mimuw-diffusion-thesis/diffusion-thesis-models

Diffusion thesis models

Model weights for the bachelor's thesis:

Score-Based Diffusion Models: Theoretical Foundations and Optimal Sampling Schedules
Łukasz Leszko, Mateusz Scharmach
University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, 2026

Code: github.com/leszkolukasz/diffusers

Structure

Folder	Thesis experiment	Dataset	Contents
exp1/	Experiment 1: Prediction Target	MNIST	noise, score and x0 predictors
exp2/	Experiment 2: Same network, different solvers	CIFAR-10	EDM medium model
exp3/	Experiment 3: Same network, different reverse processes	CIFAR-10	EDM medium model
exp7/	Pattern-dataset experiments	2D patterns	Gaussian 8×8, 8×4, 6×6, Spiral5, Beehive
edm2/	External reference	ImageNet-64	Official NVIDIA EDM2-S checkpoint

File types

For each experiment:

• files without prefix — final EMA/inference weights,
• raw_* — raw model weights,
• trainer_* — training state (epoch, optimizer and scaler).

Usage

Download a checkpoint:

from huggingface_hub import hf_hub_download

path = hf_hub_download(
    "whistleroosh/diffusion-thesis-models",
    "exp2/exp2_x0_norm_x_predictor_edm_medium_cifar10.pth",
)

Load it with the thesis code:

uv run main.py generate \
    --model-path "$path" \
    --schedule <schedule> \
    --sampling-schedule <sampling-schedule> \
    --equation <equation> \
    --solver <solver> \
    --n-steps 32 \
    --n-samples 1

EDM2

edm2/edm2-img64-s-1073741-0.075.pkl is NVIDIA's official EDM2 ImageNet-64 S checkpoint (edm2-img64-s-fid, FID 1.58). It was not trained by us. See the EDM2 repository.

License

The thesis checkpoints are released under the MIT license.

The EDM2 checkpoint is NVIDIA's official model and is covered by CC BY-NC-SA 4.0. It is included here for non-commercial research use.