Merge pull request #125 from invoke-ai/docs-cleanup

Fix merge conflict in SDXL finetuning + Docs cleanup

README.md

@@ -17,6 +17,7 @@ https://invoke-ai.github.io/invoke-training/
   - DreamBooth LoRA
   - Textual Inversion
 - Stable Diffusion XL
+  - Full finetuning
   - LoRA
   - DreamBooth LoRA
   - Textual Inversion

docs/tutorials/stable_diffusion/robocats_finetune_sdxl.md

@@ -124,15 +124,15 @@ python src/invoke_training/scripts/_experimental/lora_extraction/extract_lora_fr
   --lora-rank 32
 ```
 
-## 5 - Import into InvokeAI
+## 6 - Import into InvokeAI
 
 If you haven't already, setup [InvokeAI](https://github.com/invoke-ai/InvokeAI) by following its documentation.
 
 Import your finetuned diffusers model or your extracted LoRA from the 'Models' tab.
 
 Congratulations, you can now use your new robocat model! 🎉
 
-## 6 - Comparison: Finetune vs. LoRA Extraction
+## 7 - Comparison: Finetune vs. LoRA Extraction
 
 As noted earlier, the LoRA extraction process is lossy for a number of reasons. (There are some significant improvements coming to the LoRA extraction process soon to improve the fidelity of the extracted LoRA model.)
 

src/invoke_training/pipelines/stable_diffusion_xl/finetune/train.py

@@ -131,6 +131,7 @@ def train(config: SdxlFinetuneConfig, callbacks: list[PipelineCallbacks] | None
 
     logger.info("Loading models.")
     tokenizer_1, tokenizer_2, noise_scheduler, text_encoder_1, text_encoder_2, vae, unet = load_models_sdxl(
+        logger=logger,
         model_name_or_path=config.model,
         hf_variant=config.hf_variant,
         vae_model=config.vae_model,