Files

T

Rahul Raman 5c8115c765 example: Train Instruct pix2 pix with lora implementation (#6469 )

* base template file - train_instruct_pix2pix.py

* additional import and parser argument requried for lora

* finetune only instructpix2pix model -- no need to include these layers

* inject lora layers

* freeze unet model -- only lora layers are trained

* training modifications to train only lora parameters

* store only lora parameters

* move train script to research project

* run quality and style code checks

* move train script to a new folder

* add README

* update README

* update references in README

---------

Co-authored-by: Rahul Raman <rahulraman@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

2024-12-23 13:02:05 +05:30

README.md

example: Train Instruct pix2 pix with lora implementation (#6469 )

2024-12-23 13:02:05 +05:30

train_instruct_pix2pix_lora.py

example: Train Instruct pix2 pix with lora implementation (#6469 )

2024-12-23 13:02:05 +05:30

README.md

InstructPix2Pix text-to-edit-image fine-tuning

This extended LoRA training script was authored by Aiden-Frost. This is an experimental LoRA extension of this example. This script provides further support add LoRA layers for unet model.

Training script example

export MODEL_ID="timbrooks/instruct-pix2pix"
export DATASET_ID="instruction-tuning-sd/cartoonization"
export OUTPUT_DIR="instructPix2Pix-cartoonization"

accelerate launch finetune_instruct_pix2pix.py \
  --pretrained_model_name_or_path=$MODEL_ID \
  --dataset_name=$DATASET_ID \
  --enable_xformers_memory_efficient_attention \
  --resolution=256 --random_flip \
  --train_batch_size=2 --gradient_accumulation_steps=4 --gradient_checkpointing \
  --max_train_steps=15000 \
  --checkpointing_steps=5000 --checkpoints_total_limit=1 \
  --learning_rate=5e-05 --lr_warmup_steps=0 \
  --val_image_url="https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png" \
  --validation_prompt="Generate a cartoonized version of the natural image" \
  --seed=42 \
  --rank=4 \
  --output_dir=$OUTPUT_DIR \
  --report_to=wandb \
  --push_to_hub

Inference

After training the model and the lora weight of the model is stored in the $OUTPUT_DIR.

# load the base model pipeline
pipe_lora = StableDiffusionInstructPix2PixPipeline.from_pretrained("timbrooks/instruct-pix2pix")

# Load LoRA weights from the provided path
output_dir = "path/to/lora_weight_directory"
pipe_lora.unet.load_attn_procs(output_dir)

input_image_path = "/path/to/input_image"
input_image = Image.open(input_image_path)
edited_images = pipe_lora(num_images_per_prompt=1, prompt=args.edit_prompt, image=input_image, num_inference_steps=1000).images
edited_images[0].show()

Results

Here is an example of using the script to train a instructpix2pix model. Trained on google colab T4 GPU

MODEL_ID="timbrooks/instruct-pix2pix"
DATASET_ID="instruction-tuning-sd/cartoonization"
TRAIN_EPOCHS=100

Below are few examples for given the input image, edit_prompt and the edited_image (output of the model)

Here are some rough statistics about the training model using this script

References

InstructPix2Pix - https://github.com/timothybrooks/instruct-pix2pix
Dataset and example training script - https://huggingface.co/blog/instruction-tuning-sd
For more information about the project - https://github.com/Aiden-Frost/Efficiently-teaching-counting-and-cartoonization-to-InstructPix2Pix.-