ComboStoc-XL/2 (256×256, UNSYNC_ALL @ 800K)
Re-hosted pre-trained checkpoint for ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models (ACM Transactions on Graphics, SIGGRAPH 2026).
- Paper: https://arxiv.org/abs/2405.13729
- Project page: https://ruixu.me/html/ComboStoc/index.html
- Original code: https://github.com/(ComboStoc authors) — this repo mirrors the weights originally distributed via the authors' Google Drive.
- Authors: Rui Xu, Jiepeng Wang, Hao Pan, Yang Liu, Xin Tong, Shiqing Xin, Changhe Tu, Taku Komura, Wenping Wang.
What this is
This is the ComboStoc-XL/2 image diffusion model (SiT-style transformer,
XL depth, patch size 2) trained on class-conditional ImageNet 256×256 with the
UNSYNC_ALL combinatorial-stochasticity setting, at 800K training steps. The
checkpoint file is ALL_0800000.pt.
ComboStoc studies the combinatorial complexity of diffusion training: the space spanned by combinations of dimensions and attributes is under-sampled by standard training. ComboStoc constructs stochastic processes that fully exploit these combinatorial structures, accelerating training and enabling asynchronous per-dimension/per-attribute time steps at test time.
Files
ALL_0800000.pt— model checkpoint. Follows the SiT checkpoint convention; the dict contains anemaentry which is the recommended weights for sampling.
Usage
Load with huggingface_hub.hf_hub_download and the ComboStoc model definition
(ComboStoc-XL/2, num_classes=1000, image_size=256) plus the
stabilityai/sd-vae-ft-mse VAE. See the accompanying demo Space for a full example.
License
GNU AGPL-3.0, matching the original ComboStoc source repository. If you use these weights, you must comply with AGPL-3.0 terms.
Citation
@misc{xu2024combostoccombinatorialstochasticitydiffusion,
title={ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models},
author={Rui Xu and Jiepeng Wang and Hao Pan and Yang Liu and Xin Tong and Shiqing Xin and Changhe Tu and Taku Komura and Wenping Wang},
year={2024},
eprint={2405.13729},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2405.13729},
}
These weights are a community re-host to enable a Hugging Face Space demo. All credit for the model belongs to the original authors.