A general model for secondary painting.

Integrated into Huggingface Spaces 🤗 using Gradio. Try it out

Try it On

Stable Diffusion Reference Only Automatic Coloring 0.1.2 is released. Trained for 850,000 steps on anime images at 512 resolution.

If you are interested in better stable diffusion reference only models such as in future work and have idle computing resources, feel free to contact me. Due to copyright issues with the dataset images, I'm not sure if I can make them public.

It is recommended to create and use a torch virtual environment, such as conda.

• Install conda

  wget https://mirrors.ustc.edu.cn/anaconda/miniconda/Miniconda3-latest-Linux-x86_64.sh
  bash  Miniconda3-latest-Linux-x86_64.sh

• Create a virtual environment

  It is compatible with general torch environments. If you have a torch environment, you can try using it directly.

  conda create -n stable_diffusion_reference_only python=3.10 
  conda activate stable_diffusion_reference_only 

Make sure the torch environment is normal, such as installing through conda:

conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia

The latest diffusers (0.22.3) currently do not support loading pre-trained models directly from huggingfacehub. You may need to fix the diffusers version in advance, e.g. use 0.21.0

pip install diffusers==0.21.0

install this package:

pip install git+https://github.com/aihao2000/stable-diffusion-reference-only.git

You may need to clear the background, you can install:

pip install git+https://github.com/aihao2000/anime-segmentation.git

For usage examples, please refer to .

You can deploy a version similar to huggingface space locally.

Clone this repository:

git clone git@github.com:aihao2000/stable-diffusion-reference-only.git
cd stable-diffusion-reference-only

Install dependencies:

pip install -r requirements.txt

Run Web-UI:

python3 app.py

Next, let’s use web ui for secondary creation.

• Upload the colored character reference image to prompt. If it contains a background and you do not want the background to be colored, you can click character segment to clear to enhance the effect.
• Upload the target character line art to blueprint.
• If you expect to perform a style transfer task, you may not have a line art of the target character. You can upload a color drawing of the target character, click character segment to clear the background (optional), then click get line art to obtain the line art of the character, and finally click color inversion gets blueprint with black background and white lines.
• If you are a professional artist doing secondary creation, you can directly upload the drawn line art to blueprint, and you can still try to click get line art (recommended). It is slightly different from the line art drawn manually. The result is slightly different. Finally, click color inversion to get the final blueprint with black background and white lines.
• Finally, click inference to generate a secondary creation diagram of the target character. According to tests, num_inference_steps of 20 can achieve good results. You can still try other parameters.
• For the style transfer task, you can directly upload the color images of the two characters, and then click style transfer, which will automatically execute character segment, get line art, color inversion, inference.
• For line art automatic coloring tasks, you can directly upload the reference character image to the manual line art, and then click automatic coloring, which will automatically perform color inversion and inference.

The required model will be automatically downloaded (line art automatic coloring) from huggingfacehub. You can also download it manually at https://huggingface.co/AisingioroHao0/stable-diffusion-reference-only-automatic-coloring-0.1.2.

Then change

automatic_coloring_pipeline = StableDiffusionReferenceOnlyPipeline.from_pretrained(

  "AisingioroHao0/stable-diffusion-reference-only-automatic-coloring-0.1.2"

).to(device)

to

automatic_coloring_pipeline = StableDiffusionReferenceOnlyPipeline.from_pretrained(

  "local_model_path"

).to(device)

Input Extensions->Install from URL->URL for extension's git repository: https://github.com/aihao2000/stable-diffusion-reference-only

Then click Install,Installed->Apply and restart UI

prompt	blueprint	result

Take the line art automatic coloring task as an example. If you want to perform other tasks, you can refer to and modify train/train_sd_reference_only.py.

You need a dataset with similar characters labeled. datasets/datasets.py will be responsible for generating training data and transport it to the training script.

datasets.py requires a meta_data.json to mark similar data.

Tip: You can use CLIP's image encoder to retrieve similar images on large datasets, or directly use existing or generated similar images.

You may use and refer to:

• dataset/gen_charactters.py

  It will clear the background of the task and generate a characters dataset with the same structure in the upper-level directory.

• dataset/compute_clip_features.py

  It will calculate the clip feature of the current data set and save it for calculating similar images.

• dataset/compute_similar_pairs.py

  Calculate similar images based on clip fetures and save them to meta_data.json

Please just for reference, they use the image_retriever library, which is an unstable version, and please be careful that the script needs to be executed with the dataset as the root directory.

datasets.py will generate an object like this:

{
        "image1": datasets.features.Image(),
        "image1_path": datasets.Value("string"),
        "image2": datasets.features.Image(),
        "image2_path": datasets.Value("string"),
        "similarity": datasets.Value("float32"),
}

To train the model you can use train/train_sd_reference_only_automatic_coloring.py or train/train_sd_reference_only.py.

train_sd_reference_only_automatic_coloring.py will use image1 as the reference image, use adaptive threshold to process image2 as blueprint, and use image2 as the desired generated image for noise addition and denoise.

train/train_sd_reference_only.py can freely configure datasets.py to provide new blueprint types.

Next, the specific parameters you may use are as follows:

• pretrained_model_name_or_path

  Path to store initial model.

• dataset_name

  If your dataset is stored on huggingface hub, you can use the online dataset path: huggingface_user_name/dataset_name, otherwise, please use the storage path of the local dataset, which should contain the dataset.py mentioned above.

• prompt_column

  If you use a custom dataset and have different column names, please pass in the column name corresponding to "image1".

• image_column

  Corresponds to "image2".

• dataset_config_name

  The configuration name of the data set is generally "similar_pairs". In the past, I processed image2 in the dataset.py to get the blueprint, which was too time-consuming.

• train_data_dir

  The storage path of the training data. dataset.py will use this path and connect it to the path in meta_data.json to read the image.

• load_dataset_num_proc

• dataloader_num_proc

• output_dir

• tracker_project_name

• report_to

  Tracker name, optional tensorboard or wandb.

• train_batch_size

• max_train_steps

• learning_rate

• lr_scheduler

• lr_warmup_steps

• checkpointing_steps

• validation_steps

• validation_prompt

• validation_blueprint

• use_8bit_adam

• gradient_checkpointing

• seed

Finally, run the training script, just like train/train_automatic_coloring_0.1.2.sh.

• Training at 768 resolution, and fine-tuning at multiple resolutions
• Module separation, compatible with Stable Diffusion, Stable Diffusion XL
• ......

If you're interested in funding, computing resources, or code contributions, feel free to contact me.