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Elucidating-Classifier-Guided-Diffusion (ICLR2024)

This is the codebase for Elucidating The Design Space of Classifier-Guided Diffusion Generation, accepted by ICLR2024 as poster

This repository contains three main folders, targeting the off-the-shelf classifier guidance for DDPM, EDM and DiT respectively.

off-the-shelf classifier guided DDPM

Environment variables and files

export PYTHONPATH="${PYTHONPATH}:...{Folder_Path}/EluCD-main/DDPM"
pip install blobfile
pip install mpi4py

Download pre-trained models

For all the pre-trained diffusion, classifier models and reference batch, please place them in the ./DDPM/pretrained_models folder: For DDPM diffusion models, the ImageNet128x128 Diffusion model and fine-tuned classifier is from DDPM diffusion model,

We use the off-the-shelf Pytorch ResNet classifier: ResNet50 and ResNet101 classifier for guided-sampling.

For FID evaluation, use the ImageNet 128x128 reference batch .

Off-the-shelf Guidance for DDPM

Firstly go to folder ./DDPM, which contains all files for the off-the-shelf classifier guidance for DDPM diffusion model. you can directly run ./DDPM/guided_sample.sh. All the model checkpoints are stored in the ./DDPM/pretrained_models/ folder.

For FID evaluation, use ./pytorch-fid-master/src/evaluation_image.sh, and replace the filename with the sample folder name you created.

Off-the-shelf Classifier guided DDPM sampling

Run ./guided_sample.sh, to generate sample the off-the-shelf classifier guided sampling

  • off-the-shelf ResNet101 DDPM guided:
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_type resnet101 --classifier_scale 1.0 --softplus_beta 3.0 --joint_temperature 1.0 --margin_temperature_discount 0.5 --gamma_factor 0.3 --classifier_use_fp16 True"

DDPM Results

This table summarizes our conditional ImageNet128x128 generation results for 250 steps of DDPM guided sampling:

ImageNet 128x128 FID Precision Recall
Diffusion Baseline 5.91 0.70 0.65
Diffusion Finetune classifier Guided 2.97 0.78 0.59
Diffusion Classifier-Free 2.43 - -
Diffusion ResNet50 Guided (Ours) 2.36 0.77 0.60
Diffusion ResNet101 Guided (Ours) 2.19 0.79 0.58

off-the-shelf classifier guided EDM

Download pre-trained models

For all the pre-trained diffusion, classifier models and reference batch, please place them in the ./EDM/pretrained_models folder: For EDM diffusion models, the ImageNet64x64 Diffusion model is from EDM diffusion model,

We use the off-the-shelf Pytorch ResNet classifier: ResNet50 and ResNet101 classifier for guided-sampling.

For FID evaluation, use the ImageNet 64x64 reference batch.

Off-the-shelf Guidance for EDM

Firstly go to folder ./EDM, which contains all files for the off-the-shelf classifier guidance for EDM diffusion model. you can directly run ./EDM/guided_sample.sh. All the model checkpoints are stored in the ./EDM/pretrained_models/ folder.

For FID evaluation, use ./pytorch-fid-master/src/evaluation_image.sh, and replace the filename with the sample folder name you created; the reference batch uses ImageNet 64x64 reference batch.

Off-the-shelf Classifier guided EDM sampling

Run ./guided_sample.sh, to generate sample the off-the-shelf classifier guided sampling

EDM sampling Results

This table summarizes our conditional ImageNet64x64 generation results for diverse sampling steps of EDM-guided sampling:

ImageNet 64x64 Classifier FID Steps
EDM baseline - 2.35 36
EDM Res101 guided Off-the-Shelf 2.22 36
EDM baseline - 2.54 18
EDM Res101 guided Off-the-Shelf 2.35 18
EDM baseline - 3.64 10
EDM Res101 guided Off-the-Shelf 3.38 10

To be continued

We will release the code of the off-the-shelf classifier guided sampling for DiT soon.