Stable Diffusion Inference

Welcome to the experimental stable diffusion inference repository. This repository aims to test and evaluate the model as a new benchmark candidate for MLPerf inference .

References

• Diffusers Documentation
• MLPerf-SSD
• MLCommons

Getting Started

1. Setting Up the Environment

To set up the environment, build and launch the container using the following commands:

docker build . -t sd_mlperf_inference
docker run --rm -it --gpus=all -v {PWD}:/workspace sd_mlperf_inference bash

Note : The subsequent commands are assumed to be executed within this container.

2. Dataset Overview

This repository leverages the coco-2014 validation set for image generation and computation of FID and CLIP scores. COCO (Common Objects in Context) is a diverse dataset instrumental for object detection, segmentation, and captioning tasks. It boasts a substantial validation set with over 40,000 images encompassing more than 200,000 labels.

For benchmarking purposes, we utilize a random subset of {TBD} images and their associated labels, determined by a preset seed of {TBD}. As the focus is on image generation from labels and subsequent score calculation, downloading the entire COCO dataset is unnecessary. The required files for the benchmark, already part of the repository:

• captions.tsv: Contains processed coco2014 validation annotations with 40,504 prompts and their respective IDs. Essential for image generation and CLIP scoring.
• val2014.npz: Consists of precomputed statistics of the coco2014 validation set. Utilized for FID scoring.

For details on file generation, refer to Appendix A .

3. Image Generation

Execute the main.py script to generate images:

python main.py \
    --model-id 2 \         # xl for SD-XL, xlr for SD-XL + Refiner
    --guidance 8.0 \
    --precision fp16 \     # fp16, bf16 and fp32
    --scheduler ddim \
    --steps 50

For additional execution options:

python main.py --help

4. Compute FID Score

python fid/fid_score.py \
    --batch-size 1 \                 # batch size for the inception network. keep it 1.
    --subset-size 35000 \            # validation subset size, if you want to score the full dataset don't set the argument
    --shuffle-seed 2023 \            # the seed used for random the random subset selection
    ./val2014.npz \                  # ground truth (coco 2014 validation) statistics
    ./output                         # folder with the generated images

For more options:

python fid/fid_score.py --help

5. Compute CLIP Score

python clip/clip_score.py \
    --subset-size 35000 \          # validation subset size, if you want to score the full dataset don't set the argument
    --shuffle-seed 2023 \          # the seed used for random the random subset selection
    --tsv-file captions.tsv \      # captions file
    --image-folder ./output          # Folder with the generated images
    --device cuda                  # Device in which CLIP model is run (cpu, cuda)  

For more options:

python clip/clip_score.py --help

Appendix A: Generating Dataset Files

To create the captions.tsv and val2014.npz files:

1. Download the coco2014 validation set:

scripts/coco-2014-validation-download.sh

1. Process the downloaded annotations (provided in JSON format):

python process-coco-annotations.py \
    --input-captions-file {PATH_TO_COCO_ANNOTATIONS_FILE} \                 # Input annotations file
    --output-tsv-file captions.tsv \                                        # Output annotations
    --allow-duplicate-images                                                # Pick one prompt per image

1. Generate ground truth statistics:

python fid/fid_score.py \
    --batch-size 1 \                      # inception network batch size
    --save-stats {COCO_2014_IMAGES} \     # Input folder with coco images
    val2014.npz                           # Output file