example: Train Instruct pix2 pix with lora implementation (huggingface#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 <[email protected]>
Co-authored-by: Sayak Paul <[email protected]>

Author: 3 people

examples/research_projects/instructpix2pix_lora/README.md

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+# InstructPix2Pix text-to-edit-image fine-tuning
+This extended LoRA training script was authored by [Aiden-Frost](https://github.com/Aiden-Frost).
+This is an experimental LoRA extension of [this example](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix.py). This script provides further support add LoRA layers for unet model.
+
+## Training script example
+
+```bash
+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```. 
+
+```bash
+# 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
+
+```bash
+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)
+
+<p align="center">
+    <img src="https://github.com/Aiden-Frost/Efficiently-teaching-counting-and-cartoonization-to-InstructPix2Pix.-/blob/main/diffusers_result_assets/edited_image_results.png?raw=true" alt="instructpix2pix-inputs" width=600/>
+</p>
+
+
+Here are some rough statistics about the training model using this script
+
+<p align="center">
+    <img src="https://github.com/Aiden-Frost/Efficiently-teaching-counting-and-cartoonization-to-InstructPix2Pix.-/blob/main/diffusers_result_assets/results.png?raw=true" alt="instructpix2pix-inputs" width=600/>
+</p>
+
+## 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.-