Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching
Authors: Zizheng Pan, Bohan Zhuang, De-An Huang, Weili Nie, Zhiding Yu, Chaowei Xiao, Jianfei Cai, anima anandkumar
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that T-Stitch is training-free, generally applicable for different architectures, and complements most existing fast sampling techniques with flexible speed and quality trade-offs. On Di T-XL, for example, 40% of the early timesteps can be safely replaced with a 10x faster Di T-S without performance drop on class-conditional Image Net generation. We further show that our method can also be used as a drop-in technique to not only accelerate the popular pretrained stable diffusion (SD) models but also improve the prompt alignment of stylized SD models from the public model zoo. |
| Researcher Affiliation | Collaboration | 1Monash University 2NVIDIA 3University of Wisconsin, Madison 4Caltech |
| Pseudocode | No | The paper describes methods conceptually and with mathematical equations, but does not include any explicit 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper mentions using a 'public model zoo on Diffusers (von Platen et al., 2022)' and 'Hugging Face.co and Civitai.com', which are third-party resources or communities. It does not provide any explicit statement from the authors about releasing their own code for the methodology described in this paper. |
| Open Datasets | Yes | Following Di T, we conduct the class-conditional Image Net experiments based on pretrained Di T-S/B/XL under 256 256 images and patch size of 2. |
| Dataset Splits | Yes | We use the reference batch from ADM (Dhariwal & Nichol, 2021) and sample 5,000 images to compute FID. |
| Hardware Specification | Yes | For example, even with a high-performance RTX 3090, generating 8 images with Di T-XL (Peebles & Xie, 2022) takes 16.5 seconds with 100 denoising steps, which is 10 slower than its smaller counterpart Di T-S (1.7s) with a lower generation quality. |
| Software Dependencies | No | The paper mentions 'Diffusers' implicitly through a citation, but no specific version numbers are provided for any software libraries or dependencies used for the implementation of T-Stitch. |
| Experiment Setup | Yes | By default, we adopt a classifier-free guidance scale of 1.5 as it achieves the best FID for Di T-XL, which is also the target model in our setting. |