Denoising Diffusion Policy Optimization

Training code for the paper Training Diffusion Models with Reinforcement Learning. This codebase has been tested on Google Cloud TPUs (v3 for RWR and v4 for DDPO); it has not been tested on GPUs.

UPDATE: We now have a PyTorch implementation that supports GPUs and LoRA for low-memory training here!

prompt_fn	filter_field	Weights and Demo
imagenet_animals	jpeg	ddpo-compressibility
imagenet_animals	neg_jpeg	ddpo-incompressibility
from_file(assets/common_animals.txt)	aesthetic	ddpo-aesthetic
nouns_activities(assets/common_animals.txt, assets/activities_v0.txt)	llava_bertscore	ddpo-alignment

Installation

conda env create -f environment_tpu.yml
conda activate ddpo-tpu
pip install -e .

Running DDPO

python pipeline/policy_gradient.py --dataset compressed-animals

The --dataset flag can be replaced by any of the configs defined in config/base.py. The first config dict, base, defines common arguments that are overridden in specific configs further down. Some arguments are shared between methods; DDPO-specific hyperparameters are in the pg field.

The most important arguments are prompt_fn and filter_field, which define the prompt distribution and reward function, respectively. See training/prompts.py for prompt functions and training/callbacks.py for reward functions.

Running RWR

For standard RWR, where the weights are a softmax of the rewards:

bash pipeline/run-rwr.sh

For RWR-sparse, where only samples above a certain percentile of the reward distribution are kept and trained on:

bash pipeline/run-sparse.sh

These methods run the outermost training loop in bash rather than Python. They run the pipeline/sample.py script to collect a dataset of samples and rewards, run pipeline/finetune.py to train the model on the most recent dataset, and repeat for some number of iterations. The sampling step and finetuning step have different configs, which are labeled "sample" and "train", respectively, in config/base.py.

Running LLaVA Inference

LLaVA inference was performed by making HTTP requests to a separate GPU server. See the llava_bertscore reward function in training/callbacks.py for the client-side code, and this repo for the server-side code.

Reference

@inproceedings{black2023ddpo,
      title={Training Diffusion Models with Reinforcement Learning},
      author={Kevin Black and Michael Janner and Yilun Du and Ilya Kostrikov and Sergey Levine},
      year={2023},
      eprint={2305.13301},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}