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Re-hosted pre-trained checkpoint for ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models (ACM Transactions on Graphics, SIGGRAPH 2026).
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Re-hosted pre-trained checkpoint for ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models (ACM Transactions on Graphics, SIGGRAPH 2026).
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2 extraitsThis 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.
ALL_0800000.pt — model checkpoint. Follows the SiT checkpoint convention; the
dict contains an ema entry which is the recommended weights for sampling.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.
GNU AGPL-3.0, matching the original ComboStoc source repository. If you use these weights, you must comply with AGPL-3.0 terms.
@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.
--- license: agpl-3.0 tags: - diffusion - image-generation - class-conditional - imagenet - siggraph-2026 pipeline_tag: unconditional-image-generation library_name: pytorch --- # 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 an `ema` entry 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 ```bibtex @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/2...
Source context: 0 downloads · 0 likes · Pipeline unconditional-image-generation · Library pytorch · Repo suvadityamuk/ComboStoc-XL-2-256x256