Pyramid Patchification Flow for Visual Generation
Authors: Hui Li
Baoyou Chen
Li jiaye
Jingdong Wang
Siyu Zhu
Venue: ICLR 2026 Poster (2026)
Keywords: visual generation，flow matching，pyramidal patchification
Primary area: generative models
TL;DR: A new method to accelerate diffusion model with pyramidal patchification.

Abstract: Diffusion Transformers (DiTs) typically use the same patch size for $\operatorname{Patchify}$ across timesteps, enforcing a constant token budget across timesteps. In this paper, we introduce Pyramidal Patchification Flow (PPFlow), which reduces the number of tokens for high-noise timesteps to improve the sampling efficiency. The idea is simple: use larger patches at higher-noise timesteps and smaller patches at lower-noise timesteps. The implementation is easy: share the DiT's transformer blocks across timesteps, and learn separate linear projections for different patch sizes in $\operatorname{Patchify}$ and $\operatorname{Unpatchify}$. Unlike Pyramidal Flow that operates on pyramid representations,, our approach operates over full latent representations, eliminating trajectory ``jump points'', and thus avoiding re-noising tricks for sampling. Training from pretrained SiT-XL/2 requires only $+8.9\%$ additional training FLOPs and delivers $2.02\times$ denoising speedups with image generation quality kept; training from scratch achieves comparable sampling speedup, e.g., $2.04\times$ speedup in SiT-B. Training from text-to-image model FLUX.1, PPFlow can achieve $1.61 - 1.86 \times$ speedup from 512 to 2048 resolution with comparable quality.

Published as a conference paper at ICLR 2026
PYRAMIDAL PATCHIFICATION FLOW FOR VISUAL GENERATION
Hui Li , Baoyou Chen Liwei Zhang 3, Jiaye Li ? Jingdong Wang". Siyu Zhu 1,3,1 Fuuan University. Baidu Inc.
SShanghai Innovation Institute Shanghai Academy of AI for Science
2.00
1.5
Test
1.0
0.5
(c)
Figure 1: Pyramidal Patchification Flow (PPFlow) achieves state-of-the-an art image generation qual- ity with accelerated denoising processes. (a) and (b) show visual samples from two of our class- conditional PPF-XL-2 and PPF-XL-3 trained on ImageNet. (c) indicates that PPF-XL-2 and PPF- XL-3 obtains 1.6 and 2.0 x inference acceleration with comparable FID scores.
ABSTRACT
Diffusion Transformers (DiTs) typically use the same patch size for Patchify across timesteps, enforcing a constant token budget across timesteps. In this paper, we introduce Pyramidal Patchification Flow (PPFlow), which reduces the number of tokens for high-noise timesteps to improve the sampling effi- ciency. The idea is simple: use larger patches higher-noise timesteps and smaller patches at lower-noise timesteps. The implementation is easy: share the DiT's transformer blocks across timesteps, and learn separate linear pro- jections for different patch sizes in Patchify and Unpatchify. Unlike Pyrami- dal Flow that operates on pyramid representations, our approach operates over full latent representations, eliminating trajectory jump points, and thus avoid- ing re-noising tricks for sampling. Training from pretrained SiT-XL/2 requires only +8.9% additional training FLOPS and delivers 2.02x denoising speedups with image generation quality kept; training from scratch achieves comparable sampling speedup, e.g., 2.04x speedup in SiT-B. Training from text-to-image model FLUX.1, PPFlow can achieve 1.61 - 1.86x speedup from 512 to 2048 resolution with comparable quality. The code and checkpoint at https: / //github. com/ www.anngeeraaiiee--i.... P
INTRODUCTION
Diffusion (Ho et al., 2020; Song & Ermon, 2019; Song et al., 2021b; Rombach et al., 2022; Saharia al., 2022b) and flow-based models (Papamakarios et al., 2021; Xu et al., 2022: Liu et al., 2023b; Lipman et al., 2022; Esser et al., 2024) set the state of the art in visual generation. They comprise a noising process that maps data noise and denoising process that iteratively evaluates learned

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network to transport sample from Gaussian noise to the data distribution. While highly effective, the denoising trajectory typically requires many expensive network evaluations.
A large body of work reduces denoising cost mainly along three lines: (i) reducing the number of function evaluations (c.g., DDIM (Song et al., 2021a), distillation (Salimans & Ho, 2022; Meng et al., 2023b; Yin et al., 2024a), consistency models (Song al., 2023; Luo et al., 2023), and one- step diffusion (Yin et al., 2024b; Liu et al., 2023c; Frans et al., 2024)); (ii) lowering the cost of each function evaluation via model compression and architectural choices, including quantization (He et al., 2023; Fang et al., 2023; Zhao et al., 2024; Li et al., 2023; Huang et al., 2024), pruning (Xi et al.. 2025; Xia et al., 2025; Zhang et al., 2025a;b; Xie et al., 2024), and reducing token counts in denoising process using coarse representations or cascaded designs (Ho et al., 2022b; Saharia et al., 2022a; Ho al., 2022a; Saharia et al., 2022c; Pernias et al., 2023; Gu et al., 2023; Atzmon et al., 2024; Jin et al., 2024; Chen et al., 2025c); and (iii) other ways such as removing or amortizing classifier-free guidance (Ho & Salimans, 2021; Fan et al., 2025; Chen et al., 2025a; Meng et al., 2023a). Among these, approaches that vary spatial resolution over time (e.g. pyramidal or cascaded generation) reduce tokens at early, noisier timesteps but can introduce resolution "jumps", which complicate training and inference and break trajectory continuity.
The interest of this paper lies in improving the denoising network evaluation efficiency with a focus on reducing the number of tokens input to DiT blocks. We present simple and -mplemeepeeeeetee approach, Pyramidal Patchification Flow (PPFlow). Diffusion Transformer (DiT) Peebles & Xie (2023); Ma et al. (2024); Esser et al. (2024) exploits a patchify operation to control the number of tokens and accordingly the computation complexity. It applies the same patch size, typically, 2 2 for all the time steps. Our approach introduces simple modifications. It adopts a pyramid patchification scheme. The patch size is larger for timesteps with higher noise. The patch sizes of a -wo-pyramid-leee example are: 4 and 2 x 2. Each level has its own parameters for linear projections mapping patch representations to token representations in Patchify. Similarly, each level has its own linear projection parameters in Unpatchily. All the levels adopt the same parameters in DiT blocks.
Our approach is related to and clearly different from the recently-develope Pyramidal Flow Jin et al. (2024) and PixelFlow Chen et al. (2025c). The similarity lies in that the number of tokens at the timesteps with higher noise is smaller. The differences include: (i) Our approach operates over full-resolution latent representations; Pyramidal Flow operates over pyramid representations. This is illustrated in Figure 2. (ii) Our approach still satisfies the Continuity Equation. Pyramidal Flow Jin et al. (2024) indicates that it does not satisfy the equation as the representation resolution varies along the trajectory. (iii) Our approach has no issue of "jump points" Camphell et al. (2023b), and the inference is the same as normal DIT. Pyramidal Flow adopts carefully-designeed renoising trick.
We evaluate PPFlow in two training regimes: training from scratch and adaptation from pretrained DiTs. From scratch, two- and three-stage PPFlow achieve comparable or better quality than SiT-B/2, with FID 3.83 and 4.43 versus 4.46, while yielding 1.61x and 2.04x denoising speedups. When initialized from pretrained DiTs (c.g., SiT- XL/2,, PPFlow requires only 8.9% and 7.1% additional training FLOOPs for two- and three-stage variants, maintains similar generation quality, and delivers 1.60x and x.02x inference speedups, respectively. Qualitative examples are shown in Figure 1. Training from text-to-image model FLUX.1, PPFlow can achieve 1.61 - 1.86x speedup from 512 to 2048 resolution with comparable quality.
2 RELATED WORK
Reducing the number of function evaluations. The early algorithm, e.g., DDIM (Song et al., 2021a), greatly reduces the number of function evaluation. Distillation techniques are also widely- studied (Salimans & Ho, 2022; Meng et al., 2023b; Yin et al., 2024a). Consistency models (Song et al., 2023; Luo et al., 2023) distill pre-trained diffusion models to models with small number of sampling steps, including multi-step and single-step sampling. Recently, various one-step diffusion frameworks (Yin et al., 2024b; Liu et al., 2023c; Frans et al., 2024) have been developed.
Reducing the cost of the function evaluation. Model quantization is applied to diffusion/flow- based models for faster inference, including post-training or quantization-awan training (He al.,
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110-10
X0
Xts1
X1.z
X1
(a) PPFlow
|--
[[oif
XL.s
Xla2
X1
(b) Pyramidal Flow (Jin et al., 2024)
Figure 2: Conceptual comparison. (a) A three-level PPFlow example. The patch sizes in Patchify are larger for higher-noise timesteps and smaller for lower-noise timesteps. The representation res- olutions for all the three levels are the same and full. (b) Pyramidal Flow Jin et al. (2024). We illustrate for image generation. It operates over pyramid representations: smaller representation resolution for higher noise and larger representation resolution for lower noise.
2023; Fang et al., 2023; Li et al., 2023; Huang et al., 2024). Structural and video-specific quantiza- tion methods are studied in (Zhao et al., 2024).
Multi-scale and cascaded generation. Multi-scale generation progressively generate images or la- tent representations from low resolution high resolution (Ho et al., 2022b; Saharia et al., 2022a; Ho et al., 2022a; Saharia et al., 2022c; Pernias et al., 2023; Gu et al., 2023; Atzmon et al., 2024; Jin al., 2024; Chen al., 2025c). Pyramidal flow (Jin al., 2024), closely related our ap- proach, reinterprets the denoising trajectory as series of pyramid stages, where only the final stage operates at the full resolution. The inference introduces renoising trick carefully handle jump points (Campbell et al., 2023a), i.e., latent representation resolution change, across stages, ensuring continuity of the probability path. Our approach operates over full-resolution representations, and does not require such renoising trick.
Patchification with varying patch sizes. Training one ViT model with varying patch sizes is stud- ied in FiT (Beyer et al., 2023). It is extended to train one diffusion/flow matching model with varying patch sizes (in implementation, few parameters in the model are dedicated to each patch size) in the concurrent works, FlexiDiT (Anagnostidis al., 2025) and Lumina-Vides (Liu et al., 2025). The denoising process in FiexiDiT and Lumina- Video, is similar to ours: larger patch sizes for higher-noise time steps, and smaller patch sizes for lower-noise time steps. The training pro- cess is different from our approach: They train the model of each patch size for all the timesteps of all the noise degrees, optionally with more lower-noise timesteps for training the model with
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smaller patch size and more higher-noise timesteps for training the model with larger patch size in Lumina- Video. This difference leads to the inconsistency between the training and testing processes for FiT and Lumina-Videe size for the range of the corresponding timesteps is that the model of a patch size can better handle the corresponding specific noise degrees. This benefit is verified by our empirical results.
One benefit from our approach training the model of each patch
METHOD
3.1 PRELIMINARIES: FLOW MATCHING AND DIT PATCHIFICATION
Flow matching. Flow-based generative models (Papamakarios et al., 2021; Xu et al., 2022; Liu et al., 2023b; Lipman et al., 2022; Esser et al., 2024) and diffusion models (Song & Ermon, 2019; Ho et al., 2020) offer powerful framework for learning complex data distributions q. The core idea of flow matching (Lipman et al., 2022) is to construct a continuous sequence from Xo NN,, 1) to X1 q by linear interpolation: X = ix + (1 t)x.. The velocity field can be represented as u,
Xo. A network is trained to learn the time-dependent velocity (x), by minimizing
Blllv(x,,)) ()) (x) (xx-))
The denoising process transforms sample from standard Gaussian distribution into clean data sample progressively. By numerically integrating the learned velocity v(x,,t), from timestep 0, ",,, 1..... l2 to endpoint 1, we can get Xt,, Xt, and x1 according to dx. ar = v(x,t).
DiT patchification. The diffusion transformer (Peebles & Xie, 2023) for estimating v(x,,t) con- sists of three main components: Patchify + DiT blocks + Unpatchify.
Patchify is a process of converting the spatial input, noisy latents in diffusion, into L tokens. Each token is represented by a vector of dimension d, obtained by linearly projecting each patch represen- tation of dimension p x p x C. The patch size p X p determines the number of tokens: T. ( x T is the spatial size of the input noisy latent. The computation complexity of DiT depends on the number of tokens L, and thus the patch size p X p. DiT (Peebles & Xie, 2023) selects the patch size 2 x 2 for good balance between performance and efficiency. Unpatchify is a reverse process converting the d-dimensional representation output from DiT blocks back to the noisy latent space, e.g. p x x C' for velocity estimation. Figure illustrates the Patchify operation.
3.2 PYRAMIDAL PATCHIFICATION FLOW
Pyramidal patchification. Our approach, Pyramidal Patchification Flow (PPFlow), divides the timesteps into multiple stages. We use a three-stage example. {[,, f,, [te, stee)te)) tez), Itag [4asg1]] 1]], for de- scribing our approach. We illustrate our approach in Figure 2 (a). We adopt three-level pyramid way to form patch sizes for each stage: large, medium, small patch sizes, Psj x Ps1 Psg x Psg. and Pas X Pas (set to be 2 x 2 as the normal DiT' in our implementation), for the three stages.
Each patch is a representation vector of dimension da, = Cp We keep the dimensions of the token representations d the same for all the three stages. The linear projection matrices, mapping patch representations to token representations, are different sizes for the three stages: W., E gaxd,, W., E d and W E R Each stage has its own projection matrices. Similarly, each stage has its own linear projection matrices for Unpatchify.
Complexity. The patch size change does affect the token representation dimension, the struc- tures and parameters of DiT blocks. In our approach, all the parameters in the DiT blocks are shared for all the three stages. In summary, our approach adopts different linear projections in Patchify and Unpatchify, and the same parameters for DiT blocks in the three stages.
The costs of linear projections in Patchify (and Unpatchify) are the same for the three stages:
Lg d d (T/p.)² (v² C) d C²Cd.
This indicates that the costs of linear projections do not depend on the patch size. Differently, the cost of DiT blocks is dependent on the number of tokens: the time complexity of linear projections and MI.Ps is linear with respect the number of tokens, and the complexity of self-attention is
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IxIxe
(1/pa) Cp
Px
Linear projection W.
len. (2/px) 2 dim.
Figure 3: Patchify. After flattening noisy latent, the layer maps the P. Ps patch representation into a d-dimensional token representation through a linear projection W. E Rdxd, (d, Cp²). Unpatchify is reverse process, mapping the token representation, output from DiT blocks the predictions. For example, for the velocity predictions, the linear projection matrix is of size d, x d: W e Rd,xd
quadratic. The complexity of each block is:
O +
This indicates that reducing the number of tokens with larger patch sizes effectively reduces the computation complexity. The DiT-XL/2 model (with feature dimension d 1132 and 28 layers), approximately 99.8% of the computational FLOPs lie in the DiT blocks.
In our approach, the number of tokens at the high-noise stage is smaller. The number of tokens at the low-noise stage is larger and the same as normal DiTs. Thus, the time complexity our approach is much lower than normal DiT's for both training and testing. The two-level and three-level PPFlow reduce the FLOPs by 37.8% and 50.6% for 256 256 image generation.
3.3 TRAINING AND INFERENCE
We implement training regimes and recommend initializing from pretrained DiT when avail- able, as this preserves performance while only needs limited training cost. In all cases, the flow- matching objective and the stage schedule (time partitions and patch sizes) remain fixed during training.
Training from scratch. We train the linear projections in Patchify/nppatchify and the parameters in the shared DiT blocks by initializing the parameters normal DíT training and using the standard training setting. The linear projections in Patchiff/UUnpatchify for different patch sizes are trained only using the noisy latents of the timesteps corresponding to the patch size.
Training from pretrained DIT. We copy the weights in DiT blocks from normal DiT's to our ap- proach. The linear projections in Patchify are initialized by averaging. Suppose the patch size is x 2 in normal DiTs, and the patch size in the second stage our approach psa x Paz 4 x 4. The linear projection matrix in the second stage is initialized as: W2 = 1. [, W, W, Wj. Here, comes from 2x2 This intuitively means that four 2 patches averaged and then mapped to 4x4
the d-dimensional token.
The linear projection matrix Unpatchify is initialized by duplicating: W [W""T, (W")T (W")T, (W") WTT where W Bd This intuitively means that the out- puts for the four x patches are initially the same. Such initializations are easily extended to other patch sizes.
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Inference. The sampling process is almost the same as the normal DiTs: start from noise Xu, e.g., Gaussian noise, and gradually generate less noisy samples X Xtz..... Xtx (tK = 1) from timesteps t,, .,,...,,,.... till getting clear sample X1. The only difference lies in that the Patchify and Unpatchify operations for different stages are different: use the patch size and the linear pro- jections that correspond to the timesteps for sampling.
Pyramidal Flow and PixelFlow (Chen et al., 2025c; Jin et al., 2024) need a carefully-designa renois- ing trick to handle the representation size change issue across stages. Our approach keeps the spatial size of the latent representations unchanged and thus does not need such a renoising scheme.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets. We train class-conditional PPFlow models on ImageNet (Deng et al., 2009). Following latent diffusion practice, we use the Stable Diffusion VAE encoder (Rombach et al., 2022) map
an RGB image x # W Wx3 to a latent E /R#//x//xxx with H E {256, 512]. For text-to- image, we use curated LAION (Schuhmann et al., 2022), JourneyDB (Sun et al., 2023), BLIP3o- 60k (Chen et al., 2025b) and IM images generated by FLUX.1 dev (Labs, 2024) utilizing prompts from LLaVA-pretrain (Liu et al., 2023a).
Implementation. We adopt SiT (Ma et al., 2024) the main baseline and starting point for PPFlow. For clnss-conditional models, we evaluate at 256 x 256 and 512 x 512. Unless otherwise noted, models are trained with AdamW (Kingma & Ba, 2014; Loshchilov & Hutter, 2017), learning rate x 10-1, no weight decay, batch size 256, and horizontal flips as the only augmentation. We maintain EMA with decay 0.9999 and report all metries with EMA weights.
Models are denoted by capacity and stage count, e.g., PPF-XL-2 is an XLarge model with two patchification stages, For two-level,
patches for 00, 0.5): x 2 patches for E [0.5, 1.0]. For three-level, 4 x 4 for t E 0.5): 4 x 2 for t E [0.5. 0.75): 2 x 2 for t (0.75, 1.0). We use a stage-wise CFG schedule and Patch n' Pack (Dehghani et al., 2023) to pack variable-length token sequences into batches, reducing the training FLOPs in an iteration compared with normal DIT.
For text-to-image, we integrate two-stage PPFlow into pretrained FLUX.1-der model. We adopt progressive training scheme, where the model is trained sequentially at resolutions of 512 x 512, 1024 x 1024, 2048 x 2048, with corresponding batch sizes of 256, 128, and 16 (4 gra- dient accumulation steps), respectively. We use fixed learning rate of 1 10- for the entire 90k training iterations.
Metrics. For class-conditional generation, we report FID-50K (Heusel et al., 2017), IS (Salimans et al., 2016), sFID (Nash et al., 2021), and Precision/Recall (Kynkäänniemi et al., 2019) using ADM's TensorFlow evaluation suite (Dhariwal & Nichol, 2021). For text-to-image, report GenEval (Ghosh et al., 2023), DPG-bench (Hu et al., 2024), and T21-CompBench (Color/Shape/- Texture).
4.2 RESULTS ON COASS-CONDIIIONA IMAGE GENERATION
We study two training regimes: (i) training from scratch and (ii) training from pretrained DiTs (SiT- B/2, SiT-XL/2, DiT-XL/2).
Training from Scratch. We train PPF-B-2 and PPF-B-3 for 11M steps from scratch, the PPF- models achieve better or comparable FID-50K to SiT-B/2 at substantially lower testing FLOPS, while consistently improving IS. Quantitative results are summarized in Table 1. We use stage-wiss CFG schedules: [1.5, 3.5] for PPF-B-2 and [1.5, 3.5, 4.0) for PPF-B-3, inspired by adaptive guid- ance (Chang et al., 2022; Kynkäänniemi et al., 2024; Wang et al., 2024). Visual comparisons with the same noise inputs (Appendix Figure 5) show that pyramidal patchification preserves image qual- ity while improving compute efficiency. Overall, PPFlow attains approximately 1.6x (two-level) and 2.0x (three-level) inference speedups relative to SiT-B/2 while keeps comparable generation quality.

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Training steps
Training FLOPs (%)
Testing FLOPs (%)
Method
FID-50k
sFID
1
Pre. 1
Rec.
SiT-B/2
7M
100
100
4.46
4.87
180.95
0.78
0.57
PPF-B-2
7M
62.5
62.0
4.12
5.71
211.36
0.79
0.53
PPF-B-2
11M
98.2
62.0
3.83
5.70
223.00
0.81
0.53
PPF-B-3
7M
50.0
49.1
4.71
5.78
212.84
0.81
0.49
PPF-B-3
11M
78.5
49.1
4.43
5.85
230.72
0.83
0.48
Table I: Train from scratch comparison of our approach to normal SiT-B/2. The result of SiT-B/2 from (Ma et al., 2024; Dao et al., 2023). Our approach, trained from scratch, with more training steps but smaller training FLOPs, performs similarly: better for three metrics and worse for other two metrics. Our approach obtains 1.6x and 2.0x inference speedup.
Training steps
Training FLOPs (%)
Testing FLOPs (%)
Method
Size.
FID-50K
sFID
IS f
Pre. t
Rec.
S/T-B/2
256
100
100
4.46
4.87
180.95
0.78
0.57
PPF-B-2
256
1M
8.9
62.0
4.22
5.49
252.10
0.85
0.49
PPF-B-3
256
1M
7.1
49.1
4.57
6.06
236.53
0.83
0.48
SiT-XL/2
256
100
100
2.15
4.60
258.09
0.81
0.60
PPF-XL-2
IM
62.6
1.99
5.52
271.62
0.78
0.63
PPF-XT.-3 256
1M
7.1
49.4
2.23
5.50
286.67
0.78
0.64
DiT-XL/2
100
3.04
5.02
240.82
0.84
0.54
PPF-XL-2510
400k
7.6
58.7
3.01
5.24
249.98
0.84
0,54
PPF-XL-3 512
400k
5.8
45.4
3.06
5.31
249.91
0.83
0.53
Table 2: Train from pretrained model comparison of our approach to normal SiTs and DīT. Our approach is trained from the corresponding pretrained model with less than 10% training FLOPs of the pretrained model (7M training steps for 256 size, 3M training steps for 512 size). Our approach achieves overall comparable performance with less testing FLOOss
Training from Pretrained DiTs. We train PPFlow from pretrained SiTs with only 10% of the pretraining FLOPs and evaluate at testing reduced FLOPs (Table 2). For multiple model configs (B and XL), multiple image resolution (256 and 512), PPFlow can save 37.4% 41.3% testing FLOPs for two-level and 50.6% 54.6% for three-level with comparable FID score and better IS.
Stage-wise CFG schedules used here are [1.0, 3.0] for PPF-XL-2 and [1.0, 3.5, 3.75) for PPF-XL-3. Visual comparisons can be seen in Appendix (Figure 6) confirm quality is preserved with substan- tially lower compute.
4.3 RESULTS TEXT-TO-IMAGE
We integrate two-stage PPFlow into the pretrained FLUX. model at multiple resolutions: 512 x 512, 1024 x 1024, 2048 x 2048. We fine-tune the FLUXX1 model using our collected dataset as the baseline of our approach, and we denote the model as F-U We train PPFlow from the model F-UX.1-ft.
From Table 3, one can see that PPFlow's 's results (GenEval for compositional aspects, DPG Bench for complex textual prompts and T21-CompBench for alignment with complex semantic relationships) are comparable with the I-,,. consistent to the observation in class-conditional image gener- ation. The results three different resolutions indicate that PPFlow is scalable to higher-resolution generation, and the testing FLOPs progressively decrease as the input resolution is increasing.
4.4 ABLATION STUDY
Patch-Level embedding and stage-wise CFG. We ablate two network components on PPF-B- 2 training for 1M steps from pretrained SiT-B/2 (Table 6). Adding learned petch-level (stage) embedding improves FID from 4.44 to 4.30. Adding wise CFG further improves FID to 4.22 and markedly boosts IS.

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Testing FLOPs (%)
T21-CompBench
Method
GenEval f
DPG Bench 1
Color f
Shape f
Texture
512 resolution
FLUX.1
100
0.67
82.51
0.7534
0,5060
0.6306
FLUX.1-ft
100
0,68
82.87
0.7556
0,5070
0,6310
PPF-FLUX.1
62.2
0.68
82.90
0.7560
0.5066
0.6310
1024 resolution
FLUX.1
100
0.68
83.14
0.7529
0,5056
0.6312
FLUX.I-ft
100
0.68
83.89
0.7568
0.5098
0.6400
PPF-FLUXX11
59.1
0.68
84.00
0.7566
0.5100
0.6423
2048 resolution
FLUX.1
0.66
82.75
0.7510
0.5066
0.6300
1-ft.1-ft
100
0.67
83.02
0.7515
0.5082
0.6308
PPF-FLUX.1 53.9
0.67
83.10
0.7520
0.5086
0.6310
Table 3: Performance of PPFlow applied to FLUX. across multiple resolutions (512 x 512 to
2048 x 2048). We compare against FLUX.I-ft, a fine-tuned baseline in a same dataset, on three benchmarks: GenEval, DPG Bench, and T21-Com T21-CompBench. The results indicate that PPFlow's per- formance comparable with the fine-tuned model, showing its potential for text-to-iman tttaiiiage task.
Method
Testing FLOPs (%)
FID-50k
DiT-XL/2
100
2.27
FlexiDiT
64
2.25
FlexiDiT
46
2.64
PPF-DiT-XL/2
62.6
2.15
PPF-DIT-XL/3
49.4
2.31
Table 4: Comparison of our approach to
FlexiDiT based on DiT-XL/2 of 256 resolu- tion. At around 63% FLOPs budget, PPFlow achieves FID 2.15, outperforming Flex- iDiT's 2.25. Further, at around 50% FL.OPs, PPFlow's s FID of 2.31 substantially better than 2.64 FID of FlexiDiT.
Figure 4: Visualization comparison between pyra- mid representations renoising (left), Lumina-Vides method (middle) and our PPF-B-2 (right). One can see that right generation results are visually better.
Number of patch sizes. We explore influence of increasing number of stages on PPFlow, specif- ically, varying stage number from 2 to 5 top of pretrained SIT-B/2 (Table 7). Increasing stages consistently lowers test-time FLOPs. With additional training, models recover the two-stage quality significantly reduced FLOPs, demonstrating a trade-off by stage count and training budget.
Timestep segmentation. We investigate different time segmentations for two- and three-stage PPFlow (Table 8). The results show that for PPFlows with different time segmentations, the test- ing FLOPs vary, and they need different training steps to maintain comparable performance as the normal SiT-B. In general, lower FLOPs needs more training steps.
Pyramid representations. We implement PyramidalFlow (Jin et al., 2024) on two-stage variants of the SiT-B and train them from scratch for 1M steps. The results in Table show: PPFlow attains the best FID, sFID, IS, and Precision/Recall. Only using pyramid representations produces blurred images suffering from block-lik like artifacts (Jin al.. 2024). The test-time renoising trick only partially alleviates the issue.
Method
Training steps
FID-50k 4
sFID |
IS f
Pre.t
Rec.1
Pyramid Rep.
IM
164.48
61.58
8.29
0.09
0.14
Pyramid Rep. + Remoising
IM
27.69
11.16
73.20
0.55
0.57
Lumina- Video method
IM
18.77
6.17
79.12
0.64
0.57
PPF-B-2
IM
15.68
5.82
88.78
0.65
0.58
Table 5: Comparison of approach to pyramid representation--asse flow, Lumina- Video. The results at IM training steps from scratch are reported. Overall performance of PPFlow is better.
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that our approach achieves better Published as a conference paper at ICLR 2026
Training steps
FID-50k +
sFID L
IS 1
Pre. t
Rec.
PPF-B-2 level Emb.
1M
4.44
5.55
201.12
0.80
0.55
1M
4.30
5.50
212.70
0.81
0.55
stage CFG
IM
4.22
5.49
252.10
0.85
0.49
Table 6: Ablation study for patch-level embedding and stage-wise CFG. Level embedding and stage- wise CFG can help improve FID and IS PPFlow.
Training steps
Testing FLOPs (%)
Model
FID-50K
sFID J
IS
Pre.
Rec. 1
SiT-B/2
100
4.46
4.87
180.95
0.57
PPF-B-2
IM
62.0
4.22
5.49
252.10
0.85
0.49
PPF-B-3
IM
49.1
4.57
6.06
236.53
0.83
0.48
PPF-B-4
IM
46.5
4.78
6.23
227.12
0.82
0.48
2.5M
46.5
4.41
5.77
245.12
0.83
0.49
PPF-B-5
1M
38.3
5.30
6.66
220.13
0.80
0.49
4M
38.3
4.51
5.99
236.89
0.83
0.48
Table 7: Ablation study for different stages in PPFlow. PPFlow can be applied with more stages for more efficient inference with the performance maintained through more training steps.
Time
Training steps
Testing FLOPs (%)
Model
FID-50k
sFID +
IS 1
Pre.
Rec.
segmentation
SiT-B/2
100
4.46
4.87
180.95
0.78
0.57
PPF-B-2
[0.50, 1.0]
1.0M
62.0
4.22
5.49
252.10
0.85
0.49
[0.25, 1.0]
0.5M
4.25
5.29
249.12
0.49
[0.75, 1.0]
2.5M
43.4
4.40
5.70
242.10
0.84
0,50
PPF-B-3 [0.50, 0.75, 1.0]
1.0M
4.57
6.06
236.53
0.83
0.48
[0.25, 0.50, 1.0]
0.8M
68.3
4.21
5.30
240.11
0.84
0.49
[0.50, 0.90, 1.0]
3.0M
41.8
4.59
6.02
235.57
0.81
0.50
Table 8: Ablation study for time segmentation in PPFlow. The results show PPFlows with different time segmentations, the testing FLOPs vary, and they need different training steps maintain com- parable performance as the normal SiT-B.
Training with varying patch sizes. We study the performance of the alternative methods of training with varying patch sizes (Liu et al., 2025; Anagnostidis et al., 2025), which train the model of each patch size for all the timesteps. We implement the training process in sumina-Vides (Liu al., 2025) whose inference is similar to ours. It trains the models of different patch sizes over all the timesteps, and adopts
saapledg scheme: sample more lower-noise timesteps the model with smaller patch size and less higher-noise timesteps for training the model with a larger patch size. This leads to inconsistency between the training and testing. Our approach, training the model of each patch size for the range of the corresponding timesteps, introduces an additional benefit: the model of a patch size can better handle the corresponding specific noise degrees. From the results in Table 5 and Figure 4, results at the IM training iteration.
training
The results from FlexiDiT (Anagnostidis al., 2025) whose training is similar to Lumina- Video (Liu et al., 2025) but without adopting the shift-sampling scheme are reported in Table _ As FlexiDiT (Anagnostidis al., 2025) does not report the result from SiT, we apply method to DiT. Our approach demonstrates superior performance over FlexiDiT at comparable computational levels. around 63% FLOPs budget, PPFlow achieves FID of 2.15, outperforming FlexiDiT's 2.25. Further, at around 50% FLOPs, PPFlow's FID of 2.31 is substantially better than the 2.64 FID of FlexiDiT.
sFID discussion. In Table. and Table. 2. our sFID score is slightly worse than the baseline. We suppose this is related to the spatial sensitivity that sFID evaluates. We analyze this from three
FlexiDiTT is open-sourced. The results from the paper (Anagnostidis al., 2025).

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Published as a conference paper at ICLR 2026
observations: (1) Continue the training to how sFID changes with training. After 11M training steps from scratch, the FID score stabilizes, while the sFID score progressively reduces to 5.03, approaching the 4.87 baseline of the normal SiT-B/2 model. (2) From visualization results, obvious implications except some slight spatial difference the position and size of the main object compared the baseline shown in Fig. 5 and Fig. 6; (3) The position evaluation metric (0.20) in GenEval for PPF-FLUX.1 is same with FLUX. I-ft and FLUX.1. This gives a more evidence: in the SoTA tex--to-image model, our approach does influence the image generation quality in position relationships.
5 CONCLUSION
We introduce PPFlow, pyramidal patchification scheme that adaptively reduces token counts high-noise timesteps while maintaining latent representation resolutions unchanged across timesteps. PPFlow keeps inference identical to standard DiT avoiding re-noising and resolu- tion jumps. Across training from scratch and from pretrained models, PPFlow achieves 1.6-2.0 2.0 denoising speedups with comparable or improved image quality for class-conditional image gener- ation. Our approach is also demonstrated for text-to-image generation: nearly 50% sampling cost reduction and image generation quality kept. The approach is simple and casily-implemented.
ACKNOWLEDGMENTS
This work was supported in part by the Shanghai Municipal Commission of Economy and Informa- tization (No. WZL-RRZZ--BBB-OOO
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A USAGE OF LLM
During the preparation of this manuseript, we utilized Large Language Models (LLMs) as a writing assistant. The tool was employed enhance grammar, clarity, and overall readability. The authors reviewed and edited all Al-generated suggestions to ensure that the final text accurately and faithfully reflects our original ideas and contributions.
B LIMITATIONS AND FUTURE WORK
Our evaluation is limited class-coniitional image generation and text-to-image generation, and fixed, manually designed stage schedule with discrete patch sizes; generalization to video and other visual domains as well as alternative noise/solver settings, remains untested. Future work includes: (i) jointly learning stage boundaries and patch sizes end-to-end, (ii) introducing stage- aware conditioning or selectively untying parameters across stages, and (iii) integrating PPFlow with step-reduction, distillation, and compression techniques to further improve the efficiency-quallee trade-off.
C VISUALIZATION RESULTS
Figure and Figure 6 report the results training from scratch and training from pretrained models of our approach as well as the results for the commlll--taiined SiT models.
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Increasing putchification stages
Figure 5: Visualization results for normal SiT-B/2, PPF-B-2, and PPF-B-3. The results are sampled from the same noise. The models of our approach are trained from scratch. The results of the three methods are visually comparable.
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Incrensing patchification singes
*
Figure 6: Visualization results for normal SiT-XL/2, PPF-XL-2, and PPF-XL-3. The results are sampled from the same noise. Our models are trained from pretrained normal SiT-XL/2. The results of the three methods are visually comparable.
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It takes AT AND RAIN TO MAKE A.,. rainbow
l It takes
It takes and rain to Make Rainbow
Al
Al
and rain to make a ra bow
(a) A visually striking digital art piece featuring the phrase "It takes AI and rain to make a rainbow" set against a deep black background...
Krowledge is Power
Knowledge is Power
Knowledgee is Power
(b) beautifully aged antique is positioned carefully studio close-up up, revealing rich, dark brown leather cover. The words "Knowledge is Power" are prominently featured in the center with thick...
fe-fety-H
chu Representation Learning
Hi-g-et-
Representation Learning
Representation Learning
Kyrg= 21
In Rave
CAF//
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(c) In the image, sleek, metallic humanoid robot stands before dusty chalkboard, on which it has carefully scrawled "Representation Learning' in eloquent cursive...
Local pig eats prize pumpkin
Local pig eats prize pumpkin
Local pig eats prize pumpkin
(d) An image of newspaper lies flat, bold hcadline 'Local pig cats prize pumpkin' emblazoned across the top in large lettering...
Figure 7: Example results demonstrate that PPFlow maintains visual quality, specifically for text rendering. Left: FLUX. Middle: FLUX.1-ft; and Right: PPF-FLUX.I.
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(a) anthropomorphic wolf, clad in crisp white shirt a patterned tie...
(b) An imaginative digital artwork that features a fantasy female character, stylized with the intricate detail and ethereal qualities reminiscent of Peter Mohrbacher's 's angelic designs...
(c) meticulously crafted Art Nouveau screenprint featuring dog's face, characterized by its remarkable symmetry and claborate detailing...
Relardda
(d) An interior space featuring collection of yellow ceramic ornaments neatly arranged shelf. In the center the stands table with clear glass filled with bouquet of fresh flowers...
Figure 8: Example results demonstrate that PPFlow maintains visual quality. Left: FLUX.1; Middle: 1LUX.1-ft; and Right: PPF-FLUXX1
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Training Loss Curve
FlexiDiT "Lumina-VVile training
0.785
PPFlow training (Ouns)
Loss
0.770
0.765
0.760
100000
200000
300000
400000
500000
Truining steps
Figure 9: Training loss comparison. PPFlow (blue) achieves consistently lower loss and stability than FlexiDiT (orange). This validates that training strategy works better, avoiding the conflict- ing optimazation in handling large and small patch sizes simultaneously.
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Extract baseline model patchification projection weights
srtrcc__proo_weeeee state ceaat_dicttie mexx__mbedderrpp.e weight") source_proj_bias = state_dict "x httpraccccccleteeeeeeeeeeoo prabeedder.poo.piiee.
Initialize patchificatio
projection (deal with larger patch size)
weights with scaled and repeated weights
iiitiallie_ jayer_weights( weights(
searrce_proi__eiee
model model.x_embes
weight
repeat_factor=4,
10
scalc_factor=4.00
11
dim-1
12
13 Initialize bias
14 initialize_layer_bil
15
source_proy source_proj_bias,
16
Sroel.x_embedder. projj large_patch
18
19
def initialliz_ (source_weight, target_layer, repeat_factor, scale_factor-1.0, dim-l):
20
Initializes the weights of target layer by scaling and repeating.
23
24
with serch.no_gradil
25
Scale and repeat the source weight target_weight source_weight initialized weight target_weight, repeat repeat_interreeeeeeeee interleave repeat_factor, dim-din)
factor
28
Copy the initialized weights to the target layer Carget_Layer..eeee Layer. data
weight)
31
32
Layer def initialize layer bias (source bias, Sarget_layees facttor_ dim
-0):
34
Initializes the blas of a target layer.
35
36
with terch.
if repeat_factor 1:
initialized_bie bias scurce bias repeatreeatt_nneee ie repeat_factor dim=din)
39
else:
40
- scurce_bias
42
Copy initialized bias to the target layer
43
target layer.biat piperbbbps..dtt..o......o (initialieed bias)
Listing 1: PyTorch implementation for initializing the patchification projection layers.
21


Reviews:
[{"review_id": "9YjeB3TLAe", "reviewer": "Reviewer_uvnV", "summary": "This paper proposes Pyramidal Patchification Flow (PPFlow), a method that dynamically varies the patch size of DiT across timesteps—using larger patches in higher-noise timesteps to reduce token count and smaller patches in lower-noise timesteps to preserve visual fidelity. PPFlow operates on the full-resolution representation throughout the generation process and trains the model for each patch size only over the corresponding timestep range. As a result, PPFlow achieves faster inference without generation quality degradation.", "questions": "- In Table 7, the results show that increasing the number of training steps helps recover generation quality (line 445 vs 446 & line 447 vs 448). In Table 2, instead of matching the number of training steps, what happens if we match the training FLOPs between the two-level and three-level methods? How does the generation quality compare under this training-cost-matched condition?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is well-structured, providing clear explanations of design choices and experimental results. It includes comprehensive evaluations and ablation studies that effectively validate the proposed approach.\n- The proposed PPFlow patchification strategy can be applied to various transformer-based diffusion models due to its simplicity and comparable generation quality to conventional fixed patch-size models.", "weaknesses": "- There are several typographical errors in the paper. For example, there are double commas in the abstract (line 36, p1); in line 222 (p.5), the word “depent” should be “dependent”; and in Table 5 (p.8), “pyrimid rep” appears to be a typo.", "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": "3MfX5zbDvC", "reviewer": "Reviewer_wyzG", "summary": "The paper explores adaptive patch sizes for DiT models trained for image generation. In contrast to prior works, it argues for separating the patch sizes for different time segments to better align with the optimal test-time inference strategy. It tests on top of SiT for class-conditional image generation on ImageNet and Flux-dev for text-to-image generation. It reports better qualitative results for both setups.", "questions": "- Is it possible to support autoguidance (using a large-patch model as the weak one)?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper presents several useful ablations for adaptive patch size strategies: patch-level embedding, stage-wise CFG, \n- The paper is written well\n- Overall, this paper looks like a useful reference for adaptive patch size exploration", "weaknesses": "- FLOPs are not a good metric to measure training speed. Clock wall time should be reported as well.\n- Figure 3 is not too informative of the patchification process since it presents the input and output the same. Also, the input to patchification is a 2D image patch, not flattenning is a part of patchification.\n- Typo on L248: [WWWW] reads like a matrix multiplication of 4 matrices W. Same for L252\n- The paper claims to train Flux-dev, but does not show a single generated image from it. GenEval is not a reliable benchmark and cannot be the only source of assessment.\n- The authors claim that Lumina-Video's strategy to use all the patch sizes for all the timesteps is inferior. But it's unclear if its cfg is just as tuned and if the proposed strategy supports autoguidance like Lumina-Video does.", "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": "OF5FTLS54h", "reviewer": "Reviewer_SsjJ", "summary": "The authors propose Pyramidal Patchification Flow (PPFlow), an efficient diffusion model that reduces the number of tokens at high-noise timesteps to accelerate generation and potentially also training. Instead of using a fixed patch size, PPFlow uses a multi-stage patch size schedule, which uses larger patches for earlier timesteps and smaller patches for later timesteps. All denoising transformer blocks are shared across stages. Thanks to this design, PPFlow speeds up inference by at least $1.6\\times$ without compromising image generation quality.", "questions": "- Regarding lines 280–283, are the time intervals empirically determined? It would be interesting to explore whether adaptive interval selection, based on certain metrics such as the signal-to-noise ratio (SNR), could further improve performance or stability.\n- The choice of a $4\\times 2$ patch configuration seems interesting, as most patch-based designs adopt square-shaped patches. Have the authors experimented with larger square configurations, such as $8\\times8$ patches, especially for higher-resolution synthesis? Such an ablation could clarify whether the asymmetric patching contributes meaningfully to the model’s efficiency or generative quality.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Speedup without compromising quality: PPFlow achieves $1.6\\times - 2.0\\times$ faster inference on image generation tasks while maintaining comparable image generation quality.\n- Thanks to its design, all denoising steps happen in the same resolution, and it naturally resolves the \"jump points\" issues introduced in prior works. This leads to a simpler sampling scheme, which does not require tricks like renoising but also leads to a better image generation quality.\n- Extensive experiments demonstrate the effectiveness of the method.", "weaknesses": "- Varying CFG scales: My primary concern lies in attributing the reported performance improvements to architectural innovations rather than to the varying classifier-free guidance (CFG) scales applied across different intervals. As stated in lines 357–359, the model adopts distinct CFG levels at different denoising stages. While this is a reasonable design choice for PPFlow, it closely parallels the Guidance Interval technique [1], which argues that guidance should not be applied uniformly throughout the denoising process since guidance at earlier timesteps can negatively impact sample diversity and quality. It would strengthen the paper if the authors could provide a direct comparison with Guidance Interval applied to SiTs to confirm that the observed quality gains are not merely due to this scheduling strategy.\n\n- Diminishing returns with more stages: While adding more pyramid stages can further cut computation, it also compromises image generation quality. This seems like a trade-off to me, where aggressive token reduction demands more training but also negatively impacts the generation quality.\n\n---\n[1] Kynkäänniemi, T., Aittala, M., Karras, T., Laine, S., Aila, T., & Lehtinen, J. (2024). Applying guidance in a limited interval improves sample and distribution quality in diffusion models. Advances in Neural Information Processing Systems, 37, 122458-122483.", "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": "urbQX5dgDu", "reviewer": "Reviewer_R4cM", "summary": "This paper introduces \"Pyramidal Patchification Flow\" (PPFlow), a method designed to improve the inference efficiency of Diffusion Transformers (DiT) / Flow Transformers (e.g., SiT, FLUX.1). Standard DiTs use a fixed patch size (e.g., 2x2) across all denoising timesteps, resulting in a constant number of tokens. PPFlow's core idea is simple: use larger patch sizes (e.g., 4x4) for earlier, higher-noise timesteps and smaller patch sizes (e.g., 2x2) for later, lower-noise timesteps, thereby reducing the token count in early stages and accelerating inference.\n\nThe implementation is straightforward:\n\n1. Shared DiT Body: The main Transformer blocks (DiT Blocks) are shared across timesteps (and thus, different patch sizes).\n\n2. Independent Patchify/Unpatchify Layers: Separate linear projection layers ($W_s$ for Patchify, $W_s^u$ for Unpatchify) are learned for each distinct patch size (e.g., one set for 4x4, another for 2x2).\n\nA key distinction from methods like Pyramidal Flow (Jin et al., 2024) is that PPFlow operates consistently on full-resolution latent representations, merely changing how they are patchified, whereas Pyramidal Flow operates on pyramid representations of varying resolutions. This allows PPFlow to avoid \"jump points\" caused by resolution changes and eliminates the need for complex re-noising tricks during sampling.\n\nExperiments show that fine-tuning PPFlow (three-stage) from a pretrained SiT-XL/2 model requires minimal additional training cost (+7.1% FLOPs) while achieving ~2.02x inference speedup and maintaining comparable image generation quality (FID). Similar speedups are observed when training from scratch. When applied to the text-to-image model FLUX.1, PPFlow achieves 1.61-1.86x speedup across various resolutions with comparable quality.", "questions": "**Question 1: What are the fundamental trade-offs between PPFlow's staged training and FlexiDiT/Lumina-Video's full-timeline training?**\n\nThe paper suggests PPFlow's staged training allows models to better specialize for specific noise levels, and experiments show better performance than a re-implementation of the Lumina-Video approach. However, FlexiDiT claims their full-timeline training offers more \"flexibility,\" potentially allowing the use of any patch size at any timestep during inference. Could the authors elaborate on the trade-offs? Is PPFlow's specialized approach inherently better under fixed schedules, while FlexiDiT's generalist approach offers more adaptability if the inference schedule (patch sizes vs. timesteps) needs modification? A direct experimental comparison of these training strategies (e.g., using PPFlow's architecture but FlexiDiT's training method) would be valuable.\n\n**Question 2: How sensitive are the results to the choice of stage divisions (timesteps) and patch size combinations?**\n\nThe current stage divisions and patch sizes are set manually. How much do the final speedup and image quality depend on these specific choices? Are there better combinations? Could these parameters potentially be learned?\n\n**Question 3: How does PPFlow perform on tasks requiring fine spatial structure or long-range dependencies?**\n\nDoes using larger patches (which discard early high-frequency information) in high-noise stages negatively impact tasks that require precise spatial layout or fine-grained textures (e.g., complex scenes, generating images with text)? While ImageNet and T2I benchmark results are strong, how does performance hold up in more challenging scenarios? Suggest adding relevant experiments or discussion.\n\n**Question 4: Could 1x1 patch sizes be considered for the final, very low-noise stages?**\n\nTo potentially capture the finest details in the last stages (close to t=1), would incorporating a 1x1 patch size (equivalent to operating directly on pixels/tokens) be beneficial? This might further improve image quality but would require careful consideration of the trade-off, given the drastic increase in token count (e.g., from $(I/2)^2$ to $I^2$) and the associated computational cost surge. What are the authors' thoughts on this possibility?\n\n**Question 5: Clarification on Patchify Implementation and Weight Initialization?**\n\nOriginal DiT often implements Patchify using a Conv2D layer with kernel_size=stride=patch_size. However, Fig 3 and Sec 3.1 describe it as \"extract patches + linear projection ($W_s$)\". Are these formulations equivalent? If not, why choose the latter? Furthermore, could the authors clarify precisely how the weight initialization described in Sec 3.3 (averaging/duplicating) is applied to these independent linear layers $W_s$ and $W_s^u$ to avoid confusion?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. **Simple and Effective**: The core idea—dynamically adjusting patch size based on noise level to reduce token count—is intuitive, simple, and easy to implement (only modifying Patchify/Unpatchify layers).\n\n2. **Inference Acceleration**: Experimental results (Tables 1, 2, 3) clearly demonstrate that PPFlow achieves substantial inference speedups (approx. 1.6x to 2x) across multiple models (SiT-B, SiT-XL, DiT-XL, FLUX.1) and resolutions (256 to 2048), while maintaining comparable image generation quality (FID). This is a highly practical contribution.\n\n3. **Avoids \"Jump Point\" Issues**: Compared to methods like Pyramidal Flow that switch between different representation resolutions, PPFlow's consistent operation on full-resolution latents avoids the complexities associated with \"jump points\" and re-noising sampling strategies, keeping the inference process simple.\n\n4. **Easy Adaptation from Pretrained Models**: The paper shows that PPFlow can be efficiently adapted from pretrained DiT models via fine-tuning (Table 2), requiring only about 7-9% additional training FLOPs to gain significant inference speedups. This greatly increases the practical appeal of the method.\n\n5. **Clear Concept**: The paper is well-written with a clear concept. The experimental design is sound, covering both training-from-scratch and fine-tuning scenarios, validated on class-conditional and text-to-image generation. Ablation studies (Tables 6-8) are reasonably thorough, supporting the design choices.", "weaknesses": "1. **Limited Novelty**: While effective, the core idea (adjusting computation/token count based on noise level) is not entirely new. Related works like multi-scale/cascaded Diffusion, Pyramidal Flow (Jin et al., 2024), and especially concurrent works FlexiDiT (Anagnostidis et al., 2025) and Lumina-Video (Liu et al., 2025) explore the exact same core mechanism (time-varying patch sizes). PPFlow's primary novelty lies in its specific implementation (avoiding jump points via full-resolution operation) and its staged training strategy.\n\n2. **Insufficient Comparison with Concurrent Work**: The paper mentions concurrent works FlexiDiT and Lumina-Video in Sec 2 and 4.4. Although it claims PPFlow's training strategy (staged training) is superior to their approach (full-timeline training) and experiments (Tables 4, 5) show better results for PPFlow, the argumentation for why PPFlow's strategy is better, and the trade-offs against the claimed \"flexibility\" of FlexiDiT, could be discussed more deeply.\n\n3. **Arbitrary Stage Divisions and Patch Sizes**: The timestep points for stage transitions (e.g., 0.5, 0.75) and the patch sizes used (e.g., 4x4, 4x2, 2x2) are manually chosen. The paper lacks sensitivity analysis on these hyperparameters or exploration of more automated selection methods. Increasing the number of stages (Table 7) seems to have diminishing returns and requires more training.\n\n4. **Missing Key Visualizations**: While Table 3 presents metric results for applying PPFlow to FLUX.1 (text-to-image model), the paper lacks corresponding visual samples. Given the subjective nature of text-to-image tasks, adding visual comparisons between PPF-FLUX.1 and the baseline FLUX.1-ft would significantly strengthen the conclusions.", "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)