Improved Denoised Diffusion Probabilistic Models

This is my implementation of Improved Denoising Diffusion Probabilistic Models. It also has an implementation of Denoising Diffusion Implicit Models.

Install

Install the environment with conda, then:

pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu116

We do this because conda's environment.yml cannot specify extra arguments to pip.

Training Statistics

Commit 052985162b85ebf28903c79dc781703507ae99e3

Samples

Trained on CelebHQ for 36000 batches with batch size of 302. Loss of 0.02573 with $L_\text{simple}$ objective.

Features

Implemented:

• [x] $L_\text{simple}$ objective
• [x] Cosine schedule
• [x] Training + Generating
• [x] $L_\text{hybrid}$ objective / learned variance
  • TODO: Verify sample quality
• [x] Faster sampling
• [x] Sampling from DDIM

Unplanned:

• [ ] $L_\text{vlb}$ objective with loss-aware sampler

Repository Guide

This repository is super messy. It has a lot of scratch files that represent my attempts at figuring things out / debugging escapades. Nevertheless, here's the overview:

• openai-ddm/ contains a forked version of improved-diffusion. I spent a while stripping out features (fp16, checkpointing) to get to the bare-minimum training loop.
• All training is done using composer which supports fp16, checkpointing, automatic wandb logging, etc.
• tests/ has some tests that verify my UNet / diffusion is identical to OpenAI's unet/diffusion
  • I haven't written tests for sampling yet
• run_unet.py prints out the architecture of the UNet in a friendly form & verifies the UNet works
• make_torchdata.py contains a script to generate a dataset from a folder of image files. It outputs parquet files.

Why?

This repository is for my personal use. But, there are a few nice things you might get from it:

• The diffusion code is written simply w/o support for a ton of different options in a single class so it's easy to understand
• Same goes for the UNet
• (TODO), I will port some comments from my ml-experiments repository to the UNet, so you can see why certain things are done
• Usage of MosaicML's trainer means you don't get bogged down by FP16/checkpointing/logging & can focus on the important stuff

Credit

• The "losses.py" and "nn.py" file inside of the "diffusion" folder are copy-pasted from the OpenAI codebase. I haven't had time to re-implement them yet.
• The for_timesteps function is heavily based off of a function in lucidrain's imagen-pytorch repository.