[Experimental] Diffusion LoRA DPO training (#6422)

* add: experimental script for diffusion dpo training.

* random_crop cli.

* fix: caption tokenization.

* fix: pixel_values index.

* fix: grad?

* debug

* fix: reduction.

* fixes in the loss calculation.

* style

* fix: unwrap call.

* fix: validation inference.

* add: initial sdxl script

* debug

* make sure images in the tuple are of same res

* fix model_max_length

* report print

* boom

* fix: numerical issues.

* fix: resolution

* comment about resize.

* change the order of the training transformation.

* save call.

* debug

* remove print

* manually detaching necessary?

* use the same vae for validation.

* add: readme.

examples/research_projects/diffusion_dpo/REAMDE.md

+# Diffusion Model Alignment Using Direct Preference Optimization
+
+This directory provides LoRA implementations of Diffusion DPO proposed in [DiffusionModel Alignment Using Direct Preference Optimization](https://arxiv.org/abs/2311.12908) by Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik.
+
+We provide implementations for both Stable Diffusion (SD) and Stable Diffusion XL (SDXL). The original checkpoints are available at the URLs below:
+
+* [mhdang/dpo-sd1.5-text2image-v1](https://huggingface.co/mhdang/dpo-sd1.5-text2image-v1)
+* [mhdang/dpo-sdxl-text2image-v1](https://huggingface.co/mhdang/dpo-sdxl-text2image-v1)
+
+> 💡 Note: The scripts are highly experimental and were only tested on low-data regimes. Proceed with caution. Feel free to let us know about your findings via GitHub issues. 
+
+## SD training command
+
+```bash
+accelerate launch train_diffusion_dpo.py \
+  --pretrained_model_name_or_path=runwayml/stable-diffusion-v1-5  \
+  --output_dir="diffusion-dpo" \
+  --mixed_precision="fp16" \
+  --dataset_name=kashif/pickascore \
+  --resolution=512 \
+  --train_batch_size=16 \
+  --gradient_accumulation_steps=2 \
+  --gradient_checkpointing \
+  --use_8bit_adam \
+  --rank=8 \
+  --learning_rate=1e-5 \
+  --report_to="wandb" \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --max_train_steps=10000 \
+  --checkpointing_steps=2000 \
+  --run_validation --validation_steps=200 \
+  --seed="0" \
+  --report_to="wandb" \
+  --push_to_hub
+```
+
+## SDXL training command
+
+```bash
+accelerate launch train_diffusion_dpo_sdxl.py \
+  --pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0  \
+  --pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix \
+  --output_dir="diffusion-sdxl-dpo" \
+  --mixed_precision="fp16" \
+  --dataset_name=kashif/pickascore \
+  --train_batch_size=8 \
+  --gradient_accumulation_steps=2 \
+  --gradient_checkpointing \
+  --use_8bit_adam \
+  --rank=8 \
+  --learning_rate=1e-5 \
+  --report_to="wandb" \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --max_train_steps=2000 \
+  --checkpointing_steps=500 \
+  --run_validation --validation_steps=50 \
+  --seed="0" \
+  --report_to="wandb" \
+  --push_to_hub
+```
+
+## Acknowledgements
+
+This is based on the amazing work done by [Bram](https://github.com/bram-w) here for Diffusion DPO: https://github.com/bram-w/trl/blob/dpo/. 
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examples/research_projects/diffusion_dpo/requirements.txt

+accelerate>=0.16.0
+torchvision
+transformers>=4.25.1
+ftfy
+tensorboard
+Jinja2
+peft
+wandb
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