponytail, black jacket, hooded jacket, open jacket, hood down, blue neckwear" \
  --negative_prompt "worst quality, abstract, clumsy pose, deformed hand, fused fingers, extra digits, fewer digits, fewer fingers, extra fingers, extra arm, missing arm, extra leg, missing leg, signature, artist name, multi views, disfigured, ugly" \
  --validation_image "examples/anime_canny/condition_0.png" \ # input canny image
  --output_dir "examples/anime_canny" \
  --load_weight_increasement # load weight increasement

We use a controlnet_scale factor to adjust the strength of the control condition.

We recommend to only save & load the weights difference of the UNet's trainable parameters, i.e., $\Delta W = W_{finetune} - W_{pretrained}$, rather than the actual weight. This is useful when adapting to various base models since the weights difference is model-agnostic.

Depth Condition

# examples/vidit_depth/run.sh
python run_controlnext.py  --pretrained_model_name_or_path "stabilityai/stable-diffusion-xl-base-1.0" \
    --unet_model_name_or_path "Eugeoter/controlnext-sdxl-vidit-depth" \
    --controlnet_model_name_or_path "Eugeoter/controlnext-sdxl-vidit-depth" \
    --controlnet_scale 1.0 \
    --vae_model_name_or_path "madebyollin/sdxl-vae-fp16-fix" \
    --validation_prompt "a diamond tower in the middle of a lava lake" \
    --validation_image "examples/vidit_depth/condition_0.png" \ # input depth image
    --output_dir "examples/vidit_depth" \
    --width 1024 \
    --height 1024 \
    --load_weight_increasement \
    --variant fp16

Run with Image Processor

We also provide a simple image processor to help you automatically convert the image to the control condition, such as canny.

# examples/anime_canny/run_with_pp.sh
python run_controlnext.py --pretrained_model_name_or_path "neta-art/neta-xl-2.0" \
  --unet_model_name_or_path "Eugeoter/controlnext-sdxl-anime-canny" \
  --controlnet_model_name_or_path "Eugeoter/controlnext-sdxl-anime-canny" \
  --controlnet_scale 1.0 \
  --vae_model_name_or_path "madebyollin/sdxl-vae-fp16-fix" \
  --validation_prompt "3d style, photorealistic style, 1girl, arknights, amiya (arknights), solo, white background, upper body, looking at viewer, blush, closed mouth, low ponytail, black jacket, hooded jacket, open jacket, hood down, blue neckwear" \
  --negative_prompt "worst quality, abstract, clumsy pose, deformed hand, fused fingers, extra digits, fewer digits, fewer fingers, extra fingers, extra arm, missing arm, extra leg, missing leg, signature, artist name, multi views, disfigured, ugly" \
  --validation_image "examples/anime_canny/image_0.png" \ # input image (not canny)
  --validation_image_processor "canny" \ # preprocess `validation_image` to canny condition
  --output_dir "examples/anime_canny" \
  --load_weight_increasement