guided-diffusion

This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

This repository is based on openai/improved-diffusion, with modifications for classifier conditioning and architecture improvements.

Usage

Training diffusion models is described in the parent repository. Training a classifier is similar. We assume you have put training hyperparameters into a TRAIN_FLAGS variable, and classifier hyperparameters into a CLASSIFIER_FLAGS variable. Then you can run:

mpiexec -n N python scripts/classifier_train.py --data_dir path/to/imagenet $TRAIN_FLAGS $CLASSIFIER_FLAGS

Make sure to divide the batch size in TRAIN_FLAGS by the number of MPI processes you are using.

Here are flags for training the 128x128 classifier. You can modify these for training classifiers at other resolutions:

TRAIN_FLAGS="--iterations 300000 --anneal_lr True --batch_size 256 --lr 3e-4 --save_interval 10000 --weight_decay 0.05"
CLASSIFIER_FLAGS="--image_size 128 --classifier_attention_resolutions 32,16,8 --classifier_depth 2 --classifier_width 128 --classifier_pool attention --classifier_resblock_updown True --classifier_use_scale_shift_norm True"

For sampling from a 128x128 classifier-guided model, 25 step DDIM:

MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --image_size 128 --learn_sigma True --num_channels 256 --num_heads 4 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
CLASSIFIER_FLAGS="--image_size 128 --classifier_attention_resolutions 32,16,8 --classifier_depth 2 --classifier_width 128 --classifier_pool attention --classifier_resblock_updown True --classifier_use_scale_shift_norm True --classifier_scale 1.0 --classifier_use_fp16 True"
SAMPLE_FLAGS="--batch_size 4 --num_samples 50000 --timestep_respacing ddim25 --use_ddim True"
mpiexec -n N python scripts/classifier_sample.py \
    --model_path /path/to/model.pt \
    --classifier_path path/to/classifier.pt \
    $MODEL_FLAGS $CLASSIFIER_FLAGS $SAMPLE_FLAGS

To sample for 250 timesteps without DDIM, replace --timestep_respacing ddim25 to --timestep_respacing 250, and replace --use_ddim True with --use_ddim False.

This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

Related tags

Overview

guided-diffusion

Usage

Owner

OpenAI

Pytorch-3dunet - 3D U-Net model for volumetric semantic segmentation written in pytorch

CoANet: Connectivity Attention Network for Road Extraction From Satellite Imagery

The modify PyTorch version of Siam-trackers which are speed-up by TensorRT.

Real-Time Social Distance Monitoring tool using Computer Vision

An energy estimator for eyeriss-like DNN hardware accelerator

A benchmark dataset for mesh multi-label-classification based on cube engravings introduced in MeshCNN

Applicator Kit for Modo allow you to apply Apple ARKit Face Tracking data from your iPhone or iPad to your characters in Modo.

A synthetic texture-invariant dataset for object detection of UAVs

A Lightweight Experiment & Resource Monitoring Tool 📺

Let Python optimize the best stop loss and take profits for your TradingView strategy.

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

.NET bindings for the Pytorch engine

Final project for machine learning (CSC 590). Detection of hepatitis C and progression through blood samples.

Poisson Surface Reconstruction for LiDAR Odometry and Mapping

PyTorch implementation for the paper Pseudo Numerical Methods for Diffusion Models on Manifolds

Code for our EMNLP 2021 paper “Heterogeneous Graph Neural Networks for Keyphrase Generation”

Cartoon-StyleGan2 🙃 : Fine-tuning StyleGAN2 for Cartoon Face Generation

git《Beta R-CNN: Looking into Pedestrian Detection from Another Perspective》(NeurIPS 2020) GitHub:[fig3]

Studying Python release adoptions by looking at PyPI downloads

Image Classification - A research on image classification and auto insurance claim prediction, a systematic experiments on modeling techniques and approaches