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e7f5be3934
- Implemented `sdxl_train_leco.py` for training with LECO prompts, including argument parsing, model setup, training loop, and weight saving functionality. - Created unit tests for `load_prompt_settings` in `test_leco_train_util.py` to validate loading of prompt configurations in both original and slider formats. - Added basic syntax tests for `train_leco.py` and `sdxl_train_leco.py` to ensure modules are importable.
94 lines
2.1 KiB
Markdown
94 lines
2.1 KiB
Markdown
# LECO Training
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This repository now includes dedicated LECO training entry points:
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- `train_leco.py` for Stable Diffusion 1.x / 2.x
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- `sdxl_train_leco.py` for SDXL
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These scripts train a LoRA against the model's own noise predictions, so no image dataset is required.
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## Current scope
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- U-Net LoRA training only
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- `networks.lora` is the default network module
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- Prompt YAML supports both original LECO prompt pairs and ai-toolkit style slider targets
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- Full ai-toolkit job YAML is not supported; use a prompt/target YAML file only
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## Example: SD 1.x / 2.x
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```bash
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accelerate launch train_leco.py ^
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--pretrained_model_name_or_path="model.safetensors" ^
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--output_dir="output" ^
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--output_name="detail_slider" ^
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--prompts_file="prompts.yaml" ^
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--network_dim=8 ^
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--network_alpha=4 ^
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--learning_rate=1e-4 ^
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--max_train_steps=500 ^
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--max_denoising_steps=40 ^
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--mixed_precision=bf16
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```
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## Example: SDXL
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```bash
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accelerate launch sdxl_train_leco.py ^
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--pretrained_model_name_or_path="sdxl_model.safetensors" ^
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--output_dir="output" ^
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--output_name="detail_slider_xl" ^
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--prompts_file="slider.yaml" ^
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--network_dim=8 ^
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--network_alpha=4 ^
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--learning_rate=1e-4 ^
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--max_train_steps=500 ^
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--max_denoising_steps=40 ^
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--mixed_precision=bf16
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```
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## Prompt YAML: original LECO format
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```yaml
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- target: "van gogh"
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positive: "van gogh"
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unconditional: ""
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neutral: ""
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action: "erase"
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guidance_scale: 1.0
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resolution: 512
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batch_size: 1
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multiplier: 1.0
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weight: 1.0
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```
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## Prompt YAML: ai-toolkit style slider target
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This expands internally into the bidirectional LECO pairs needed for slider-style behavior.
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```yaml
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targets:
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- target_class: ""
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positive: "high detail, intricate, high quality"
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negative: "blurry, low detail, low quality"
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multiplier: 1.0
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weight: 1.0
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guidance_scale: 1.0
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resolution: 512
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neutral: ""
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```
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You can also provide multiple neutral prompts:
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```yaml
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targets:
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- target_class: "person"
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positive: "smiling person"
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negative: "expressionless person"
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neutrals:
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- ""
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- "studio photo"
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- "cinematic lighting"
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```
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