Repository for Diffusion Project

Hyun Kang, Dohae Lee, Myungjin Shin, In-Kwon Lee

Implemented Baseline Models

{
	'stable_diffusion': StableDiffusionPipeline,
	'composable_diffusion': ComposableStableDiffusionPipeline,
	'structure_diffusion': StructureDiffusionPipeline,
	'attend_and_excite': AttendAndExcitePipeline,
	'syntax_guided_generation': SynGenDiffusionPipeline,
}

Dataset

Dataset should be in '.csv' format in which "prompt" column contains target prompts

Experiments

Baseline models' default configurations are in "configs/models" file

Configure experiment file (.yaml) to run multiple models all at once

python -m run configs/exp/example.yaml

Evaluation

Prerequisites

• clean-fid==0.1.35
• Clone https://github.com/mjsh34/T2I-CompBench, install requirements (in a separate environment).

Running

Evaluate a single dataset (specified in ./configs/exp/example.yaml) on fid, blip-vqa, clipscore and unidet metrics:

python eval.py \
    --config ./configs/exp/example.yaml \
    --methods fid blip-vqa clipscore unidet \
    --t2i_compbench_path /path/to/T2I-CompBench/ \
    --t2i_compbench_pyexe /path/to/T2I-CompBench/venv/bin/python \
    --fid_ref_images_dir /path/to/coco/

Evaluate datasets (specified in configs inside ./configs/datasets/) on fid, blip-vqa, clipscore and unidet metrics (if evaluation on some dataset results in an error the program will move on to the next one without aborting the whole process):

python eval.py \
    --config ./configs/datasets/ \
    --methods fid blip-vqa clipscore unidet \
    --t2i_compbench_path /path/to/T2I-CompBench/ \
    --t2i_compbench_pyexe /path/to/T2I-CompBench/venv/bin/python \
    --fid_ref_images_dir /path/to/coco/

To see all options for evaluation run python eval.py --help.

ComfyUI Extension