mirror of https://github.com/bmaltais/kohya_ss
28 lines
1.0 KiB
Markdown
28 lines
1.0 KiB
Markdown
# Finetuning Resource Guide
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This guide is a resource compilation to facilitate the development of robust LoRA models.
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-Need to add resources here
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## Guidelines for SDXL Finetuning
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- Set the `Max resolution` to at least 1024x1024, as this is the standard resolution for SDXL.
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- The fine-tuning can be done with 24GB GPU memory with the batch size of 1.
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- Train U-Net only.
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- Use gradient checkpointing.
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- Use `--cache_text_encoder_outputs` option and caching latents.
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- Use Adafactor optimizer. RMSprop 8bit or Adagrad 8bit may work. AdamW 8bit doesn't seem to work.
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- PyTorch 2 seems to use slightly less GPU memory than PyTorch 1.
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Example of the optimizer settings for Adafactor with the fixed learning rate:
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```
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optimizer_type = "adafactor"
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optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False" ]
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lr_scheduler = "constant_with_warmup"
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lr_warmup_steps = 100
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learning_rate = 4e-7 # SDXL original learning rate
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```
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## Resource Contributions
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If you have valuable resources to add, kindly create a PR on Github. |