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https://github.com/aoirint/sd-scripts-docker
Dockerfile for https://github.com/kohya-ss/sd-scripts
https://github.com/aoirint/sd-scripts-docker
Last synced: about 1 month ago
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Dockerfile for https://github.com/kohya-ss/sd-scripts
- Host: GitHub
- URL: https://github.com/aoirint/sd-scripts-docker
- Owner: aoirint
- License: mit
- Created: 2023-07-06T10:56:47.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-10T00:43:44.000Z (5 months ago)
- Last Synced: 2024-08-10T01:37:16.820Z (5 months ago)
- Language: Dockerfile
- Homepage: https://hub.docker.com/r/aoirint/sd_scripts
- Size: 32.2 KB
- Stars: 4
- Watchers: 1
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# sd-scripts-docker
Dockerfile for [kohya-ss/sd-scripts](https://github.com/kohya-ss/sd-scripts).
## Requirements
- Docker Engine >= 23.0
- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
## 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).
### Example: LoRA-LierLa training with `DreamBooth、class+identifier方式` for Waifu Diffusion 1.5 Beta 2.
Create permanent directories to mount on container.
```shell
mkdir -p "./base_model" "./work" "./cache/huggingface/hub"
sudo chown -R 1000:1000 "./base_model" "./work" "./cache/huggingface/hub"
```
Download `wd-1-5-beta2-fp32.safetensors` from [waifu-diffusion/wd-1-5-beta2](https://huggingface.co/waifu-diffusion/wd-1-5-beta2).
```shell
wget "https://huggingface.co/waifu-diffusion/wd-1-5-beta2/resolve/main/checkpoints/wd-1-5-beta2-fp32.safetensors"
echo "764f93581d80b46011039bb388e899f17f7869fce7e7928b060e9a5574bd8f84 wd-1-5-beta2-fp32.safetensors" | 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](https://github.com/kohya-ss/sd-scripts/blob/v0.6.4/train_README-ja.md#dreamboothclassidentifier%E6%96%B9%E5%BC%8F%E6%AD%A3%E5%89%87%E5%8C%96%E7%94%BB%E5%83%8F%E4%BD%BF%E7%94%A8%E5%8F%AF). 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
- 0002.png
- ...
- reg_img/
- transparent_1.png
- transparent_2.png
- ...
- output/
- logs/
Here is a example `config.toml`.
```toml
[general]
enable_bucket = true
[[datasets]]
resolution = 768
batch_size = 4
[[datasets.subsets]]
image_dir = '/work/my_dataset-20230715.1/img'
class_tokens = 'shs girl'
num_repeats = 10
[[datasets.subsets]]
is_reg = true
image_dir = '/work/my_dataset-20230715.1/reg_img'
class_tokens = 'girl'
num_repeats = 1
```
Execute training.
```shell
sudo docker run --rm --gpus all \
-v "./base_model:/base_model" \
-v "./work:/work" \
-v "./cache/huggingface/hub:/huggingface/hub" \
aoirint/sd_scripts \
--num_cpu_threads_per_process 1 \
train_network.py \
--pretrained_model_name_or_path=/base_model/wd-1-5-beta2-fp32.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_steps=400 \
--learning_rate=1e-4 \
--optimizer_type="AdamW8bit" \
--xformers \
--mixed_precision="fp16" \
--cache_latents \
--gradient_checkpointing \
--save_every_n_epochs=1 \
--network_module=networks.lora \
--v2 \
--v_parameterization
```
### Example: WD14 Captioning (Tensorflow)
```shell
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" \
/work/my_dataset-20230715.1/img
```