Evolutionary Caching to Accelerate Your Off-the-Shelf Diffusion Model
Authors: Anirud Aggarwal
Abhinav Shrivastava
Matthew Gwilliam
Venue: ICLR 2026 Poster (2026)
Keywords: diffusion caching
image generation
efficient deep learning
diffusion transformers
inference acceleration
Primary area: generative models
TL;DR: Genetic algorithm to quickly learn model-specific caching schedules to make diffusion inference more than twice as fast without sacrificing quality.

Abstract: Diffusion-based image generation models excel at producing high-quality synthetic content, but suffer from slow and computationally expensive inference. Prior work has attempted to mitigate this by caching and reusing features within diffusion transformers across inference steps. These methods, however, often rely on rigid heuristics that result in limited acceleration or poor generalization across architectures. We propose **E**volutionary **C**aching to **A**ccelerate **D**iffusion models (ECAD), a genetic algorithm that learns efficient, per-model, caching schedules forming a Pareto frontier, using only a small set of calibration prompts. ECAD requires no modifications to network parameters or reference images. It offers significant inference speedups, enables fine-grained control over the quality-latency trade-off, and adapts seamlessly to different diffusion models. Notably, ECAD's learned schedules can generalize effectively to resolutions and model variants not seen during calibration. We evaluate ECAD on PixArt-alpha, PixArt-Sigma, and FLUX.1-dev using multiple metrics (FID, CLIP, Image Reward) across diverse benchmarks (COCO, MJHQ-30k, PartiPrompts), demonstrating consistent improvements over previous approaches. On PixArt-alpha, ECAD identifies a schedule that outperforms the previous state-of-the-art method by 4.47 COCO FID while increasing inference speedup from 2.35x to 2.58x. Our results establish ECAD as a scalable and generalizable approach for accelerating diffusion inference. Our project page and code are available here: https://research.aniaggarwal.com/ecad

Published as a conference paper at ICLR 2026
EVOLUTIONARY CACHING TO ACCELERATE YOUR OFF-THE-SHELL DIFFUSION MODEL
Anirud Aggarwal
Abhinav Shrivastava Matthew Gwilliam University of Maryland, College Park (ani rud, mgwillia abhinav2) @umd edu
ABSTRACT
Diffusion-basee image generation models excel at producing high-quality synthetic content, but suffer from slow and computationally expensive inference. Prior work has attempted to mitigate this by caching and reusing features within diffusion transformers across inference steps. These methods, however, often rely rigid heuristics that result in limited acceleration or poor generalization across architec- tures. We propose Evolutionary Caching to Accelerate Diffusion models (ECAD), a genetic algorithm that learns efficient, per-model, caching schedules forming Pareto frontier, using only small set of calibration prompts. ECAD requires no modifications to network parameters or reference images. It offers significant inference speedups, enables fine-gria fine-grained control over the quality-latency, trade-off, and adapts seamlessly to different diffusion models. Notably, ECAD's learned schedules can generalize effectively to resolutions and model variants not seen during calibration. We evaluate ECAD on PixArt-a: PixArt-E, and FLUX.1-dew using multiple metrics (FID, CLIP, Image Reward) across diverse benchmarks (COCO, MJHQ-30k, PartiPrompts), demonstrating consistent improvements over previous approaches. On PixArt-a, ECAD identifies a schedule that outperforms the previous state-of-the-ard method by 4.47 COCO FID while increasing inference speedup from 2.35x to 2.58x. Our results establish ECAD as a scalable and gener- alizable approach for accelerating diffusion inference. Our project page and code are available here: kettss://reearroh www.eeeeercc..aaagaaaaaaao com/ ecad
INTRODUCTION
Diffusion has emerged the backbone for state-of-the-art image and video synthesis tech- niques (Dhariwal & Nichol 202T] Ho et al. 2020 2022] Liu et al. 2024c). Unlike prior methods involving deep learning, which would train neural network generate images in single forward inference step, diffusion instead involves iterating over a prediction for many (20 to 50) steps (Lu et al 2023). This process is quite expensive, and many researchers and practitioners try reduce the latency while preserving, or even improving, the quality (Ma et al., 2023) Wimbauer et al.) 2024 Selvaraju et 2024; Meng et al. 2023 Sauer al. 2023). Some of these strategies involve training some model that can perform the inference in I to steps, particularly with model distillation (Hinton et al, 2015 Meng al., 2023). Other strategies do train tune any neural network weights, principally caching. where the diffusion model's internal features are reused across steps. allowing
that computation be skipped (Ma et al. 2023 Wimbauer al., 2024 Li et 2023a).
We introduce new conceptual and algorithmic framework for diffusion caching by reframing the problem and replacing existing heuristic-bare based approaches with a principled, optimization-drien methodology that is generalizable across model architectures. Existing caching methods typically offer a few discrete schedules, each with fixed trade-offs-for example, a 2x speedup with moderate quality loss, and 3x speedup with greater degradattion-withouu support for intermediate more aggressive configurations. However, real-world deployments often operate under variable latency or quality constraints, necessitating further flexibility. We instead formulate caching as multi-obeective optimization problem, aiming to discover a smooth Pareto frontier that reveals a wide spectrum of speed-quality trade-offs. We show frontiers for FLUX.. 1-dev (Labs] 2024 in Figure

---

Published as a conference paper at ICLR 2026
FLUX
0.75
700
0.70
1.50
0.60
FORA (2 (2.4x)
0.55
0.50
0.45
Qurs (2.63x)
Total TMACs
Good caching schedules evolve to optimize image quality versus speed
Figure 1: We conceptualize diffusion caching as a Pareto optimization problem over image quality and inference time and propose ECAD to discover such Pareto frontiers using genetic algorithm. Left: performance progression over generations for ILUX.I-dew Right: example 1024x 1024 results with corresponding speedups.
Such frontiers are very challenging to produce given how caching schedules are currently derived. State-of-the-ee approaches motivated by heuristics, and key hyperparameters must be carefully hand-tuned by human practitioners based on performance on some set of key metrics (Selvaraju et al., 2024 Zou et al. 2025 Liu et al., [202bb Zou al., 2024 Liu et al. 2025a). We propose different paradigm that does not rely on numan-defined heuristics or hyperparameters, instead discovering effective caching schedules via genetic algorithm.
Our Evolutionary Caching to Accelerate Diffusion models (ECAD) requires two components: (i) some small set of text-only "calibration" prompts and (ii) some metric which computes image quality given a prompt and generated image-we we use Image Reward (Xu et al. (023a). We formulate caching schedules such that the genetic algorithm can automatically discover which features to cache (in terms of blocks and layer types) and when (which timestep). ECAD can be initialized with either random schedules or some set of promising schedules based on prior works such Selvaraju et al.) (2024); [iu et al. (2024b). Thus, while ECAD presents a different paradigm compared to prior works, it can also build on their valuable findings. ECAD takes these initial schedules and gradually evolves them according to the mating rules of genetic algorithm, optimizing their "fitness" according to Multiply-Accumllaa Operations, aka MACs).
quality and computational complexity (measured
This strategy is extremely flexible. While other methods are entirely designed around whether they cache entire block outputs, intermediate layer outputs (such as attention or feedforward outputs), or specific tokens, ours is orthogonal to all of these. We offer framework to optimize caching schedules according to any well-defined criteria. We instantiate with schedule definitions and criteria in Section but the general principles can applied to arbitrary criteria and schedules find Pareto-optimal caching frontiers. For example, fitness could be defined by human ratings of generated samples, and schedule definitions could be made more granular or coarse, or incorporate heuristics from other methods. Although our experiments target text-to-in -itttoo-mmgeee synthesis, the framework is agnostic to modality and naturally extends to class-conditioned or text-to-video tasks. Furthermore, since ECAD computes no gradients and updates no weights, it introduces no memory overhead. During optimization, batch size is unrestricted (allowing use of single, small GPUs infeasible for distillation), and the process runs entirely asynchronously. Schedules could also be optimized for aggressively quantized models to further improve acceleration and quality.
Figure I showcases our method's strong performance and highlights flexibility across resolutions. Although optimized for FLUX.1-des at 256x256, the same schedule applied to 1024x 1024 still outperforms SOTA methods in both speed and quality. At 256x256 ECAD matches or surpasses accelerated PixArt-a and FLUX. 1-vv baselines with 1.97x and 2.58x latency reductions, respectively. At more aggressive 2.58x and 3.37x settings, quality slightly drops but remains competitive.
2

---

Published as a conference paper at ICLR 2026
DiT Block with Caching
Evolutionary Step Generation
Generation
SA FF SA FF SA FF
FF SA
of using
Blocks
cache
using
Cross
Feed Forward
Cached FF
Blocks
Mutate
Blocks
if using
using cache
Cross
cache
Steps
Steps
Self Attention
Cached SA
Recompute
Cached Attention (SA)
Recompute Feed. Forward (FF)
Use Cached Forward (FF)
Attention (SA)
Figure 2: In the context of transformer-basee diffusion model, describe how the transformer architecture allows for caching of attention and feedforward results separately (left). We then give toy illustration of how our method might transition from generation the next, prioritizing mating for schedules with the best quality-speed trade-offs (right).
2
RELATED WORK
2.1
DIFFUSION FOR IMAGE AND VIDEO SYNTHESIS
Diffusion models predict noise, given noised image inputs, to generate high-quality images (Ho et al.
2020 Nichol Dhariwal 2021: Dhariwal Nichol 2021) and videos (Ho et al., 2022 Blattmann
all 2023 Liu et al. 2024c). To save time and reduce feature sizes, these computations are typically performed in the latent space (Rombach et al. 2022 of a pre-trained variational autoencoder (Kingma
& Welling 2014). Although carlier works U-Net backbones (Ronneberger al. 2013). more
models (Vaswani et al. 2017 Dosovitskiy et al. 2020 Peebles & Xie 2023) Bao et al.] 2023). especially Diffusion Transformers (DiT's (Peebles &
recent methods rely mainly on transformer-
Xıe, 2023), which dominate the current landscape due to their powerful scaling properties (Chen
al. 2023 Esser et 2024 Liu et al. 2024c Labs| 20024.. Text-conditioning with multimodal
models like CLIP (Radford et al.) (2221) ), or extremely powerful text models like TS (Raftel et al.
2023). allows for more granular control over image content (Saharia et al. 2022a Ramesh al.
2022 Nichol et 2022 Ruiz et al. 2023) Podell et al.) 2023). not only in generative pipelines but
also for editing (Kawar et al. 2023 Brooks et al. 2023
Sun et al. 2024
Ceylan et al. 2023 Chai
et all 2023).
2.2
ACCELERATING DIFFUSION INFERENCE
Training. Many works accelerate diffusion by training fine-tuning models. Knowledge distilla- tion (Hinton et al. 2015) trains a smaller or faster model to mimic the teacher, reducing steps but high training cost and some quality loss (Salimans & Ho 2022 Meng al.
2023 Luo al. 2023 Lee et al.] 2024 Sauer et al.] [023 Kohler et al. 2024 Yin et al. 2023 Xu et al.) 2023b). Other approaches train auxiliary modules to predict skip connections (Jiang et al.) (023). internal features (Gwilliam et al. 2025). caching configurations (Ma et al. 2024), or adaptive step sched-
ules (Zhang et al. (023). Network compression via pruning (Zhu et al. 2024 Fang al.. (023) or quantization similarly requires retraining to recover accuracy, while post-training quantization offers
limited gains in speed (Li et al. 2023b. Shang et al. 2023).
Training-free An alternative direction accelerates inference without modifying model parameters by caching and reusing intermediate features. Early strategies designed for U-Nets (Li et al.) 2023a Ma et al., 2023 Wimbauer et al., 2024) do not transfer well to DiTs (M et al.) 2024). which lack encoder decoder hierarchy and rely only on within-block skip connections. Pioneering DiT caching works show promise, but some only cache entire blocks at fixed timestep intervals (Selvaraju et al.) 2024), which sacrifices image quality, while others cache only attention layers (Liu et al.) (202b)) which limits potential speedups. Recent works pursue finer-grained caching but depend heavily
3

---

Published as a conference paper at ICLR 2026
Algorithm 1 Evolutionary Caching to Accelerate Diffusion models (ECAD)
Require: Diffusson model M, calibration prompts P. population size n, generations G, crossover
probability Pe. mutation probability
Pu t ImittalieePopulationn)]
Random and heuristic- based schedules
2: for to G' do for each schedule E P do
3:
4:
[ Ms(P)
D Generate images / using schedule S on prompts P
5:
Compute quality metric Q(P.I)
Immae Reward score >> MACs
6:
Compute computational cost ((S)
7:
end for
8:
Ps Selection( (P (Pg-1)
D NSGA-II with Tournament Selection b Recombine schedules with 4-Point Crossover Bit-flip mutation
9: P. < Crossover(P,,, (Po.Pe)
Pg Mutation (Pa Pp.Pm)
11: end for
ComputcParecoFrooiee(PP: P2...., Pc)
Pareto frontier across all generations
13: return F
heuristics and extensive hyperparameter tuning to balance efficiency and quality (Chen et al. 2024 Zou al. 2025 Yuan al. 2024 Liu et 2024a 2025d Qiu et al. 2025 Sun et 2025 Zou et all 2024 Liu et al., 2025b a Bu et al. 2025). We build on these caching methods by replacing manual heuristic design and human in thop hyperparameter tuning with a genetic algorithm, leading to superior image quality.
3 METHODS
We begin by outlining key preliminaries for caching with DiTs (see Appendix A. for a general diffusion background). We then detail our method for modeling caching as a Pareto optimization problem over speed and quality, and the genetic algorithm used to optimize these frontiers.
3.1 PRELIMINARY: CACHING DIFFUSION TRANSFORMERS
DiTss utilize a modified transformer architecture optimized for the diffusion denoising process. typical DiT block takes three inputs: sequence of tokens z' representing the noisy image, conditioning vector c (c.g., text embeddings), and a timestep embedding t. Caching in DīTs exploits temporal coherence between consecutive denoising steps. As the diffusion process proceeds from 2f to 2/_1> the inputs to each block change gradually, creating an opportunity to reuse computed features from previous timesteps (Ma al. 2023] /elvaraju al., 2024)). Rather than caching entire blocks, we employ component-level caching. For each transformer block, we selectively cache the outputs of specific functional components: self-attention (SSA), cross-attention (Sca), and feedforward networks
(fFeN). Formally, for component foomp in block b at timestep t, we can decide whether to compute directly or reuse its cached value:
feimple/st, (2)st, compute(2/,,c. c, if recompute caccee[fSmpp + if cached
When recomputing, the new value is stored in the cache for potential reuse in subsequent steps. Figure demonstrates this for DiT block with two components: self-attention and feedforward. The DiT's s per-component residual connections allow features from the current inference step be smoothly combined with cached features from previous steps.
This selective computation strategy can be represented as binary tensor {0, Nx where N is the number of diffusion steps, B is the number of transformer blocks, and C is the number of cacheable components per block. A value of 0 at position (n, b, e) in S, which we show with shades of red in Figure 2 indicates that we reuse the cached value of component in block b at diffusion step n rather than recomputing it. A caching schedule directly impacts both computational efficiency and generation quality; aggressive caching (more Os in S) reduces computation but may degrade output quality. Our method finds caching schedules with optimal trade-offs between computation and quality by identifying which components be safely cached, in which blocks and timesteps.
4

---

Published as a conference paper at ICLR 2026
Table 1: Main results, 256 2256, 20-step text-to-image generation. We select schedules from our evolutionary Pareto frontier and compare them to prior works across various datasets and models on Image Reward, CLIP Score, and FID. Despite being optimized only on Image Reward with just 100 calibration prompts, our method achieves superior results across other metrics and for unseen prompts.
Settings
Latency
Calibrarion
PartiPrompes
MS-COC02013-00
MJHQ-KIK
mss / img (speedupp)
Image Reward
Image Reward
Model
Caching
Setting
TMACsL
CLIP1
RDD
CLIPT
FID
CLIP1
None
5.71
165.74 (1.00s)
0.90
0.97
32.01
24.84
31.29
9.75
32.77
TGATE
15,
4.86
144.77 (1.14k)
0.78
0.87
31.70
23.90
31.12
10.38
32.33
TGATE
(1.53k)
-0.27
28.29
29.38
FORA
N =2
2.87
100.57 (1.65x)
0.83
0.91
32.03
24.80
31.37
10.33
32.74
PORA
M
2.02
82:55 (2.01%)
0.60
0.83
31.94
24.50
31.35
11.11
32.63
N R-= 60%
(1 (.833)
0.76
30.99
32.37
ToCa
N 3. 90%
2
70.58 (2.35x)
0.60
0.68
31.35
24.01
30.92
11.80
32.35
DuCa
N J.R 60%
3.20
72.53 (2.29x)
0.76
0.79
31.53
23.13
31.03
11.69
32.48
N 90%6
2:30
0.74
24.00
32.39
Ours
2.13
84.09 (1.97))
0.96
0.99
31.94
20.58
31.40
8.02
32.78
Ours
faster
1,46
69.17 A0X)
0.90
0.88
31.44
21.93
31.10
9.92
32.34
Ours
1,18
64.24 (2.58k)
0.77
19.54
31,28
32.24
None
5.71
167.62 (1 (1000)
0.85
1,08
31. 31.90
24.63
31.11
10.53
32.65
FORA
N
2.02
82.12 (2.04k)
0.65
0.81
31.91
27.69
31,16
12.70
32.28
PixAn--:
N R 60%
3.17"
94, 28 (1 (1.78x)"
0.19
54.80
35.42
30.64
ToCa
N 3. 90%
2.13
73,03 (2.30x)
0.07
0.14
30.89
56.48
30.25
36.53
30.55
1.91
1.02
22.17
32.52
None
198.69
2620.09 (1.00x)
0.69
1.04
31 31.88
25. 76
30.95
17:37
31.06
FORA
=
69.80
1073.70 (2.4k)
0.67
0.93
31.88
23:51
31,30
19.38
31.10
ToCa
= 90%
42.96
1576.97 (1 66x))
0.63
0.95
31.81
23.78
31.26
21.59
30.88
FLUX.1 1-Uxx
TaylorSeer N 5.0 -2
DiCache
59.88
62.23
1028.
1161.66 (2.26x)
(2.55x))
0.29
0.61
0.54
0.97
31.16
31.97
26.18
29.66
30.19
31.12
24.36
20.70
31.18
30.64
"aylorSeer
=
49'97"
865.97 (3.03))
-0.07
0.02
29.88
29.02
37.98
29.38
Ours
fast
63.02
1016.59 (2,58x)
0.83
1.04
32.24
21.61
31.58
16.14
31.69
Ours
fastest
43.60
778.17 (3,37))
0.69
0.89
32.27
26.66
31.63
21.43
31.67
(ToCa is not optimized for PixArt- so we reuse the hyperparameters from PixArt-a. Suboptimal results do not indicate that ToCa is not suitable for PixArt-E; instead, ToCa should be hand-optimized per-model. "Refer to Appendix A.ll for detailed explanation of MAC and latency calculations.
3.2 GENETIC ALGORITHM A PARADIGM FOR CACHING
Caching, as Pareto frontiers. The caching optimization problem inherently exhibits trade-off between computational efficiency and generation quality. This can be formalized as a multi-objective optimization problem:
min(C(S), Q(S))
S
where C'S) denotes the computational cost function (lower better) and Q(S) represents the generation quality metric (lower is better, e.g., FID) for caching schedule S. This optimization operates directly on the binary caching tensor E {0, 1) NxCC introduced previously. Possible configurations for S naturally induce sets of solutions that form Pareto frontiers improving one objective necessarily degrades the other. However, this search space is intractable to exhaustively explore, even for small DiTs, given current compute. Prior acceleration methods have predominantly relied on fixed hcuristics that typically provide only isolated operating points. By contrast, our proposed approach explores greater search space and discovers Pareto-optimal configurations, enabling practitioners to select schedules based on application-specie
constraints.
Evolutionary Caching to Accelerate Diffusion models (ECAD). We introduce ECAD, an evolution- ary algorithm-basee based framework for discovering efficient caching schedules for diffusion models, in Aigorithm T Our approach's key insight is that the optimal caching configuration can be discovered through a population-based search over the space of possible caching schedules, using a small set of calibration prompts to evaluate candidate solutions. ECAD is framework with 4 simple customizable components.
The practitioner may adjust granularity with the (1) binary caching tensor shape by adjusting N, B. and C' (the defaults define for S allow any component with a skip connection to be cached, on any block, for any timestep). While does not require any image data, ECAD needs (2) calibration prompts. which we instantiate with the 100 prompts from the Image Reward Benchmark (Xu et al.) 2023a). The practitioner can also select their preferred (3) metrics, where ideally both can be computed quickly online. We use Image Reward for quality, and MAC's for speed (to avoid hardware
5

---

Published as a conference paper at ICLR 2026
1.0
100
0.9
Image Reward
0.8
0.8
0.7
Schedule Type
Ours
Schedule Type
ToCa
0.6
Ours
Unaccelerated
0.6
TaylorSeer
ToCa
Ours - Fast
FORA
DuCa
V
Ours Faster
Unaccelerated
0.5
FORA
Ours Fastest
Ours Fast
TGATE
Ours Fastest
0.4
0.4
Latency (ms)
Latency (ms)
Figure 3: PartiPrompt Pareto frontiers 256 x 256 for PixArt-a (left) and FLUX.I-dew (right).
dependencies). Then, we choose an (4) initial population of caching schedules, which should be diverse, and can be seeded based on prior knowledge (such using FORA schedules) initialized randomly. We utilize NSGA-II (Deb & Jain 2013) for our genetic algorithm due to its efficient non-dominated sorting approach and proven effectiveness in multi-criteria optimization problems.
With all components defined, ECAD runs for the desired number of generations. In each generation, images generated per caching tensor, and top-performing tensors (in quality and speed) evolve to form the next generation. This process incrementally improves Pareto frontiers for the selected model, scheduler, and timestep combination.
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Model Architectures We provide experiments three popular text-to-image DiT models: PixArt-a, PixArt-E, FLUX. 1-dev. Each model uses its default sampling method at 20 steps: DPM- Solver++ (Lu al. 2023) for both PixArt models and Now.VacchfulerDDscreeeeeeeee
(Esser
et al. 2024) for FLUX.1 1-ev..-dev. Guidance scales are 4.5 for PixArt models and 5 for FLUX.I-de Both PixArt models employ 28 identical transformer blocks containing three components we enable caching for: self-attention, cross- cross-attention, and feedforward. In contrast, FLUX. I-dev implements an MMDiT-based architecture (Esser et al. 2024) with "full" and 38 "single" blocks. We enable caching for attention, feedforward, and feedforward context components in full blocks, and attention, MLP projection, and MLP output for single blocks. Cacheable component selection is discussed in Appendix A.2 We calibrate all models at 256 256 but evaluate at both 256 and 1024x 1024.
Evaluation Metrics 2020). and CLIP score (Zhengwentai (202) with ViT-B/32 (Dosovitskiy al. 22200 on the Image Reward Benchmark prompts set (Xu et al.) 2023a), the PartiPrompts set (Yu et al. 2022), MS-
We evaluate performance using Image Reward (Xu et al. 2023a FID (Setzer
COCO2017-30X (Lin
2015) (we use the same prompts and images as ToCa (Zou al. 2025) and MJHQ-30K (Li et al. 2024). On the Image Reward Benchmark prompts set, we generate each of 100 prompts at 10 different, fixed seeds for 1,000 total images, For PartiPrompts we generate single image for each of the 1,632 prompts. To measure the speed of a particular caching schedule, we use two metrics: Except where otherwise stated, utilize calflops YYe 2023) measure MACs. We average end-to-end image generation latency using precomputed text embeddings on I NVIDIA A6000 GPU after discarding warmup runs; full details in Appendix A.11
operations (MACs) and direct image generation latency.

---

Published as a conference paper at ICLR 2026
three-quanees front view .. blue 1977 Porsche
section of the Girent Wall in the mountains, detailed clarcoal sketch.
blue red leaves and yellow fruit field impressionisiic painting
still life painting of shoes
portniit of mountain road
old man
Partherion
Unaccelerane
ToCa (1.83)
Ours (1 1999
Figure 4: Qualitative results comparing our "fast" schedule for PixArt-a 256 x56 with ToCa; see Figure f6 FLUX.I-dev. "..." represent omitted text, see Appendix A.13 for full prompts.
Inference
13
Block Number
Block Number
Figure 5: Our "fast" schedule for PixArt-a (left) and FL.UX. (right). Reds are cached compo- nents and grays are recomputed (for PixArt-a, from left right: self-attention, cross-attention, and feedforward). See Appendix A. A.14 more details.
4.2
MAIN RESULTS
We optimize ECAD on three diffusion models: PixArt-a, PixArt-E, and FLUX. 1-dev and present results for select schedules in Table For PixArt-a 256 resolution with 20 inference steps, we run 550 generations with 72 candidate schedules per generation, where each candidate generates 1,000 images (10 for each of 100 Image Reward Benchmark prompts). For FLUX. I-dev reduce the population to 24 schedules and evolve for 250 generations under otherwise identical settings. We initialize both using variants inspired by FORA and TGATE, detailed Appendix A.8 For PixArt-E. we transfer 72 schedules from PixArt-a's 200th-generation Pareto frontier and run 50 additional generations, leveraging the models' shared DIT architecture.
Across all models, ECAD achieves strong performance on Image Reward (which correlates strongly with human preference (Xu al. 2023at) and FID. On PixArt-o, our "fastest' schedule reduces FID by 9.3 over baseline and by 2.51 over C best setting. On PixArt- and FLUUX.1-dev, ECAD schedules outperform prior work and baseline by significant margin. On FLUX.. 1-dev,, our 'fast' schedule at 2.58x matches baseline Image Reward and the 'fastest" schedule at 3.37x maintains competitive quality. For prompt-image alignment, measured via CLIP score, ECAD roughly matches prior works, which is expected as caching should not affect prompt-image alignment.
We show full Pareto frontiers Figure on unseen prompts. ECAD discovers schedules that consistently outperform prior works across evaluation metrics while providing fine-graines control over the quality-latene trade-off. We provide qualitative comparisons for PixArt-a in Figure 4 with
additional results in Figure and and further FL.UX.1-de comparisons in Figures 26
28
We show
7

---

Published as a conference paper at ICLR 2026
Table 2: Genetic scaling results. We show performance changes as more iterations (generations) of ECAD run in terms of latency, PartiPrompts Image Reward, and MJHQ-30K FID. select the schedule with highest TMACs per generation.
Table Model transfer results. ECAD is first optimized on PixArt-a for 200 generations, and the resulting schedules used to initialize optimization on PixArt- for an additional 50 generations (shown in the last row). Settings for both schedule discovery and evaluation are detailed below. We report TMACs, latency, Image Reward on the calibration and PartiPrompts set, and
FID for MJHQ-30K. Transferring ECAD schedules between these two models results in only slight penalties to performance.
Gens
(speedup1)
ms iing.
Image Reward
FIDJ
Model
Generic Settings
Gens
Model
Evaluttion Settings
Res.
TMACs]
si iingi (specdup")
Latency
Calibrationt
Meeics
PariPPrompss
[TD]
145.09 (1.14x)
1.00
9.40
PixAat-
200
PixAat-
256
2.59
94.04 (1.76x)
119%
1.02
X,00
50
92.76 (1.7%x)
0.98
7.97
RixAn-n
200
PixAn-E
256
2.39
103.47 (L.2xs)
0.84
1,9
9.27
150
87.11 (1.90x)
1.00
8.11
PixAn-a
250
PixArt-
256
2:22
86.59 (1 91x)
0.96
0.99
8,09
300
86.62 (1.91x)
0.99
8.04
PixAn-a
PaxAn-E
256
2:22
1/66
9.06
(2.17%)
PicAn-3:
256
1.91
Table 4: FLUX.1-dev detailed transfer results, 1024 x 1024 resolution, 20-step text-to-image generation. We reuse our 'fast" schedule trained on FLUX. 1-dev at 256x256 resolution, as well as an older, 'slow' schedule. We apply them for 1024 x 1024 image generation and compare them to prior works in terms of Image Reward, CLIP Score, and FID. Our results are competitive with prior work despite being evaluated at different resolution than optimization.
Model Settings
Latency
Calibration
PartiPrompts
MS-CDC02011-300
MJHQ-30K
Caching
Setting
TMACs]
s/ingl (spcodupf)
Image Rewardf
Image Rewardf
CLIP
FIDL
CLIP?
FIDJ
CLIP
18.30
25.45
None
40% steps
476.10
7.61 (2.41x)
0.43
0.83
31.38
25.20
30.73
21.68
30.99
FORA
N
416.88
7.62 (2.40k)
0.27
0.69
31.20
29.45
30.52
24.65
30.69
300.41
(2.47x)
TaylorSeer
N 5,0
357.39"
7,20 (2.54x)
0.50
0.94
32.28
42.81
31.74
29.89
31.92
slowgre 11024
Ours
fust 1004
376.62
6.96 (2.63x)
0.71
1.05
31.88
26.69
30.91
17.76
31.99
the composition of the "fast" ECAD schedules for PixArt-a and FLUX.I-dew in Figure with more schedules in Appendix A..4
Scaling Properties. Unlike existing approaches, practitioners have the flexibility to run ECAD for as many generations as their time and compute constraints allow. While competitive schedules emerge within few iterations, continued optimization yields steady improvements. To illustrate this, track the 'slowest' schedule throughout the genetic process for PixArt-a and report results in Table After just 50 generations, this schedule outperforms the unaccelerated baseline and all prior methods on Image Reward for unseen PartiPrompts and MJHQ FID. Further generations reduce latency at slight cost in quality. Figure 6 shows the Pareto frontier for each generation on the calibration prompts; initial generations rapidly improve while later generations show incremental improvements.
4.3 EMERGENT GENERALIZATION CAPABILITIES
Model Transfer Results. To demonstrate ECAD's advantage handcrafted heuristics, transfer pre-optimized schedules between model variants. In Table we select the "slowest" schedule from the Pareto-frontier across the first 200 generations of PixArt-a ECAD optimization and evaluate on PixArt-E as is. to demonstrate direct transfer results. Then, we perform an additional 50 optimization generations on PixArt-E using 72 schedules transferred from the PixArt-a ECAD frontier 200 generations. Although with direct transfer from PixArt-a, PixArt-E has higher latency than Pix/Art-o at 200 generations, after only 50 generations of optimization, surpasses PixArt-a's speedup while improving calibration Image Reward and MJHQ FID. By comparison, simply transferring the 250- generation PixArt-a configuration yields only a 1.79x speedup instead 1.98x, and has worse calibration Image Reward and MJHQ FID. This is departure from recent caching innovations; for example, ToCa's s carefully tuned PixArt-a settings cannot be transferred to PixArt-E (see Table T). despite the similarities between the two models.
8

---

Published as a conference paper at ICLR 2026
1.0
500
0.0
400
Generation Number
Image Reward
Reward Rewar Image
0.6
300
0.4
200
Schedule Type
0.2
Paruto Frontier Per Geniration
Schedule Type
100
0.2
Size images Frompe. 100 Prompts
Size 72, Images per Proppt 100 Prompts
Size 72, 10 images per Prompe, 33 Prompts
Frantier Across All Generations
[Ful] Size 72, 10 Images per Prompt, 100 Prompts
Unsccelerated
Unaccelerated
6.0
Total TMACs
Total TMACs
Figure 6: ECAD evolution. ECAD iteratively improves quality/time trade-offs as it evolves across generations as measured by Image Re- ward (PixArt-a 256x
Figure 7: Faster ECAD optimization strategies. We compare "Full" ECAD smaller popula- tion size, fewer images per prompt, and fewer prompts (PixArt-o 256x256).
Resolution Transfer Results. We present ECAD's performance FLUX. at 1024x 1024 resolution after optimization on 2 256 in Table[4. and highlight its superior performance compared FORA and the "None" approaches. We apply schedules as-is, with no further optimization the higher resolution. While it is likely preferable to optimize ECAD at the target evaluation resolution sufficient compute is available, we show this is not necessary practice. In addition to the same "fast' FLUX. 1-dev schedule from Table I at 256x256 resolution, we select 'slow' schedule from just 50 generations training at 256. Despite ToCa being optimized for high resolution and ours for low resolution, our "fast" setting achieves superior Calibration Image Reward (a proxy for human preference) and COCO FID, and further surpasses concurrent TaylorSeer on unseen-prr unseen-prompt Image Reward, while avoiding its prohibitive memory overhead that reduced its batch size by 66%.
4.4 ABLATION ANALYSIS
To better explore the evolutionary algorithm's 's behavior, especially with respect to optimization time, we run three ablations with different hyperparameters on PixArt-a for 100 generations, varying the population size (from 72 to 24), the number of images generated per prompt (from 10 to 3), and the number of prompts used (from 100 to 33, selected randomly), each approximately reducing GPU time by 66%. The shape of the frontier of the reduced population setting in Figure resembles previous generations of full populations settings, suggesting that reducing the population size is akin running the model for fewer generations. Reducing the number of images per prompt is not notably harmful, while using smaller set of only 33 prompts is very detrimental. However, as shown Appendix A.4 this effect stems from size rather than diversity: smaller sets degrade quality, but equally sized sets with less diversity do not. Appendix A.4 further shows that a 100- prompt calibration set generated via ChatGPT performs comparably to the human-curated Image Reward set, demonstrating that large, diverse prompt collections are straightforward to assemble. In addition, we include ablations on NSGA-II hyperparameters Appendix A.5 and display the effectiveness of alternative quality metries in Appendix A.3
5 DISCUSSION
Limitations and Broader Impacts. Optimizing on automatic metrics ties our performance to the quality of those metrics. We use Image Reward for the sake of cost and time; however, if replace with ranking by human users, for example, results could improve. ECAD does not introduce new societal risks beyond those inherent to diffusion models. While reduced inference cost may increase

---

Published as a conference paper at ICLR 2026
potential for misuse, also promotes broader image-genene gengreeinnrttonn accessibility and mitigates some environmental impact of image generation.
Conclusion. In this work, reconceptualize diffusion caching Pareto optimization problem that enables fine-grained trade-offs between speed and quality. We provide a method, ECAD, which converts this problem into search over binary masks, and can discover best-case caching Pareto frontier. With only 100 text prompts, our method runs asynchronously with much lower memory requirements than training or fine-tuning diffusion model. We achieve state-of-the-ar results for training-free acceleration of diffusion models in both speed and quality.
ACKNOWLEDGMENTS
This work was partially supported by NSF CAREER Award (#2238769) to AS. The authors ac- knowledge UMD''s supercomputing resources made available for conducting this research. The U.S. Government is authorized to reproduce and distribute reprints for Govermeental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of NSF or the U.S. Government.
REFERENCES
Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Zhu, All are worth words: A vit backbone for diffusion models, 2023. BtLLhttp::/// http://wwwwwww.oaa//oooooaoo
12152
Blank and K. Deb. pymoo: Multi-objective optimization in python. IEEE Access, 8:89497-89509, 2020.
Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pp. 22562225622222 22575, June 2023.
Tim Brooks, Aleksander Holynski, and Alexei A. Efros. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18392-18402, June 2023.
Jiazi Bu, Pengyang Ling, Yujie Zhou, Yibin Wang. Yuhang Zang, Tong Wu, Dahua Lin, and Jiaqi Wang. Dicache: Let diffusion model determine its own cache. arXiv preprint arXiv:2508. 17356, 2025.
Duygu Ceylan, Chun-Hao Paul Huang, and Niloy Mitra. Pix2video: Video editing using image diffu- sion. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 23149-23160, 2023. doi: 10. N0.11099CCCV51000.222...222
Wenhao Chai, Xun Guo, Gaoang Wang. and Yan Lu. Stablevideo: Text-driven consistency-awee diffusion video editing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 23040-23055 2023.
Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, al. Pixart-o: Fast training of diffusion transformer for photorealistic text-to-image synthesis. arXiv preprint arXiv:2310.001
2023.
Pengtao Chen, Mingzhu Shen, Peng Ye, Jianjian Cao, Chongjun Tu, Christos-Savee -Savvas Bouganis, Yiren Zhao, and Tao Chen. 8-dit: A training free acceleration method tailored for diffusion transformers, 2024. URL https: http///axvvvv/o org abs /20...40066.. 01125
Kalyanmoy Deb and Himanshu Jain. An evolutionary many-objective optimization algorithm using sefereeceepoint-bbseee nondominated sorting approach, part i: solving problems with box constraints. IEEE transactions on evolutionary computation, 18(4):577-601, 2013.
Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780-8794 2021.
10

---

Published as a conference paper at ICLR 2026
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghami, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition scale. arXiv preprint arXiv:2010. 1929, 2020.
Patrick Esser. Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller. Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach. Scaling rectified flow trans- formers for high-resolution image synthesis, 2024. URL 03206
Gongfan Fang, Xinyin Ma, and Xinchao Wang. Structural pruning for diffusion models. In Advances in Neural Information Processing Systems, 2023.
Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt. Geneval: An object-focused framework for evaluating text-to-image alignment. In A. Oh, T. Naumann, A. Glober- son, K. Saenko, M. Hardt, and S. Levine (eds.), Advances in Neural Information Pro- cessing Systems, volume 36, pp. 52132-52152. Curran Associates, Inc., 2023. URL
https::/prroceedngg....uuu neuriss wctppap..cc/pppp.rrr..eeeeeppepe Ftlees/ppeer2023/E1 33577cc/608000cooofffeeee12e1213
and Benchmarks.
pdi.
Matthew Gwilliam, Han Cai, Di Wu, Abhinav Shrivastava, and Zhiyu Cheng. Accelerate high-quality diffusion models with inner loop feedback. arXiv preprint 2rXiv:2501.1310, 2025.
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network, 2015. URL httttt:ss///xiiii org abs /103. 02531
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in
Neural Information Processing Systems, 33:6840-688 6851, 2020.
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David Fleet. Video diffusion models. Advances in Neural Information Processing Systems, 35:8633-8646, 2022.
Xiwei Hu, Rui Wang, Yixiao Fang, Bin Fu, Pei Cheng, and Gang Yu. Ella: Equip diffusion models with I1m for enhanced semantic alignment, 2024.
Zeyinzi Jiang, Chaojie Mao, Yulin Pan, Zhen Han, and Jingfeng Zhang. Scedit: Efficient and controllable image diffusion generation via skip connection editing, 2023. URL https://
arxiv.orq/abs/2312.2
Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani. Imagic: Text-based real image editing with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6007-6017, J June 2023.
Diederik P Kingma and Max Welling. Auto-encoding variational bayes, 2014. URL https
7arxxvv. wwt.://rr/abb////3/ /atee.131..66 6114
Jonas Kohler, Albert Pumarola, Edgar Schönfeld, Artsiom Sanakoyeu, Roshan Sumbaly, Peter Vajda, and Ali Thabet. Imagine flash: Accelerating emu diffusion models with backward distillation, 2024. URL htt.atts////iii.oo////////
als 05224
Black Forest Labs. Flux.
bcck forest--toooo
2024.
Youngwan Lee, Kwanyong Park, Yoorhim Cho, Yong-Ju Lee, and Sung Ju Hwang. Koala: Empirical
lessons toward memory-efficient and fast diffusion models for text-to-image synthesis, 2024, URL
https:://arxiss wwtpa/wwwwwwwwo.////a/aaaa/
2000000002/11200553533 04005
Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet, Linmiao Xu, and Suhail Doshi. Playground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation, 2024.
11

---

Published as a conference paper at ICLR 2026
Senmao Li, Taihang Hu, Fahad Shahbaz Khan, Linxuan Li, Shiqi Yang, Yaxing Wang, Ming-Ming -iig Cheng, and Jian Yang. Faster diffusion: Rethinking the role of unet encoder in diffusion models, 2023a. |tttt::
org/ abs 2312. 09608
Xiuyu Li, Yijiang Liu, Long Lian, Huanrui Yang. Zhen Dong, Daniel Kang, Shanghang Zhang, and Kurt Keutzer. Q-diffusion: Quantizing diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 17535-17545, Octoher 2023b.
Tsung- Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitmick, and Piotr Dollár. Microsoft coco: Common objects in context, 2015. Utt.https://aaxiiii org. abs .05.0012
Feng Liu, Shiwei Zhang, Xiaofeng Wang, Yujie Wei, Haonan Qiu, Yuzhong Zhao, Yingya Zhang, Qixiang Ye, and Fang Wan. Timestep embedding tells: It's time to cache for video diffusion model, 2024a. URL hwt.:httts://arttee http://awwwvvvor.aaaaa/// wbwp:///rgg/a/oooooooooooo/ss 24 19108
Haozhe Liu, Wentian Zhang, Jinheng Xie, Francesco Faccio, Mengmeng Xu, Tao Xiang, Mike Zheng Shou, Juan-Manue, Perez-Rua, and Jürgen Schmidhuber. Faster diffusion via temporal attention decomposition, 2024b. URL (tttss:::: http:///axvvvvve haagg//ba//// 2404. 02747
Jiacheng Liu, Chang Zou, Yuanhuiyi Lyu. Junjic Chen, and Linfeng Zhang. From reusing to forecasting: Accelerating diffusion models with taylorseers. arXiv preprint arXiv:2503.06923; 2025a.
Jiacheng Liu, Chang Zou, Yuanhuiyi Lyu, Junjie Chen, and Linfeng Zhang. From reusing to forecasting: Accelerating diffusion models with taylorseers, 2025b. URL httts:://a/aa: Drc/abs/2500.0692.
Jiacheng Liu, Chang Zou, Yuanhuiyi Lyu, Fei Ren, Shaobo Wang, Kaixin Li, and Linfeng Zhang. Speca: Accelerating diffusion transformers with speculative feature caching, 2025c. URL https
//a22iv.orr/aas//50033 11628.
Yixin Liu, Kai Zhang, Yuan Li, Zhiling Yan, Chujie Gao, Ruoxi Chen, Zhengqing Yuan, Yue Huang, Hanchi Sun, Jianfeng Gao, Lifang He, and Lichao Sun. Sora: A review on background, technology, limitations, and opportunities of large vision models, 2024c. URL https:://axxv..or
abs/ 2os/2402.10 1777
Ziming Liu, Yifan Yang, Chengruidong Zhang, Yiqi Zhang, Lili Qiu, Yang You, and Yuging Yang. Region-adaptive sampling for diffusion transformers, 2025d. URL https:://axxiv..ot
Bbs/ 2502 OKEE
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models, 2023. URL httphhtt///// //arxiv Stc//ab/22211.100
Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. Latent consistency models:
Synthesizing high-resolution images with few-step inference, 2023. URL htttss///aaxii
prq/aba/2310 04378.
Xinyin Ma, Gongfan Fang, and Xinchao Wang, Deepcache: Accelerating diffusion models for free, 2023. URL http:::/t://aaat
org/
www.or////bb////eeee.o.oeeeeeeee abs/ 00858
Xinyin Ma, Gongfan Fang, Michael Bi Mi, and Xinchao Wang. Leaming-to-cache: Accelerring dif- fusion transformer via layer caching, 2024. htt.httpss//axii..rrr/aa//////// 01..4066..7.33 01733
Chenlin Meng. Robin Rombach, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans. On distillation of guided diffusion models, 2023. URL httl/attt//xxiv..ott
abs/2 Bos/2210 U. 03142
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models, 2022. URL htt.:/ntttss//aasi..orrr/aaaasss
12

---

Published as a conference paper at ICLR 2026
Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. In International Conference on Machine Learning, pp. 8162-8177 PMLR, 2021.
William Peebles and Saining Xie. Scalable diffusion models with transformers, 2023. URL https (aaxxiv..rr//ab//22222000
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023. https:/:: /ttppps://axiivi orooor////oo..o abs////b///a/ 2307. 01 952
Junxiang Qiu, Shuo Wang. Jinda Lu, Lin Liu, Houcheng Jiang, Xingyu Zhu, and Yanbin Hao. Accelerating diffusion transformer via error-optimized cache, 2025. URL https:://axaxt
org/ab8/2501 19243.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark. Gretchen Krueger, and llya Sutskever. Learning transferable visual models from natural language supervision, 2021. URL https
//arxiv.orc/abs//2
00020.
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer, 2023. URL https:: /tapiii.//arvv...o/ wrg.oorggaaa........ 910. 1068)
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text- conditional image generation with clip latents, 2022. URL https::/arxxi...tt./////// wwt.oogg//.a.....ooooo.......a/t /204
U6 245
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models, 2022. URL httpp::/
abs/2112. 10752
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation, 2015. httpl:ttt:////
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Pro- ceedings of the IEEE/CV Conference Computer Vision and Pattern Recognition (CVPR), pp. 22500-22510, June 2023.
Chitwan Saharia, William Chan. Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David Fleet, and Mohammad Norouzi. Photorealistic text-to-image diffusion models with deep language understanding. In S. Koyejo. S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (eds.), Ad- vances in Neural Information Processsig Systems, volume 35, pp. 36479-36494. Curran Associates, Inc., 2022a. 2022/ 1007:11/22.55eeee002223333550323
URL http:://ppoceeeeisss.eerr.rppppp papper files/paper)
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L. Denton, Kam- yar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan. Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi. Photorealistic eext-to-image diffusion models with deep language understanding. In Advances Neural Information Processing Systems, vol- neuri happrr/appe___e lireeer_flleee files/paper) 1022:11eee////2eeeeeee1222222222
ume 35, 2022b. URL
Tim Salimans and Jonathan Ho. Progressivv distillation for fast sampling of diffusion models, 2022. 00512
URL /tlLLttttss//ccclooo//////s//
Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach. distillation, 2023. URL /tL.tttps///arxvv.orr////
Adversarial diffusion
17042
Maximilian Seitzer. pytorch-fide fid: FID Score for PyTorch. https: http//gitttubbb ht httt:s//ggihbb..com//meeeeeee wttp://w.////eeeeeeemmee pytorch- fid, August 2020. Version 0.3.0.
13

---

Published as a conference paper at ICLR 2026
Pratheba Selvaraju, Tianyu Ding, Tianyi Chen, Ilya Zharkov, and Luming Liang. Fora: Fast-forwart caching diffusion transformer acceleration, 2024. URL
2407
UT425
Yuzhang Shang, Zhihang Yuan, Bin Xie, Bingzhe Wu, and Yan Yan. Post-training quantization on diffusion models. CVPR, 2023.
Wenhao Sun, Rong-Cheng Tu, Jingyi Liao, and Dacheng Tao. Diffusion model-based video editing: A survey, 2024. URL https: arxiv. org/ wttp/bbb///...000//// 0701
Wenzhang Sun, Qirui Hou, Donglin Di, Jiahui Yang, Yongjia Ma, and Jianxun Cui. Uniep: A unified caching and pruning framework for efficient video generation, 2025. URL https:
0ttpii/...r//////552222 04393.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention all you need. Advances in neural information processing systems, 30, 2017.
Jianyi Wang, Kelvin CK Chan, and Chen Change Loy. Exploring clip for assessing the look and feel of images. In AAAI, 2023.
Lilian Weng. What are diffusion models? lilianweng.githubbio, Jul 2021. URL https: //
11 Flliawenn github. 1ttpmaa...ooppssa/22200011111100 mode-uuuoo--meeeea models/
Felix Wimbauer, Bichen Wu, Edgar Schoenfeld, Xiaoliang Dai, Ji Hou, Zijian He, Artsiom Sanakoyeu, Peizhao Zhang, Sam Tsai, Jonas Kohler, Christian Rupprecht, Daniel Cremers, Peter Vajda, and Jialiang Wang. Cache me if you Accelerating diffusion models through block caching, 2024.
URL https: httpthtsss///.ii...tot wttpx/xxvvorrrg/e//e abs//312/222222 03209
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, 2023a.
URL httts::////: org. www.rr//abbbaaaa absp/////2222./.. 05977
Jiazheng Xu, Yu Huang, Jiale Cheng, Yuanming Yang, Jiajun Xu, Yuan Wang, Wenbo Duan, Shen Yang, Qunlin Jin, Shurun Li, Jiayan Teng, Zhuoyi Yang. Wendi Zheng, Xiao Liu, Ming Ding, Xiaohan Zhang, Xiaotao Gu Shiyu Huang, Minlie Huang, Jie Tang, and Yuxiao Dong. Visionreward: Fine-grained multi-dimennsionll human preference learning for image and video generation, 2024. URL htt.httts: lttttps://aaii...toooo htt//aiivvv.o////// www. abs 2412. 21059
Yanwu Xu, Yang Zhao, Zhisheng Xiao, and Tingbo Hou. Ufogen: You forward once large scale text-to-image generation via diffusion gans, 2023b. URL.
www.abss.rg//ss.../ /2111
09257
Xiaoju Ye. calfiops: a flops and params calculate tool for neural networks in pytorch framework, 2023. URL http:htttt/ttttttt
com Mx cal celllulaae culate- flops. www.pppppp....... .yttrch
Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T. Freeman, and Taesung Park. One-step diffusion with distribution matching distillation, 2023. URL https
Varxiv. www.vv.orgabb///..
1882..
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Bureu Karagol Ayan, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason Baldridge, and Yonghui Wu. Scaling autoregressive models for content-rich text-to-image generation, 2022. URL hattts::/arxivv.rst http://pe///zzuu...rr//////00//0
Zhihang Yuan, Hanling Zhang, Pu Lu, Xuefei Ning, Linfeng Zhang, Tianchen Zhao, Shengen Yan, Guohao Dai, and Yu Wang. Ditfastattn: Attention compression for diffusion transformer models, 2024. URL httpt: /taps:://axivv http//awwvvrgg/.e./// abs 2406. /
Hui Zhang, Zuxuan Wu, Zhen Xing, Jie Shao, and Yu- Yu-Gang Jiang. Adadiff: Adaptive step selection for fast diffusion, 2023. URL http///axvvv..////e /attttsss//rratv.oo/////////1/1 14768
14

---

Published as a conference paper at ICLR 2026
Zhixin Zheng, Xinyu Wang, Chang Zou, Shaobo Wang, and Linfeng Zhang. Compute only 16 tokens in one timestep: Accelerating diffusion transformers with cluster-driven feature caching, 2025.
URL /RR/ittss///xxx..rrrraaa//////// httpc//abb/55000 1001
SUN Zhengwentai. clip-score: CLIP Score for PyTorch. http:://github.ccm/raateet
+ 11p--cores March 2023. Version 0.2.1.
Haowei Zhu, Dehua Tang, Ji Liu, Mingjie Lu, Jintu Zheng, Jinzhang Peng, Dong Li, Yu Wang, Fan Jiang, Lu Tian, Spandan Tiwari, Ashish Sirasao, Junhai Yong. Bin Wang, and Emad Barsoum. Dip-go: A diffusion pruner via few-step gradient optimization. In A. Glober- son, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomezak, and C. Zhang (eds.), Ad- vances in Neural Information Processing Systems, volume 37, pp. 92581-92604 Curran Asso- ciates, Inc., 2024. wttp:///t/////eeeeeiaaaaa.......
Chang Zou, Evelyn Zhang, Runlin Guo, Haohang Xu, Conghui He, Xuming Hu, and Linfeng Zhang. Accelerating diffusion transformers with dual feature caching, 2024. URL https:
//arxiv. (atxiv.rrg/abs/221...2
Chang Zou, Xuyang Liu, Ting Liu, Siteng Huang, and Linfeng Zhang. Accelerating diffusion transformers with token-wise feature caching, 2025. httpst http://wxwvw.../...e/ /ttp://wwww.or//b////000
053
15

---

Published as a conference paper at ICLR 2026
A APPENDIX
A.1 DIFFUSION PRELIMINARY
Diffusion models have emerged as powerful generative models capable of producing high-quality images. In this section, we provide brief overview of the diffusion process, the denoising objective, and the specific formulation for Diffusion Transformers (DiT).
The diffusion process follows Markov chain that gradually adds Gaussian noise to data. Given an image zo sampled from data distribution q(zo), the forward diffusion process sequentially transforms the data into a standard Gaussian distribution through timesteps by adding noise according to a pre-defined schedule. This forward process can be formulated as:
Basic Difffusion Process:
1 = (ze: / B, 3,I)
(I)
where {,B, E (0,1) represents the noise schedule (Weng. 2021 We define a 1 - Be and oe = II a, for convenience. key property arising from this process is that we sample 2i any arbitrary timestep / directly from zo without having to sample the intermediate states as:
It Varo + vI āye
where e /V, I).
This property particularly useful during training as it allows for efficient parallel sampling across different timesteps.
Denoising Objective: The denoising process aims to reverse the forward diffusion by learning to predict the noise added at each step. This is typically accomplished by training a neural network (1 1) to estimate the noise component Fe. Its training objective is formulated as:
L = E, del [l|e co(x,, +
(3)
where is uniformly sampled from 2,.,..., T), xp from the data distribution, and from (O, I). During sampling. the noisy image is gradually denoised using various strategies. In the DDPM algorithm (Ho et al. 22200) the reverse process takes the form:
Po( = Ni t), ofI)
(4)
where (o t)
8. (o 1)) While effective, DDPM typically requires hundreds Var It VI-ai
to thousands of denoising steps. For more efficient sampling, DPM-Solver++ (Lu al. 2023) (used in both PixArt-a and PixArt-E) reformulates the diffusion process as an ordinary differential equation of the (simplified) form below:
dx 1 &.V di 3-V3 (z)
(5)
DPM-Solver++ then applies high-order numerical methods solve this ODE more efficiently. This leads to update rules that enable high-qualit image generation in as few as 20 steps rather than the hundreds required by DDPM. However, each step still requires a forward pass through the noise prediction network, making the sampling process computationally intensive and primary target for acceleration.
DiT-specific Processing Diffusion Transformers (DiT) adapt the transformer architecture for diffusion models, offering improved scalability compared to conventional UNet architectures. The processing pipeline for DiTs follows several key steps: first, the input image E IR CC is encoded into a nower-dimensions latent representation using a pre-trained variational autoencoder (VAE): 2 E(x) hxxxd, where h, w, and d represent the height, width, and channel dimensions of the latent space, respectively. The latent representation is then divided into non-overlapping patches and linearly projected to form a sequence of tokens z' = Patch(2) E R
N xxd' where = Aw p" is the number of patches with patch size p p. and d' is the embedding dimension of the transformer. Additionally, timestep embeddings and class or text condition embeddings are incorporated into the model to condition the generation process. Finally, the DiT model processes these tokens through a series of transformer blocks, each typically containing self-attention and cross cross-attente attention (or joint attention as in FL.UX.1-dew and feedforward network components.
16

---

Published as a conference paper at ICLR 2026
Table 5: Computation breakdown of a single transformer block forward pass at 256 x 256 resolution. We report GMACs and each component's 's share of total block computation for PixArt-a. PixArt-E, and FLUX.1-dew Components marked as cache-enabled are those selected for caching in ECAD, as they dominate the computational cost. Components not selected are omitted for efficiency, due to any fundamental limitation in their cacheability.
Model
Component
Cache-Enabled
GMACs % of Block Total
Feedforward
Yes
5.440
53.6 %
Self-Attention
Yes
2.720
26.8 %
PixArt-a
Cross-Attention
Yes
2.000
19.7 %
and PixArt-3
Ada Layer Norm Single
No
0.000
0.0 %
Total: PixArt Transformer Block
10.150
100 %
Feedforward (Context)
Yes
77.310
44,39 %
Joint Attention (Mutli-stream)
Yes
57.980
33.29 %
Feedforward (Regular)
Yes
38.650
22.19 %
Ada Layer Norm Zero
No
0.226
0.14 %
Layer Norm
No
0.000
0.00 %
Total: Flux Transformer Block, Full
174.170
100
FLUX.I-dew
Linear (MLP Input Projection)
Yes
72.480
41.66 %
Lincar (MLP Output Projection)
Yes
57.980
33.32 %
Joint Attention (Single-streand Ada Layer Norm Zero Single
Yes
43.490
24.99 %
No
0.057
0.03 %
GELU
No
0.000
0.00 %
Total: Flux Transformer Block, Single
174.000
100 %
Table Comparison ECAD performance using Image Reward (IR) versus weighted sum of CLIP Score and CLIP Image Quality Assessment (IQA) quality metric. The first 4 rows duplicated from Table I while the final row displays results after running ECAD for 150 generations, where optimize for TMACs and weighted combination of CLIP Score (30%) and CLIP IQA. For CLIP IQA we specifically use Good (30%), Clean (20%). and Sharpness (20%) scores. CLIP Score encourages prompt-alignment, while CLIP IQA ensures the generated images of high quality.
Method
ms /iggl (spcodupt)
TMACs
MJHQ FID.,
COCO
Calibration IR
PartiPrompts IR
Prt-rt- Baseline
164.74 (1.00))
5.71
9.75
24,84
0.90
0.97
FORA N 2
100.57 (1.65x)
2.87
10.33
24,80
0,83
0.91
ToCa R 60%
90.71 (1.8kx)
3.17
12.01
22.05
0.71
0.76
Ours (IR)
84.09 (1.97x)
2.13
8.02
20.58
0,96
0.99
Ours (CLIP)
97.65 (1.68x)
2.00
9.86
23.86
0.80
0.82
A.2 CACHEABLE COMPONENT SELECTION
To enable ECAD off-the-shed model, must first select which components cacheable. Any computation whose output can be stored at one step and reused at another-while introducing only minimal, acceptable inaccuracy-can be considered for caching. The number of such components determines the value C' in the binary caching tensor S E (0, BxC introduced in Section 3 Since the search space grows linearly with C, careful selection is essential ensure efficient and effective caching.
Note that the tensor notation simplified for clarity. In cases where the model uses k different types of DiT blocks, each with a different number of cacheable components, the caching tensor would instead take the form (0, 1) Nx. NX B,)
Table enumerates the computational complexity of each DiT block's 's forward pass. We enable caching for the three most computationally expensive components per block, they collectively dominate the total cost. Computations outside the DiT block's forward pass are not currently considered they contribute less than 1% of the total compute.
17

---

Published as a conference paper at ICLR 2026
A.3 QUALITY METRIC SELECTION
We select Image Reward it strong indicator of human preference and fast (Xu et al. 2023a). However, the ECAD framework supports a Bring-Your-ONe Your-Own-Reward paradigm, since image evaluation done offline after each generation with image-prompt pairs. This makes human scoring easier than other online methods.
We include an ablation in Table/uutilizee a weighted combination of CLIP Score and CLIP Image Quality Assessment (Wang al.) 2023), aka IQA, score demonstrate the feasibility of alternative rewards. Since CLIP Score and CLIP IQA can be computed using the same CLIP image features, and can precompute the text features for the calibration prompts offline, still only need to perform a single ViT-L. forward pass on the image to compute the metric. Thus, the cost is essentially the same Image Reward. However, Image Reward is generally superior metric CLIP Score and CLIP IQA to judge image quality, so it is unsurprising that the results using it outperform CLIP variants.
Still, ECAD's robustness allows it to achieve good results, even with other metrics, which is favorable property for application in more niche use cases. For example, one could utilize a human preference for video model, such as VisionReward (Xu al., 2024). for text-to-video generation. Note that any number of metrics can be ensembled to dampen noisy reward signals (such as combination of Image Reward, CLIP Score, and CLIP IQA).
Generate 100 prompts for benchmarking image generation models (such as PixArt Alpha, Stable Diffusion, etc.). The prompts should be diverse and cover wide range of styles, including photorealism, painting, anime, pixel art, and more. Each prompt should be crafted to evaluate aspects like aesthetics, compositional accuracy, text rendering, and subject diversity, in order to comprehensively test model quality.
Figure 8: ChatGPT prompt used to generate a set of 100 diverse prompts.
"Oil painting of rolling hills at sunrise, vibrant sky, wildflowers in foreground"
"Impressionist painting of snowy mountain pass at twilight, soft pastels"
Watercolor painting of a misty forest in autummn golden leaves, tranquil stream" "
CClsssical landscape painting of medieval village by river, ornate details"
"Surrealist painting of a desert landscape with floating rocks and melting clocks" "Abstract painting coastal landscape, bold shapes, bright primary colors"
Figure 9: Sample of painting-style landscape prompts.
Generate 100 prompts for benchmarking image generation models (such as PixArt Alpha, Stable Diffusion, etc.). The prompts should be brief not granular), with no more than 5 words per prompt.
Figure 10: ChatGPT prompt used to generate the set of 100 short (5-word) prompts.
Generate 10 prompts for benchmarking image generation models (such as PixArt Alpha, Stable Diffusion, etc.). The prompts should be diverse and cover a wide range of styles, including photorealism, painting, anime, pixel art, and more. Each prompt should be crafted to evaluate aspects like aesthetics, compositional accuracy, text rendering, and subject diversity, in order comprehensively test model quality.
Figure 11: ChatGPT prompt used to generate the compact set of 10 diverse prompts.
18

---

Published as a conference paper at ICLR 2026
A.4 CALIBRATION PROMPT SELECTION
To demonstrate the ease of assembling calibration prompt set and to isolate the factors that influence schedule quality, such as prompt source, domain specificity, and granularity, we evaluate ECAD across five distinct calibration strategies.
Source and Curation: We first compare the baseline Image Reward Benchmark (100 prompts) against two alternatives: DrawBench200 (Saharia al., 2022b). human-curated set of 200 prompts with frequent repetitions, and a GPT-Generated set of 100 diverse prompts created via ChatGPT (see Figure (). As shown in Table 7 and Figure 20] the schedule calibrated on the ChatGPTTgeenerai achieves nearly identical performance to the baseline. Interestingly, the Image Reward-calibrand schedule demonstrates superior generalization
set
DrawBench220 compared the reverse scenario, suggesting that a smaller, more diverse set (Image Reward) provides more robust foundation than larger, repetitive one (DrawBench).
Domain Specificity: To test if the calibration domain restricts generalizability, we generate a set of 100 prompts exclusively describing Painted Landscapes (see Figure Despite this narrow semantic focus, the resulting schedule maintains competitive performance on general-purpon purpose benchmarks (COCO and MJHQ), performing similarly to the baseline. This indicates that ECAD relies less on semantic content matching and more on the complexity of the generation task itself.
Finally, we probe the limits of calibration data efficiency. We construct 5- BoWrd Prompts set (100 coarse, short prompts; Figure TOp and minimal 10 Prompts set (see Figure II Table 8 reveals that prompt granularity is not a bottleneck; the schedule learned from 5-word prompts outperforms prior state-of-the-art methods with only slight reduction in speedup. However, reducing the number of prompts to 10 causes noticeable degradation MJHQ FID) (increased 10.02).
Quantity and Granularity:
These results suggest that the quantity of prompts is the primary driver of schedule robustness, whereas the specific source, length, or semantic domain of the prompts is secondary. As such, gathering sufficiently large (approx. 100) set of calibration prompts for ECAD surprisingly simple and flexible.
Conclusion:
Table 7: Calibration prompt set source ablation. Comparison of ECAD performance when calibrated on human-curated prompt sets Image Reward Benchmark and DrawBench200 versus ChatGPT-generated set. Metrics include Image Reward (IR) on each prompt set, MJHQ-30K FID, CLIP score, and latency. Each result reflects the highest-TMMAs schedule from the Pareto frontier after 100 generations. IRB, DB200, GPT-Gen, and PP refer the Image Reward Benchmark, DrawBench200,, GPT Generated, and PartiPrompts prompt sets, respectively.
Caliborion Prompt
calib, prompts
imgs per promyt
img. (spoolupp))
GPT--en IRT
Image Benchmark
(1.65))
1.21
Drwe Reach300
200
99.53 (1.67)
0.79
1.19
GPT Generated
100
10
104.66 533
0.9
0.79
1.24
8.05
32.85
Table 8: Calibration prompt diversity and size ablation. We compare the baseline (Image Reward Benchmark) against domain-specifie (Painted Landscapes), coarse (5-Word), and minimal (10 Prompts) calibration sets. Metrics include Image Reward (IR), MJHQ, and COCO. Each result reflects the highest-TMACss schedule from the Pareto frontier after 100 generations, except for "5 Word Prompts (Faster)", which is selected to provide an additional reference point for high-speed performance.
Calibration Set
ringg (spexdupt)
Calib, Sei IR!
PP IR?
MIHQ FID)
MJHQ(CIIP
COCO HID)
COCO
ImageReward Benchmark
100.68 (1.65x)
Painted Landscapes
95.11 (1.74x)
1,20
0.97
8.55
32.85
20.82
31.58
10 Prompts
97.88 (1.69%)
1.31
0.94
10.02
32.84
25.27
31.51
Word Prompts (Hligesst TMACs)
109.18 (1.52x)
1.01
0.98
7.42
32.82
20.05
31.46
Prompts (Faster)
95.27
19

---

Published as a conference paper at ICLR 2026
A.5 HYPERPARAMETER ABLATIONS
To better characterize the behavior of ECAD, we conduct two ablations on two different sets of hyperparameters. The first is over the hyperparameters that are agnostic to the genetic algorithm used - poopulaton size, the number of images generated per prompt, and the number of prompts. The second is over the NSGA-II hyperparameters, but should be noted that other genetic algorithms can be employed.
The former ablation is shown in Figure and include plots of the evolution over generations for each configuration. Figure 16 illustrates the impact of reducing the population size. This setting results in slightly noisier frontiers and slight performance degradation across all metrics: the MJHQ- 30K FID worsens slightly and latency increases by 22 ms over the baseline-the largest increase among all ablations. Figure 17 examines effect of reducing the number of images per prompt from to 3, while keeping 100 prompts and population of 72. This configuration achieves the fastest latency at 100,30 ms, the highest calibration Image Reward of 0.96, and the smallest increase in MJHQ-30K FID. In Figure 19, we reduce the number of prompts from 100 to 33 while maintaining 10 images per prompt. This setup exhibits the cleanest convergence behavior but significantly underperforms calibration Image Reward and its final Pareto frontier is dominated by other settings. However, its PartiPrompts score remains competitive and it produces the best FID, suggesting the subset of prompts challenging enough for some generalization. Detailed results for the highestTTMACS schedule after 100 generations under each hyperparameter setting are shown in Table 10
The latter ablation, with results in Table modifies one each of the following hyperparameters: the number of crossover points, mutation probability, and whether direct copies are allowed. Refer to Appendix A.7 for the purpose each of these. The results show disallowing direct copies of parents improves inference speed but significantly worsens FID (8.60 - 9.64), as strong schedules are more frequently 'churned' with lower quality ones. Reducing the mutation rate 1% has the greatest inference speedup, as it reduces exploration and increases exploitation, but results in poor quality, Conversely, reducing crossover to point and increasing mutation rate to 15% both slow convergence. The high mutation rate promotes exploration and seems prevent the high-FID local-minima 1% mutation rate. Metrics across most configurations remain relatively stable, meaning a set of good-enough standard hyperparameters for the genetic algorithm is sufficient for ECAD.
Table 9: Ablation comparing the effects of hy perparaaeess NSGA-II Each modifies exactly one hyperparameter, examining effects on computational cost (TMACs), latency, image quality metrics (Calibration Image Reward (IR), PartiPrompts IR), and MJHQ FID. All experiments use the highest-TMAC schedule after 100 generations, generating 3 images per prompt, with 100 prompts from the Image Reward prompt set, and with population size of 72. Crossover probability (P(Cross)) is the probability the parent's DNA is not directly copied to the offspring. Point crossover refers to the number of splices made connect the parent's DNA, and the P(Mut) the probability that an offspring will be mutated.
Experiment Condition
TMAC's
ms ms/imgl /m// (speedup1)
PartiPrumpts
FID.
Baseline: P(Cross) = 0.9, 4 Point Cross, P(Mut) = 0 0.06)
2.89
100. 30 (65x)
0.96
0.99
8:60
No Direct Capies (eross 1.0)
2.46
94,02 (1.76x)
0.97
0.99
9.61
Point Crossover
(1.44k)
6 Point Crosover
2.
93.52 (1.77k)
0.98
1.01
8.57
P(Mutation) 0.01
2.35
90.40 (1.83)
0.97
0.98
9.11
P(Mutation) 0.16
3.97
127.83 (1.30x)
0.96
1,00
Table 10: Genetic hyperparameter ablation. Performance of ECAD when varying population size, number of images per prompt, and number of calibration prompts. We report latency. Image Reward on calibration and unseen PartiPrompts, and MJHQ-30K FID. Each result corresponds to the highest-TMACC schedule lying on the Pareto frontier after 100 generations.
Population Size
# imgs per prompt
# of calibration prompts
ms img. (spoodupt)
Calibration IR
PartiPrompts IR?
FID]
10
100.68 (1.66 (1.65%)
0.94
1.00
8.18
24
10
100
122.96 (1.35x)
0.93
1.00
8.92
72
3
100
100.30 (1.65)
0.96
0.99
8.60
10
110.44 (1.50x)
0.85
0.99
7.52
20

---

Published as a conference paper at ICLR 2026
A.6 NUMBER OF INFERENCE STEPS ABLATION
We examine how the number of inference steps affects the performance of ECAD-generated schedules. Since ECAD produces schedules optimized for particular step count, this ablation evaluates their robustness when applied at a different inference step setting.
We first learn ECAD schedules using 10 steps on DPM-Solver++ for 100 generations with standard hyperparameters, then upscale the binary mask of the schedule with the highest TMACs to 20 steps by duplicating each step. Formally, given a 10-step schedule S10. we define step of the corresponding 20-step schedule S20 as
20 =
i= 0, 0.......,19
We similarly learn schedules at 20 steps and downscale to 10 steps by caching component step of Sin only if it is cached in both corresponding steps 2i and 2i + 1 of S20. Recalling that 0 indicates caching S, for each block and component e we define
Siol i, b, S2ol 2i, b,
V S 2i 1,b,c], i= 0,
Table I1 presents evaluation results. Applying a 10-step ECAD schedule at 20 inference steps yields improvements in both Image Reward and FID compared the unaccelerated baseline. Conversely, using a conservative downscaling strategy reduces the overall speedup but still maintains performance gains over the baseline. Note that while the schedules here are evaluated as-is, they could also serve as starting points for further refinement or adaptation.
Table 11: Inference step ablation. We optimize ECAD PixArt-a for 100 generations with standard hyperparameters, for and 20 inference steps on OPM-Solver+++ We then evaluate the highest-TMAAss
TMACs schedule from the Pareto frontier for both 10 and 20 steps, up- and down-scaling the learned caching schedules appropriately. Reported metrics include latency, Image Reward performance the Image Reward prompt set (Calib. IR) and the unseen PartiPrompts set (PP IR), as well as both FID and CLIP scores on MJHQ-30K and MS-COCC2017-300K
Acceleration
Train Steps
Eval
mss / imgl (specdupf)
Calib. I8
PP IK?
MJHQ FID.
MJHQ CLIP
COCO FID)
COCO CLIP
(15.74(1000)
9.75
32.77
ROAD
(1.6%)
1.00
8.18
32,88
21.40
BCAD
20
121.44 (1.7X)
0.94
1.01
8.80
32.74
21.67
31.33
None
10
10
89.85 (1 (000
0.81
0.90
10.83
32.77
25.82
31.42
BCAD
66.89 (1.35)
0.93
0.97
8,36
32.62
22.02
$1.40
(1.19)
32,87
A.7 GENETIC ALGORITHM EVOLUTIONARR STEP IN DETAIL.
The evolutionary step occurs once the end of each generation to create offspring for the subsequent generation. This step takes negligible time (< minute) and does not require a GPU. Formally, this step can be understood follows:
Given a population Pg of size n at generation ECAD employs the NSGA-II algorithm (Blank & Deb 2020 Deb & Jain 2013) to produce the next generation Py+1 through the following steps:
1. Selection and Offspring Generation: An offspring population Qg. also of size is generated from Pg via binary tournament selection by repeating the following process until Q, is filled. Two pairs of candidates are randomly sampled from Pg- Within each pair, a tournament is conducted by first comparing candidates by Pareto rank, then breaking ties using crowding distance. The winners from each pair undergo erossover, followed by mutation, to generate offspring.
2. Crossover: With probability of 0.9, we apply 4-point crossover to the binary caching tensors of the parent schedules. Four distinct crossover points are randomly selected along the flattened tensor, and two offspring are created by alternating segments between parents. With probability 0. the offspring are direct copies of their respective parents.
3. Mutation: Each candidate in Q, undergoes bit-flip mutation with probability of 0.05. If selected, each bit in the binary tensor S E (0, 1) NxBxC is independently flipped with
21

---

Published as a conference paper at ICLR 2026
probability NxBxC- Note that after this step, force all components in all blocks to be recomputed on the first step, since there is no 'cached' value to be reused.
4. Non-Dominated Sorting: The union Pg Qg (size 2n) is sorted into Pareto fronts Fo. Fi,...,FF based on dominance. For each candidate c, we compute (R), the number of candidates that dominate in some set of candidates R. Fronts defined iteratively as:
Fn = E Pg UQ, Doma UQg) =0]
Fi e (P, (Q,) \ Fo Dome((P, ((p, UQ₂) \ (Fo) 0)
i-1
i-1 U F2) = j=0
F; := (P, (P,
\ U Dome (P, 5=0
Note that candidates in front F; said be of Pareto rank i; lower-rank candidates 'fitter' solutions. Each front F; contains candidates not dominated by any candidate in fronts of higher rank.
5. Population Selection:
The next generation Po+1 filled by sequentially adding complete fronts Fo, Fi,... until the population size is reached. If front FR cannot be fully accommodated, it is sorted by crowding distance. The most diverse candidates- those with the fewest close neighbors-are selected to fill the remaining slots, always including the extrema to preserve frontier diversity.
A.8 POPULATION INITIALIZATION
We initialize the first generation of schedules for PixArt-a using a diverse set of heuristic strategies informed by prior work. Each heuristic varies caching behavior based on step/block selection patterns:
Cross-Attention Only: Cache cross-attention at evenly spaced steps. each selected step, cache the cross-attention of DIT blocks, evenly spaced across the total 28 blocks.
Self-Attention Only: Identical to the above, but cache only self-attention.
Feedforward Only: Identical to the above, but cache only feedforward layers.
Cross- & Self-Attention, All Blocks: Cache both cross- and self-attention for all blocks every nth step.
FORA-inspired:
Following (Selvaraju et al., 2024), cache cross-attention, self-attention, and feedforward layers for all blocks at every vith step.
Following the gating mechanism from (Liu et al. 2024b), set gate step and interval k. After the first warm-up steps, compute self-attention every & steps, caching and reusing otherwise. After step m, self-attention is computed every step, while cross-attention is not recomputed and reuses the cached output from step m. Unlike TGATE, which averages the cross attention activation on text and null-text embeddings, we cache only the result from the embedding.
TGATE-inspired:
The resulting Pareto frontiers for these heuristics are shown Figure 12 From the complete set of generated schedules, randomly select 72 to initialize ECAD's first generation for PixArt-a.
For PixArt-E, as summarized in Section we initialize with 72 schedules randomly sampled from the Pareto frontier of PixArt-a after 200 generations of ECAD optimization.
For FLUX. I-dev, we start with a FORA-inspired schedule, apply few rounds of mutation and crossover, and randomly select 24 candidates initialize ECAD.
When initializing populations. it is suggested to include at least one schedule that nearly identical to the uncached baseline and one that is nearly fully-cu fulle-cached. The former will allow ECAD to find schedules with the highest image quality possible, and the latter will promote faster convergence to efficient schedules.
22

---

Published as a conference paper at ICLR 2026
To better understand this, analyze two random initialization strategies. We find that naive random sampling of binary masks (True Random) suboptimal; due to the Central Limit Theorem, candidate schedules cluster around the mean sparsity, failing to explore the extremes of the Pareto frontier (Figure 13).
To address this, we propose Uniform Random Initialization, which samples uniformly across the computational cost spectrum [0, Cmaz] We first sample target budget C (O, Cmaz). We then determine valid integer counts ke for each component c E {FF, SA, CA} with GMAC cost we by solving the linear Diophantine equation Σ Weke s C This solved efficiently by iterating over the highest-weighted component (kpF) and solving the remaining two-variable equation using the Extended Euclidean Algorithm:
wSAKSA + wCAKea C UFFFFF
From the solution set, we sample a tuple (kpr, kSA, kca) and distribute the active flags uniformly across the N x B spatiotemporal positions.
As detailed in Table T2, while Heuristic initialization yields the best performance (1.65x speedup, 8.18 MJHQ FID). Uniform Random significantly outperforms True Random (1.60x 1.28x speedup) and prevents the population diversity collapse observed in the naive approach.
1.0
0.8
Image Reward
0.6
0.4
Schedule Type
Caching Cross-Attention Evenly Across Blocks
Caching Self-Attention Evenly Across Blocks
0.2
Caching Feed Forward Evenly Across Blocks
Caching Self-, Cross-Attention
Blocks Every N Steps
FORA
TGATE Inspired
Unaccelerated
0.0
3
4
Total TMACS
Figure 12: Pareto frontiers Image Reward vs. computational cost for the handcrafted schedules described in Section A.8 evaluated on the Image Reward Benchmark. Notably, caching a single component (e.g., cross-attention or feedforward) offers slight gains over baseline. Among all heuristics, FORA achieves the best trade-off, with slightly lower quality but superior efficiency.
23

---

Published as a conference paper at ICLR 2026
Inttalization
Rendom Intiiitcation Over Compate Complexity
Infintaction Over Scheoale Texaor
TMAGS
Test TMACS
Figure 13: Schedule initialization Ablation. We initialize the first 72 candidates of generation I with three methods: heuristics as described in Section A.8] random sample over the binary caching tensors, and sampling such that we have uniform spread of compute complexity (TMACs). Hcuristics converge the quickest, and achieve higher Image Reward performance. Uniform sampling over TMACs performs well, while randomly sampling caching schedule results in heavy grouping which prevents ECAD from optimizing effectively.
Table 12: Initialization strategies ablation. Comparison of ECAD performance using different initialization methods. Heuristic initialization yields the highest speedup and fidelity metrics compared to random initialization baselines. All evaluations are conducted on the schedule with the highest TMACs after 100 generations on default settings, except for 'Uniform Random (Closest)', which is selected to have the closest speedup to the Heuristic schedule for more fair comparison.
Initialization
ms img. (specdupp)
ImageReward IR
PPIR1
MJHQ FID.
MLIQ
MJHQ CLIP
COCO FIDI
COCO CLIP1
Heuristic
100.68
0.94
Uniform Random
150.52 (1.10x)
0.92
0.95
9.37
32.71
22.35
31.32
Uniform Ramdom (Closest)
103. 79 (1 (.660s)
0.91
0.93
9.29
32,70
23,40
31.34
True Random
129.88 (1.28k)
0.64
0.61
13.77
31.74
24.14
30.68
Table 13: FLUX.I-dev performance on GenEval and DPG Bench. We compare our and other methods from Table on the GenEval and DPG Bench benchmarks using FLUX.1-dev (20 steps, 256 x 256). Our method does not impact GenEval Overall score at 2.58 x acceleration while other methods result in 2% to 22% quality decrease for lower acceleration. Our method achieves the highest speedups while even slightly improving the DPG Bench score, whereas other aggressive caching strategies degrade performance.
Setting
Latency
GenEval Overall
DPG Bench
Caching
Setting
TMACs
ms/ / img. (speedupt)
Score
% Decrease
Score
% Decrease
None
198.69
2620.09 (1.00x)
0.5842
22.7058
ToCa
4,
90%
42.96
1576.97 (1.66x)"
0,5517
5.56%
22.8215
-0.51%
DiCache
62.23
1161.86 (2.26x)
0.5699
2.45%
22. 22.6946
0.05%
TaylorSeer
5.0
59.88
1028.66 (2.55x)*
0.4531
22.44%
22.4695
1.04%
TaylorSeer
6,0
49.97*
865.97 (3.03x)"
0.3399
41.81%
21.6869
4.49%
Ours
Fast
63.02
1016.59 (2.58x)
0,5892
-0.86%
22.8364
-0.58 %
Ours
Fastest
43.60
778.17 (3.37x)
0.5258
10.00%
23.5098
-3.54%
"Refer to Appendix A.11 for a detailed explanation of MAC and latency calculations.
A.9 ADDITIONAL FLUX.1-DEL RESULTS
To further demonstrate the robustness our method, we include supplementary quantitative results on the GenEval (Ghosh et al. 2023) and DPG Bench (Hu et al. 2024) in Table 13 Both benchmarks use the official prompt sets provided by each respective method, with 4 images generated per prompt. Our method's "Fast" schedule from [ 2.58x acceleration with slightly higher performance on each metric as compared with the uncached baseline, while other methods result in quality degradation. Our "Fastest" schedule trades only some image quality to achieve 3.37x acceleration.
24

---

Published as a conference paper at ICLR 2026
A.10 OPTIMIZATION COST AND LIMITATIONS OF ECAD
ECAD introduces an offline optimization phase that searches over binary caching schedules, This search is a one-time cost per model family: once schedule (or of schedules) is learned, can be reused for that architecture and shared with downstream users, who simply choose an operating point on the quaeity-latency frontier.
In our PixArt configurations, the full "fast/faster/fastess" frontier requires ~ 700 NVIDIA A6000 GPU-hours with a research-oriented implementation. However, competitive operating points can be obtained much more cheaply: the "fast" schedule with SOTA performance is discovered in only 358 generations (just 470 GPU-hours). 100 generations with default settings costs 145 GPU-hours, yielding schedule with 16% reduction in MJHQ FID over baseline. But with minor engineering changes, we achieve a schedule with an identical 1.65 speedup, 11.8% MJHQ FID reduction over baseline, in only 44 GPU-hours. As such, these figures should be viewed as upper bounds given an under-optimized research framework.
The focus of this work is the algorithmic framework: formulating diffusion caching as multi- objective optimization problem and demonstrating that a simple genetic algorithm can discover strong Pareto fronts across models and resolutions. System-level engineering e.g.. optimized kernels, greater hardware utilization, and torch. compile integration is orthogonal to ECAD and can further reduce wall-clock search time without changing the method.
key limitation is that ECAD adds an up-front compute cost, Nevertheless, unlike saining-based accelerations, ECAD does not require gradients or weight updates, has lower VRAM requirements, and can be run asynchronously across heterogencous, lower-end GPUs. For large-scale services employing ECAD, the one-time optimization cost is quickly amortized by per-sample latency savings.
Table 14: Parameters used for latency evaluation. W is the number of warm-up batches discarded, N is the number of batches used to compute the average latency, and B is the largest batch size that fits in memory on a single NVIDIA A6000 GPU. All values are empirically chosen to ensure stable, consistent measurements and to match the quality benchmarking batch size.
Model Name
Resolution
Warm-up (W) Measured (N) Batch Size (B)
PixArt-a
256 x 256
100
PixArt-E
256 x 256
100
FLUX.1 1-X.1-dev
256 x 256
18
FLUX.1-dev
1024 1024
3
A.11 MAC AND LATENCY COMPUTATIONS
Latency Setup: Latency measurements are conducted on single NVIDIA A6000 GPU for all models. For each model, we discard the first W warm-up batches and compute the mean latency over the subsequent N measured batches, using prompts from the Image Reward Benchmark. The reported per-image latency is obtained by dividing the average batch latency by the batch size B, except in the case of ToCa (see section below). Detailed configuration parameters provided in Table 14
The publicly available implementation for some prior works, denoted by in tables, differed substantially from the infrastructure employed in our framework. While all methods use the same GPU (NVIDIA A6000) and identical warm-up and batch settings, ToCa and DuCa, for example, consistently produce higher latency measurements. To enable fair comparison, we normalize reported latencies by computing the relative speedup of each setting over its own baseline, then applying this speedup to our unaccelerated baseline latency:
Latency Results:
Latency Other
cached
Normalized Latency Other
X Latency Ours
aaccellrrted
Latency Other
umaccelerated
This procedure ensures that the reported values reflect performance improvements relative to each method's own baseline, enabling direct comparison across implementations. See Table 15 for details.
25

---

Published as a conference paper at ICLR 2026
operation (MAC) counts for ToCa derived using the analytical formulations provided the original work (Zou et al. 2025). specifically Section A.4. The relevant expressions are:
ToCa MAC Results:
5 N MACSSA R 4N 2 2N
5 2
MACSCA & 2N + N2) 2N N
MACSFFN 8N, 8N, 12NI
Here, N1 and N2 denote the number of image and text tokens respectively. D is the hidden state dimensionality, DEEN refers to the dimensionality within the feedforward network, and H is the number of attention heads. Results from DuCa (Zou et al., 2024), concurrent method that builds upon ToCa, confirm that these approximations closely match empirical MAC counts.
We compute MACs and FLOPs for all DiT models with the cal f flops from Ye (2023). However, when matching our configuration to that reported in Liu al. (2025a), we find our computed FLOPs to always be different by factor of exactly 1. x due to differences in implementation. As such, we report our computed values as is for consistency with other models, and note this scaling factor here.
TaylorSeer MAC Results:
Table 15: Latency normalization details across different models and resolutions. "True ms/ img" refers to direct latency measured from the official implementation. "Speedup" is computed relative to each method's 's unaccelerated bascline, and "Normalized ms / img" applies that speedup to our unaccelerated latency for fair comparison. Note we reduced batch size for TaylorSeer due to its high VRAM requirements.
Model
Resolution
Inplementaation
Caching
Setting
True ms / ling.
Speedup"
Normalized ms img.
Ours
None
165. 165.736 736
ToCa
None
948.688
1.000x
165.
ToCa
ToCa
N = 3,R 60%
$19. 258
1.827x
90.715
ToCa
ToCa
N
90%
403.989
2.348x
70.577
DuCa
None
981.263
1.000x
165.736
DuCa
DuCa
3.R 60%
429, 405
2.285%
72.527
DuCa
DuCa
N 3,R 90%
379.411
2.586x
64.083
Ours
None
167.624
ToCa
925.024
1.000x
PixAnt-:
256
ToCa
ToCa
N 3, 60%
520.286
1.778x
94.281
ToCa
ToCa
3,R 90%
403.038
2.295x
73.035
Ours
None
2620.095
Tooa
None
3385.153
1.000x
2620.095
ToCa
N 4,R 90%
2037.433
1.661x
1576.965
ToCa
ToCa
N = 5,R 90%
1935.554
1.747x
1499.949
256
TaylorSeer
batch
TaylorSeer
TaylorSeer
N 5,0
1043.457
2.547x
1028.66) 16.6661
TaylorSeer
None
batch
2630.581
1.000x
TaylorSeer
TaylorSeer
N 66
869.438
3.026x
865.972
Ours
None
18297.603
FLUX.1-dev
ToCa
34109.719
1.000x
18297.603
ToCa
ToCa
N = =
13832.0K 082
2.466x.
7419.995
1024x1024
TaylorSeer
hatch
18947.390
TaylorSeer
ThylorSeer
5=500=
7452.669
2.542x
7197.085
TaylorSeer
TaylorSeer
==
6219.621
3.046x
6006.323
A.12 COMPARISON TO CONCURRENT WORKS
Although our method thoroughly evaluated against established baselines, comparison with concur- rent works is limited. As such, restrict our comparisons methods with publicly available code at the time of submission. We note several relevant differences nonetheless. First, concurrent methods such SpeCa (Liu et al. 2025c) and ClusCa (Zheng et al.) 2025) report high acceleration figures partly attributable to a 50-step inference setting in addition to their strong method. Our experiments 20 steps, which is already approximately 2.5x faster than 50-step inference with minimal quality
26

---

Published as a conference paper at ICLR 2026
loss. Second, while ClusCa reduces memory overhead relative to TaylorSeer Liu et al. still incurs roughly 10% additional cost (Zheng et al., 2025), which practice constrains batch size. In contrast, ECAD introduces memory overhead. Finally, because both SpeCa and ClusCa rely on manually tuned hyperparameters (e.g., propagation ratio, cluster size, cache interval), ECAD's optimization framework could be utilized to automatically discover such configurations.
2025ap,
A.13 ADDITIONAL ECAD OPTIMIZATION PLOTS
Figure 14 iillustrates the progression of ECAD optimization for PixArt-Ei and FLUX. at 256x256 resolution. PixArt-E converges rapidly, likely due to its initialization from pre-optimized schedules learned on PixArt-a. FLUX. I I-dev converges to steeper Pareto frontier, with its resulting schedules substantially outperforming the unaccelerated baseline on the Image Reward benchmark. We hypoth- esize that this steep convergence is facilitated by an initial population with a relatively high mean acceleration. See Section A.8 for additional details population initialization.
Additionally, we include the Pareto frontier of PixArt-E. as measured by Image Reward on the unseen PartiPrompts set vs. image generation latency in Figure [5 Our method achieves Pareto dominance over FORA but does not reach the unaccelerated baseline's 's level of performance.
0.9
50
Generation Number
Generation Number
Image Reward
0.6
-15
3.4
0.3
0.2
Schedule Type
10
0.2
Schedule Type
50
0.1
Forttier
Trantler Acrom Coneration
Acroas Generatins
Unaccolorated 5.72 TMACs
Unacos wrated 298.69 TMACs
3.0
2.2
140
160
Total TMACS
Total TMACS
Figure 14: Progress of ECAD optimization measured by Image Reward and TMACs. Left: PixArt-E optimized for 50 generations, initialized using 200 generations of PixArt-a optimization. Right: FLUX.1-dev optimized for 250 generations, initialized using basic heuristics.
A. VISUALIZING ECAD SCHEDULES
To better understand how ECAD optimizes caching schedules under different constraints and settings, visualize selected schedules using heatmaps. Each heatmap represents schedule, where red shades indicate cached components and gray shades indicate recomputed components. For Pix models, the component order left-to-right self-attention, cross-attention, and feedforward. FLUX. dev uses two types of DiT blocks. Block numbers O to 18 are 'full' FLUX DiT blocks, whose components are multi-stream joint-attention, feedforward, and feedforward context. Blocks to 56 are 'single' blocks with components single-stream joint-attention, linear MLP input projection, and linear MLP output projection. Figure 18 and Figure 24 show representative schedules for PixArt-a and PixArt-E used throughout the paper. Figure 22 compares FLUX. 1-dev'd 'slow" and 'fastest' schedules. Furthermore, Figure 23 visualizes how ECAD schedules evolve over time for PixArt-a, comparing the highest-TMAA TMACs candidate generations 50, 200, and 400. Finally, Figure 25 presents the highest-TMer TMACs schedules resulting from our genetic hyperparameter ablations, illustrating how variations in population size impact the structure of learned caching strategies.
27

---

Published as a conference paper at ICLR 2026
Image Reward
0.6
0.4
Schedule Type
Ours
PixArt-Z
FORA
Unaccelerated
Ours Fast
60
80
100
120
140
160
Latency (ms)
Figure 15: PartiPrompt Image Reward vs. latency for PixArt-E. Note that ToCa is not optimized for PixArt-E and its parameters are transferred from PixArt-a. Our method achieves Pareto dominance with significant margin, but does not reach baseline performance.
Block Number
10
Inference Step
Inference Step
Figure 18: ECAD schedules for PixArt-a from Table 1 "faster" (left) and "fastest" (right). Despite being separate schedules with no guarantee of relation, the "faster" schedule has near identical structure to "fast", with more caching along steps 6 and 16. Furthermore, it appears cross-attention matters less than self-attention and the feedforward network during steps 16 and 17 and can safely be cached.
A.15 FURTHER QUALITATIVE RESULTS
In addition to the PixArt-a 256x results shown in Figure 4 present further qualitative
comparisons using FLUX. -- at 256 (Figure 26) and 1024x 1024 (Figure 27). Notably, in prompts such as "I want supplement vitamin please help paint related food," method exhibits stronger prompt adherence than both the uncached baseline and ToCa. This behavior is likely influenced by ECAD's optimization for the Image Reward metric, which emphasizes semantic alignment with the prompt.
Full Prompts from Figure 4 from left right:
28

---

Published as a conference paper at ICLR 2026
"Three-quarter front view of a blue 1977 Porsche 911 coming around curve in mountain road and looking over a green valley on cloudy day."
"a portrait of old man"
"A section of the Great Wall in the mountains. detailed charcoal sketch."
"a still life painting of pair of shoes"
"a blue cow standing next a tree with red leaves and yellow fruit. the cow standing in a field with white flowers. impressionistic painting"
"the Parthenon"
Full Prompts from Figure 26 27 from top-to-bottom:
"Drone view of waves crashing against the rugged cliffs along Big Sur's Garay Point beach. The crashing blue waters create white-tipped waves, while the golden light of the setting sun illuminates the rocky shore."
"Bright scene, acrial view, ancient city, fantasy, gorgeous light, mirror reflection, high detail, wide angle lens."
"3d digital art of an adorable ghost, glowing within, holding a heart shaped pumpkin, Halloween, super cute, spooky haunted house background"
"8k uhd A man looks up at the starry sky, lonely and ethereal, Minimalism, Chaotic compo- sition Op Art"
"I want to supplement vitamin c, please help me paint related food."
"A deep forest clearing with mirrored pond reflecting a galaxy-filled night sky."
"A person standing on the desert, desert waves, gossip illustration, half red, half blue, abstract image of sand, clear style, trendy illustration, outdoor, top view, clear style, precision art, ultra high definition image"
Full Prompts from Figure 28 from top-to-bottom:
"Eiffel Tower was Made up of more than 2 million translucent straws to look like a cloud, with the bell tower at the top of the building, Michel installed huge foam-making machines in the forest to blow huge amounts of unpredictable wet clouds in the building's 's classic architecture."
"Mural Painted of Prince in Purple Rain on side of 5 story brick building next garden vacant lot the urban center district, rgh"
"Editorial photoshoot of a old woman, high fashion 2000s fashion Steampunk makeup, in the style of vray tracing, colorful impasto, uhd image, indonesian art, fine feather details with bright red and yellow and green and pink and orange colours, intricate patterns and details, dark cyan and amber makeup. Rich colourful plumes. Victorian style."
Full Prompts from Figure 29 from top-to-bottom:
handsome villain in his early 40s with very short bleach blonde hair and glowing red eyes wearing a blue armor and red cape. hyperrealistic, mythological, regal, 8k, medieval."
"logo, simplistic, art style, multiple parallel universes together, different ages and themes over an open book
"professional Food photography, BeerenProteinSmmoohie a glass decorated with a mint leaf, high quality, hyper, detailed, beautifully color, beautifully color graded, cinematic
"iphone wallpaper, conceptual art colorful design, splash of colors, racing car drifting, ultra fine detailed art
29

---

Published as a conference paper at ICLR 2026
Ovenll Frontier
Image Reward
Generation Frontier
Generaaiio Frontia
Total TMACs
Illustrative example of per-generation and overall Pareto frontiers in ECAD. Points represent candidate schedules, with lines interpolated between them for visualization. Half-colored points lie both the generational and overall frontiers. In this example, the frontier from generation G appears to exceed the overall frontier, highlighting interpolation 'artifacts' that can occur between discrete candidate solutions.
Figure 21:
A.16 CLARIFYING FRONTIER VISUALIZATIONS
Several frontier plots-such as Figures 16 17 and 1h-show both the Pareto frontier of individual generations (typically shown in color) and the overall frontier aggregated across all generations (typically in black). At first glance, it may seem that a generational frontier occasionally surpasses the overall frontier. This apparent contradiction arises from interpolation between discrete candidate schedules. illustrated in Figure 2T] the frontier from generation appears to extend beyond the overall frontier. However, the aggregated frontier integrates more finely sampled points, including high-pes perforeeformnn candidates from earlier generations (c.g., generation G-1), which are not always aligned with the interpolated curves of later generations. The overall frontier, therefore, forms tighter envelope of all known Pareto-optimal schedules, even if it may visually appear to exceeded due to interpolation artifacts.
A.17
LLM USAGE
We utilized LLMs to proofread, check grammar, and suggest revisions during the writing of this manuscript.
30

---

Published as a conference paper at ICLR 2026
0.8
Generation Number
Image Reward
0.4
Schedule Type
Pareto Frontier Per Generation
Frontier Generatiens
Unaccelerated
0.0
Total TMACs
Figure 16: ECAD optimization progress and final Pareto frontier using reduced population size of 24 (compared to the default 72), with 100 prompts and 10 images per prompt. The resulting frontiers are noisier and exhibit slower convergence.
.00
Generation
Image Reward
Schedule Type
Pareto Prontier Por Ganeration
Frontier Acress Generations
Unaccelerated
0.0
Total TMACs
Figure 17: ECAD optimization progress and final Pareto frontier using only 3 images per prompt (default is 10), with 100 prompts and a population size of 72. This configuration demonstrates stable convergence and achieves stronger overall performance.
31

---

Published as a conference paper at ICLR 2026
1.0
100
Generation Number
Image Reward
0.6
Schedule Type
20
Frontier Generation
Frontler Acress All Generations
Unaccelerated
0.0
Total TMACs
Figure 19: ECAD optimization progress and final Pareto frontier using only 33 prompts (a random subset of the default 100), with 10 images per prompt and population size 72. Although convergence relatively smooth, the final frontier is constrained by the reduced prompt diversity.
Evaluated on GPT Generated
1.2
1.0
Evaluated Benchmark
Image Reward
0.8
Evaluated DrawBench220
0.6
Calibration Set
IR Benchmark
DrawBench200
GPT Generated
Unaccelerated
0.0
1
3
5
Total TMACS
Figure 20: ECAD calibration prompt set ablation. We show performance change when using the human-curated BrawBeech200 prompts set the CharGPT-gennrated prompt set for calibration instead of the Image Reward set. Performance measured in Image Reward (IR) on all combinations of each prompt as used for calibration and evaluation.
32

---

Published as conference paper at ICLR 2026
Block Number
2
3
5
8
10
11
12
13
14
15
16
17
18
19
Block Number
Inference Step
Figure 22: ECAD schedules "slow" (top) and "fastest" (bottom) for FLUX. from Table
and Table respectively. Despite being almost 200 generations apart, both schedules share similar structures for the first 5 steps, particularly at step 2 for blocks 9 through 12.
33

---

Published as a conference paper at ICLR 2026
Block Number
10
21
23
24
25
26
2
5
10
12
13
15
18
Block Number
10
21
23
24
25
26
2
10
12
13
15
18
Block Number
23
24
25
26
27
9
11
12
14
17
19
Inference Step
Figure 23: Highest-TMACs schedules from generation (top), 200 (center), and 400 (bottom) during PixArt-a ECAD optimization. While steps between 8 and 15 remain somewhat similar in structure, early and late steps change more.
34

---

Published as a conference paper at ICLR 2026
Block Number
20
21
22
23
24
25
26
27
Inference Step
Figure 24: ECAD schedule for PixArt-Y "fast" from Table 1 Initial DiT blocks in steps 6, 9, and 12 are more important to recompute than the final blocks. Cross-attention has less of an impact than the other components in the final three steps, with it the only component cached in step 17.
Black Number
Block Number
10
12
13
14
15
Step
Step
Figure 25; Highest-TMAC's schedules after 100 generations for PixArt-a under different hyperparam- eter ablations: (top-left) reduced population size; (top-right) fewer images per prompt; (bottom-left) fewer prompts; (bottom-rrggh) baseline configuration. All configurations realize the cacheability of cross attention for steps where other components cannot safely be cached.
35

---

Published as a conference paper at ICLR 2026
Unaccelerated (1.00x)
ToCa (1.75x)
Ours (1.97x)
waves crashing aguinst the rugged cliff's along Big Sur's 's
Garay Point white-tipped waves ... golden light ... illuminates the rocky shore
Bright scene, aerial view, ancient city, fantasy, gorgeous light, mirror reflection, high detail, wide angle lens.
3d digital art of an adorable ghost, glowing within, holding a heart shaped pumpkin, Halloween, super cute, spooky haunted house background
man looks up the starry sky, lonely and ethereal, Minimalism, Chaotic composition...
want to supplement vitamin c, please help paint related food.
A deep forest clearing with mirrored pond reflecting galaxy-fille filled night sky.
Figure 26: FLUX..1-de 256x256 qualitative comparisons. Displayed left-to-right generations from the uncached baseline, ToCa (N' = 5, R. 90%: 1.75x speedup), and our "fast" ECAD schedule (Table [ 1.97x speedup). ECAD consistently yields sharper images with improved prompt adherence.
36

---

Published as a conference paper at ICLR 2026
ToCa (2.47x)
A person standing on the desert, desert waves half red, half blue, abstract image of sand ... top view, clear style, precision art
Ours (2.63x)
Figure 27: FLUX.I-dev 1024x1024 qualitative comparisons. Outputs, top-to-bottom, ToCa (N = 4, R 90%: 2.47x speedup), and our "fast" ECAD schedule (as shown in Table 4 2.63x speedup). Our method yields greater visual complexity with stronger prompt-alignment, despite higher acceleration.
37

---

Published as a conference paper at ICLR 2026
Ours (2.63x)
TaylorSeer (2.54x)
Ours (2.63x)
TaylurSeer (2.54x)
handsome villain in his early 40s with very short bleach blonde hair und glowing red eyes wearing blue armor and rod cape
professional Food photography, Beeren Preeisnh in gluss decorated with mint leaf' detailed, beautifully color graded
logo, simplistic multiple parallel universes together.... ... over open book
iphone wallpaper. conceptual splash of colors, racing drifting, ultra fine detailed...
Figure 28: FLUX.I-dey 1024x 1024 further qualitative comparisons. Outputs, left-to-right, are our "fast" ECAD schedule (as shown in Table 2.63x speedup), and TaylorSeer (N = 5, O = 2; 2.54x speedup). Prompts, from the MJHQ-30K set, and are shown without omission in Appendix A. TaylorSeer's method leads to visible patches solid backgrounds, lower resolution, and color distortion.
38

---

Published as a conference paper at ICLR 2026
Unaccclerated (1.00x)
DuCa (2.29x)
Ours (2.40x)
Eiffel Tower Made
of translucent
straws to look like cloud
foam-making machines... blow huge ... clouds in the building...
Mural Painted of Prince in Purple Rain on ... brick building next ... vacant lot
Editorial photoshoot of a old woman 2000s fashion
Steampunk makeup feather... ... with bright red and yellow and green and pink and orange colours dark cyan and amber makeup
Figure 29: PixArt-a 256x256 further qualitative comparisons. Outputs, left-to-right, are the unac- celerated baseline, DuCa (N 3, 60%; 2.29x speedup), and our "faster" ECAD schedule (as shown in Table I 2.40x speedup). Our method introduces fewer artifacts and distortions.
39


Reviews:
[{"review_id": "zA9fFKlbpL", "reviewer": "Reviewer_Mtyb", "summary": "This paper, “Evolutionary Caching to Accelerate Your Off-the-Shelf Diffusion Model (ECAD)”, addresses the problem of slow and computationally intensive inference in diffusion-based generative models, especially Diffusion Transformers (DiTs). Traditional caching methods reuse previously computed features during sequential inference steps to reduce redundant computation, but existing approaches rely heavily on rigid heuristics, hand-tuned hyperparameters, and limited trade-off flexibility. ECAD reframes feature caching as a multi-objective optimization problem over image quality and computational efficiency, solved via a genetic algorithm (specifically, NSGA-II). The algorithm evolves caching schedules—which specify what components (self-attention, cross-attention, feedforward outputs) to cache, and when—to form a Pareto frontier of quality–latency trade-offs. ECAD is model-agnostic, training-free, and learns these schedules using only a small set of “calibration prompts” and a quality metric (e.g., Image Reward). Experiments on three major diffusion models (PixArt-α, PixArt-Σ, FLUX-1.dev) show that ECAD provides higher-quality outputs at faster inference speeds compared to previous caching baselines such as FORA, ToCa, DuCa, and TaylorSeer. The method is also shown to generalize across resolutions (e.g., 256×256 to 1024×1024) and related model architectures without retraining.", "questions": "1. How computationally heavy is ECAD’s optimization stage in practice (e.g., total GPU-hours)? Is the cost justified by the inference savings for typical users?\n    \n2. What are the performance trade-offs if the number of calibration prompts is reduced further (e.g., to 10 or 20)? How stable is ECAD under smaller calibration sets?\n    \n3. Could ECAD be combined with other acceleration strategies (e.g., distillation or quantization)? Would the Pareto optimization remain consistent?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **Novel framing and methodological clarity.**  \n    The paper introduces a principled optimization perspective (Pareto frontier with NSGA-II) that replaces heuristic-based caching rules. This reframing is conceptually elegant and well-motivated.\n    \n2. **Training-free and model-agnostic.**  \n    ECAD operates without any weight updates or retraining, making it practical for various diffusion models—especially for users without access to large compute resources.\n    \n3. **Comprehensive experimental evaluation.**  \n    Evaluations cover multiple high-profile models (PixArt and FLUX families), a range of datasets (COCO, MJHQ-30K, PartiPrompts), and metrics (FID, CLIP, Image Reward), showing consistent improvements.on prompts, and different reward functions\n    \n4. **Clear writing and structured presentation.**  \n    The paper is well-organized, readable, and includes detailed algorithmic pseudocode and visualizations.", "weaknesses": "1. **Compute inefficiency during optimization.**  \n    Although no network training is required, ECAD’s optimization process (hundreds of generations, thousands of images per prompt) might still be computationally expensive. The paper could better quantify this cost relative to real-world savings.\n    \n2. **Reliance on automatic quality metrics.**  \n    The optimization depends on Image Reward or similar automatic metrics, whose correlation to human preference can be imperfect or domain-specific. The paper does not show any human evaluation.\n    \n3. **Potential overfitting to calibration prompts.**  \n    While generalization is discussed, there’s limited theoretical grounding for why a small prompt set suffices to generalize to unseen data.\n    \n4. **Reproducibility complexity.**  \n    The large-scale evolutionary optimization (e.g., 500 generations × 72 candidates) may be difficult for others to reproduce without substantial GPU time.", "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": "Z54tKnECke", "reviewer": "Reviewer_X7Lz", "summary": "This paper introduces ECAD, a method that uses genetic algorithms to learn optimal caching schedules for accelerating Diffusion Transformers. The authors demonstrate speedups on models like PixArt and Flux while preserving or even improving image quality across multiple benchmarks.", "questions": "- what's the generalizability of a schedule optimized for a certain reward, e.g. Image Reward, when evaluated with a different metric?\n- how stable are the Pareto frontiers across random seeds and initializations of population, versus being seeded with heuristic schedules?\n- can you analyze the selected prompt in terms of how representative they are of real-world use, and how sensitive is the performance of the proposed method towards prompts with different length, granularity, etc?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- framing diffusion caching as a multi-objective Pareto optimization problem to balance quality and efficiency is novel.\n- the proposed method is lightweight, and can adapt across prompts, model variants, and resolutions.\n- strong empirical performance that achieves state-of-the-art inference acceleration with competitive image quality across several models.\n- the paper is well-written and the comprehensive ablation studies in the appendix provide supports for the design choices.", "weaknesses": "- the performance is dependent on the choice of the quality metric used during optimization, and the caching schedules may not generalize perfectly to all possible evaluation metrics.\n- the experiment setup (e.g., 100 prompts) may not be representative of complex prompts from real-world applications.", "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": "5cB01yEJ0X", "reviewer": "Reviewer_NiYD", "summary": "This paper introduces Evolutionary Caching to Accelerate Diffusion models (ECAD), a genetic algorithm-based framework designed to optimize caching schedules for diffusion models, significantly improving inference speed without compromising image quality. Unlike heuristic-based methods, ECAD formulates caching as a Pareto optimization problem, enabling fine-grained trade-offs between speed and quality. The method is scalable, generalizable across model architectures, and requires no modifications to network parameters. Experiments on PixArt-α, PixArt-Σ, and FLUX-1.dev demonstrate state-of-the-art performance, achieving up to 3.37x speedups while maintaining or improving image quality metrics like FID and Image Reward. ECAD also exhibits strong generalization to unseen resolutions and model variants.", "questions": "see weakness", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ The proposed evolutIionary caching framework is novel and effective.", "weaknesses": "- The authors emphasize in the introduction that \"there are no restrictions on batch size.\" However, it is unclear whether they measure generation performance per image and acceleration ratio under different batch sizes. Doing so would provide strong evidence to support this claim.\n\n- For the strongest model, FLUX, it would be more compelling to evaluate its performance on more comprehensive benchmarks, such as GenEval and DPG-Bench, rather than relying solely on toy datasets.", "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": "9LKzoFmVvd", "reviewer": "Reviewer_NeEf", "summary": "This paper proposes a training-free accelerated sampling algorithm for diffusion transformers. The method achieves its speedup by caching and reusing features across multiple inference steps, thereby avoiding redundant computations within the model.\n\nThe core innovation of this work is the introduction of the Pareto frontier to construct efficient, model-specific caching schedules. By leveraging evolutionary algorithms to search for this frontier, the authors identify optimal schedules that achieve a favorable trade-off between inference speed and generation quality, using only a minimal set of calibration prompts.\n\nThe authors evaluate their method, ECAD, on state-of-the-art models such as PixArt-α, PixArt-Σ, and FLUX-1.dev. Their experiments are conducted across diverse benchmarks (COCO, MJHQ-30k, and PartiPrompts) and assessed with multiple metrics (FID, CLIP Score, and Image Reward), demonstrating consistent performance improvements over previous approaches.", "questions": "No", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper presents a highly novel approach to the cache-based accelerated sampling problem in diffusion models by leveraging concepts from traditional machine learning. I find the most compelling contribution to be the elegant design of the cache schedule and, in particular, the use of an evolutionary algorithm to efficiently search for the optimal schedule. This represents a creative and effective way to frame and solve the problem. 💡\n\n2. The experimental validation in this work is exceptionally thorough and convincing.\n\n- Comprehensive Evaluation: The authors employ a suite of evaluation metrics, including FID, CLIP Score, and ImageReward, ensuring a multi-faceted assessment of generation quality. Their method is rigorously tested on a diverse range of powerful, state-of-the-art models like PixArt-α, PixArt-Σ, and FLUX-1.dev.\n\n- Insightful Ablations and Visualizations: The paper is further strengthened by thorough ablation studies and clear visualizations. The ablation on the number of evolutionary generations, for instance, provides a clear insight into the trade-off between quality (FID) and efficiency (acceleration ratio), showing how FID first decreases before increasing while the speedup consistently grows. Furthermore, the visualizations of the discovered cache schedules and the points on the Pareto frontiers provide strong, intuitive evidence for the effectiveness of the proposed search strategy.\n\nOverall, the rigorous and well-executed experiments strongly substantiate the claims made in the paper.", "weaknesses": "I find several points in the paper confusing, primarily as follows:\n\n1. On the Behavior of Optimization Objectives (ImageReward vs. MACs). I have a question regarding the behavior of the evaluation metrics during the evolutionary search. The paper's objectives are to maximize ImageReward (quality) and minimize MACs (computational cost). I understand how the NSGA-II algorithm operates to find a Pareto frontier for this multi-objective problem. However, the results show that as the number of generations increases, the MACs consistently decrease, while ImageReward does not exhibit a monotonic improvement; instead, it appears to fluctuate. Could the authors clarify this dynamic? My understanding is that while reducing MACs can be achieved by simply caching more, this aggressive caching might negatively impact image quality. Is the fluctuation in ImageReward a result of the algorithm exploring this trade-off, where some generations might sacrifice quality for significant computational savings before finding a better balance? A brief explanation of this trade-off during the search process would be beneficial.\n\n2. On the Representation of the Cache Schedule (S). I know $S \\in \\{0,1\\}^{N,B,C}$, but I don't seem to see $C$ in the visualization. I'd like to understand what role $C$ actually plays.\n\n3. I observe two implicit but critical constraints in the cache schedules: Initial Step Constraint: \n\n- The first inference step (n=0) must always be a full recomputation, as there is no prior cache to reuse. This is reflected in the visualizations. \n\n- Intra-Step Dependency: Within a single inference step n, if a block b is set to recompute, all subsequent blocks (b+1,b+2,…) in that same step must also recompute. A reuse decision for a later block would be illogical, as its input depends on the output of the recomputed block b, invalidating the stored cache.\n\nMy question is: how are these logical constraints enforced during the evolutionary search, specifically within the Crossover and Mutation operations? A random mutation or crossover could easily generate an invalid schedule that violates these rules. Do the authors employ constraint-aware genetic operators, or is there a \"repair\" function that corrects invalid schedules after they are generated? Detailing this mechanism would strengthen the paper's description of the algorithm.", "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)