Intro

This is the training code for Diffusion-DPO. The script is adapted from the diffusers library.

Model Checkpoints

The below are initialized with StableDiffusion models and trained as described in the paper (replicable with launchers/ scripts assuming 16 GPUs, scale gradient accumulation accordingly).

StableDiffusion1.5

StableDiffusion-XL-1.0

Use this notebook to compare generations. It also has a sample of automatic quantative evaluation using PickScore.

Setup

pip install -r requirements.txt

Structure

• launchers/ is examples of running SD1.5 or SDXL training
• utils/ has the scoring models for evaluation or AI feedback (PickScore, HPS, Aesthetics, CLIP)
• quick_samples.ipynb is visualizations from a pretrained model vs baseline
• requirements.txt Basic pip requirements
• train.py Main script, this is pretty bulky at >1000 lines, training loop starts at ~L1000 at this commit (ctrl-F "for epoch").
• upload_model_to_hub.py Uploads a model checkpoint to HF (simple utility, current values are placeholder)

Running the training

Example SD1.5 launch

# from launchers/sd15.sh
export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export DATASET_NAME="yuvalkirstain/pickapic_v2"

# Effective BS will be (N_GPU * train_batch_size * gradient_accumulation_steps)
# Paper used 2048. Training takes ~24 hours / 2000 steps

accelerate launch --mixed_precision="fp16"  train.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --dataset_name=$DATASET_NAME \
  --train_batch_size=1 \
  --dataloader_num_workers=16 \
  --gradient_accumulation_steps=1 \
  --max_train_steps=2000 \
  --lr_scheduler="constant_with_warmup" --lr_warmup_steps=500 \
  --learning_rate=1e-8 --scale_lr \
  --cache_dir="/export/share/datasets/vision_language/pick_a_pic_v2/" \
  --checkpointing_steps 500 \
  --beta_dpo 5000 \
   --output_dir="tmp-sd15"

Important Args

General

• --pretrained_model_name_or_path what model to train/initalize from
• --output_dir where to save/log to
• --seed training seed (not set by default)
• --sdxl run SDXL training
• --sft run SFT instead of DPO

DPO

• --beta_dpo KL-divergence parameter beta for DPO
• --choice_model Model for AI feedback (Aesthetics, CLIP, PickScore, HPS)

Optimizers/learning rates

• --max_train_steps How many train steps to take

• --gradient_accumulation_steps

• --train_batch_size see above notes in script for actual BS

• --checkpointing_steps how often to save model

• --gradient_checkpointing turned on automatically for SDXL

• --learning_rate

• --scale_lr Found this to be very helpful but isn't default in code

• --lr_scheduler Type of LR warmup/decay. Default is linear warmup to constant

• --lr_warmup_steps number of scheduler warmup steps

• --use_adafactor Adafactor over Adam (lower memory, default for SDXL)

Data

• --dataset_name if you want to switch from Pick-a-Pic
• --cache_dir where dataset is cached locally (users will want to change this to fit their file system)
• --resolution defaults to 512 for non-SDXL, 1024 for SDXL.
• --random_crop and --no_hflip changes data aug
• --dataloader_num_workers number of total dataloader workers

Citation

@misc{wallace2023diffusion,
      title={Diffusion Model Alignment Using Direct Preference Optimization}, 
      author={Bram Wallace and Meihua Dang and Rafael Rafailov and Linqi Zhou and Aaron Lou and Senthil Purushwalkam and Stefano Ermon and Caiming Xiong and Shafiq Joty and Nikhil Naik},
      year={2023},
      eprint={2311.12908},
      archivePrefix={arXiv},
      primaryClass={cs.CV}

Ethical Considerations

This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.