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
Venue: ICLR 2025 Poster (2025)
Keywords: diffusion model
transformers
model stitching
Primary area: generative models
TL;DR: We propose Trajectory Stitching, a simple but effective technique that leverages small pretrained diffusion models to accelerate sampling in large pretrained diffusion models without training.

Abstract: Sampling from diffusion probabilistic models (DPMs) is often expensive for high-quality image generation and typically requires many steps with a large model. In this paper, we introduce sampling Trajectory Stitching (T-Stitch), a simple yet efficient technique to improve the sampling efficiency with little or no generation degradation. Instead of solely using a large DPM for the entire sampling trajectory, T-Stitch first leverages a smaller DPM in the initial steps as a cheap drop-in replacement of the larger DPM and switches to the larger DPM at a later stage. Our key insight is that different diffusion models learn similar encodings under the same training data distribution and smaller models are capable of generating good global structures in the early steps. 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 DiT-XL, for example, 40% of the early timesteps can be safely replaced with a 10x faster DiT-S without performance drop on class-conditional ImageNet 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. Finally, the explicit model allocation strategy of T-Stitch significantly reduces the need of training or searching, delivering high deployment efficiency.

Published as a conference paper at ICLR 2025
T-STITCH: ACCELERATING SAMPLING IN PRE- TRAINED DIFFUSION MODELS WITH TRAJECTORY STITCHING
Zizheng Pa
Bohan Zhuang!f
De-An Huang?
Weili Nic²
Zhiding Yu²
Chaowei X
Jianfei Cai!
Anima Anandkumar
Monash University
?NVIDIA Universityy of Wisconsin, Madison *Caltech
ABSTRACT
Sampling from diffusion probabilistic models (DPMs) is often expensive for high- quality image generation and typically requires many steps with large model. In this paper, we introduce sampling Trajectory Stitching (T-Stitch), a simple yet efficient technique to improve the sampling efficiency with little or no generation degradation. Instead of solely using a large DPM for the entire sampling trajec- tory. T-Stitch first leverages smaller DPM in the initial steps replacement of the larger DPM and switches to the larger DPM at a later stage. Our key insight is that different diffusion models learn similar encodings under the same training data distribution and smaller models are capable of generating good global structures in the early steps. Extensive experiments demonstrate that T-Stitch is training-free generally applicable for different architectures, and com- plements most existing fast sampling techniques with flexible speed and quality trade-offs. On DiT-XL, for example, 40% of the early timesteps can be safely replaced with 10x faster DiT-S without performance drop on class-conditional ImageNet 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. Finally, the explicit model allocation strategy of T-Stitch significantly reduces the need of training or searching, delivering high deployment efficiency.
cheap drop-in
INTRODUCTION
Diffusion probabilistic models (DPMs) (Ho al. 2020) have demonstrated remarkable success in generating high-quality data among various real-world applications, such as text-to-image genera- tion (Rombach et al., 2022). audio synthesis (Kong al. 2021) and 3D generation (Poole al.) 2023), etc. Achieving high generation quality, however, is expensive due to the need to sample from large DPM, typically involving hundreds of denoising steps, each of which requires high com- putational cost. For example, even with a high-performann
RTX 3090, generating 8 images with DiT-XI. (Peebles Xie 2022) takes 16.5 seconds with 100 denoising steps, which 10x slower than its smaller counterpart DIT-S (1.7s) with lower generation quality.
Recent works tackle the inference efficiency issue by speeding up the sampling of DPMs in two ways: (1) reducing the computational costs per step or (2) reducing the number of sampling steps. The former approach can be done by model compression through quantization et al., 2023b) and pruning (Fang et al. 2023), or by redesigning lightweight model architectures (Yang et al.) 2023
Lee et al. 2023). The second approach reduces the number of steps either by distilling multiple
denoising steps into fewer ones (Salimans & Ho 2022
Song al. 2023
Ziheng et al. 2023
Luo
"Work done during internship at NVIDIA
Corresponding author: bohan mhhaan. @ gmail.com

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Figure 1: Top: FID comparison on class-conditional ImageNet when progressively stitching more DİT-S steps at the beginning and fewer DiT-XL, steps the end, based on DDIM 100 timesteps and classifier-free guidance scale of 1.5. FID is calculated by sampling 5000 images. Bottom: One example of stitching more DīT-S steps to achieve faster sampling, where the time cost is measured by generating 8 images
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A ghibli style princess with golden hair in New York City
A photo a white cat on a tropical beach, nvinkpunk style
By directly adopting small SD in the model zoo, T-Stitch naturally interpolates the speed, style, and image contents with a large styled SD, which also potentially improves the prompt alignment, e.g., "New York City" and "tropical beach" in the above examples.
Figure 2:
et al. 2023 Sauer et al. 2023) or by improving the differential equation solver (Song et al. 202 la Lu al. 2022] Zheng al. 2023). While both directions can improve the efficiency of large DPMs, they assume that the computational cost of each denoising step remains the same, and single model used throughout the process. However, we observe that different steps in the denoising process exhibit quite distinct characteristics, and using the same model throughout is suboptimal strategy for efficiency.
Our Approach. In this work, we propose Trajectory Stitching (T-Stitch), a simple yet effective strategy improve DPMs' efficiency that complements existing efficient sampling methods by dy- namically allocating computation to different denoising steps. Our core idea is to apply small DPMs at the early denoising steps instead of using the same large model at all steps, as in previous works. We show that by first applying smaller DPM in the early denoising steps followed by switching to larger DPM the later denoising steps, we can reduce the overall computational costs without sacrificing the generation quality. Figure [ shows example of our approach using two DiT models (DiT-S and DiT- where DiT-S is computationally much cheaper than DiT-XL.. With the increase in the percentage of steps from DiT-S instead of DīT-XL in T-stitch, can keep increasing the inference speed. In our experiments, we find that there is no degradation of the generation quality (in FID), even when the first 40% of steps using DiT-S, leading around 1.5x lossless speedup.
Some prior arts Liu et al. (023): Yang et al. (024) share a similar spirit with ours, i.e., applying different models at different steps. However, compared to their costly trained and searched model schedules, our method is distinct in two new key insights: (1) Recent work suggests a common latent space across different DPMs trained on the same data distribution (Song et al., 2021b Roeder et al. 2021). Thus, different DPMs tend to share similar sampling trajectories. which makes it possible to stitch across different model sizes and even architectures, without significant training or searching. (2) From the frequency perspective, the denoising process focuses generating low- frequency components at the early steps while the later steps target the high-frequent frequency signals (Yang effective for high-frequency details, they still
et al. 2023). Although the small models generate a good global structure at the beginning. In our experiments, we show that T-Stitch achieves model schedules.
comparable even better trade-offs than training/searchnng--
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With comprehensive experiments, we demonstrate that T-Stitch substantially speeds up large DPMs without much loss of generation quality. This observation is consistent across a spectrum of architec- tures and diffusion model samplers. This also implics that T-Stithh can be directly applied to widely used large DPMs without any re-training (e.g-, Stable Diffusion (SD) |Robbach et al. (2022). Fig- ure [ shows the results of speeding up stylized Stable Diffusion with relatively smaller pretrained SD model Kim et al. (2023). Surprisingly, we find that T-Stithh not only improves speed but also improves prompt alignment for stylized models. This is possibly because the fine-tuning process of stylized models (e.g., ghibli, inkpunk) degrades their prompt alignment. T-Stitch improves both efficiency and generation quality here by combining small SD models to complement the prompt alignment for large SD models specialized in stylizing the image.
Note that T-Stitch complementary to most existing fast sampling approaches. The part of the trajectory that is taken by the large DPM can still be sped up by reducing the number of steps taken by it, or by reducing computational cost with compression techniques. In addition, while T-Stitch can already effectively improve the quality-efficieney trade-off's without any overhead of re-training, we show that the generation quality of T-Stitch can be further improved when we fine-tune the stitched DPMs given trajectory schedule (Section By fine-tuning the large DPM only on the timesteps that it is applied, the large DPM can better specialize in providing high-frequency details and further improve generation quality. Furthermore, we show that the training-free Pareto frontier generated by T-Stitch improves quality-efficiency trade-offs to training-based methods designed for interpolating between neural network models via model stitching (Pan et al./ 2223a b). Note that T- Stitch is not limited to only two model sizes, and is also applicable to different DPM architectures.
We summarize our main contributions as follows:
We propose T-Stitch, simple yet highly effective approach for improving the inference speed of DPMs, by applying a small DPM at early denoising steps while a large DPM at later steps. Without retraining, we achieve better speed and quality trade-offs than individ- ual large DPMs and even non-trivial lossless speedups.
We conduct extensive experiments to demonstrate that our method is generally applicable to different model architectures and samplers, and is complementary to existing fast sampling techniques.
Notably, without any re-training overhead, T-Stitch not only accelerates Stable Diffusion models that are widely used in practical applications but also improves the prompt align- ment of stylized SD models for text-to-image generation.
2 RELATED WORKS
Efficient diffusion models. Despite the success, DPMs suffer from the slow sampling speed (Rom- bach et al., 2022, Ho et al. 2020) due to hundreds of timesteps and the large denoiser (e.g.. U-Net).
To expedite the sampling process, some efforts have been made by directly utilizing network com- pression techniques to diffusion models, such as pruning (Fang et al.) (023) and quantization (Shang et al. 2023 Li et al. 2023b). On the other hand, many works seek for reducing sampling steps, which can be achieved by distillation (Salimans & Ho 2022 Zheng et al. 2023) Song et al.) 2023
Luo al. 2023 Sauer al. 2203), implicit sampler (Song al. [2021a). and improved differential equation (DE) solvers (Lu et 2002 Song et al. 202 Jolicoeur Joliceur-Marrtinnn et al. 2021 Liu et al.,
2022). g. Another line of work also considers accelerating sampling by parallel sampling Zheng et al.
2023) ; Shih et al. (2023). As complementary technique to the above methods, T-Stitch accelerates
large DPM sampling by leveraging pretrained small DPMs early denoising steps, while leaving sufficient space for large DPMs at later steps. Some recent works also explore using different mod- els at different sizes to achieve better speed-quality trade-offs. For example, OMS-DPM Liu et al. (2023) trains predictor that predicts the model performance given model schedule. DDSM Yang ct al. (2024) trains a supernet and adopts evolutionary search. Compared to them, T-Stitch is training- free. Under time budget, the search in T-Stitch is much more efficient since the fraction of the small DPM directly defines a unique efficiency and generation quality trade-off.
Multiple experts in diffusion models. Previous observations have revealed that the synthesis be- havior in DPMs can change at different timesteps (Balaji et al.) 2022)) Yang et al. 2023). which has inspired some works to propose an ensemble of experts different timesteps for better performance.
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Figure 4: Trajectory Stitehing (T-Stitch); Based on pretrained small and large DPMs, we can leverage the more efficient small DPM with different percentages the early denoising sampling steps to achieve different speed-quality trade-offs.
For example, Balaji et al. (2022) trained an ensemble of expert denoisers at different denoising in- tervals. However, allocating multiple large denoisers linearly increases the model parameters and does not reduce the computational cost. Yang et al.] (2023) proposed a lite latent diffusion model (i.e., L.M) which incorporates a gating mechanism for the wavelet transform in the denoiser to con- trol the frequency dynamics at different steps, which can be regarded as ensemble of frequency experts. Following the same spirit, Lee et al. (2023) allocated different small denoisers at different denoising intervals to specialize on their respective frequency ranges. Nevertheless, most existing works adopt the same-sized model over all timesteps, which barely consider the speed and quality trade-offs between different-sized models. In contrast, explore flexible trade-off between small and large DPMs and reveal that the early denoising steps can be sufficiently handled by a much efficient small DPM.
Stitchable neural networks. Stitchable neural networks (SN-Net) (Pan et al. 2023a) is motivated by the idea of model stitching (Lene & Vedaldi 2015 Bansal al. 2021 Csiszárik al. 2021 Yang eet al. 2022). where the pretrained models of different scales within a pretrained model family can be splitted and stitched together with simple stitching layers (i.e., performance drop. Based on the insight, SN-Net inserts a few stitching layers among models of different sizes and applies joint training obtain numerous networks (i.e., stitches) with different speed-performance trade-offs. The following work of SN-Netv2 (Pan et al. 2023b) enlarges its space and demonstrates its effectiveness on downstream dense prediction tasks. In this work, we compare our technique with SN-Netv2 to show the advantage of trajectory stitching over model stitching in terms of the speed and quality trade-offs in DPMs. Our T-Stitch is a better, simpler and more general solution.
convs) without significant
TRAJECTORY STITCHING
In this section, we introduce Trajectory Stitching (T-Stitch) with focus on the class of score-based diffusion models in continuous time (Song et al.) 2021b). Due to the limited space, we leave the preliminary Appendix Section A.T
Why can different pretrained DPMs be directly stitched along the sampling trajectory? First of all, DPMs from the same model family usually takes the latent noise inputs and outputs of the same shape, (e.g., 4 32 x 32 in DiTs). There is no dimension mismatch when applying different DPMs at different denoising steps. More importantly, as pointed out in (Song et al., 2021b), different DPMs that are trained on the same dataset often learn similar latent embeddings. We observe that this is especially true for the latent noises at early denoising sampling steps, as shown in Figure where the cosine similarities between the output latent noises from different DiT models reach almost 100%
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Figure 5: T-Stitch of two model combinations: DiT-XL/S, DiT-XL/B and DiT-B/S. We adopt DDIM 100 timesteps with classifier-free guidance scale of 1.5.
early steps. This motivates us to propose T-Stitch, a novel step-level stitching strategy that leverages pretrained small model at the beginning to accelerate the sampling speed of large diffusion models.
Principle of model selection. Figure f shows the framework of our proposed T-Stitch for different speed-quality tradeoffs. In principle, the fast speed or worst generation quality we achieve is roughly bounded by the smallest model in the trajectory, whereas the slowest speed or best genera- tion quality is determined by the largest denoiser. Thus, given large diffusion model that want to speed up. we select a small model that is 1) clearly faster, 2) sufficiently optimized, and 3) trained on the same dataset as the large model or at least they have learned similar data distributions (e.g.. pretrained or finetuned stable diffusion models).
Pairwise model allocation. By default, T-Stitch adopts pairwise denoisers in the sampling trajec- tory as it performs very well in practice. Specifically, we first define a denoising interval as range of sampling steps in the trajectory, and the fraction of it over the total number of steps T is denoted as r, where [0, 1]. Next, we treat the model allocation as compute budget allocation problem. From Figure 3 we observe that the latent similarity between different scaled denoisers keeps de- cheap
creasing when I flows to O. To this end, our allocation strategy adopts small denoiser replacement at the initial intervals then applics the large denoiser at the later intervals. In particular, suppose have small denoiser D1 and a large denoiser D2- Then we let Dj take the first [r]]] steps and D2 takes the last [r steps, where 1.1 denotes a rounding operation and 72 = 1 T1- By increasing 71, naturally interpolate the compute budget between the small and large denoiser and thus obtain flexible quality and efficiency trade-offs. For example, in Figure [ the configuration r1 0.5 uniquely defines trade-off where it achieves 10.06 FID and 1.76x speedup.
More denoisers for more trade-offs. Note that T-Stitch is not limited to the pairwise setting. In fact, we can adopt more denoisers in the sampling trajectory obtain more speed and quality trade- off's and a better Pareto frontier. For example, by using medium sized denoiser in the intermediate interval, we can change the fractions of each denoiser to obtain more configurations. In practice, given a compute budget such as time cost, we can efficiently find few configurations that satisfy this constraint via a pre-computed lookup table, as discussed Section A.2
Remark. Compared to existing multi-experts DPMs, T-Stitch directly applies models of different sizes in a pretrained model family. Thus, given compute budget, we consider how to allocate different resources across different steps while benefiting from training-free, Furthermore, specu- lative decoding (Leviathan et al., 2023) shares a similar motivation with us, i.e., leveraging a small model speed up large language model sampling. However, this technique is specifically designed for autoregressive models, whereas it is not straightforward to apply the same sampling strategy to diffusion models. On the other hand, our method utilizes the DPM's property and achieves effective speedup.
4 EXPERIMENTS
In this section, we first show the effectiveness of T-Stitch based on DiT (Pebless & Xie 2022) as provides a convenient model family. Then extend into U-Net and Stable Diffusion models. Last, we ablate our technique with different sampling steps, and samplers to demonstrate that T-Stitch is generally applicable in many scenarios.
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Figure 6: T-Stitch based on three models: DiT-S, DiT-B and DiT-XL. We adopt DDIM 100 timesteps with classifier-free guidance scale of 1.5. We highlight the Pareto frontier in lines.
Table 1: T-Stitch with LDM (Rombach et al. 2022) and LDM-S on class-conditional ImageNet. All evaluations are based on DDIM and 100 timesteps. We adopt a classifier-free guidance scale of 3.0.
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4.1 DIT EXPERIMENTS
Implementation details. Following DIT, conduct the class-conditional ImageNet experiments based on pretrained DiT-S/B/XL under 256 226 images and patch size of 2. A detailed comparison of the pretrained models is shown in Table 6 As T-Stitch is training-free, for two-model setting, we directly allocate the models into the sampling trajectory under our allocation strategy described in Section 3 For three-moue model setting, we enumerate all possible configuration sets by increasing the fraction by 0.1 per model one at a time, which eventually gives rise to 66 configurations that include pairwise combinations of DiT-S/XI,, DiT-S/B, DiT-S/XI., and three model combinations DiT-S/B/XI_ By default, we adopt classifier-free guidance scale of 1.5 as it achieves the best FID for DiT-XL, which also the target model our setting.
Evaluation metries. We adopt Fréchet Inception Distance (FID) (Heusel et al. 2017)
default metric to measure the overall sample quality as captures both diversity and hideiity (lower values indicate better results). Additionally, we report the Inception Score it remains solid performance measure on ImageNet, where the backbone Inception network (Szegedy et al., 2016) is pretrained. We use the reference batch from ADM (Dhariwal & Nichol 2021) and sample 5,000 images to compute FID. In the supplementary material, we show that sampling more images (e.g., 50K) does affect our observation. By default, the time cost measured by generating 8 images on single RTX 3090 in seconds.
Results. Based on the pretrained model families, we first apply T-Stitch with any two-model com- binations, including DiT-XL/S, DiT-XL/B, and DiT-B/S. For each setting, we begin the sampling steps with relatively smaller model and then let the larger model deal with the last timesteps. In Figure we report the FID comparisons different combinations. In general, we observe that us- ing a smaller model the early 40-50% steps brings minor performance drop for all combinations. Besides, the best/worst performance is roughly bounded by the smallest and largest models in the pretrained model family.
Furthermore, we show that T-Stitch can adopt medium-sizee model at the intermediate denoising intervals to achieve more speed and quality trade-offs. For example, built upon the three different- sized DiT models: DiT-S, DiT-B, DiT-XL, start with DiT-S the beginning then use DiT-B the intermediate denoising intervals, and finally adopt DiT-XL to draw fine local details. Figure 6 indicates that the three-model combinations effectively obtain smooth Pareto Frontier for both FID and Inception Score. In particular, at the time cost of 10s, we achieve 1.7x speedups with comparable FID (9.21 9.19) and Inception Score (243.82 82 245.73). We show the effect of using different classifier-free guidance scales in Section A.5
4.2 U-NET EXPERIMENTS
In this section, we show T-Stitch is complementary to the architectural choices of denoisers. We experiment with prevalent U-Net as it is widely adopted in many diffusion models. We adopt the

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Figure 7: Based on a general pretrained small SD model, T-Stitch simultaneously accelerates large general SD and complements the prompt alignment with image content when stitching other finetuned/styiized large SD models, i.e, "park" InkPunk Diffusion. Better viewed when zoomed in digitally.
Table 2: T-Stitch with BK-SDM Tiny Kim et al. 2023) and SD v1.4. We report FID, Inception Score (IS) and CLIP score (Hessel et al., 2021 on MS-COCO 256 benchmark. The time cost is measured by generating one image on one RTX 3090.
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class-conditional ImageNet implementation from the latent diffusion model (LDM) (Rombach al.,
2022). Based on their official implementation, we simply scale down the network channel width from 256 to 64 and the context dimension from 512 256. This modification produces 15.8 smaller LDM-S. A detailed comparison between the two pretrained models is shown in Table 7
Results. We report the results on T-Stitch with U-Net in Table In general, under DDIM and 100 timesteps, we found the first 550 steps can be taken by an efficient LDM-S with comparable or even better FID and Inception Scores. At the same time, we observe an approximately linear decrease in time cost when progressively using more LDM-S steps in the trajectory. Overall, the U-Net experiment indicates that our method is applicable to different denoiser architectures. We provide the generated image examples in Section A.15 and also show that T-Stitch can be applied with even different model families in Section
4.3 TEXT-TO-IMAGE STABLE DIFFUSION
Benefiting from the public model zoo on Diffusers (von Platen et al. 2022), we can directly adopt a small SD model to accelerate the sampling speed of any large pretrained or styled SD models without any training. In this section, we show how to apply T-Stitch to accelerate existing SD models in the model zoo. Previous research from Kim et al. (0023) has produced multiple SD models with different sizes by pruning the original SD v1.4 and then applying knowledge distillation. We then directly adopt the smallest model BK-SDM Tiny for our stable diffusion experiments. By default, use guidance scale of 7.5 under 50 steps using PNDM (Liu et al. 2022) sampler.
Results. In Table we report the results by applying T-Stitch to the original SD v1.4. addition the FID and Inception Score, we also report the CLIP score for measuring the alignment of the image with the text prompt. Overall, we found the early 30% steps can be taken by BK-SDM Tiny without a significant performance drop in Inception Score and CLIP Scores while achieving even better FID. We believe a better and faster small model in future works can help to achieve better quality and efficiency trade-offs. Furthermore, we demonstrate that T-Stitch is compatible with other large SD models. For example, shown in Figure 7 under the original SD vl.4, we achieve a promising speedup while obtaining comparable image quality. Moreover, with other stylized SD models such as Inkpunk style] observe natural style interpolation between the two models. More importantly, by adopting small fraction of steps from general small SD, we found it helps the image to be more aligned with the prompt, such the "park" in InkPunk Diffusion. In this case,

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we assume finetuning in these stylized SD may unexpectedly hurt prompt alignment, while adopting the knowledge from general pretrained SD can complement the early global structure generation. Overall, this strongly supports another practical usage of T-Stitch: Using small general expert at the beginning for fast sketching and better prompt alignment, while letting any stylized SD at the later steps for patiently illustrating details. Furthermore, we show that T-Stitch is compatible with ControlNet, SDXL, LCM Section A.12 and zero-shot Text-to-
Text-to-Video Video generation in Section A.20
4.4 ABLATION STUDY
Effect of T-Stitch with different steps. To explore the efficiency gain on different numbers of sam- pling steps, we conduct experiments based on DDIM and DiT-S/XL. As shown in Figure T-Stitch achieves consistent efficiency gain when using different number of steps and diffusion model sam- plers. In particular, we found the 40% early steps can be safely taken by DIT-S without significant performance drop. indicates that small DPMs can sufficiently handle the early denoising steps where they mainly generate the low- newfrreuency components. Thus, we can leave the high-free seguffrryeency generation of fine local details to a more capable DiT-XL. This is further evidenced by the gener- ation examples in Figure 17 where across different total number of steps. Overall, we demonstrate that T-Stitch not competing but complementing other fast diffusion approaches that focus reducing sampling steps.
provide the sampled images at all fractions of DiT-S steps
Effect of T-Stitch with different samplers. Here we show T-Stitch also compatible with ad- vanced samplers (Lu et al.) 2022) for achieving better generation quality in fewer timesteps. To this end, we experiment with prevalent samplers to demonstrate the effectiveness of T-Stitch with these orthogonal techniques: DDPM (Ho
2020), DDIM (Song al. 2021a) and DPM-Solver++ (Lu) et al. 2022). In Figure g we use the DīT-S to progressively replace the early steps of DiT-XL un- der different samplers and steps. In general, we observe consistent efficiency gain the initial sampling stage, which strongly justifies that our method is a complementary solution with existing samplers for accelerating DPM sampling.
T-Stitch vs. model stitching. Previous works (Pan et al. 2023a b) such as SN-Net have demon- strated the power of model stitching for obtaining numerous architectures that with different com- plexity and performance trade-offs. Thus, by adopting one of these architectures as the denoiser for sampling. SN-Net naturally achieves flexible quality and efficiency trade-offs. To show the advan- tage of T-Stitch in the Pareto frontier, we conduct experiments to compare with the framework of model stitching proposed in SN-Netv2 (Pan et al.] 2023b). We provide implementation details in Section A.9 In Figure I0, compare T-Stitch with model stitching based on DDIM sampler and 100 steps. Overall, while both methods can obtain flexible speed and quality trade-offs, T-Stitch achieves clearly better advantage model stitching across different metries.
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M-Stich T-Stitch
M-Stitch T-Stichh
60
Inception Score
200
0 40
100
20
5
10 Time Cost (s)
15
10
15
Time Cost (s)
Figure 10: T-Stitch vs, model stitching (M-Stitch) Pan et al. (2023b) based on DiTs and DDIM 100 steps, with a classifier-free guidance scale of 1.5.
Table 3: Comparison between T-Stitch and training/searching based model schedules on CIFAR- -0. "#Eval Runs" indicates the number of FID evaluation runs required for both training and searching. Baselines are the largest networks in their model families. For T-Stitch, we only adopt the smallest network in their model family to accelerate the baselines.
(a) Comparison with DDSM Yang et 2024). (b) Comparison with OMS-DPM
Liu et al. 2023
Name
Time per Img (s)
FID
#Eval Runs
Name
Time per Img (s)
FID
#Fval Runs
Baseline 0.70
7.9
Baseline
0.13
8.4
DDSM 0.45
8.3
500
OMS-DPM
0.03
11.2
26,000
T-Stitch 0.44
8.1
T-Stitch
0.04
11.5
1
Compared to training- acceleration methods. The widely adopted training-based methods
for accelerating DPM sampling mainly include lightweight model design Zhhoo et al. (2023); Lee
et al. (2023), model compression Kim et al.] (2023), and steps distillation [Saimaan & Ho (2022);
Song et al. (203): Luo et al. (2023). Compared to them, T-Stitch is a training-free and complemen-
tary acceleration lechnique since it agnostic to individual model optimization. In practice, T-Stitch
achieves wide compatibility with different denoiser architectures (DiT and U-Net, Section 4.1 and
Section 4.2) and any already pruned (Section A.8) or step-distilled models (Section 17.
Compared to other training-free ffree acceleration methods. Recent works Li et al. (002a)); Ma
jtt al. (2023; ; Wimbauer et al. (2023) proposed to cache the intermediate feature maps in U-Net
during sampling for speedup. T-Stitch is also complementary to these mache-based methods since
the individual model can still be accelerated with caching, as shown in Section A.18 In addition, T-
A.
Stitch can also enjoy the benefit from model quantization [hang et al. (2023) Li et al. 2023b), VAE decoder acceleration Kodaira et al. (2023) and token merging Bolya et al. (023) (Section A.19)
since they are orthogonal approaches.
model schedules. Given a time budget such as the time cost for generating an image, there is line of work Yang al. (2024); Liu et al. (2023) searching for a model schedule. In contrast to these methods, we provide a clear allocation strategy, which essentially reduces the search space, thus significantly reducing the search cost, compared to training or searching-based methods which have a much larger search space. For example, under 1,000 steps and 4 different network widths in DDSM (Yang et al., 2024), the search space is 41,000 Our work points out a simple but effective way by progressively allocating a small model at the early steps, resulting in only 1,000 configurations at most fine-grained level. Benefiting from such small space, our T-Stitch is practically way faster than existing works since the corresponding model schedule under a time cost can be obtained by directly querying a pre-calculated lookup table on CPU within a second. At the same time, our result is comparable or even better, as shown in Table
Compared to training/searching--ees
Additional memory and storage overhead of T-Stitch. Intuitively, T-Stitch adopts a small DPM which can introduce additional memory and storage overhead. However, in practice, the large DPM still the main bottleneck of memory and storage consumption. In this case, the additional overhead from small DPM is considerably minor. For example, shown in Table compared to DiT-XL, T- Stitch by adopting 50% steps of DiT-S only introduces additional 5% parameters, 4% GPU memory cost, 10% local storage cost, while significantly accelerating DiT-XL sampling speed by 1.76x.
Compared with more stitching baselines. By default, we design T-Stitch to start from small DPM and then switch into large DPM for the last denoising sampling steps. To show the effectiveness of this design, we compare our method with several basclines Table 5 based on DiT-S and DiT-XL. These baselines listed below,

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Table Local storage and memory cost comparison between DiT-S, DiT-XL and T-Stitch. Memory and time cost are measured by generating 8 images in parallel on one RTX 3090.
Name
Parameter (M)
Local Storage (MB)
Memory Cost (MB)
Time Cost (s)
DIT-S
33
263
3,088
1.7
DIT-XI
675
2,576
3,166
16.5
T-Stitch (50%)
708 1.04)
2,839 (.1))
3,296 (x1.04)
9.4 (x 1.76)
Table 5: Compared to other trajectory stitching baselines based on DiT-S/XL, DDIM 100 steps and
guidance scale of 1.5. FID is calculated by 5K images. Memory and time cost measured by batch size of on one RTX 3090.
Method
FID
Inception Score
Time Cost
Interleave
19.02
120.04
10.1
Decreasing Prob
12.94
163.45
9.8
Large Small
27.61
72.60
10.0
Small to Large (Ouns)
10.06
200.81
9.9
Interleaving. During denoising sampling, we interleave the small and large model along the trajectory. Eventually, DiT-S takes 50% steps and DīT-XL takes another 50% steps.
Linearly decreasing the probability of using DīT-S from 1 to 0 during the denoising sampling steps.
Decreasing Prob.
Large to Small. Adopting the large model at the early 50% steps and the small model at the last 50% steps.
Small to Large (our default design). The default strategy of T-Stitch by adopting DiT-S at the early 50% steps and using DīT-XL at the last 50% steps.
As shown in Table 5 in general, our default design achieves the best FID and Inception Score with similar sampling speed, which strongly demonstrate its effectiveness.
Remarks on the small model. While small model is required for T-Stitch, our key motivation is that the increasingly expanded large public model zoo more or less addressed this concern prac- tice. First, under T-Stitch, small model that lies in a similar data distribution as the target large model can accelerate all its finetuned or stylized versions for free. For example, single BK-SDN KDM Tiny is able to simultaneously accelerate SDv1.4, any stylized SDs based on it, e.g., ControlNet, as shown Section A2 Furthermore, T-Stitch also works for different model families. Benefit- ing from it, we can easily accelerate U-VIT H sampling with a small DīT-S, as shown in Table B Second, free lunch is generally available in large public model zoos. The open-source community (e.g.. HuggingFacc.ccv and Civitai.com has released tens of thousands of pretrained models, includ- ing different scales (Segmind, 2023; Kim et al. 2023), which are pruned and knowledge distilled versions of large stable diffusion models. In our experiments, have never trained SD model from scratch, while being able to demonstrate broad applications of T-Stitch on SDXL, ControlNet, and step-distilled diffusion models.
CONCLUSION
We have proposed Trajectory Stitching, an effective and general approach to accelerate existing pre- trained large diffusion model sampling by directly leveraging pretrained smaller counterparts the initial denoising process, which achieves better speed and quality trade-offs than using an individ- ual large DPM. Comprehensive experiments have demonstrated that T-Stitch achieved consistent efficiency gain across different model architectures, samplers, as well as various stable diffusion models. Besides, our work has revealed the power of small DPMs at the early denoising process. Future work may consider disentangling the sampling trajectory by redesigning or training experts of different sizes at different denoising intervals. For example, designing better, faster small DPM at the beginning to draw global structures, then specifically optimizing the large DPM the later stages to refine image details. Besides, more guidelines for the optimal trade-off and more in- analysis of the prompt alignment for stylized SDs can be helpful, which leave for future work.
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A APPENDIX
We organize our supplementary material as follows.
In Section
A.
we introduce the preliminary of T-Stitch.
In Section
A.2
we provide guidelines for practical deployment of T-Stitch.
In Section
A.3
we provide frequency analysis during denoising sapmling process based on
DiTs.
In Section
A.4
we report the details of our adopted pretrained DiTs and U-Nets.
In Section
A.5
we show the effect using different classifier- free guidance scales based
DiT's and TtStich.
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1+ 999
190
Log amplluut
555
1
0.0m
0.2m
0.411
0.6m
0.8m
Frequency
Figure 11: Frequency analysis denoising process of DiT-XL, based on DDIM steps and guid- ance scale of 4.0. We visualize the log amplitudes of Fourfer-transformed latent noises at each step. Results are averaged over 32 images.
In Section A.6
we compare FID evaluation under T-Stitch with 5,000 images and 50,000
images.
In Section
A.7
we compare T-Stitch with directly reducing sampling steps.
In Section
A.8
8
we show T-Stitch compatible with pruned and knowledge distilled mod-
els.
In Section
A.9
we describe the implementation details of model stitching baseline under
SN-Netv2 Pan et al. (2023b).
In Section A.10 we show image examples when using T-Stitch with different sampling steps based on DiTs.
In Section A.
demonstrate that T-Stitch is applicable to different pretrained model families, e.g., stitching DiT with U-ViT Bao et al. (2023).
In Section 12
we show more image examples in stable diffusion experiments, including the original SDv 1.4, stylized SDs, SDXL, ControlNet.
In Section A.13 13
we report our finetune experiments by further finetuning the large DiTs
their allocated steps.
In Section A.
we report the precision and recall metrics on class conditional ImageNet- 256 based on DiTs.
14
In Section A.15
we show image examples of T-Stitch with DiTs and U-Nets.
In Section training iterations.
A.16
we evaluate FID under T-Stitch by using pretrained DiT-S at different
In Section A.17 we demonstrate that T-Stitch can still obtain smooth speed and quality trade-off under 2-4 steps with LCM Luo et al. (2033)
In Section A.18 we show T-Stitch also complementary to cache-based methods such as DeepCache Ma et (2023) achieve further speedup.
A.19 we evaluate T-Stitch and show image examples by applying ToMe Bolya et 20033 simultaneously.
In Section
we show examples of using T-Stitch to accelerate zero-shot Eext-to-Vides generation Khachatryan et al. (003)..
In Section A.20
In Section A.21
we report the 1.2-distance of latent embeddings between DiTs at different denoising steps.
In Section A.22
we discuss the relation with early-exit works.
In Section
A.2
discuss the limitations of T-Stitch.
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Performance comparison of pretrained DiT model family on class-conditional ImageNet. FLOPs are measured by a single forward process given a latent noise in the shape of 4 x 32 x 32.
Table 6:
Parameters (M)
FLOPs (G)
Train Iters
Time Cost (s)
FID
DiT-S
33.0
5.5
5000K
1.6
33.46
DiT-B 130.5
21.8
1600K
4.0
12.30
DiT-XL
675.1
114.5
16.5
9.20
Table 7: Performance comparison of LDM and LDM-S on class-conditional ImageNet.
Model
Param (M)
Train Iter
Time Cost (s)
FID
LDM-S
25
400K
2.9
40.92
LDM
394
200K
7.1
20.11
A.1 PRELIMINARY
Diffusion models. We consider the class of score-based diffusion models in a continuous time (Song al. b02 and following the presentation from Karras al. (2022). Let Paata(Xx) denote the data distribution and ot): [0, + R_ is user-specified noise level schedule, where t E {o..... T and (t ((t-1) 1) (t). Let p(x: 0)) denote the distribution of noised samples by injecting va-riiancee Gaussian noise. Starting with high-variance Gaussian noise XT, diffusion models gradually de- noise XT into less noisy samples 1XT-1,XT-22, ..., Xo), where x, ~ p(x:; (()). Furthermore, this iterative process can be done by solving the probability flow ordinary differential equation (ODE) if knowing the score V log Pt (z), namely the gradient of the log probability density with respect to data,
dx = Vx
at)) dt,
(1)
where (t) denote the time derivative of (t). Essentially, diffusion models aim to learn a model for the score function, which can be reparameterized
Vx lo R (Do(x;o)->)) -xo))xxx/aa x)/o²
where Dx(x; a) is the learnable denoiser. Given a noisy data point x + n and a conditioning signal c, where n ~ V (0, d²T), the denoiser aim to predict the clean data Xp. In practice, the mode is trained by minimizing the loss of denoising score matching,
Ei pw..ee)))paa........... (c.)))pp.
[>]] Do(xo +n;a,c) x xol12]
(3)
+ R4 weighting function (Ho al. 2020), p(a, n) W and p(a) is distribution over noise levels
where Ag -
This work focuses on the denoisers D in diffusion models. common practice, they are typically large parameterized neural networks with different architectures that consume high FLOPs each timestep. In the following, we use "denoiser" or "model" interchangeably to refer to this network. We begin with the pretrained DiT model family to explore the advantage of trajectory stitching on efficiency gain. Then we show our method is a general technique for other architectures, such as U-Net (Rombach al. 2022) and U-ViT' (Bao al. 2023).
that require an additional network to provide conditioning guidance, classifier-free guidance (Ho Salimans, 2022) is technique that jointly trains conditional model and an unconditional model in one net- work by replacing the conditioning signal with a null embedding. During sample generation, adopts guidance scale 2 0 to guide the sample to be more aligned with the conditioning signal by jointly considering the predictions from both conditional and unconditional models,
Classifier-free guidance. Unlike classifier- based denoisers (Dhariwal & Nichol 2021
D* = (1+ s) D) o, sD(x; o).
Recent works have demonstrated that classifier-fre guidance provides clear improvement in gen- eration quality. In this work, we consider the diffusion models that are trained with classifier-free guidance due their popularity.
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S 1.5
400
$ 2.0
Inception Score
3.0
300
20
200
=
15
S 2.0
10
100
S 3.0
5
15
5
15
Time Cost (s)
Time Cost (s)
Figure 12: Trajectory stitching based on three models: DiT-S, DiT-B, and DiT-XL. We adopt DDIM 100 timesteps with classifier-free guidance scale of 1.5, 2.0 and 3.0.
A.2 PRACTICAL. DEPLOYMENT OF T-STITCH
In this section, we provide guidelines for the practical deployment of T-Stitch by formulating our model allocation strategy into compute budget allocation problem.
Given set of denoisers { D2, ..., DK} and their corresponding computational costs {C1, C2...., [x] for sampling in a T-steps trajectory. where CB-1 < Ck. we aim to find an op- timal configuration set fri, r2...., rk that allocates models into corresponding denoising intervals to maximize the generation quality, which can be formulated as
max 22o2
M (F(D1, 11) F()2, ... ... F(DK,
(5)
K
subject to
Σ Tr s CR. Σ Tk 1=
(6)
where F(DE, rk) refers to the denoising process by applying denoiser D3 at the k-th interval indi- cated by Tke denotes to composition, M represents metric function for evaluating generation performance, and CR the compute budget constraint. Since [C1, C2 ..., Cx } is known, we can efficiently enumerate all possible fraction combinations and obtain lookup table, where each frac- tion configuration set corresponds to a compute budget (i.e., time cost). In practice, we can sample few configuration from this table that satisfy a budget and then apply to generation tasks.
A.3 FREQUENCY ANALYSIS DENOISING PROCESS
We provide evidence that the denoising process focuses on low frequencies at the initial stage and high frequencies in the later steps. Based DiT-XL, we visualize the log amplitudes of Fourier- transformed latent noises at each sampling step. As shown in Figure [1] the low- low-frequeccy ampli- tudes increase rapidly the early timesteps (i.e., from 999 555), indicating that low frequencies are intensively generated. At the later steps, especially for / = 111 and = 0, we observe the log amplitude high frequencies increases significantly, which implies that the later steps focus on detail refinement.
A.4 PRETRAINED DITS AND U-NETS
In Tad Table 7 we provide detailed comparisons the pretrained DiT model family, as well reproduced small version of U-Net. Overall, as mentioned earlier in Section 3 make sure the models at each model family have a clear gap in model size between each other such that we can achieve clear speedup.
A.5 EFFECT OF DIFFERENT CLASSIFIER GUIDANCE ON THREE-MODEL. T-STITCH
In Figure 12 we provide the results by applying T-Sittch with DiTs using different guidance scales under mree- settings. In general, T-Stitch performs consistently with different guidance scales,
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Figure 13: Trajectory stitching based three models: DiT-S, DiT-B, and DiT-XL. We adopt DDPM 250 timesteps with a classífier-free guidance scale of 1.5.
Pretrained
Finetuned
Figure 14: Image quality comparison by stitching pretrained and finetuned DYT-B and DiT-XL the later steps, based on T-Stitch schedule of DiT-S/B/XL. of 50% 30% 20%.
where it interpolates smooth Pareto frontier between the DiT-S and DiT-XL. As common practice in DPMs adopt different guidance scales to control image generation, this significantly underscores the broad applicability of T-Stitch.
A.6 FID-50K vs. FID-5K
For efficiency concerns, report FID based on 5,000 images default. Based DiT, apply T- Stitch with DDPM 250 steps with a guidance scale 1.5 and sample 50,000 images for evaluating FID. As shown in Figure 13, the observation between FID-50K and FID-5K similar, which indicates that sampling more images like 50,000 does not affect the effectiveness.
A.7 COMPARED TO DIRECTLY REDUCING SAMPLING STEPS
Reducing sampling steps has been a common practice for obtaining different speed and quality trade-offss during deployment. Although we have demonstrated that T-Stitch can achieve consistent efficiency gain under different sampling steps, we show in Figure 15 that compared to directly reducing the number of sampling steps, the trade-offs from T-Stitch very competitive, especially for the 50-100 steps region where the FIDs under T-Stitch are even better. Thus, T-Stitch is able to serve a complementary an alternative method for practical DPM sampling speed acceleration.
A.8 COMPARED TO MODEL COMPRESSION
In practice, T-Stitch is orthogonal individual model optimization/compressio For example, with a BK-SDM Tiny and SDv1.4, we can still apply compression into SDv1.4 4 in order to reduce the computational cost at the later steps from the large SD. In Figure [6] we show that by adopting
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Figure 15: Based on DDIM, we report the FID and speedup comparisons DiT-XL using T-Stitch and directly reducing the sampling step from 100 to 10. "s" denotes the classifier-free guidance scale. Trajectory stitching adopts the three-model combination (DIT-S/B/XI.) under 100 steps.
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backpack the snow
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Figure 16: Comparison of T-Stitch by adopting SDv1.4 and its compressed version (i.e., BK-SDM Small) at the later steps.
compressed SD vl.4, i.e., BK-SDM Small, we can further reduce the time cost with a trade-off for image quality.
A.9 IMPLEMENTATION DETAILS OF MODEL STITCHING BASELINE
We adopt a LoRA rank of 64 when stitching DiT-S/XI., which leads to 134 stitching configurations. The stitched model is finetuned 8 A100 GPUs for 1,700K training iterations. We pre-extract the ImageNet features with a Stable Diffusion AutoEncoder (Rombach et al. (2022) and do not apply any data augmentation. Following the baseline DiT, we adopt the Adam W optimizer with constant learning rate of 1 x 10 The total batch size is set as 256. All other hyperparameters adopt the default setting as DIT.
A. IMAGE EXAMPLES UNDER THE DIFFERENT NUMBER OF SAMPLING STEPS
Figure 17 shows image examples generated using different numbers of sampling steps under T-Stitch
and DIT-S/XL. As the figure shows, adopting a small model at the early 40% steps has a negligible effect the final generated images. When progressively increasing the fraction of DiT-S, there is a visible trade-off between speed and quality, with the final image becoming more similar to those generated from DIT-S.
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Figure 17: Based on DDIM and classifier free guidance scale of 1.5, we stitch the trajectories from DĪT-S and DiT-XL and progressively increase the fraction (%) of DiT-S timesteps the beginning.
Table T-Stitch with DiT-S and U-ViT H. under DPM-Solver++, 50 steps, guidance scale of 1.5.
Fraction of DīT-S
0%
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FID
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13.68
12.44
12.76
14.19
17.6
29.4
53.75
74.14
84.33
121.95
Time Cost
15.90
13.21
11.91
10.61
9.42
7.92
6.57
5.23
3.84
2.50
1.40
A.11 T-STITCH WITH DIFFERENT PRETRAINED MODEL FAMILIES
As different pretrained models trained on the same dataset to learn similar encodings, T-Stitch is able to directly integrate different pretrained model families. For example, based U-ViT H (Bao jet al., (023). we apply DiT-S at the early sampling steps just as we have done for DiTs and U- Nets. Remarkably, shown in Table it performs very well, which demonstrates the advantage of T-Stitch as it can be applied for more different models in the public model zoo.
A.12 MORE EXAMPLES IN STABLE DIFFUSION
We show more examples by applying T-Stitch to SD v1.4, InkPunk Diffusion and Ghibli Diffusion with a small SD model, BK-SDM Tiny (Kim al. 2023). For all examples, we adopt the default scheduler and hyperparameters of StableDifflusoo Pipeline in Diffusers: PNDM scheduler, 50 steps, guidance scale 7.5. In Figure 25 we observe that adopting small SD in the sampling trajectory of SD v1.4 achieves minor effect on image quality at the small fractions and obtain flexible trade-offs speed and quality by using different fractions.
Stylized SDs. For stylized SDs, such Figures 26 and 27 that T-Stitch helps to complement the prompt alignment by effectively utilizing the knowledge of the pretrained small SD. Benefiting from the interpolation speeds, styles and image contents, T-Stitch naturally increases the diversity of the generated images given prompt by using different fractions of small SD.
and Ghibli-Diffusior]] we show in
Generality of T-Stitch In Figure 28 we show T-Stitch performs favorably with more complex prompts. Besides, by adopting smaller and distilled SSD-1B, can easily accelerate SDXL while being compatible with complex prompts and ControlNet (Zhang et al.) 2023) for practical art generation, as shown in Figure 29 and Figure 30 Furthermore, we demonstrate that T-Stitch is robust in practical usage. As shown in Figure 31 consecutive runs can generate stable images with great quality.
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Table 10: Precision and Recall evaluation based on DiT-S/XL, with DDIM scale of 1.5.
100 steps and guidance
Fraction of DIT-S
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20%
30%
40%
50%
60%
70%
80%
90%
100%
FID
9.20
9.17
8.99
9.03
8,95
10.06
12.46
18.04
25.44
30.11
33.46
Precision
0.81
0.81
0.81
0.81
0.80
0.76
0.72
0.67
0.62
0.59
0.58
Recall
0.74
0.74
0.74
0.74
0.75
0.75
0.74
0.73
0.69
0.65
0.63
A.13 FINETUNING ON SPECIFIC TRAJECTORY SCHEDULE
When progressively using a small model in the trajectory, observe a non-negligible performance drop. sim-
Table 9: Performance comparison of stitching pretrained
and finetuned DiTs at the later steps. We set the denois-
ing interval of DiT-S/B/XL with 50% 30% 20%
However, show that ply finetune the model at the allo- cated denoising intervals improve the generation quality. For example, based on DDIM and 100 steps, allo- cating DiT-S at the early 50%, DīT-B the subsequent 30%, and DiT-XL. at the last 20% obtains an FID of 16.49. In this experiment, we separately finetune DīT-B and DřT- XL at their allocated denoising intervals, with additional 250K iterations on ImageNet- IK under the default hyperparameters in DiT (Peebles & Xie 2022). In Table 9 we observe clear improve- ment over FID, Precision and Recall by finetuning at stitched interval. This strategy also achieves better performance than finetuning for all timesteps. Furthermore, we provide comparison of the generated images in Figure 14 where we observe that finetuning clearly improves local details.
FID
Inception Score
Pretrained
16.49
123.11
Finetuned all timesteps
16.04
125.81
Finetuned at stitched interval
13.35
155.35
A. PRECISION AND RECALL MEASUREMENT OF T-STITCH
Following common practice (Dhariwal Nichol 2021), we adopt Precision to measure fidelity and Recall to measure diversity or distribution coverage. In Table [T] we show that T-Stitch introduces a minor effect on Precision and Recall the early 40-50% steps, while the later steps we observe clear trade-offs, which is consistent with FID evaluations.
A.15 IMAGE EXAMPLES OF T-STITCH ON DITS AND U-NETS
In Figures 23 and 24 we provide image examples that generated by applying T-Stitch with DiT- S/XL, LDM-S/LDM, respectively. Overall, we observe that adopting small DPM at the begin- ning still produces meaningful and high-quality images, while at the later steps it achieves flexible speed and quality trade-offs. Note that different from DiTs that learn null class embedding during classifier-free free guidance, LDM inherently omits this embedding in their official implementation During sampling, LDD and LDM-S have different unconditional signals, which eventually results in various image contents under different fractions.
A.16 EFFECT OF DIT-S UNDER DIFFERENT TRAINING ITERATIONS
In our experiments, we adopt a DiT-S that trained with 5,000K iterations as it can be sufficiently optimized. In Figure [8 adopting DiT-S at the initial stages of the sampling trajectory also has minor effect on the overall FID. The main difference the later part of the sampling trajectory. Therefore, implies the early denoising sampling steps can be easier to learn and be handled by compute-efficies small model.
indicate that under short training schedule of 400K iterations,
A.17 COMPATIBILITY WITH LCM
T-Stitch can further speed up an already accelerated DPM via established training- methods, such step distillations (Luo et al. 2023 Song et al. 2023 For example, as shown in Figure 32
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Figure 18: Effect of different pretrained DiT-S in T-Stitch for accelerating DiT-XL, based on DDPM, 250 steps and guidance scale of 1.5, For example, "400K"" indicates the pretrained weights of DiT-S at 400K iterations.
given a distilled SDXL from LCM (Luo et al. 2023). T-Stitch can achieve further speedup under 4 steps with high image quality by adopting a relatively smaller SD. In Table I1] Table 12 we report comprehensive FID, inception score and CLIP score evaluations by stitching LCM distilled SDXL and SSD-IB, where we show that T-Stitch smoothly interpolates the quality between SDXL and SSD-IB IB. Finally, we assume a better and faster small model in T-Stitch will help to obtain more gains in future works.
and Figure 33
Table 11: T-Stitch based on LCM (Luo et
2023 distilled models: LCM-SDXL and LCM-SSD-
IB, under 2 sampling steps.
Faction of SSD-1B
0
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FID
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23.96
24.36
IS
28.48
27.60
28.22
CLIP Score
0.2929
0.2895
0.2844
Time Cost (ms)
768
685
634
A.18 COMPATIBILITY WITH DEEPCACHE
In this section, we demonstrate that recent cache-based methods Ma et al. (2023): Wimbauer et al. (2023) such DeepCache Ma al. (2023) can be effectively combined with T-Stitch to obtain more benefit. Essentially, T-Stitch directly drops off the pretrained SDs, we can adopt DeepCache to simultaneously accelerate both small and large diffusion models during sampling to achieve further speedup. The image quality and speed benchmarking as shown in Figure Figure2]]ave have demonstrated that T-Stitch works very well along with DeepCache, while potentially further improving the prompt alignment for stylized SDs. We also comprehensively evaluate the FID, Inception score, CLIP score and time cost in Figure 19 where we observe combining T-Stitch with DeepCache brings
Table 12: T-Stitch based LCM (Luo et al. 2023) distilled models: LCM-SDXL and LCM-SSD-
IB, under 4 sampling steps. Time cost is measured by generating one image on RTX 3090 in seconds.
Faction of SSD-IB
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Figure 19: Effect of combining T-Stitch and DeepCache Ma et al. (2023) We report FID, Inception Score and CLIP score (Hessel et al. 202T) MS-COCO 256 benchmark under 50 steps. The time cost measured by generating one image on one RTX 3090. We adopt BK-SDM Tiny and SDv1.4 the small and large model, respectively. For DeepCache, we adopt an uniform cache interval of 3.
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Figure 20: Effect of combining T-Stitch and ToMe Bolya et al. (022)). We report FID, Inception Score and CLIP score (Hessel et al. 2021) on MS-COCO 256 256 benchmark under 50 steps. The time cost is measured by generating one image one RTX 3090. We adopt BK-SDM Tiny and SDv1.4 .4 as the small and large model, respectively. For ToMe, we adopt a token merging ratio 0.5.
improvement over all metrics. Note that under DeepCache, BK-SDM Tiny is 1.5x faster than SDv1 .4, thus the speedup gain from T-Stitch is slightly smaller than applying T-Stitch only where the BK-SDM Tiny is 1.7x faster than SDv1.4. addition, we observe DeepCache cannot work well with step-distilled models and ControlNet, while T-Stitch is generally applicable to many scenarios, as shown in Section A.12
A. COMPATIBILITY WITH TOKEN MERGING
Our technique also complements Token Merging Bolya et (2023). For example, during the de- noising sampling, we can still apply ToMe into both small and large U-Nets. In practice, it brings additional gain in both sampling speed and CLIP score, and slightly improves Inception score, as shown in Figure 20, We also provide image examples in Figure 22
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ue cake topped with beach scone.
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sqyimal the park, rvinkppukk style
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sky hiled with vibrant hot air ballonns, ghibil
Figure 21: Image examples of combining T-Stitch with DeepCache Ma et 2023) We adopt BK-SDM Tiny as the small model in T-Stitch and report the percentage on the top of images. All images are generated by default settings diffusers yon Platen et al. 2002): 50 steps with guidance scale of 7.5.
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thre horzes a snowy feeld with trees backigrond
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ToMe man is riding red matoraycle and mvinkuunk.
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ghibli style village, onws eotingg by river,
Figure 22: Image examples of combining T-Stitch and ToMe Bolya et al. 2023 We adopt BK-
SDM Tiny as the small model T-Stitch and report the percentage on the top images. All images generated by the default settings in diffusers yon Platen al. (2022): 50 steps with guidance scale of 7.5. We adopt a token merging ratio of 0.5 in ToMe.
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crate
eoral_fungus
restaurant
pillow
admiral
dowitcher
gyromitra
spotted
salamandar
convertible
Figure 23: Image examples of T-Stitch DIT-S and DIT-XL.. We adopt DDIM and 100 steps, with a guidance scale of 4.0. From left to right, we gradually increase the fraction of LDM-S steps at the beginning, then let the original LDM to process later denoising steps.
A.20 EXPERIMENTS ON ZERO-SHOT TEXT-TO-VINE GENERATION
In this section, we show T-Stitch works very well with text-to-video generation. Based on Text2Video-Zero Khachatryan et al. (2023), we found T-Stitch can also speedup video generation by adopting small model like BK-SDM Tiny. We also observed slight improvement prompt alignment. For example, the "outer space" Figure 34 where we progressively allocate BK-SDM Tiny at the 10-50% early sampling steps.
A.21 L2-DISTANCE COMPARISON OF LATENT EMEBDDINGS FOR DITS
As complementary analysis to Figure 3 present the L2-distance comparison of latent embed- dings across different steps for DiTs in Figure 35 Overall, we observe that the latent embeddings from small and large DiTs notably more similar during the early steps compared to the later steps, aligning with the patterns observed in Figure
A.22 COMPARISON WITH EARLY-EXIT WORKS
In general, we aim to explore the compute budget allocation for diffusion model sampling, which is orthogonal with individual model acceleration techniques such early-exiting model compres-
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promontory
macco
aircreft_cco carrier
Berman_shhpheene
beascn
sewing machine
heri
English_aprinnn Lepringer
bison
Figure 24: Image examples T-Stitch on U-Net-based LDM and LDM-S. We adopt DDIM and 100 steps, with guidance scale of 3.0. From left to right, gradually increase the fraction of LDM-S steps at the beginning, then let the original LDM to process later denoising steps.
sion that specifically focus model, discussed in Section A.8 with early-exit works as below.
A.8
We provide short discussion
Deediff Tang al. 2023) introduces Timestep-Aware Uncertainty Estimation Module (UEM), which includes new trainable parameters. Compared to it, T-Stitch does not require any new pa- rameters train since directly drop the small model the early denoising steps. Adaptive Score Estimation (ASE) Moon et al.] (2023) heuristically designs block-exiting strategies based on differ- ent architectures, then finetuning the target diffusion model with substantial training cost. Based on DiT-XL, we found our speed speed-quality trade-off's are clearly better, as shown below.
Table 13: Comparison of DiT models in FID-5K scores and acceleration gains, as reported in Moon et al. (2023).
Name
FID-5K
Acceleration
DIT-XL (Moon et al. 2023) implementation)
9.10
D1-DiT
8.89
14.38%
D3-DIT
8.99
20.99%
D4-DiT
9.19
28.70%
D6-DIT
11.41
36.80%
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10%
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50%
SD-v1.4
bowl that has vegetables Inside of
30%
40%
50%
SD-v1.4
a brown and white cat laying on a bed
40%
50%
60%
SD-v1.4
very large tower has clock on it
Figure 25: T-Stitch based on Stable Diffusion vl.4 and BK-SDM Tiny. We annotate the faction of BK-SDM on top of images.
In Table 13 we briefly summarize the main results in Moon et al. (2023). FID-SK is evaluated based
on ImageNet-256 and DDIM sampler. "Acceleration" refers the acceleration in sampling speed. "n" in "Dn-DiT" represents the acceleration scale. Details for different settings can found in Table 2 of ASE et al. 20033..
The results in Table 14 are from our Figure which is based on the same experimental setting: ImageNet-256, DDIM sampler, and FID-5K. Note that due different implementations, DiT-
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Inkpunk SD
A polar bear mars, nvinkpunk style
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lion wearing suit meeting room, nvinkpunk style
10%
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Inkpunk
train pulling into station on a cloudy day, rvinkpunk style
Figure 26: T-Stitch based on Inkpunk-Diffusin SD an BK-SDM Tiny. We annotate the faction of BK-SDM on top of images.
XL baseline performance can be slightly different. Furthermore, compared to these training-bart based approaches, T-Stitch is training-free and can be directly applicable to many various scenarios.
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10%
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500%
Ghibli SD
a ghibli style princess with golden hair in New York City
20%
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Ghibli SD
ghibli style beautiful Caribbean beach tropical (sunset)
60%
70%
80%
90%
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Ghibli SD
ghibli style ice field white mountains ((northern lights)) starry sky low horizon
Figure 27: T-Stitch based Ghibli-Diffusion SD and BK-SDM Tiny. We annotate the faction of BK-SDM on top of images.
A.23 LIMITATIONS
T-Stitch requires a smaller model that has been trained on the same data distribution as the large model. Thus, sufficiently optimized small model that shares the similar latent space the target large model is required. Besides, adopting an additional small model for denoising sampling will slightly increase memory usage (Table 4). Lastly, since T-Stitch provides free lunch from small
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SD-v1.4
A clock and several vases sit on table in front of gold framed mirror
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photography of dewdrops on spiderweb, with moming sunlight creating rainbows.
10%
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SD-v1.4
Aerial photography a winding river through autumn forests, with vibrant and orange foliage.
Figure 28: T-Stitch with more complex prompts based on Stable Diffusion v1.4 and BK-SDM Tiny. We annotate the faction of BK-SDM on top of images.
model for sampling acceleration, the speedup gain is bounded by the efficiency of the small model. In practice, we suggest using T-Stitch when a small model is available and much faster than the large model. Furthermore, when applying T-Stitch with very different architectures, the main challenge is that the two model may have different spatial dimension for their latents, thus making it difficult to directly switching models during sampling. We leave better stitching strategies for future works.
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SDAL, 13.65
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40%, 11.08s
60%, 10.85
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100%,,
Prompt: highly detailed albert einstein at an epic laboratory office, shelves with detailed items in background, (llong shot)), highly detailed realistic painting by grandmaster, unreal engine, octane render, 4k, trending on artstation
Prompt:
concept dragon flying town, clouds. digital artwork, illustrative, painterly, matte painting, highly detailed, cinematic composition
Negative Prompt: photo, photorealistic, realism, ugly
Prompt:
anime artwork an empty classroom. anime style, key visual, vibrant, studio anime, highly detailed
Negative Prompt:
photo, deformed, black and white, realism, disfigured, low contrast
Prompt: claymation style captain jack sparrow on tropical island. sculpture, clay art, centered composition, play-doh
Negative Prompt: sloppy, messy, grainy, highly detailed, ultra textured, photo, mutated
Prompt: 16-bit pixel art, cozy cafe side view, beautiful day
Negative Prompt: sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic
Figure 29: T-Stitch with more complex prompts based on SDXL (Podell et al. 2023) and SSD- iB (Segmind) 2023). We annotate the faction of SSD-1B on top of images. Time cost is measured by generating one image on RTX 3090.
The optimal threshold for T-Stitch. In our observation, stitching at the early 40% minorly af- fects the generation quality, while at the larger fractions, T-Stitch provides clear trade-off between
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T Stitch
T Stite h
Image 40%, 17.58
Canny edges 60%, 16.68
SDXL, 19.7s
20%, 18.49
80%, 15.68
100%, 14.8
Stiloh scitch
Stitch
Stitch
Stitch
Stitch
Prompt: highly detailed cinematic aerial view of futuristic research complex Antarctica, 4K Negative Prompt: blur, low quality, bad quality, sketches
Image
Depth
SDXL, 19:48
20%, 18.4s
40%, 17.38
60%, 16.4s
80%, 15.58
100%, 14.6
Prompt: beautiful bosai tree, masterpiece, 4K Negative Prompt: blur, low quality, bad quality, sketches
Image
Pose
SDXL, 19.4s
20% 18.4s
40%, 17.5s
60%, 18.5s
80%, 15.0s
100%, 14.78
Prompt:
Ironman dancing in a futuristic city, high quality
Negative Prompt: low quality, bad quality
Figure 30:
T-Stitch with SDXL-based ControlNet. We annotate the faction of SSD-IB on top of images. Time cost is measured by generating one image on one RTX 3090.
small and large model. This phenomenon has been observed across various architectures and samplers. However, note that this 40% threshold might not hold for all use cases.
show that different classifier-fres guidance (CFG) scales may affect this optimal threshold during our experiments with DiT models. By default, we set the CFG scale to 1.5 as is the default value in DiT evaluation. However, under the CFG scale of 2.0, observe this optimal
In Figure 36
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Published as a conference paper at ICLR 2025
10%
20%
30%
40%
50%
80%
70%
80%
90%
100%
runs
Cluttered the anime, painting. high resolution, cottagecore, ghibli Inapired,
Figure 31:
Based on Stable Diffusion v1.4 and BK-SDM Tiny, we generate images by different GPU. T-Stitch demonstrates stable
fractions of BK-SDM for consecutive runs (a for-loop) performance for robust image generation. Best viewed in digital version and zoom in.
Table 14: Summary of FID-5K and acceleration results for various configurations in Figure
Name
FID-5K
Acceleration
DiT-XL (our implementation)
9.20
T-Stitch (10%)
9.17
7.84%
T-Stitch (20%)
8.99
18.71%
T-Stitch (30%)
9.03
32.00%
T-Stitch (40%)
9,95
50.00%
T-Stitch (50%)
10.06
75.53%
threshold occurs at around 60%. But we should not assume larger CFG scale would always help T-Stitch since FID sometimes cannot reflect the desired image quality, as mentioned in the SDXL. report. We aim to demonstrate that the optimal threshold could be affected by CFG scale, as of the limitations of T-Stitch.
It is also intuitive that different models behave differently when using T-Stitch. For example, In Figure 377 the best cut-off for DiT at CFG scale of 1.5 around 40%. b) On the other hand, the experiment BK-SDM Tiny and SD v1.4 indicates that the optimal cut-off exists before the 40% estimate. c) Table 1, we have already shown that LDM-S can replace 50% early steps for the better FID.
baseline LDM with comparable
32

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Published as a conference paper at ICLR 2025
LCM-SDXL, 768ms
50% 685ms
100%, 634ms
Prompt: Astronaut in jungle, cold color palette, muted colors, detailed, 8k
LCM-SDXL
50%
100%
Prompt: A picture of a cute Welsh Corgi in bucket
Figure 32: T-Stitch based on distilled models: LCM-SDXL (Luo et al, 2023) and LCM-SSD- IB (Luo et al. 2023), under 2 sampling steps. We annotate the faction of LCM-SSD-IB on top of images. Time cost is measured by generating one image on RTX 3090 in milliseconds.
LCM-SDXL, 1029ms
25%, 968ms
50%, 921ms
75%, 870ms
100%, 823ms
Prompt: Self-portrait oil painting, beautiful cyborg with golden hair, 8k
LCM-SDXL
25%
50%
75%
100%
Prompt: close-up picture of an old man standing the rain
Figure 33: T-Stitch based distilled models: LCM-SDXL. (Luo et al. 2023 and LCM-SSD- IB (Luo et al. (202)), under 4 sampling steps. We annotate the faction of LCM-SSD-IB on top of images. Time cost measured by generating one image on RTX 3090 in milliscconds.
At this stage, determining the optimal switching point in T-Stitch can done very efficiently, as discussed in general response. For optimal results, recommend conducting these efficient
33

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Published as a conference paper at ICLR 2025
Frames
Baseline 34.3s
T-Stitch 10%
33.1s
T-Stitch 20% 30.4s
T-Stitch 30% 28.6s
T-Stitch 40% 27.4s
T-Stitch 50%
26.7s
Prompt:
"an astronaut dancing in the outer space"
Figure 34: T-Stitch based Text2Video-Zert Khachatryan et (2023
DIT-S/B Latents
DIT-B/XL Latents
DIT-S/XL Latents
Distance
Distance
Distance
Mean: 1.82 1.95
100
80
60
40
20
100
80
40
20
100
80
40
20
Denaising stops
Denoising steps
Deneising steps
Figure 35: L2-distance comparison of latent embeddings different denoising steps between DiT models. Results are averaged over 32 images.
preliminary experiments to determine the ideal switching point for specific model pairs. We leave more interesting explorations in future works.
T-Stitch beyond pairwise. The requirement of pretrained models naturally becomes one limita- tion for T-Stitch beyond pairwise since it relies on publicly available model weights. Furthermore, different models may have different optimal CFG scales. This means that combining multiple mod- els along the same sampling trajectory creates much larger space compared pairwise combina- tions, making comprehensive evaluation challenging.
34

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Published as a conference paper at ICLR 2025
CFG 1.5
CFG
20
cut-off
28.
17.91
25
23.76
15.01
RID
18. /6
15
12.71
12
11.76 6
11.02
10.651
11..4
9.18 8.87
8.94
9.41
9.
10
9.61
40
60
80
100
20
40
60
80
100
(G)
(G)
Figure 36: FLOPs difference versus FID. We report the DiT-S/XL comparison based on classifier- free guidance scale of 1.5 and 2.0 under 50 timesteps and DDIM sampler.
DIT-S & DIT-XL
BK-SDM Tiny & SD v1.4
40% cutoff
40% cutoff
16.92,6.50
6.50
30 1/
16.57
16
15.99
25
14.
15
20
14
13:85
12.96
13,07
13
10.00
12.59
10
9.17 8:99
9.03
8.05
12.2
60
80
60
100
120
FLOPS Difference (G)
FLOPs Difference (G)
Figure 37: FLOPs difference versus FID. We report the DiT-S/XL. comparison based on ImageNet- 1K and 100 timesteps. The experiments BK-SDM Tiny and SD v1.4 are based MS-COCO benchmark and 50 timesteps.
35


Reviews:
[{"review_id": "3ifioWY7zT", "reviewer": "Reviewer_Aq9T", "summary": "This paper introduces T-Stitch, a training-free approach to accelerate sampling in diffusion models by strategically utilizing different-sized models across the denoising trajectory. The key insight is that small and large models trained on the same data distribution learn similar encodings, particularly in early steps where low-frequency components dominate. By leveraging this property, T-Stitch uses smaller models for early steps (global structure) and larger models for later steps (fine details), achieving significant speedup without quality degradation. The method demonstrates broad applicability across various architectures (DiT, U-Net, Stable Diffusion) and shows interesting benefits for stylized models' prompt alignment. Extensive experiments validate the effectiveness across different settings, samplers, and guidance scales.", "questions": "- How does the method perform when architectural differences between small and large models are more significant? Are there specific architectural compatibility requirements?\n- The improved prompt alignment for stylized models is intriguing. Could you provide more analysis of why this occurs and how generally applicable this finding is?\n- What are the primary failure modes of T-Stitch? Are there specific scenarios where the method consistently underperforms?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "### Novel & Foundational Insight\n  - Deep understanding of diffusion models' behavior across timesteps\n  - Thorough empirical validation of latent space similarity between models\n  - Clear frequency analysis supporting the theoretical foundation\n  - Novel perspective on leveraging model size differences temporally\n\n### Practicality\n- Training-free nature enables immediate deployment\n- Compatible with existing acceleration techniques\n- Works across various architectures and model families\n- Clear implementation guidelines and deployment considerations\n\n\n### Comprehensive Empirical Validation\n- Extensive experiments across multiple architectures\n- Thorough ablation studies covering various aspects\n- Clear demonstration of speedup-quality tradeoffs\n\n\n### Broader Impact & Applications\n- Unexpected benefits in prompt alignment for stylized models\n- Natural interpolation between style and content\n- Practical applications in Stable Diffusion ecosystem\n- Potential implications for efficient model deployment", "weaknesses": "### Critical Absence of Limitations Analysis\n- Paper lacks a dedicated section for discussing limitations\n- No systematic analysis of failure cases\n- Insufficient discussion of edge cases and potential risks\n- Missing critical self-reflection on method boundaries\n\n### Theoretical Gaps\n- No mathematical justification for the 40% threshold\n- Lack of theoretical guarantees for quality preservation\n- Missing analysis of optimal model size ratios\n- Incomplete understanding of feature compatibility requirements\n\n### Architectural Considerations\n- Limited analysis of cross-architecture compatibility\n- No clear guidelines for multi-model (>2) scenarios\n- Insufficient investigation of feature space alignment\n- Missing discussion of architecture-specific optimization\n\n### Practical Implementation Challenges\n- Memory overhead management not thoroughly addressed\n- Pipeline complexity implications understated\n- Limited guidance for scenarios without suitable small models\n- Deployment considerations in resource-constrained environments lacking\n\n\n### +)\n- The absence of a dedicated limitations section limits the paper's completeness", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0fME0jiDtp", "reviewer": "Reviewer_4dLD", "summary": "The paper proposes a method to accelerate sampling in diffusion models by using a sequence of two denoising networks, a smaller one followed by larger one (instead of using the same network for all sampling steps as is traditionally done). In their experiments, they show their method can lead to meaningful computational savings at little to no cost to the quality of generated images.", "questions": "- Your idea reminds me a bit of works on early-exit diffusion models [1,2] where the depth on the denoising network is made adaptive based on the estimated difficulty of the sampling step. Could be interesting to draw further parallels between early-exit and your stitching approach\n\n\n[1] [AdaDiff: Accelerating Diffusion Models through Step-Wise Adaptive Computation](https://arxiv.org/abs/2309.17074)\n\n[2] [DuoDiff: Accelerating Diffusion Models with a Dual-Backbone Approach](https://arxiv.org/abs/2410.09633)", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- Their main idea of leveraging model of various sizes throughout the diffusion sampling process is simple, yet it is shown to be effective. The simplicity is an added benefit in my opinion, as it makes the method more reproducible and more likely to be adopted\n- I also believe their idea to be novel (though I am not fully up to date with the diffusion literature due to its high pace)\n-  The experiments are very comprehensive, they try out their trajectory-stitching approach on various backbone architectures (DiT, UNet), with various samplers, for unconditional/conditional cases etc.\n- Also, I like how instead of proposing yet another new efficient diffusion model (and thus contributing to the model zoo), the authors find a smart way to combine/reuse the existing models via their trajectory-stitching approach", "weaknesses": "- I think the writing can be improved. For camera-ready it would make sense to move the background/preliminaries to the main text and perhaps to move some of the experiments to the appendix. Also I find Section 3 quite chaotic (it talks about too many different things, from motivation to model design and connection to the other efficiency techniques like speculative decoding)\n- It is not clear how to select the switching point/threshold between the small and large model (r1). While I understand that by varying it you can get a Pareto frontier, however, that still requires running/evaluating a large number of candidate thresholds.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jfu6FIX6Nx", "reviewer": "Reviewer_GeS8", "summary": "The paper introduces a new method to speed up the sampling efficiency of diffusion models. By using smaller models for the initial denoising steps and larger models in later stages, this approach greatly boosts sampling speed while keeping quality on par. Notably, this method is distinct from existing training-based or training-free techniques aimed at improving sampling efficiency. The authors demonstrate the effectiveness of their approach through extensive experiments, highlighting improved quality-efficiency trade-offs with a clear Pareto frontier.", "questions": "The work primarily relies on the observation of the alignment of noise predictions between models of different scales during the early stages of the denoising process (Fig. 3). While this is an intriguing phenomenon, the paper does not provide sufficient explanation for why this occurs. Furthermore, the magnitude of the latent vectors is also important. Does the $L^2$-distance exhibit a similar pattern as shown in Fig. 3?\n\nI believe that the requirement for a shared latent space is a strict condition for this method. It is unclear whether this method is also robust for models trained with different configurations, such as varying noise schedules (like variance-preserving versus cosine) and different diffusion steps (e.g., T=100 versus T=500).\n\nIs it possible that small models trained only over larger T steps (for example, $t \\sim [70, 100]$ with a total $T=100$) yield better results?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Overall this is a well-written paper which presents a simple but effective approach for accelerating sampling speed of large diffusion models. The authors convey their ideas clearly and support their approach through extensive experiments. I guess the key significance of this stitching approach is that it is orthogonal to other techniques, like model distillation or improved ODE solvers, allowing it to be easily combined with other methods to further reduce inference time.", "weaknesses": "Even if we ignore the inference time needed for running the small models, the time to generate samples of comparable quality can still be reduced by 30-40% at most. It is hard to say this method is a groundbreaking technique for improving sampling efficiency in diffusion models. While the paper presents a comparison of the Pareto frontiers between T-stitching and M-stitching, it might be more insightful to compare it with methods like progressive distillation, which can be much faster and does not need store multiple models.\n\nAdditionally, the approach uses models of different scales along the same denoising trajectory, which necessitates that both the small and large models predict score functions in the same latent space. This requirement may limit its applicability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "yuco9aUugf", "reviewer": "Reviewer_1Rg8", "summary": "This paper introduces a training-free acceleration technique named T-Stitch for diffusion models. The core spirit of the approach is to employ a compact model for early timesteps and a more substantial model for later stages. The authors have provided empirical evidence that model performance remains unaffected even when the lighter model is employed for 40% of the initial steps. While the proposed method is simple and efficacious, parts of its evaluation appear to rely heavily on empirical evidence, and in my opinion, falls to a typical trap of this type of papers of not including further limit studies.", "questions": "Please see the two points about limitations I have raised in my Weakness section", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "Generally, I believe this is a good paper backed by solid experimentation. It has extensive comparative analysis involving various timesteps and samplers, and also compares itself against other methods, including those that are training-based, training-free, and search-based.\n\nThe paper is also well written and clearly motivated.", "weaknesses": "In my view, theoretical insights and empirical studies hold equal value; the simplicity of an idea does not de-value from its merit if it is proven effective, especially if it is a topic like Efficient AI. However, my primary issue with such papers lies in my preference for a clear explanation of the method's limitations, also through sets of experiments.\n\nFirst, the authors of the T-Stitch paper state that 40% is an appropriate cutoff for switching models, a decision purely grounded in empirical evidence. This raises the question of how well-founded this choice is. If I were to apply this switching method to a different set of diffusion pairs of the models, would the 40% value still be relevant? Intuitively, the cutoff point likely hinges on the performance disparity between the more and less powerful models. From that perspective, if you put the model difference (Strong model FLOPs - Weak Model FLOPs) on x-axis, and the best cut-off point on y-axis, do you simply expect a flat line at 40% cut-off?\n\nSecond, although the authors did claim that the method can go beyond pari-wise, and have demonstrated how switching (I would maybe actually call this switching rather than stitching) can happen across 3 models, it is unclear about the limitation on this. Clearly the increased number of models would complicate the decision making on where to switch, and potentially make this method becomes a search-based one. More importantly, there must exhibits certain limitation on this switching especially when one has limited diffusion time steps. When you have N models to stitch/switch with M time steps. When N becomes larger or M becomes smaller, the return of this optimization should inevitably becomes diminishing.  \n\nAlso something minor: Figure 5: the bottom of the images are cropped.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)