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.

Download pre-trained models

We have released checkpoints for the main models in the paper. Before using these models, please review the corresponding model card to understand the intended use and limitations of these models.

Here are the download links for each model checkpoint:

64x64 classifier: 64x64_classifier.pt
64x64 diffusion: 64x64_diffusion.pt
128x128 classifier: 128x128_classifier.pt
128x128 diffusion: 128x128_diffusion.pt
256x256 classifier: 256x256_classifier.pt
256x256 diffusion: 256x256_diffusion.pt
256x256 diffusion (not class conditional): 256x256_diffusion_uncond.pt
512x512 classifier: 512x512_classifier.pt
512x512 diffusion: 512x512_diffusion.pt
64x64 -> 256x256 upsampler: 64_256_upsampler.pt
128x128 -> 512x512 upsampler: 128_512_upsampler.pt
LSUN bedroom: lsun_bedroom.pt
LSUN cat: lsun_cat.pt
LSUN horse: lsun_horse.pt
LSUN horse (no dropout): lsun_horse_nodropout.pt

Sampling from pre-trained models

To sample from these models, you can use the classifier_sample.py, image_sample.py, and super_res_sample.py scripts. Here, we provide flags for sampling from all of these models. We assume that you have downloaded the relevant model checkpoints into a folder called models/.

For these examples, we will generate 100 samples with batch size 4. Feel free to change these values.

SAMPLE_FLAGS="--batch_size 4 --num_samples 100 --timestep_respacing 250"

Classifier guidance

Note for these sampling runs that you can set --classifier_scale 0 to sample from the base diffusion model. You may also use the image_sample.py script instead of classifier_sample.py in that case.

64x64 model:

MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --dropout 0.1 --image_size 64 --learn_sigma True --noise_schedule cosine --num_channels 192 --num_head_channels 64 --num_res_blocks 3 --resblock_updown True --use_new_attention_order True --use_fp16 True --use_scale_shift_norm True"
python classifier_sample.py $MODEL_FLAGS --classifier_scale 1.0 --classifier_path models/64x64_classifier.pt --model_path models/64x64_diffusion.pt $SAMPLE_FLAGS

128x128 model:

MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --image_size 128 --learn_sigma True --noise_schedule linear --num_channels 256 --num_heads 4 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
python classifier_sample.py $MODEL_FLAGS --classifier_scale 0.5 --classifier_path models/128x128_classifier.pt --model_path models/128x128_diffusion.pt $SAMPLE_FLAGS

256x256 model:

MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
python classifier_sample.py $MODEL_FLAGS --classifier_scale 1.0 --classifier_path models/256x256_classifier.pt --model_path models/256x256_diffusion.pt $SAMPLE_FLAGS

256x256 model (unconditional):

MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
python classifier_sample.py $MODEL_FLAGS --classifier_scale 10.0 --classifier_path models/256x256_classifier.pt --model_path models/256x256_diffusion.pt $SAMPLE_FLAGS

512x512 model:

MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --image_size 512 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 False --use_scale_shift_norm True"
python classifier_sample.py $MODEL_FLAGS --classifier_scale 4.0 --classifier_path models/512x512_classifier.pt --model_path models/512x512_diffusion.pt $SAMPLE_FLAGS

Upsampling

For these runs, we assume you have some base samples in a file 64_samples.npz or 128_samples.npz for the two respective models.

64 -> 256:

MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --large_size 256  --small_size 64 --learn_sigma True --noise_schedule linear --num_channels 192 --num_heads 4 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
python super_res_sample.py $MODEL_FLAGS --model_path models/64_256_upsampler.pt --base_samples 64_samples.npz $SAMPLE_FLAGS

128 -> 512:

MODEL_FLAGS="--attention_resolutions 32,16 --class_cond True --diffusion_steps 1000 --large_size 512 --small_size 128 --learn_sigma True --noise_schedule linear --num_channels 192 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
python super_res_sample.py $MODEL_FLAGS --model_path models/128_512_upsampler.pt $SAMPLE_FLAGS --base_samples 128_samples.npz

LSUN models

These models are class-unconditional and correspond to a single LSUN class. Here, we show how to sample from lsun_bedroom.pt, but the other two LSUN checkpoints should work as well:

MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --dropout 0.1 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
python image_sample.py $MODEL_FLAGS --model_path models/lsun_bedroom.pt $SAMPLE_FLAGS

You can sample from lsun_horse_nodropout.pt by changing the dropout flag:

MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --dropout 0.0 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
python image_sample.py $MODEL_FLAGS --model_path models/lsun_horse_nodropout.pt $SAMPLE_FLAGS

Note that for these models, the best samples result from using 1000 timesteps:

SAMPLE_FLAGS="--batch_size 4 --num_samples 100 --timestep_respacing 1000"

Results

This table summarizes our ImageNet results for pure guided diffusion models:

Dataset	FID	Precision	Recall
ImageNet 64x64	2.07	0.74	0.63
ImageNet 128x128	2.97	0.78	0.59
ImageNet 256x256	4.59	0.82	0.52
ImageNet 512x512	7.72	0.87	0.42

This table shows the best results for high resolutions when using upsampling and guidance together:

Dataset	FID	Precision	Recall
ImageNet 256x256	3.94	0.83	0.53
ImageNet 512x512	3.85	0.84	0.53

Finally, here are the unguided results on individual LSUN classes:

Dataset	FID	Precision	Recall
LSUN Bedroom	1.90	0.66	0.51
LSUN Cat	5.57	0.63	0.52
LSUN Horse	2.57	0.71	0.55

Training models

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.

Codebase for Diffusion Models Beat GANS on Image Synthesis.

Related tags

Overview

guided-diffusion

Download pre-trained models

Sampling from pre-trained models

Classifier guidance

Upsampling

LSUN models

Results

Training models

Owner

Katherine Crowson

[ECCV 2020] XingGAN for Person Image Generation

The official project of SimSwap (ACM MM 2020)

내가 보려고 정리한 <프로그래밍 기초 Ⅰ> / organized for me

Diffusion Probabilistic Models for 3D Point Cloud Generation (CVPR 2021)

Few-Shot-Intent-Detection includes popular challenging intent detection datasets with/without OOS queries and state-of-the-art baselines and results.

Config files for my GitHub profile.

[NeurIPS 2021] "Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems"

Pacman-AI - AI project designed by UC Berkeley. Designed reflex and minimax agents for the game Pacman.

Coursera - Quiz & Assignment of Coursera

Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning.

[CVPR2021] Invertible Image Signal Processing

Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction

Learning View Priors for Single-view 3D Reconstruction (CVPR 2019)

Backdoor Attack through Frequency Domain

Enigma-Plus - Python based Enigma machine simulator with some extra features

Simple, efficient and flexible vision toolbox for mxnet framework.

Self-Supervised CNN-GCN Autoencoder

A Survey on Deep Learning Technique for Video Segmentation

Code for Learning Manifold Patch-Based Representations of Man-Made Shapes, in ICLR 2021.

Reimplementation of Learning Mesh-based Simulation With Graph Networks