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