ZipLoRA-pytorch

This is an implementation of ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs by mkshing.

The paper summary by the author is found here.

Installation

git clone git@github.com:mkshing/ziplora-pytorch.git
cd ziplora-pytorch
pip install -r requirements.txt

Usage

1. Train LoRAs for subject/style images

In this step, 2 LoRAs for subject/style images are trained based on SDXL. Using SDXL here is important because they found that the pre-trained SDXL exhibits strong learning when fine-tuned on only one reference style image.

Fortunately, diffusers already implemented LoRA based on SDXL here and you can simply follow the instruction.

For example, your training script would be like this.

export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
# for subject
export OUTPUT_DIR="lora-sdxl-dog"
export INSTANCE_DIR="dog"
export PROMPT="a sbu dog"
export VALID_PROMPT="a sbu dog in a bucket"

# for style
# export OUTPUT_DIR="lora-sdxl-waterpainting"
# export INSTANCE_DIR="waterpainting"
# export PROMPT="a cat of in szn style"
# export VALID_PROMPT="a man in szn style"

accelerate launch train_dreambooth_lora_sdxl.py \
  --pretrained_model_name_or_path=$MODEL_NAME  \
  --instance_data_dir=$INSTANCE_DIR \
  --output_dir=$OUTPUT_DIR \
  --instance_prompt="${PROMPT}" \
  --rank=64 \
  --resolution=1024 \
  --train_batch_size=1 \
  --learning_rate=5e-5 \
  --report_to="wandb" \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --max_train_steps=1000 \
  --validation_prompt="${VALID_PROMPT}" \
  --validation_epochs=50 \
  --seed="0" \
  --mixed_precision="fp16" \
  --enable_xformers_memory_efficient_attention \
  --gradient_checkpointing \
  --use_8bit_adam \
  --push_to_hub \

In the above script, all hyperparameters such as --max_train_steps and --rank are followed the paper. But, of course, you can tweak them for your images.
You can find style images in aim-uofa/StyleDrop-PyTorch.

2. Train ZipLoRA

export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"

# for subject
export LORA_PATH="mkshing/lora-sdxl-dog"
export INSTANCE_DIR="dog"
export PROMPT="a sbu dog"

# for style
export LORA_PATH2="mkshing/lora-sdxl-waterpainting"
export INSTANCE_DIR2="waterpainting"
export PROMPT2="a cat of in szn style"

# general 
export OUTPUT_DIR="ziplora-sdxl-dog-waterpainting"
export VALID_PROMPT="a sbu dog in szn style"


accelerate launch train_dreambooth_ziplora_sdxl.py \
  --pretrained_model_name_or_path=$MODEL_NAME  \
  --output_dir=$OUTPUT_DIR \
  --lora_name_or_path=$LORA_PATH \
  --instance_prompt="${PROMPT}" \
  --instance_data_dir=$INSTANCE_DIR \
  --lora_name_or_path_2=$LORA_PATH2 \
  --instance_prompt_2="${PROMPT2}" \
  --instance_data_dir_2=$INSTANCE_DIR2 \
  --resolution=1024 \
  --train_batch_size=1 \
  --learning_rate=5e-5 \
  --similarity_lambda=0.01 \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --max_train_steps=100 \
  --validation_prompt="${VALID_PROMPT}" \
  --validation_epochs=10 \
  --seed="0" \
  --mixed_precision="fp16" \
  --report_to="wandb" \
  --gradient_checkpointing \
  --use_8bit_adam \
  --enable_xformers_memory_efficient_attention \

If you're facing VRAM limitations during training, use the --quick_release flag to help free up VRAM.

3. Inference

import torch
from diffusers import StableDiffusionXLPipeline
from ziplora_pytorch.utils import insert_ziplora_to_unet

pipeline = StableDiffusionXLPipeline.from_pretrained(pretrained_model_name_or_path)
pipeline.unet = insert_ziplora_to_unet(pipeline.unet, ziplora_name_or_path)
pipeline.to(device="cuda", dtype=torch.float16)
image = pipeline(prompt=prompt).images[0]
image.save("out.png")

Also, you can quickly interact with your ziplora by using gradio.

export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
export ZIPLORA_PATH="..."

python inference.py --pretrained_model_name_or_path=$MODEL_NAME --ziplora_name_or_path=$ZIPLORA_PATH

TODO

super quick instruction for training each loras
ZipLoRA (training)
ZipLoRA (inference)
Pre-optimization lora weights

License

mkshing/ziplora-pytorch

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