OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot
Authors: Junhan Zhu
Hesong Wang
Mingluo Su
Zefang Wang
Huan Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Pruning
Diffusion Model
Primary area: generative models
TL;DR: Provide the first efficient training-free pruning for diffusion model

Abstract: Large-scale text-to-image diffusion models, while powerful, suffer from prohibitive computational cost. Existing one-shot network pruning methods can hardly be directly applied to them due to the iterative denoising nature of diffusion models. To bridge the gap, this paper presents \textit{OBS-Diff}, a novel one-shot pruning framework that enables accurate and training-free compression of large-scale text-to-image diffusion models. Specifically, (i) OBS-Diff revitalizes the classic Optimal Brain Surgeon (OBS), adapting it to the complex architectures of modern diffusion models and supporting diverse pruning granularity, including unstructured, N:M semi-structured, and structured (MHA heads and FFN neurons) sparsity; (ii) To align the pruning criteria with the iterative dynamics of the diffusion process, by examining the problem from an error-accumulation perspective, we propose a novel timestep-aware Hessian construction that incorporates a logarithmic-decrease weighting scheme, assigning greater importance to earlier timesteps to mitigate potential error accumulation; (iii) Furthermore, a computationally efficient group-wise sequential pruning strategy is proposed to amortize the expensive calibration process. Extensive experiments show that OBS-Diff achieves state-of-the-art one-shot pruning for diffusion models, delivering inference acceleration with minimal degradation in visual quality.

Published as a conference paper at ICLR 2026
OBS-DIFF: ACCURATE PRUNING FOR DIFFUSION MODELS IN ONE-SHOT
Junhan Zhu', Hesong Wang Mingluo Su', Zefang Wang!? Huan Wang
WWestlkke University "Zhejiang University
https://githul com/ Alr ArtiAlrrghtttot gone /ooseosee--oeeeooeeeeeeeee /OB//OBS-DDE
Dense
20%
30%
40%
50%
Magnitude
DSnoTT
Wanda
Ours (OBS-DIF)
Promut: A portraít of human grewing colorful flowers from her hair. Hyperrealistia oll painting. Intrisate details. Negative Prompt: low quality, bad quality, blur, voly, worst quality
Qualitative comparison of unstructured pruning methods on the SD3-Medium model (Esser et al., 2024). We evaluate Magnitude, DSnoT (Zhang et al., 2024d), Wanda (Sun et al., 2024), and our method (OBS-Diff) at various sparsity levels (20%, 30%, 40%, and 50%) us- ing the same prompt and negative prompt. All images are generated at resolution of 512 x 512.
Figure 1:
ABSTRACT
Large-scale text-to-image diffusion models, while powerful, suffer from pro- hibitive computational cost. Existing one-shot network pruning methods hardly be directly applied to them due to the iterative denoising nature of dif- fusion models. To bridge the gap, this paper presents OBS-Diff. novel one-shot pruning framework that enables accurate and training-free compression of large- scale rext-to-image diffusion models. Specifically, (i) OBS-Diff revitalizes the classic Optimal Brain Surgeon (OBS), adapting it to the complex architectures modern diffusion models and supporting diverse pruning granularity, including unstructured, N:M semi-structured and structured (MHA heads and FFN neu- rons) sparsity; (ii) To align the pruming criteria with the iterative dynamics of the diffusion process. by examining the problem from cror-accumuultiion perspec- tive, we propose a novel timestep-aware Hessian construction that incorporates logarithmic-decreas. weighting scheme, assigning greater importance earlier timesteps to mitigate potential eтror accumulation; (iii) Furthermore, a compu- tationally efficient group-wise sequential pruning strategy is proposed amor- tize the expensive calibration process. Extensive experiments show that OBS-Diff achieves state-of-Ae satee--f-the-ata one-shot pruning for diffusion models, delivering infer- ence acceleration with minimal degradation in visual quality.
"Corresponding author: wanghuan @ westlake.edu.cn

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Published as a conference paper at ICLR 2026
INTRODUCTION
Recent advances in ext-to-imaae generation have been largely driven by large-scale diffusion mod- els (Rombach et al., 2022; Ramesh et al., 2022; Xie et al., 2024). These models, such as the Stable Diffusion and 3.5 series (Esser et al., 2024), are capable of producing stunning images from tex- tual prompts, revolutionizing fields from digital art to content creation. However, their massive parameter counts often in the billions (e.g., 8B in Stable Diffusion 3.5-Large)-creen create prohibitive computational and memory demands, severely limiting their broader accessibility.
To improve the efficiency of the diffusion models, multiple research avenues have been proposed. One major line of work focuses on accelerating the sampling process by reducing the number of denoising steps (Song et al., 2021; Lu et al., 2022; Wang & Wang, 2025) or knowledge distillation (Salimans & Ho, 2022: Sauer al., 2024). Orthogonal these efforts, model compression aims to reduce the intrinsic computational and memory footprint of the model itself. This category includes methods like quantization (He al., 2023; Li et al., 2023b; Shang al., 2023; Li et al., 2023a) and pruning, which is the primary focus of our work.
Pruning (Han et al., 2015; 2016; Wang et al., 2021; Fang et al., 2023a; Feng et al., 2024) an effec- tive way to reduce the computational and memory costs of deep neural networks. However, the rapid evolution of diffusion models underscores the severe limitations of existing pruning techniques. Cur- rent methods often lack generality (Fang et al., 2023b; Li et al., 2023c; Kim et al., 2024), as they are typically tailored to specific architectures like the U-Net and are not easily adapted to large-scale, text-to-image diffusion models with diverse structures (e.g.. Multimodal Diffusion Transformer). Moreover, the efficiency gains from pruning are frequently undermined by computationally expen- sive requirements, such as the need for gradient information during pruning (Fang et al., 2023b; Zhang et al., 2024b) or a costly post-pruning fine-tuning stage. Furthermore, unstructured and semi- structured pruning remains largely unexplored for large-scale text-to-image diffusion models. All of these motivate our central research question: Can we develop a general and training-frr prun- ing framework capable of pruning diff'usion models with diverse architectures and supporting multiple pruming granularities in one-shot manner?
In the domain of Large Language Models (LLMs), one-shot and training-free -rren pruning methods like SparseGPT (Frantar & Alistarh, 2023) and Wanda (Sun et al., 2024) have achieved remarkable suc- cess. Subsequent SlimGPT (Ling et al., 2024) and SoBP (Wei et al., 2024) further explored training- free structured pruning. These layer-wise post-training pruning method approaches efficiently com- press massive models without requiring costly retraining. However, the existing training-fri free prun- ing methods from the LLM field, such as SparseGPT, cannot be directly applied to the diffusion model. This is due to the unique challenges posed by diffusion models: their iterative nature, where parameters shared across multiple denoising steps. Furthermore, the complex architectures in- troduce additional difficulties for pruning.
To bridge this gap, we introduce OBS-Diff, a novel one-shot, training- free pruning framework de- signed specifically for large-scale text-to-image diffusion models. Our approach revitalizes the clas- sic Optimal Brain Surgeon (OBS) (Hassibi et al., 1992) and tailors it to the unique, iterative nature of the diffusion denoising process. By reformulating the pruning objective to account for the temporal dynamics of generation and introducing a computationally efficient calibration strategy, OBS-Diff efficiently removes redundant weights with minimal impact on performance, all without requiring any training during the pruning process or fine-tuning.
Our contributions are summarized as below:
We adapt the OBS framework to handle the complex architectures of modern diffusion models, such as the Multimodal Diffusion Transformer (MMDiT), and demonstrate OBS-Diff versatil- ity across unstructured, semi-structured (e.g.. 2:4 sparsity patterns), and structured pruning (e.g.. removing entire attention heads or FFN neurons).
Recognizing that errors introduced in the early stages of the iterative denoising process have a compounding effect, we propose Timestep-Aware Hessian Construction. This novel construc- tion weights the importance of parameters according to their influence across the entire denoising trajectory, prioritizing the more sensitive early steps through logarithmic weighting scheme.
To overcome the prohibitive cost of sequential calibration in iterative models, we devise a group- wise sequential pruning strategy built upon "Module Packages". This approach amortizes the
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Published as a conference paper at ICLR 2026
expensive data collection process by processing layers in batches, striking effective balance between computational time and memory requirements for the pruning process.
Extensive experiments demonstrate that OBS-Diff sets new state-of-the-art for training-free dif- fusion model pruning. It achieves inference acceleration while maintaining high visual quality, outperforming other layer-wist pruning methods across various sparsity levels and patters.
2 RELATED WORK
Pruning for Diffusion Models. Several methods have explored pruning for diffusion models. An early approach, Diff-pruning (Fang et al., 2023b), introduced gradient-basee based method for structured pruning. However, its demonstration its dependency on expensive retraining limit its applicability to modern, large-scale systems with diverse architectures. A significant line of research has since focused compressing the UNet- based text-to-image diffusion models, with works like SnapFusion (Li et al., 2023c), MobileDif- fusion (Zhao al., 2024), BK-SDM (Kim al., 2024), LAPTOP-Diff (Zhang et al., 2024a), and LD-Pruner (Castells et al., 2024) all targeting less salient components of the UNet architecture.
small-scale, non-text-to- too-ttooiimeeeee models (e.g., DDPMs) and
Other works have explored different architectures or techniques; for instance. Tinyfusion (Fang et al.. 2025a) introduced depth pruning for the DiT architecture. More recently, EcoDiff (Zhang al.. 2024b) introduced general pruning framework for text-to-image models applicable di- verse architectures; however, it remains dependent on a costly training phase to learn pruning mask and requires extensive hyperparameter tuning. A common theme among these methods is dependency on training or fine-tuning and primary focus architecture-specifi,, structured prun- ing. Furthermore, unstructured and semi-structured pruning for large-scale, text-to-image diffusion models remains largely unexplored area.
Layer-Wite Wise Pruning Methods. Early post-training compression methods, notably Optimal Brain Damage (OBD) (LeCun et al., 1989) and Optimal Brain Surgeon (OBS) (Hassibi al., 1992), utilized Hessian-basee saliency scores to prune individual weights. However, the prohibitive cost of computing and storing the full Hessian matrix limited their scalability. This challenge spurred the development of layer-wise approaches such as L-OBS (Dong et al., 2017) and Optimal Brain Compression (OBC) (Frantar & Alistarh, 2022), which approximate the Hessian locally to make pruning tractable.
As models scaled to billions of parameters, particularly in Large Language Models (LLMs), new methods emerged. SparseGPT (Frantar & Alistarh, 2023), Wanda (Sun et al., 2024), and DSnoT (Zhang et al., 2024d) focused on efficient unstructured and semi-structured (N:M pattern) prun- ing. Subsequently, SlimGPT (Ling et al., 2024) and SoBP (Wei et al., 2024) extended the OBS methodology structured granularity. Nevertheless, this family of compression methods remains unexplored in the field of diffusion models.
3 PRELIMINARIES
3.1 LAYER-WEY WISE POST-TRAINING PRUNING
Post-training pruning often decomposes the global network compression problem into series of independent, layer-wise subproblems (Hubara et al., 2021; Nagel et al., 2020; Aghasi et al., 2017). For each layer 1, the objective is to find a pruned weight matrix W, that minimizes the output reconstruction error, given input activations X, and a target sparsity Sp. This is formulated as:
argmin wi W;X: - w.X
s.t. sparsity = S
(I)
where - is the squared Euclidean norm. optimization problem for each layer.
The network is pruned by sequentially solving this
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Calbration Dataset (a small number of text prompts)
Text tokens
Vauul tokens
Timestep Weight Schome: 9,
QKV Projection
OKV Projection
Activation: X
First Modile Package
Original Hessian Matrix
Step2:
limestep-Awar
Joint Attention
Hessian Construction
Provessing Module Package
hook
Block 2n
Outaut Projection
Output Projection
Block 2n+1
Inverse Hessian
Up Projection
Up Projection
OBS Pruning
Block N-1
Down Projection
Down Projection
Weight
Remrliedd
Medule
Block N
Step 1:
Pruvedi
Collect the activations of the Module Package
To Be Uppbred
Iterative Process
Figure 2: Illustration of the proposed OBS-Diff framework applied the MMDIT architecture. Target modules are first partitioned into a predefined number of "Module Packages" and processed sequentially. For each package, hooks capture layer activations during forward pass with a calibra- tion dataset. This data, combined with weights from dedicated timestep weighting scheme, is used construct Hessian matrices. These matrices guide the Optimal Brain Surgeon (OBS) algorithm to simultaneously prune all layers within the current package before proceeding to the next.
3.2 OPTIMAL BRAIN SURGEON FOR LAYER- WISE PRUNING
The Optimal Brain Surgeon (OBS) framework (Hassibi et al., 1992) offers efficient solution to the layer-wise problem in Eq. (1). A key insight of OBS is that the f--norm objective allows the problem be decoupled into independent subproblems for each row of the weight matrix W,
For each row, OBS approximates the objective with a second-order Taylor expansion centered around the current weights. This relies on the Hessian of the reconstruction error, H X This approximation yields closed-form solution to identify the least salient weight w the one whose removal minimally increases the error-and to compute the optimal update 8w for the re- maining weights in its row. The saliency score Lg and the update are defined as:
W dw
Wy
Cg
H
2
H
.
.99
44
where H [H- lgg is the q-th diagonal element of the inverse Hessian and H is its g-th column.
This process repeated iteratively until the target sparsity St reached. After each weight is removed, the inverse Hessian must be updated. To circumvent the prohibitive cost of full re-inversion and the enror accumulation of approximate rank-one updates, SparseGPT (Frantar & Alistarh, 2023) imposes fixed pruning order. This structural constraint enables efficient and stable updates to the inverse Hessian information using methods like Cholesky decomposition (Frantar et al., 2024) as weights are progressively removed.
4 METHODOLOGY
We propose OBS-Diff, one-shot, training-free pruning framework tailored for diffusion models. As illustrated in Figure 2, our method partitions the model into sequential "Module Packages" to amortize calibration costs. Within each package, employ novel Tiwestep-Aware Hessian struction to prioritize early denoising steps, enabling the simultaneous pruning of all target layers within the current package.

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Published as a conference paper at ICLR 2026
4.1 TIMESTEP-AWARE HESSIAN CONSTRUCTION
The layer-wise pruning objective defined in Eq. (1) is effective for models with a single forward pass, but insufficient for diffusion models, which are iterative and operate over denoising trajectory parameterized by discrete timesteps E (1,... .7).' ! The impact of praning-induced errors is not uniform across this trajectory. Errors introduced in early inference steps (small t) are inherently more damaging, as they propagate and compound through all subsequent steps (t + 1,- : (T). leading to larger deviations in the final output.
Therefore, robust pruning strategy must prioritize preserving network function during these critical early stages. We reformulate the layer-wis wise optimization problem minimize weighted recon- struction error that places greater importance on earlier, higher-impact steps:
arg min [1.T] 0i WiXX,, WiX,, Wi Here, X1, is the input to layer at step i, and a₁ step-dependent weight. We define as using a simple and effective logarithmically decreasing schedule:
Omax - amin In(7 ++ E (1,2, T). In(T)
02
Ommin
(4)
This schedule ensures the weight highest at the beginning of inference and decays smoothly, such that > ... at > 0.
By incorporating this weighting, we adapt the Optimal Brain Surgeon framework (Hassibi et al., 1992). The Hessian, which captures the second-order information of this weighted loss, is now computed as a weighted sum over all inference steps:
T
HI=2
E[X,,x
(5)
which is termed as Timestep-Aware Hessian. It encapsulates the varying importance of parameters over the generation process. Saliency scores derived from its inverse are thus more sensitive to weights that are critical during the early, formative stages of the denoising process, resulting in more faithfully pruned model.
4.2 MODULE PACKAGES: GROUP-WISE SEQUENTIAL. PRUNING STRATEGY
Conventional post-training pruming methods, such SparseGPT (Frantar & Alistarh, 2023), em- ploy a sequential layer-wise calibration. This paradigm is computationally prohibitive for diffusion models, as calibrating each layer necessitates executing full, multi-step denoising trajectory. To address this bottleneck, we introduce Module Packages, a group-wise strategy that amortizes cali- bration costs by processing layers in batches.
Our approach is built upon two concepts. Basic Unit a set of layers with mutually independent inputs in a forward pass (e.g., query, key, and value projections), allowing for parallel processing. A Module Package comprises one or more Basic Units, which are pruned and calibrated collectively. Our framework processes these packages sequentially. For each package, we first execute Group- wise Data Collection phase: we run the complete denoising trajectory once across the calibration dataset, using forward hooks to concurrently gather input statistics for all modules within the pack- age. Subsequently, all modules are pruned simultaneously using their respective - Hessian matrices.
Crucially, the network state is updated sequentially between packages but remains static within package during data collection. This preserves the principle of sequential calibration at a coarser, group-wise granularity, rendering the process computationally feasible. This strategy drastically reduces the number of calibration runs, with the primary trade-off being an increased memory foot- print to store multiple Hessian matrices concurrently. Notably, our empirical results demonstrate that pruning accuracy has low sensitivity to package granularity, granting practitioners the flexibility to balance computational cost against memory constraints without significant performance sacrifice.
Herr, f denotes the sequential index of the denoising iteration during inference, where T' is the total number of inference steps (e.g., for T 28, ranges from 2, .2....22. 28).
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4.3 EXTENSION TO SEMI- REMI-STRUCTURED AND STRUCTURED PRUNING
A key advantage of our OBS-Diff framework is its adaptability. While focusing on unstructured pruning, it readily extends to both semi-structured and structured sparsity.
Semi-Structured Pruning. For semi-structured patterns like 2:4 sparsity, the extension is direct. Within each block four weights, we simply prune the two with the lowest per-weight OBS-Diff saliency scores, efficiently creating hardware-friee frienrlyf models.
Structured Pruning. For structured pruning of Feed-Forward Network (FFN) layers, we assess neuron's s importance by aggregating the saliency of its associated weights. The saliency by for an entire neuron (column g) and the corresponding weight update are:
Σ W C.y 2 "l
W
oW
H
(6)
.9
499
where the lowest-scoring neurons removed.
Similarly, for Multi-Head Attention (MHA), we prune entire heads. Our approach, inspired by SlimGPT (Ling al., 2024), quantifies the saliency of each head.
The calculation begins with the full Hessian matrix, H, for the output projection layer. For the j-th head, we consider its weight matrix W, and the corresponding Hessian block Hj. The total saliency for this head, Lj. is found by aggregating the importance of its individual weights. The saliency is calculated as:
d C = Σ k=1 (H 1)..
where (W,).. R. the k-th column of the weight matrix W,, (H, )x,,, is the k-th diagonal element of the inverse Hessian block, and d is the dimension of each head.
However, MMDiT's joint attention mechanism presents a unique challenge. Shared attention heads process concatenated mudti-modal inputs. but are fed into separate, modality-specifie output paths. This structure yields two distinct importance rankings for the same set of heads (one for each modal- ity), while OBS-Diff processes the output projection matrices after separation. To resolve this, we fuse these rankings into a single, decisive list using Reciprocal Rank Fusion (RRF):
SRRF
(8)
+ rank + rank
where ( is the rank of head j for modality A, and stabilizing hyperparameter (e.g., 60). This fused score provides a unified ranking to guide the pruning of shared attention heads.
Subsequently, the weights of the entire output projection layer are updated using the full Hessian matrix, H, following the formulation presented in Eq. (6).
5
EXPERIMENTS
5.1 SETTINGS
Models. To demonstrate the generalizability of OBS-Diff, we evaluate across a diverse range of text-to-image models: Stable Diffusion --baee (866M) (Rombach et al., 2022), Stable Dif- fusion 3-Medium (2B) (Esser et al., 2024), Stable Diffusion 3.5-Large (8B), and 1-dev (12B) (Black Forest Labs, 2024). For comparison with prior work, we also evaluate our method on DDPM (35.7M) (Ho et al., 2020) trained on the CIFAR- 10 (32 x 32) dataset (Krizhevsky et al., 2009).
Baselines. For text-to-image models, we compare against methods adapted from the Large Lan- guage Model (LLM) domain for unstructured//smmisstuuuut 2024) and DSnoT (Zhang et al., 2024d), as well as standard magnitude pruning. For structured prun- ing, we employ Ll-normm based baseline (Li et al., 2017) and EcoDiff (Zhang al., 2024b). On
sparsity, namely Wanda (Sun al.,

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Published as a conference paper at ICLR 2026
Table 1: Quantitative comparison of unstructured pruning methods on text-to-image diffusion mod- els. The best result per metric is highlighted in bold.
(a) SD v2.1 base and SD 3-medium
(b) SD 3.5 large and Flux I.dev
Base Model
Sparsity (%)
Method
FID
CLIP
ImageRRward T
Base Model
Sparsity (%)
Method
FID 1
CLIP1
ImageBeward
Derr Model
M1.35
0.3142
0. 0.9627
Deman Midel
$1.59
0:3156
0:7549
Magnmude
27.86
0.3131
0.1864
Mageiiude
55.21
0.3252
0.1465
Dono
A3
0.
-00422
DSwi
52.82
0.3113
0.2323
OBS
20.19
27.96
0.3131
0.3122
0.2061
SD 3.r-lare
Wanda
OBS Dirt
27.49
29.01
0.3142
0.3123
0.6146
0.4215
Mayrmmado
Magnindo
156.21
02302
0,2829
-11395
DSad
02706
+ 113198
Wunda
41.84
0,2458
0.4704
Wansh
OBS
0,3102
IU1356
OBS-DHI
29.15
Dams Model
36.14
0,3162
0.9029
Dense Model
39.16
0.9661
Magnmmad
231 24
0,1884
-2.2719
Mageinudo
DSnn'
61:37
0.0936
-1.9911
0:111
SD 3-madiiu
OBS Dver
Wanda
27.20
43.98
0.3000
0.3167
0.6468
..1076
Flux 1.dev
Wardh
OBS-Dill
0.2576
Maynmmado
549 53
0.1954
Magnitude
251.58
2,2271
DSnoT
211.55
2.2271
DSno't
64.35
0.2970
0.3459
170.33
0.2252
2.0641
Wanda
69.68
0.2957
0,1016
OBS--DIF
39.79
0.2986
0,3697
the CIFAR-10 10 DDPM, our method is directly compared with Diff- PfffiPr (Fang et al., 2023b). The sparsity refers to the pruning ratio of all the linear layers within MHA and FFN for each MMDiT block. For calibration, we utilize text prompts from the GCC3M dataset (Sharma al., 2018). To ensure fair comparison, all methods and baselines utilize identical configurations (computational resources provided in Appendix B).
The Wanda (Sun et al., 2024) and DSnoT (Zhang et al., 2024d) baselines are originally designed for unstructured and semi-structured pruning of LLMs. Their direct application to diffusion models is non-trivial due to the iterative nature of the diffusion model. Specifically, we extended their pruning logic by incorporating the concept of module packages, enabling them to perform unstructured and semi-structured pruning targeted at the key components of the diffusion architecture. Critically, to ensure an equitable comparison with our Hessian-based method, the adapted DSno'I' baseline is configured its Hessian-based importance score calculation mode.
Evaluation Metries. We evaluate the performance of the text-to-image -ottgoiigeeeaaao models on subset of 5K prompts from the MS-COCO 2014 validation set (Lin et al., 2014). The evaluation is based on three metrics: Fréchet Inception Distance (FID) (Heusel et al., 2017), CLIP Score (ViT-B/16) (Hessel et al., 1), and ImwaeReward (Xu et al., 2023). For the DDPM on CIFAR- 10, we report the FID score. We measure efficiency gains in terms of wall-clock time reduction and the decrease in FLOPs.
5.2 RESULTS OF UNSTRUCTURED PRUNING
The results in Table show the superiority of our OBS-Diff in terms of CLIP score and ImageReward. An interesting phe- nomenon observed with the FID metric the pruned model can occasionally out- perform the original dense model. E.g. 40% sparsity on SD v2. -base, the Magni- rude method beats the dense model in FID, while our results suggest Magnitude does not produce visually better results. thereby conceived that FID may not be a very reliable metric here to evaluate dif- ferent pruning methods.
Table 2: Performance of semi-structured (2:4 spar- sity pattern) pruning on the Stable Diffusion L.re model. Pruning is applied to the 3rd through 25th MMDiT blocks. The best result is shown in bold.
Base Model
Method
FID
CLIP 1
ImageReward
Dense Model
31.59
0.3156
0.7549
SD 3.5-Large
Magnitude
45.39
0.2945
-0.4705
is
DSnoT
32.40
0.3069
0.0307
Wanda
32.08
0.3036
-0.1363
OBS-Diff
32.13
0.3129
0,4493
Regarding the CLIP score, OBS-Diff is the best-performing method in the vast majority of test cases, exhibiting only a slight decrease compared the dense models. Most notably, OBS-Diff consistently leads in the ImageReward metric across all benchmarks, indicating superior alignment with human aesthetic preferences.
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Table 3: Performance of structured pruning on the SDXL (U-Net) model across various sparsity levels. Comparison includes the L1-norm baseline, EcoDiff, and our proposed OBS-Diff. The TFLOPs metric represents the theoretical computational cost for single forward pass of the entire
UNet. For each sparsity group, the best result per metric is highlighted in bold.
Model
Sparsity
Method
#Params
TFLOPs
FID
CLIP Score f ImageReward 1
Dense Model
2.57 B
5.98
29.21
0.3213
0.7635
L Li-norm
71.78
0.3035
-0.0006
15%
EcoDiff'
2.24 B
5.33 (110.87%)
34.18
0.3100
-0.1870
OBS-Diff (Ours)
29.08
0.3215
0.6877
SDXL
Ly-norm
133.07
0.2825
-0,7897
20%
EcoDiff
2.13 B
5.12 ((.4438%)
42.98
0,2993
-0.6172
OBS-Diff (Ours)
29.19
0.3212
0.6461
L1-norm
170.68
0.2711
-1.1694
30%
FioDiff'
1.91
4.70 (121.40%)
101.96
0,2465
-1.9161
OBS-Diff (Ours)
29.75
0.3204
0.4909
The superiority of our approach becomes most pronounced at high sparsity levels. For example, at 60% sparsity on SD 3.5-Large or 70% on Flux I.dev, the performance of all baseline methods collapses, resulting metrics that are significantly worse than ours. This quantitative degradation corresponds to qualitative failure; as illustrated in Figure I, the images generated by baseline meth- ods at high sparsity are often totally destroyed and suffer from severe artifacts, whereas OBS-Diff continues to produce high-quality and coherent results. Beyond its performance in generation qual- ity, OBS-Diff is also highly efficient. For instance, the entire pruning process for the 2B-parameter SD 3-medium model completes in under 15 minutes single NVIDIA RTX 4090, highlighting its excellent cost-effeettivenes. Detailed analyses of pruning time and the impact sparsity on ImageReward are provided in Appendix C.1.
5.3 RESULTS ON SEMI-STRUCTURE PRUNING
The results for 2:4 semi-structured pruning are presented in Table 2. Although Wanda obtains slightly better FID of 32.08 compared our 32.13, OBS-Diff shows substantial advantages in semantic-leve metrics. Notably, it surpasses the strongest baseline by large margin in both CLIP score (0.3 and IewgeReward (0.4493). This highlights our method's effectiveness in maintain- ing high-level semantic consistency and visual fidelity under hardware-friendly sparsity constraints.
5.4 RESULTS ON STRUCTURED PRUNING
The results are presented in Table and Table 3. The baseline Ln-norm pruming suffers from catas- trophic performance degradation even modest 15% sparsity, with its FID score deteriorating from 31.59 to 158.89 on SD Large. In stark contrast, our method, OBS-Diff, demonstrates remarkable resilience. At the same 15% sparsity, OBS-Diff maintains FID of 32.64, nearly iden- tical to the dense model's 's performance. This robustness persists up to 30% sparsity, where OBS-Diff sustains strong FID of 34.51 while the baseline model fails completely (FID of 327.48). These findings highlight OBS-Diff's superior ability to preserve critical model structures under aggressive structured pruning.
To benchmark our method against established techniques. we incorporate the comparison with EcoDiff (Zhang al., 2024b), state-of-the-aa structured pruning framework for text-to-image diffussonn models, directly into the main experiments. As shown in Table 3 and Table 4, while EcoDiff generally outperforms the naive In-mormm baseline on SDXL., it still exhibits significant per- formance degradation compared to our method both tables, especially at higher sparsity levels. For instance, on the U-Net based SDXL model (Table 3), EcoDiff yields an FID of 101.96 at 30% sparsity, whereas OBS-Diff achieves substantially better FID of 29.77 75. This confirms that OBS- Diff generalizes effectively across diverse architectures, outperforming baselines on both MMDiT (SD 3.5) and U-Net (SDXL) backbones.
Finally, we compare OBS-Diff with Diff- ffff-Pruning (Fang et al., 2023b), a well-recognized method that leverages gradient information for structured pruning small class-conditional DDPMs. The
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Table Performance structured pruning on the Stable Diffusion 3.5-Large model across various sparsity levels. The first and last transformer blocks were excluded from the pruning process. The TFLOPs metric represents the theoretical computational cost for single forward pass of the entire transformer. For each sparsity group, the best result per metric is highlighted in bold.
Base Model
Sparsity (%)
Method
#Params
TFLOPs
FID
CLIP
ImageReward
Dense Model
8.06
11.26
31.59
0.3156
0.7549
L1-norm
158.89
0.2376
-2.0502
15%
EcoDiff
7.28 B
9.63 (411.55%
230.97
0.2086
-2.2594
OBS-Diff
32.64
0.3157
0.6446
L1-norm
189.50
0.2124
-2,2385
SD 3.re
20%
EcoDiff OBS-Diff
7.02
(419.3%)
293.89
0,2050
-2.7744
32.46
0.3149
0.5475
Ly-norm FcoDiff OBS-Diff
228.82
0.2040
-222651
25%
6.76
8.55 (124.11%
308.96
0.2037
-2.2686
33.73
0.3128
0.3741
Lı-norm EcoDiff
327.48
0.2093
-2.2663
30%
6,54
8.10 (128.11%)
346.38
0,2024
-2.2746
OBS-Diff
34.51
0.3107
0.2221
Table 6: Ablation study of timestep weighting strategies, conducted on the SD3-Medium model 50% unstructured sparsity. (For reference, the ImageReward of uniform strategy is 0.6355.)
Table Ablation study the impact of the number of module packages on resource usage and performance, conducted on SD3-Medium model at 30% unstructured sparsity.
Weight strategy
ImageReward
Pkgs. Mem. (GB) Time (s) ImageReward
Linear increase
0.6174
1
30.67
572.20
0.8569
Linear decrease
0.6384
4
24.05
896.52
0,8442
Log increase
0.6244
10
22.75
1539.37
0.8429
Log decrease
0.6438
20
22.08
2594.95
0.8564
detailed results for this specific comparison are deferred to the Appendix C.3, where method outperforms Diff- fiff-Pruning consistently.
5.5 WALL-CLOCK TIME COMPARISON
To quantify the practical efficiency gains, measure the wall-clock time for a single for- ward pass through MMDIT block of the SD3.5-Large model, on single NVIDIA 4090
Table Wall-clock inference time (ms) and speedup for a single MMDiT block under various sparsity schemes.
GPU with batch size 4, resolution 1024 1024.
Sparsity Type
Time (ms)
Speedup
Table 5 shows that both methods effectively re- duce inference latency. The 2:4 semi-structured approach achieves a 1.23 speedup, while our structured pruning method attains x speedup at 30% sparsity. These results vali- date the tangible practical acceleration benefits of applying these pruning techniques.
Dense
14.36
Semi-structured (2:4)
11.71
1.23 x
Structured (15%)
13.96
1.03
Structured (20%)
11.95
1.20x
Structured (25%)
11.17
1.29x
Structured (30%)
10.99
1.31x
5.6 ABLATION STUDY
We perform an ablation study to analyze the impact of three key components: (1) the timestep- aware Hessian construction, (2) the number of module packages, and (3) the number of prompts in the calibration dataset. For this study, all variants are evaluated using the ImageReward metric on 1,000 prompts from the MS-COCO 2014 validation set.

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Timestep-Aware Hessian Matrix Establishment. To incorporate temporal information from the diffusion process, we introduce timestep-aware weighting during the Hessian matrix construction. This method assigns distinet weight to the hooked activations each timestep. Empirical results demonstrate that assigning greater importance to earlier inference steps yields superior performance. As shown in Table 6, logarithmic decrease strategy significantly outperforms other weight distri- bution methods.
Packaee The concept of module packages partitions the model's 's layers for layer-wise compression. 'This approach intro- duces critical trade-off between computational resources and time. Processing the model in more packages reduces peak GPU memory, as the Hessian matrix for each pruning step smaller. However, it proportionally increases the total runtime because the entire cali- bration dataset must be forwarded for each package. As shown ablation study (Table 7), while the resource trade-off is evident, the number of packages does not show a clear, predictable rela- tionship with the final pruned model's performance. Consequently, practitioners can select configuration that best fits their hardware constraints without sacrificing final model quality.
0.66
ImageReward
0.64
0.62
50
100
150
200
Number Prompts
Effect of the num- ber of prompts in calibration dataset the ImageReward.
Figure 3:
The Number of the Prompts in the Calibration Dataset. The size of the calibration dataset critical hyperparameter that directly influences the quality of the approximated Hessian ma- trix. To find an optimal size, evaluated post-pruning performance against the number of text prompts in the calibration dataset, as shown in Figure 3. The pruned model's ImageReward score improves sharply up 100 prompts and then plateaus, indicating point of diminishing returns where additional data offers no significant benefit to the Hessian approximation. Therefore, to bal- ance performance gains with computational efficiency, we selected 100 prompts for our calibration dataset in all main experiments.
CONCLUSION
This work introduces OBS-Diff, a novel one-shot, training-free pruning framework tailored for large-scale text-to-image diffusion models. By revitalizing the classic Optimal Brain Surgeon method, we address the unique challenges of iterative denoising through our proposed timestep- aware Hessian construction, which prioritizes critical early-stage generation steps. To overcome prohibitive calibration costs, we devise group-wise sequential pruning strategy that effectively bal- ances memory overhead and computational efficiency. The versatility of our framework extends across unstructured, semi-structured, and structured pruning, demonstrating its broad applicability. Extensive empirical results show that OBS-Diff establishes a new state-of-the-art in training-free dif- fusion model pruming, consistently outperforming existing methods by maintaining high generative quality, especially at high sparsity regimes.
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ACKNOWLEDGEMENT
This paper is supported by Young Scientists Fund of the National Natural Science Foundation of China (NSFC) (No. 62506305), Zhejiang Leading Innovative and Entrepreneur Team Introduc- tion Program (No. 2024R01007), Key Research and Development Program of Zhejiang Province (No. 2025C01026), Scientific Research Project of Westlake University (No. WU2025W FO03), Chi- nese Association for Artificial Intelligence (CAAT) & Ant Group Research Fund AGI Track (No. 2025CAAI-ANT-13) It is also supported by the research funds of the National Talent Program and Hangzhou Municipal Talent Program.
REFERENCES
Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J Ballard, Joshua Bambrick, et al. Accurate structure prediction of biomolecular interactions with alphafold 3. Nature, 630(5016):4935500, 2024.
Alireza Aghasi, Afshin Abdi, Nam Nguyen, and Justin Romberg. Net-trim: Convex pruning of deep neural networks with performance guarantee. NewrlPS, 2017.
Haolei Bai, Siyong Jian, Tuo Liang, Yu Yin, and Huan Wang. Ressvd: Residual compensated svd for large language model compression. arXiv preprint arXiv22505.20112, 2025.
Black Forest Labs. Flux. https: httpps/s//bacccfestlaaaat. ai/, 2024. Accessed: 2025-09-25.
Yukang Cao, Chenyang Si, Jinghao Wang, and Ziwei Liu. Freemorph: Tuning-free generalized image morphing with diffusion model. arXiv preprint arXiv:2507.01193, 2025.
Thibault Castells, Hyoung-Kye Song, Bo- Bo-Kyeong Kim, and Shinkook Choi. Ld-pruner: Efficient pruning of latent diffusion models using task-agnostic insights. In CVPR, 2024.
Xueyi Chen, Keda Tao, Kele Shao, and Huan Wang. Streamingtom: Streaming token compression for efficient video understanding. arXiv preprint arXiv:2510.18260 2025a.
Zigeng Chen, Gongfan Fang, Xinyin Ma, Ruonan Yu, and Xinchao Wang. dparallel: Learnable parallel decoding for dllms. arXiv preprint arXiv:2509.26488, 2025b.
Zigeng Chen, Xinyin Ma, Gongfan Fang, Ruonan Yu, and Xinchao Wang. Verithinker: Learning to verify makes reasoning model efficient. arXiv preprint arXiv:2505, 2025c.
Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Weihao Yu, Xiaonan Nie, Ziang Song, et al. Emerging properties in unified multimodal pretraining. arXiv preprint arXiv:2505. 14688, 14683, 2025.
Haiwen Diao, Yufeng Cui, Xiaotong Li, Yueze Wang, Huchuan Lu, and Xinlong Wang. Unveiling encoder-free vision- lanounlang models. NeurIPS, 2024.
Haiwen Diao, Mingxuan Li, Silei Wu, Linjun Dai, Xiaohua Wang, Hanming Deng, Lewei Lu, Dahua Lin, and Ziwei Liu. From pixels to words-towards native vision-language primitives at scale. arXiv preprint arXiv:2510. 14979999,, 2025a.
Haiwen Diao, Xiaotong Li, Yufeng Cui, Yueze Wang, Haoge Deng, Ting Pan, Wenxuan Wang, Huchuan Lu, and Xinlong Wang. Evev2: Improved baselines for encoder-free vision-language models. In ICCV, 2025b.
Xin Dong, Shangyu Chen, and Sinno Pan. Learning to prune deep neural networks via layer-wise optimal brain surgeon. In NeurlPS, 2017.
Wenjie Du, Li Jiang, Keda Tao, Xue Liu, and Huan Wang. Which heads matter for reasoning? rl-guided ky cache compression. arXiv preprint arXiv:2510.08522 2025.
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederie Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In /CML, 2024.

---

Published as a conference paper at ICLR 2026
Gongfan Fang, Xinyin Ma, Mingli Song, Michael Bi Mi, and Xinchao Wang. Depgraph: Towards any structural pruning. In CVPR, pp. 16091-16101, 2023a.
Gongfan Fang, Xinyin Ma, and Xinchao Wang. Structural pruning for diffusion models. In NeurlPS, 2023b.
Gongfan Fang, Kunjun Li. Xinyin Ma, and Xinchao Wang. Tinyfusion: Diffusion transformers learned shallow. In CVPR, 2025a.
Gongfan Fang, Xinyin Ma. and Xinchao Wang. Thinkless: Llm learns when to think. arXiv preprint arXiv:2505. 13379, 2025b.
Sicheng Feng, Keda Tao, and Huan Wang. Is oracle pruning the true oracle? arXiv preprint arXiv:2412.00143, 2024.
Sicheng Feng, Gongfan Fang, Xinyin Ma, and Xinchao Wang, Efficient reasoning models: A survey. TMLR, 2025a.
Sicheng Feng, Kaiwen Tuo, Song Wang, Lingdong Kong, Jianke Zhu, and Huan Wang. Rewardmap: Tackling sparse rewards in fine-grained visual reasoning via multi-stage reinforcement learning. arXiv preprint arXiv:2510.02244, 2025b.
Sicheng Feng, Song Wang, Shuyi Ouyang, Lingdong Kong, Zikai Song, Jianke Zhu, Huan Wang, and Xinchao Wang. Can milms guide me home? benchmark study on fine-grained visual reasoning from transit maps. arXiv preprint arXiv:2505. /8675, 2025c.
Elias Frantar and Dan Alistarh. Optimal brain compression: A framework for accurate post-training quantization and pruning. In NeurIPS, 2022.
Elias Frantar and Dan Alistarh. Sparsegpt: Massive language models can be accurately pruned in one-shot. In ICML, 2023.
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. Optq: Accurate quantization for generative pre-tr transformers. In ICLR, 2024.
Song Han, Jeff Pool, John Tran, and William Dally. Learning both weights and connections for efficient neural network. In NIPS, volume 28, 2015.
Song Han, Huizi Mao, and William J Dally. Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding. In ICLR, 2016.
Babak Hassibi, David G Stork, and Gregory J Wolff. Optimal brain surgeon and general network pruning. In NeurlPS, 1992.
Yefei He, Luping Liu, Jing Liu, Weijia Wu, Hong Zhou, and Bohan Zhuang. Ptqd: Accurate post- training quantization for diffusion models. In NeurlPS, 2023.
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. arXiv preprint arXiv:2/04.08718 2021.
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to local nash equilibrium. In NeurIPS, 2017.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In NeurIPS, 2020.
Itay Hubara, Yury Nahshan, Yair Hanani, Ron Banner, and Daniel Soudry. Accurate post training quantization with small calibration sets. In ICML, 2021.
Xin Jin, Siyuan Li, Siyong Jian, Kai Yu, and Huan Wang. Mergemix: A unified augmentation paradigm for visual and multi-modal understanding. arXiv, 2025.
Bo-Kyeong Kim, Hyoung-Kye Song, Thibault Castells, and Shinkook Choi. Bk-sdm: A lightweight, fast, and cheap version of stable diffusion. In ECCV, 2024.
12

---

Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf. Pruning filters for Published as a conference paper at ICLR 2026
Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models. arXiv preprint arXiv:2412.03603, 2024.
Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009.
Yann LeCun, John Denker, and Sara Solla. Optimal brain damage. In NeurlPS, 1989.
Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, et al. Llava-onevision: Easy visual task transfer. arXiv preprini arXiv:2408.033266 2024.
efficient convnets. In ICLR, 2017.
Haopeng Li, Jinyue Yang, Guoqi Li, and Huan Wang. Autoregressive image generation with ran- 2025.
domized parallel decoding. arXiv preprint arXiv:2503.
Haopeng Li. Shitong Shao, Wenliang Zhong, Zikai Zhou, Lichen Bai. Hui Xiong, and Zeke Xie.
Pisa: Piecewise sparse attention is wiser for efficient diffusion transformers, 2026. URL https:
//arxiiv.org...o... abs/ 220022007722 01077.
Muyang Li, Yujun Lin, Zhekai Zhang, Tianle Cai, Xiuyu Li, Junxian Guo, Enze Xie, Chenlin Meng, Jun- Yan Zhu, and Song Han. Svdquant: Absorbing outliers by low-rank component for 4-bit diffusion models. In ICLR, 2023a.
Xiuyu Li, Yijiang Liu, Long Lian, Huanrui Yang, Zhen Dong, Daniel Kang, Shanghang Zhang, and Kurt Keutzer. Q-diffusion: Quantizing diffusion models. In ICCV, 2023b.
Yanyu Li, Huan Wang, Qing Jin, Ju Hu, Pavlo Chemerys, Yun Fu, Yanzhi Wang. Sergey Tulyakov, and Jian Ren. Snapfusion: Text-to- Text-to-image diffusion model mobile devices within two seconds. In NeurlPS, 2023c.
Tsung-Y Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014.
Gui Ling, Ziyang Wang, and Qingwen Liu. Slimgpt: Layer-wise structured pruning for large lan- guage models. In NeurlPS, 2024.
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. In NeurlPS, 2022.
Yanzuo Lu, Xin Xia, Manlin Zhang, Huafeng Kuang, Jianbin Zheng, Yuxi Ren, and Xuefeng Xiao. bager--bage:: A unified acceleration framework for multimodal understanding and generation. arXiv preprint arXiv:2509.1 18844, 2025.
Markus Nagel, Rana Ali Amjad, Mart Van Baalen, Christos Louizos, and Tijmen Blankevoort. Up or down? adaptive rounding for post-training quantization. In ICML, 2020.
Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, and Chongxuan Li. Large language diffusion models. arXiv preprint arXiv:2502.09992, 2025.
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text- conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In CVPR, 2022.
Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. In ICLR, 2022.
Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach. Adversarial diffusion dis- tillation. In ECCV, 2024.
13

---

Published as a conference paper at ICLR 2026
Yuzhang Shang, Zhihang Yuan, Bin Xie, Bingzhe Wu, and Yan Yan. Post-training quantization diffusion models. In CVPR, 2023.
Kele Shao, Keda Tao, Can Qin, Haoxuan You, Yang Sui, and Huan Wang. Holitom: Holistic token merging for fast video large language models. In NeurIPS, 2025.
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In ACL, 2018.
Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In ICLR, 2021.
Mingjie Sun, Zhuang Liu, Anna Bair, and Zico Kolter. A simple and effective pruning approach for large language models. In ICLR, 2024.
Keda Tao, Kele Shao, Bohan Yu, Weiqiang Wang, Huan Wang, et al. Omnizip: Audio- guided dynamic token compression for fast omnimodal large language models. arXiv preprint arXiv:2511.14582, 2025.
Juanxi Tian, Siyuan Li, Conghui He, Lijun Wu, and Cheng Tan. Envision: Benchmarking unified understanding & generation for causal world process insights, 2025. URL https://arxxiv.. org/abs/ 2512. 01816.
Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wee Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, et al. Wan: Open and advanced large-scale video generative models. arXiv preprint arXiv:2503.208 14, 2025.
Chenru Wang, Beier Zhu, and Chi Zhang. Free lunch for stabilizing rectified flow inversion, 2026. URL https: //a:://aaza arrivv org/ abs/2602. 11850.
Hesong Wang and Huan Wang. Improved techniques for training smaller and faster stable diffusion. In ICLR 2025 Workshop on Deep Generative Model Machine Learning: Theory, Principle and Efficacy, 2025.
Huan Wang, Can Qin, Yulun Zhang, and Yun Fu. Neural pruning via growing regularization. In ICLR, 2021.
Jiateng Wei, Quan Lu, Ning Jiang, Siqi Li, Jingyang Xiang, Jun Chen, and Yong Liu. Structured optimal brain pruning for large language models. In EMNLP, 2024.
Enze Xie, Junsong Chen, Junyu Chen, Han Cai, Haotian Tang, Yujun Lin, Zhekai Zhang, Muyang Li, Ligeng Zhu, Yao Lu, et al. Sana: Efficient high-resolution image synthesis with linear diffu- sion transformers. urXiv preprint arXiv:2410. 10629, 2024.
Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences for text-to-image generation. In NewrlPS, 2023.
Senqiao Yang, Yukang Chen, Zhuotao Tian, Chengyao Wang, Jingyao Li, Bei Yu, and Jiaya Jia. Visionzip: Longer better but not necessary in vision language models. CVPR, pp. 19792- 19802, 2025.
Jiacheng Ye, Zhihui Xie, Lin Zheng, Jiahui Gao, Zirui Wu, Xin Jiang, Zhenguo Li, and Lingpeng Kong. Dream 7b: Diffusion large language models. arXiv preprint arXiv:2508. 15487, 2025.
Dingkun Zhang, Sijia Li, Chen Chen, Qingsong Xie, and Haonan Lu. Laptop-diff: Layer pruning and normalized distillation for compressing diffusion models, arXiv preprint arXiv:2404. 11098, 2024a.
Hang Zhang, Xin Li, and Lidong Bing. Video-llama: An instruction-tuned audio-visual language model for video understanding. arXiv preprint arXiv:2306. 02858, 2023.
Peiyuan Zhang, Yonggi Chen, Haofeng Huang, Will Lin, Zhengzhong Liu, Ion Stoica, Eric P Xing, and Hao Zhang. Faster video diffusion with trainable sparse attention. In NeurlPS, 2025.
14

---

Published as a conference paper at ICLR 2026
Yang Zhang, Er Jin, Yanfei Dong, Ashkan Khakzar, Philip Torr, Johannes Stegmaier, and Kenji Kawaguchi. Effortless efficiency: Low-cost pruning of diffusion models. arXiv preprint arXhv:2412.02882. 2024b.
Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Video instruction tuning with synthetic data. arXiv preprint arXiv:2410.02713, 2024c.
Yuxin Zhang, Lirui Zhao, Mingbao Lin, Sun Yunyun, Yiwu Yao, Xingjia Han, Jared Tanner, Shiwei Liu, and Rongrong Ji. Dynamic sparse no training: Training-free fine-tuning for sparse llms. In ICLR, 2024d.
Yang Zhao, Yanwu Xu, Zhisheng Xiao, Haolin Jia, and Tingbo Hou. Mobilediffusion: Instant text-to-image generation on mobile devices. In ECCV, 2024.
Fengqi Zhu, Rongzhen Wang, Shen Nie, Xiaolu Zhang, Chunwei Wu, Jun Hu, Jun Zhou, Jianfei Chen, Yankai Lin, Ji-Rong Wen, et al. Llada 1.5: Variance-reduced preference optimization for large language diffusion models. arXiv preprint arXiv:2505. 2025.
A DECLARATION OF LLM USAGE
The use of Large Language Models (LLMs) in this work served two purposes: (1) aid and polish the paper writing, and (2) to generate some of the text prompts used by the diffusion model to create figures that are shown in the paper.
B IMPLEMENTATION DETAILS
This section provides further details on the experimental setup, including common configurations, baseline adaptations, and the computational hardware used for our evaluations.
Common Configurations. To ensure controlled and fair comparison, all experiments, unless otherwise specified, adhere to a common set of configurations. For all text-to-image generation tasks, we set the output resolution 512 512 pixels to facilitate rapid experimentation across the diverse and large-scale models. For our method and baselines such as Wanda (Sun et al., 2024) and DSnoT (Zhang et al., 2024d), we consistently group model parameters into module packages. Furthermore, logarithmic decreasing timestep weighting scheme (log decrease) was uniformly applicd across diffusion models and pruning methods to schedule the pruning process over the diffusion timesteps.
Computational Resources. The training of the DDPM the CIFAR-10 dataset was conducted on NVIDIA A100 GPUs. For the large text-to-image models, all pruning methods are training- frree.
The pruning and evaluation for Stable Diffusion v2.1-base, Stable Diffusion 3-Medium, and Stable Diffusion 3.5-Large were performed on a single NVIDIA RTX 4090 GPU, each equipped with 48GB of VRAM. Due its substantial memory footprint, all experiments involving the FLUX.1 1- dev model, including its pruning and evaluation, was conducted on a single NVIDIA A 1000 GPU with 80GB of VRAM.
C MORE EXPERIMENTAL RESULTS
C.I MORE ANALYSIS FOR UNSTRUCTUREDLY PRUNED SD3-MEDIUM
As illustrated Figure 4, our proposed OBS-Diff method consistently outperforms all baseline approaches in terms of the ImageReward metric across all evaluated sparsity levels. The superiority of our method is particularly pronounced at higher sparsity ratios. For instance, at 60% sparsity, the performance of competing methods collapses, yielding negative ImageReward scores. In stark contrast, OBS- Diff maintains a positive score, demonstrating its exceptional robustness in high- compression scenarios.
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In terms of computational efficiency, Table indicates that OBS-Diff has the longest pruning time a single NVIDIA RTX 4090. However, the additional overhead marginal, requiring only slightly more time than DSnoT (14.95 14.25 minutes). Considering the substantial gains in genera- tion quality and model robustness, we conclude that OBS-Diff offers a superior trade-off between performance and computational cost, establishing it a highly cost-effective pruning solution.
Table 8: Pruning time of different unstruc- tured pruning methods on SD3-Medium (2B) 50% sparsity.
0.5
ImageReward
Method
Time (min)
-1.
Magnitude
R 0
Magriuce
Wanda
7.32
D5
DSnoT
14.25
-2.0
GBS-cil
Wands
OBS-Diff
14.95
2.5
Sparsity (%)
Figure 4: ImageReward vs. sparsity for var- ious unstructured pruning methods on SD3- Medium.
C.2 STRUCTURED PRUNING FOR SD3-MEDIUM
We evaluate our structured pruning method, OBS-diff, on the Stable Diffusion 3-medium model and compare it against the widely-used Lı-norm magnitude pruning baseline. As summarized in Table the baseline method suffers from severe performance degradation as sparsity increases. In contrast, our approach maintains performance remarkably close to the original dense model across all tested sparsity levels, demonstrating its effectiveness and robustness.
Table 9: Performance comparison of structured pruning methods at 10%, 15%, 20%, and 25% sparsity on the Stable Diffusson 3-medium model (2B). The first and last transformer blocks were excluded from the pruning process. The TFLOPs metric represents the theoretical computational cost for single forward pass of the entire transformer. For each sparsity group, the best result per metric is highlighted in bold.
Base Model
Sparsity (%)
Method
#Params
TFLOPs
FID 1
CLIP
ImageReward
Dense Model
2.03
2.84
36.14
0.3162
0.9029
L norm
267.32
0.2035
-2.2611
10%
1.91
2.59 (18.8%)
Ours (OBS-diff)
35.65
0,3166
0,8118
In norm Ours (OBS-ditf)
326.92
0.1942
-2.2768
3-medium
15%
1.83
2,43 (114.4%)
34.33
0.3168
0.6717
Li norm Ours (OBS-diff)
348.77
0.1926
-2.2768
20%
1.78 B
2.31 (U18.7%)
33.15
0.3163
0.4997
L1 norm
365.24
0.1906
-2.2786
25%
1.72 B
2.19 (122.9%)
Ours (OBS-diff)
32.96
0.3143
0.2782
C.3 COMPARISON WITH DIFF-PRUNING ON DDPM
To evaluate the generalizability of our method beyond large-scale text-to-image models, we adapt it to the task of structured pruning for a Denoising Diffusion Probabilistic Model (DDPM) on the CIFAR-IU 10 dataset. Our adaptation leverages the column masks identified by Diff-Pruning, which then integrated with our OBS weight update mechanism detailed in Eq. (6).
As presented Table 10, our method surpasses the current of-te-af- baseline, Diff-Pruning, by achieving superior FID score under an identical fine-tuning budget ( (100K steps). This result demonstrates not only the versatility of our approach but also suggests that the model pruned by OBS-Diff serves as more effective checkpoint for subsequent fine-tuning.
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Table 10: Performance of pruned DDPMs on CIFAR-10 (32 32). All pruned models are fine- tuned for 100K steps. Evaluations are conducted on samples generated via 100 DDIM steps. The best FID score is highlighted bold.
Method
#Params
MACs
FID
Train Steps J
Pretrained
35.7M
6.1G
4.50
800K
Random Pruning
13, 13,95M
2.1G
7.85
100K
Magnitude Pruning
13.95M
2.1G
7.91
100K
Diff- Siff-Pruning
13.95M
2.1G
7.72
100K
OBS-Diff
13.95M
2.1G
7.55
100K
D ROBUSTNESS AND GENERALIZATION ANALYSIS
To demonstrate that our calibration (using only 100 prompts) does not overfit, we evaluated the fixed pruned model (SD3-Medium, 50% Unstructured) under inference conditions significantly different from the calibration settings (CFG 7.0, Steps 25, Euler). For fast evaluation, we evaluated all tasks on the MSCOCO 2014 validation 1K subset using the ImageReward metric.
D.1 ROBUSTNESS TO CFG SCALES
We evaluated the pruned model across varying Classifier-Free Guidance (CFG) scales. As shown in Table 11, the pruned model achieves higher performance at CFG 9.0 (0.7044) than at the cal- ibration setting of CFG 7.0 (0.6425). This indicates that our pruning strategy effectively preserves the model's semantic generation capabilities and generalizes exceptionally well higher guidance scales, which critical for high-quality text-to-image synthesis.
Table 11: Robustness of the pruned SD3-Medium (50% Unstructured) across different CFG scales. The model was calibrated CFG 7.0.
CFG
Dense (ImageReward) Pruned (ImageReward)
Performance
5.0
0.8319
0.5297
7.0 (Calibrated)
0,8510
0,6425
Baseline Improved
9.0
0.8275
0.7044
D.2 ROBUSTNESS TO SAMPLING STEPS
To address the concern regarding step counts, we evaluated the pruned model (calibrated at 25 steps) across 15, 25, and 50 inference steps. The results are summarized in Table 12. Although calibrated 25 steps, the pruned model effectively leverages additional compute 50 steps to generate higher- quality images. This confirms the pruning preserves the integrity of the underlying ODE trajectory.
Table 12: Robustness across varying inference sampling steps. The model was calibrated at 25 steps.
Steps
Dense (IwageRewwrd) Pruned (ImageReward)
Trend
15
0.6988
0,4883
Fast Preview Baseline Improved Quality
25 (Calibrated)
0.8510
0.6425
50
0.9391
0.7153
D.3 ROBUSTNESS ACROSS SAMPLERS
We evaluated generalization across different solvers on both SD3-Medium (MMDiT) and SD v2.1
(U-Net).
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SD3-Medium: Calibrated on Euler (1st-order), the model generalizes zero-shot to Heun (2nd-order), showing significant quality gains.
SD v2.1: We applied 40% unstructured pruning (calibrated on PNDM). As shown in Ta- ble 13, the relative performance ranking of the samplers is preserved between the Dense and Pruned models (c.g., DPM++ remains the highest performing), indicating the pruning is solver-agnostic.
Table 13: Generalization across different samplers for SD3-Medium and SD v2.1.
Model
Sampler
ImageReward (Dense)
ImageReward (Pruned)
Euler (Calibrated)
0.8510
0.6425
SD3-Medium
Heun (2nd Order)
0.9200
0.7249
PNDM (Calibrated)
0.3432
0.1782
DPM++
0.3889
0.2246
SD v2.1
EDM
0.3442
0.1534
DDIM
0.3439
0.1579
D.4 GENERALIZATION OUT-OF-DISTRIRCTION (OOD) PROMPTS
We address the concern regarding calibration data in two ways:
1. Experimental Design: All main results in the paper already use GCC3M for calibration and MS-COCO for evaluation, representing a standard OÒD setting.
2. New Validation Experiment: To rigorously test this, we calibrated two separate mod- els els-one using MS-COCO 2014 Train (n-Distributtinn and one using GCC3M (Out- of-Distribution) and evaluated both on the MS-COCO 2014 validation 5K subset.
As shown in Table 14, the performance nearly identical. The model calibrated on OOD data (GCC3M) performs par with (and even slightly better in FID than) the ID model. This definitively proves that OBS-Dit captures generalizable features and does not overfit to the calibration prompts.
Table 14: Comparison of models calibrated on In- In-Distribution (MS-COCO) vs. Out-of-Distribne (GCC3M) datasets, evaluated on MS-COCO validation set.
Calibration Dataset
Evaluation
FID
CLIP Score
ImageReward
MS-COCO 2014 Train
In-Distribution (ID)
27.93
0.3169 0.3167
0.6547
GCC3M Train
Distrrbtiitiitti (OOD)
27.20
0.6468
E ADDITIONAL QUALITATIVE RESULTS
In this section, we provide additional qualitative results to further demonstrate the effectiveness of OBS-Diff. Figures 5 and Figure 8 showcase unstructured pruning comparisons on SD3-Medium and Flux 1.dev, respectively, benchmarking against Magnitude, DSno'T, and Wanda. Furthermore, Figures to 11 illustrate structured pruning results on SD3.5-Large compared to the L.-norm base- line. Across these diverse architectures and high sparsity regimes, OBS-Dit consistently maintains superior visual fidelity and semantic consistency compared to existing methods.
F FUTURE WORK
While OBS- demonstrates compelling effectiveness in training- free, one-shot pruning for text- to-image diffusion models, its potential extends far beyond this scope. envision three strategic directions to broaden its impact:
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Dense
20%
30%
40%
50%
Magnitude
DSnoT
Warda
Outs (OBS- DIff)
Promept: peaceful forast path covereol in outurur Leaves and soft light, oll painting style, ele Negative Promup:: low quality, bad quality, blur, uglly, worst quality
Figure 5: More Qualitative comparison of unstructured pruning methods on the SD3-Medium model. We evaluate Magnitude, DSnoT, Wanda, and our method (OBS-Diff) various sparsity levels (20%, 30%, 40%, and 50%) using the same prompt and negative prompt. All images are generated at resolution of 512 x 512.
Dense
20%
30%
40%
50%
Megnitude
Cola
DSnoT
Wande
Ours (OBS-DI)
Prompt:: photo delicious ham.burger with fries and wooder. table, professional photography.. bolech Negative Prompt: lew quality. bad quatity, blur, uglly, worst quality
Figure 6: More Qualitative comparison of unstructured pruning methods the SD3-Medium model. We evaluate Magnitude, DSno'T, Wanda, and our method (OBS-Diff) at various sparsity levels (20%. 30%, 40%, and 50%) using the same prompt and negative prompt. All images are generated at resolution of 512 x 512.
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Dense
20%
30%
40%
50%
Magnitude
Wands
Ours (0BS-DIF)
Promut::
Astronakt in A jungle, color palette, musted colors, detalted, Negative Promupt: quality, bad quality, blur, ugly,, worst quality
Figure More Qualitative comparison of unstructured pruning methods on the SD3-Medium model. We evaluate Magnitude, DSnoT, Wanda, and our method (OBS-Diff) at various sparsity levels (20% 30%, 40%, and 50%) using the same prompt and negative prompt. All images are generated at resolution of 512 x 512.
Dense
Magnitude
DSnoT
Wanda
Ours (OBS-Diff)
A steamppenke detestive London street
An astronaut planting flag ow Mars, cinematio
A cybeerpnnne street samarri
fox in the anow, photograph
Figure 8: Qualitative comparison of unstructured pruning methods on Flux L.dev at 70% sparsity. Results from Magnitude, DSnoT, Wanda, and our proposed OBS-Diff are shown.
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Dense 15%
20%
25%
30%
L1 moomm
Ours (OBS (OHF)
Promep: tranalusent, glowing jellyffai in danike
Figure 9: Qualitative comparison of structured pruning methods the SD3.5-Large model vari- ous sparsity levels (15%, 20%, 25%, and 30%). Results from the LI -oloorm baseline and our proposed OBS-Diff are shown.
Dense
15%
20%
25%
30%
L1 ---rom
Ours (OBS DIfS-DIff
Promupt: A vibrant hunmiinghird hovering wext to hibissus flower, naRovo photo
Figure 10: Qualitative comparison of structured pruning methods on the SD3.5- model at various sparsity levels (15%, 20%, 25%, and 30%). Results from the L1-norm baseline and proposed OBS-Diff are shown.
Dense
15%
20%
25%
30%
Li 1--mrm
Ours (OBS-DRF
Prowept lighthouse rockyy coest during
Figure 11: Qualitative comparison of structured pruning methods on the SD3.5-Large model at various sparsity levels (15% 20%, 25%, and 30%). Results from the L1-norm baseline and our proposed OBS-Diff are shown.
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Generalization to diverse diffusion paradigms. OBS-Diff revitalizes the classical Optimal Brain Surgeon framework through elegant designs tailored for the iterative denoising process. This theo- retical foundation is transferable to broader diffusion-based architectures, such as Diffusion LLMs (dILMs) (Nie et al., 2025; Ye et al., 2025; Zhu et al., 2025; Chen et al., 2025b), video generation models (Kong et al., 2024; Wan al., 2025), and other diffusion-basel models (Cao et al., 2025; Abramson et al., 2024; Wang et al., 2026).
Extension to other architectures. OBS-Diff revitalizes the traditional Optimal Brain Surgeon prun- ing algorithm for diffusion models by incorporating designs specifically tailored to the iterative na- ture of the process. The potential of this Hessian- based pruning paradigm can be further applied to recent advanced architectures, such as Vision- LanguaLa Models (VLMs) (Zhang et al., 2023; Li al., 2024; Zhang al., 2024c; Diao et al., 2025a; 2024; 2025b), reasoning models (Feng et al., 2025c;b; Du et al., 2025; Fang et al., 2025b; Chen et al., 2025c; Feng et al., 2025a), and unified mul- timodal models (Tian al., 2025; Lu et al., 2025; Deng al., 2025), to explore inherent sparsity and enable efficient inference.
Synergy with other efficiency techniques. To further enhance model efficiency, OBS-Diff and its possible extensions serve as weight pruning methods that are orthogonal to other acceleration techniques. It can be combined with approaches such as Token Merging (Tao et al., 2025; Shao et al., 2025; Chen et al., 2025a; Jin et al., 2025; Yang et al., 2025), sparse attention (Zhang et al., 2025; Li et al., 2026), or other compression methods (Bai et al., 2025; Li et al., 2025) to achieve greater efficiency gains.
22


Reviews:
[{"review_id": "L6Hx7pFoaa", "reviewer": "Reviewer_L2zh", "summary": "This paper introduces OBS-Diff, a one-shot, training-free pruning framework designed to compress large-scale text-to-image diffusion models. The authors identify that existing methods fail because they don't account for the iterative nature of the denoising process, where errors can accumulate. OBS-Diff addresses this by adapting the classic Optimal Brain Surgeon (OBS) method. Its core innovations are a timestep-aware Hessian construction that gives more weight to earlier, more critical denoising steps, and a group-wise pruning strategy to make the process computationally efficient.", "questions": "Same as above.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The proposed timestep-aware Hessian is an elegant and intuitive solution. Weighting the pruning criteria based on the temporal dynamics of the diffusion process is a clever way to mitigate performance degradation.\n\n2. The framework is explicitly designed to handle modern, complex architectures (like MMDiT) and supports a wide range of pruning granularities (unstructured, semi-structured, and structured). This makes it a potentially universal tool for diffusion model compression.", "weaknesses": "1. the main result (Table 1) presents the unstructured pruning results. However, unstructured pruning have limited application and cannot provide memory reduction and speedup on conventional hardware. From the table, models can be pruned up to 50%. Similar results have been presented by baseline works (SparseGPT, Wanda), which shows the redundancy of transformer weights. Therefore, showing diffusion models can be pruned by 50% in an unstructured way has limited contribution.\n2. Comparison results are mainly for unstructured pruning. Authors should show comparison results in structured pruning and semi-structured pruning settings to support the advantage of OBS-Diff in a practical scenario. From Table 2, Wanda is better than OBS-Diff under semi-structured setting. Comparison under structured setting is missing.\n3. While many models are used under unstructured setting. Only SD3.5 is used under semi-structured and structured setting.", "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": "HBIf9sNDJX", "reviewer": "Reviewer_p7Tk", "summary": "OBS-Diff is a training-free, one-shot pruning framework for text-to-image diffusion models. OBS-Diff employs optimized OBS for diffusion architectures and supports multiple granularities (unstructured, N:M semi-structured, and structured pruning of MHA heads / FFN neurons). A timestep-aware Hessian with decreasing weights prioritizes early denoising steps to optimize error accumulation, and a group-wise sequential procedure optimizes calibration cost. Experiments on SD3/3.5/Flux models report minimal visual quality loss with notable speedups on a wide range of sparsity types.", "questions": "See above", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Unified support for unstructured, N:M (e.g., 2:4), and structured sparsity in diffusion series models. \n\n- Efficient group-wise sequential pruning and effective weighted Hessian construction. \n\n- Qualitative and quantitative results across SD3-Medium/SD3.5-Large and Flux; Strong experimental metrics.", "weaknesses": "- Figure 2 needs to be cited and introduced at the beginning of the methods section. \n\n- Wall-clock numbers are shown for a single MMDiT block; whole-model, sampler-inclusive speedups are not fully established. Efficiency gain under unstructured sparsity also needed to be reported.  \n\n- The comparison set is narrow, mainly Magnitude, Wanda, DSnoT for un/semi-structured pruning and an L1-norm baseline for structured pruning (with Diff-Pruning only on CIFAR-10). Please broaden or justify the baseline choice by including (or at least discussing) recent training-based / non-one-shot diffusion compression methods \n\n- What would be the result of implementing SparseGPT like wanda? This experiment might demonstrate the advantages of the proposed approach over standard OBS. \n\n- The log-decrease schedule is intuitive but needs stronger ablation/theory vs. alternatives (e.g., cosine, exponential, other weights).  \n\n- For CIFAR-10 DDPMs, the pruned models are fine-tuned (100k steps, C.3); clarify when fine-tuning is required vs. truly training-free regimes. Also, the enhancement of the proposed method seems to be very limited compared to other methods (even random pruning), please clarify. \n\n- In Table 8, the higher the sparsity, the better the FID metrics, explain why. \n\n- How sensitive is OBS-Diff to the timestep-weighting schedule and to the number/selection of timesteps used for Hessian estimation, and do the gains persist across samplers (DDIM/DPM++/EDM) and extend to video diffusion or cascaded/staged T2I? A qualitative explanation is sufficient; empirical results are optional if running new experiments would be time-consuming.", "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": "ckqGKF81bj", "reviewer": "Reviewer_RU2d", "summary": "The paper introduces OBS-Diff, a training-free, one-shot pruning framework tailored for large text-to-image diffusion models, whose iterative denoising makes standard LLM-based pruning ineffective. Experiments show OBS-Diff achieves state-of-the-art one-shot pruning for diffusion models, delivering notable inference speedups with minimal drop in visual quality.", "questions": "My major concern lies in the experimental comparison. This paper only compares with LLM-based method.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- OBS-Diff adapts OBS with a timestep-aware Hessian that gives earlier steps more weight, which matches error build-up in denoising. It also supports many sparsity types (unstructured, N:M, and structured like MHA heads and FFN neurons), making the pruning precise and flexible without extra training.\n- It is a one-shot, training-free pipeline with a group-wise sequential strategy that cuts calibration cost. Experiments show clear speedups with little quality drop across common text-to-image backbones (e.g., SD/SDXL/DiT-style models), suggesting good practicality.", "weaknesses": "- Most comparisons are against LLM-oriented, training-free heuristics (e.g., Wanda/DSnoT/magnitude) adapted to diffusion, plus simple L1 for structured cases. This risks overstating, because these baselines were not designed around DiT denoising dynamics. The paper should add results against diffusion-native pruning on Stable Diffusion/SDXL/DiT—e.g., LD-Pruner, EcoDiff, and TinyFusion.\n- The proposed timestep weighting and the group-wise pruning are tuned on a small prompt set and tested in limited settings. It’s not clear they hold for other samplers, step counts, CFG scales, or out-of-domain prompts/styles. Wider tests across these factors would better show robustness.", "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": "A4kkrjUE0M", "reviewer": "Reviewer_ePsz", "summary": "This paper proposes a **training-free OBS-Diff framework** for pruning multi-step diffusion models to reduce computation cost. It extends the **Optimal Brain Surgeon (OBS)** algorithm to diffusion models, using a **timestep-aware Hessian** to reflect the varying importance of parameters across denoising steps. The authors also design **Module Packages** to group layers for batch pruning, reducing the cost of activation collection. OBS-Diff supports **unstructured**, **semi-structured**, and **structured** pruning. Experiments show that it outperforms existing methods in ImageReward and CLIP score. And the quantitative experiment shows that it perform much better than other methods at 30% and 40% sparsity level.", "questions": "1. Why doesn’t the paper include a **comparison of inference-time acceleration** between different pruning methods? Only pruning-time cost is reported.\n2. Is the **generalization ability limited by the calibration dataset**? Since pruning depends on calibration prompts, will prompts outside that distribution generate lower-quality results?\n3. If the **calibration dataset size and prompt diversity** increase, could neuron activations become more uniform, reducing contrast in neuron importance and potentially leading to over-pruning of useful neurons?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The method is **highly generalizable**, applicable to different diffusion architectures such as U-Net and MMDiT.\n2. It **extends OBS pruning** to diffusion models, introducing timestep-aware weighting that better preserves early-stage generation accuracy.\n3. Supports multiple pruning granularities (unstructured, semi-structured, structured) in a unified framework.", "weaknesses": "1. The pruning ratio is fixed per layer (layer-wise), without dynamic or weighted adjustment based on layer importance. Different layers may contribute unequally to the final output, so uniform sparsity could be suboptimal.", "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)