Update README.md

README.md

@@ -1,5 +1,5 @@
 ![](https://huggingface.co/Agreene5/Rhythm_Heaven_Style_LoRA/resolve/main/CivitAIExamples2/formodelcard.png "3 example images")
-# Rhythm Heaven Style LoRA for Stable Diffusion 1.5
+# Rhythm Heaven Style LoRA for Stable Diffusion 1.5 + SDXL
     Model is also on CivitAI: https://civitai.com/models/87254?modelVersionId=258514
 ## Model Details
 ### Version 1 parameters:
@@ -62,6 +62,36 @@
 **Removed "rhythm_heaven" trigger**: Seems like a style trigger isn't really necessary, removing it just saves a bit of token length.
 **Less unprompted black and white generations**: This one isn't as big but I manually added color to some of the training images to get more variety
   which consequently means you'll get less black and white generations.
+### Version 1 (SDXL) parameters:
+    steps_per_image: 20
+    total_images: 122 (61 unique images, doubled amount by mirroring them)
+    total_steps: 7320
+    training_model: anima_pencil-XL
+    optimizer: Adafactor
+    network_dim: 128
+    network_alpha: 1
+    network_train_on: both
+    learning_rate: 1.2e-3
+    unet_lr: 1.2e-3
+    text_encoder _lr: 1.2e-3
+    lr_scheduler: constant
+    lr_scheduler_num_cycles: 1
+    lr_scheduler_power: 1
+    train_batch_size: 5
+    num_epochs: 15
+    mixed_precision: bf16
+    save_precision bf16
+    save_n_epochs_type: save_every_n_epochs
+    save_n_epochs_type_value: 1
+    resolution: 1024
+    max_token_length: 75
+    clip_skip: 2
+    additional_argument: --xformers
+    training_hardware: RTX 3090
+    training_time: ~6 hours
+#### Version 1 (SDXL) Improvements:
+**Cleaner looking images**: All of the images used to train this model were upscaled 2x so outputs are less grainy.
+**Better prompt understanding**: SDXL has a better understanding of prompts so training a LoRA using it as a base makes the LoRA get a better understanding too.
 ## Model Description
 Trained on humanoid characters from the Rhythm Heaven series (and some from Wario Ware) using AnyLoRA.
 Captions were done manually using booru tags.