sd-scripts-docker

Dockerfile for kohya-ss/sd-scripts.

Requirements

• Docker Engine >= 27.0
• NVIDIA Container Toolkit

Usage

• Replace accelerate launch with sudo docker run --rm --gpus all aoirint/sd_scripts.
• Training command will run in the container by a general user (UID=1000).

WD14 Captioning (ONNX)

mkdir -p "./cache/wd14_tagger_model_cache"
sudo chown -R 1000:1000 "./cache/wd14_tagger_model_cache"

# If your cache is broken, execute
# rm -rf ./cache/wd14_tagger_model_cache/wd14_tagger_model

sudo docker run --rm --gpus all \
  -v "./work:/work" \
  -v "./cache/wd14_tagger_model_cache:/wd14_tagger_model_cache" \
  aoirint/sd_scripts \
  finetune/tag_images_by_wd14_tagger.py \
  --model_dir "/wd14_tagger_model_cache/wd14_tagger_model" \
  --onnx \
  /work/my_dataset-20230715.1/img

Training LoRA-LierLa U-Net only with DreamBooth、キャプション方式 for Animagine XL 4.0 Zero (Stable Diffusion XL)

Create permanent directories to mount on container.

mkdir -p "./base_model" "./work" "./cache/huggingface/hub"
sudo chown -R 1000:1000 "./base_model" "./work" "./cache/huggingface/hub"

Download animagineXL40_v4Zero.safetensors from Animagine XL 4.0 Zero.

wget -O "animagineXL40_v4Zero.safetensors" "https://civitai.com/api/download/models/1409042?type=Model&format=SafeTensor&size=full&fp=fp16"
echo "f15812e65c2ea7f4e19ce37fb2a8445eb65c64da450a508dd9c8f237c73f6bb8  animagineXL40_v4Zero" | sha256sum -c -

Prepare a dataset directory work/my_dataset-20230715.1 and a config file work/my_dataset-20230715.1/config.toml following train_README.

Set file ownership UID:GID = 1000:1000 (sudo chown -R 1000:1000 "./work").

You can also choose another directory structure to modify config.toml and the training command.

• work/my_dataset-20230715.1/
  • config.toml
  • img/
    • 0001.png
    • 0001.txt
    • 0002.png
    • 0002.txt
    • ...
  • reg_img/
    • transparent_1.png
    • transparent_2.png
    • ...
  • sample_prompts.txt
  • output/
  • logs/

This is an example sample_prompts.txt. Use the output of WD14 tagger as reference. Add new lines for multiple samples.

shs 1girl, 1girl, solo, simple background, white background, masterpiece, high score, great score, absurdres --w 1024 --h 1024 --d 42 --s 28 --l 5 --ss euler_a --n lowres, bad anatomy, bad hands, text, error, missing finger, extra digits, fewer digits, cropped, worst quality, low quality, low score, bad score, average score, signature, watermark, username, blurry

This is an example config.toml.

[general]
enable_bucket = true

[[datasets]]
resolution = 1024
batch_size = 1

  [[datasets.subsets]]
  image_dir = '/work/my_dataset-20230715.1/img'
  caption_extension = '.txt'
  num_repeats = 20

  [[datasets.subsets]]
  is_reg = true
  image_dir = '/work/my_dataset-20230715.1/reg_img'
  class_tokens = '1girl'
  num_repeats = 1

Execute training.

sudo docker run \
  --rm \
  --gpus all \
  -v "./base_model:/base_model" \
  -v "./work:/work" \
  -v "./cache/huggingface/hub:/home/user/.cache/huggingface/hub" \
  aoirint/sd_scripts \
  --num_cpu_threads_per_process=1 \
  sdxl_train_network.py \
  --seed=42 \
  --pretrained_model_name_or_path="/base_model/animagineXL40_v4Zero.safetensors" \
  --dataset_config="/work/my_dataset-20230715.1/config.toml" \
  --output_dir="/work/my_dataset-20230715.1/output" \
  --output_name="my_dataset-20230715.1" \
  --save_model_as="safetensors" \
  --logging_dir="/work/my_dataset-20230715.1/logs" \
  --prior_loss_weight=1.0 \
  --max_train_epochs=5 \
  --learning_rate=1e-4 \
  --optimizer_type="AdaFactor" \
  --xformers \
  --mixed_precision="fp16" \
  --cache_latents \
  --cache_text_encoder_outputs  \
  --gradient_checkpointing \
  --save_every_n_epochs=1 \
  --sample_at_first \
  --sample_every_n_epochs=1 \
  --sample_prompts="/work/my_dataset-20230715.1/sample_prompts.txt" \
  --sample_sampler="euler_a" \
  --network_module="networks.lora" \
  --network_train_unet_only