HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance
Authors: Jiazi Bu
Pengyang Ling
Yujie Zhou
Pan Zhang
Tong Wu
Xiaoyi Dong
Yuhang Zang
Yuhang Cao
Dahua Lin
Jiaqi Wang
Venue: NeurIPS 2025 poster ()
Keywords: high-resolution generation
text-to-image generation
Primary area: deep_learning
TL;DR: HiFlow is a training-free and model-agnostic framework to unlock the resolution potential of pre-trained flow models.

Abstract: Text-to-image (T2I) diffusion/flow models have drawn considerable attention recently due to their remarkable ability to deliver flexible visual creations. Still, high-resolution image synthesis presents formidable challenges due to the scarcity and complexity of high-resolution content. Recent approaches have investigated training-free strategies to enable high-resolution image synthesis with pre-trained models. However, these techniques often struggle with generating high-quality visuals and tend to exhibit artifacts or low-fidelity details, as they typically rely solely on the endpoint of the low-resolution sampling trajectory while neglecting intermediate states that are critical for preserving structure and synthesizing finer detail. To this end,  we present HiFlow, a training-free and model-agnostic framework to unlock the resolution potential of pre-trained flow models. Specifically, HiFlow establishes a virtual reference flow within the high-resolution space that effectively captures the characteristics of low-resolution flow information, offering guidance for high-resolution generation through three key aspects: initialization alignment for low-frequency consistency, direction alignment for structure preservation, and acceleration alignment for detail fidelity. By leveraging such flow-aligned guidance, HiFlow substantially elevates the quality of high-resolution image synthesis of T2I models and demonstrates versatility across their personalized variants. Extensive experiments validate HiFlow's capability in achieving superior high-resolution image quality over state-of-the-art methods.

HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance
Jiazi 1u Pengyang Ling
2.5x Yujie Zhou Pan Zhang
Tong Wu
Xiaoyi Dong Yuhang Zang² Yuhang Cao Dahua 3.5,7 Jiaqi Wang 5.6
'Shanghai Jiao Tong University University of Science and Technology of China
'The Chinese University of Hong Kong "Stanford University SShanghai AI Laboratory Shhanhaii Innovation Institute "CPII under InnoHK gi github. io/hiflow gi github.r. io/
2046 RONS
2546 2046
BOPER 9922
9072 4096
9072 2015
Figure 1: Gallery of HiFlow. The proposed HiFlow enables pre-trained text-to-image flow models [Flux.1.0-de. integrated with various LoRA models) to synthesize high-resolution images with high fidelity and rich details in training-free free manner. All prompts are listed in the appendix.
'Equal contribution. |Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Abstract
Text-to-image (T2I) diffusion/flow models have drawn considerable attention re- cently due to their remarkable ability to deliver flexible visual creations. Still, high-resolution image synthesis presents formidable challenges due to the scarcity and complexity of high-resolution content. Recent approaches have investigated training-free strategies to enable high-resolution image synthesis with pre-trained models. However, these techniques often struggle with generating high-quality vi- suals and tend to exhibit artifacts or low-fidelity details, as they typically rely solely on the endpoint of the low-resolution sampling trajectory while neglecting interme- diate states that are critical for preserving structure and synthesizing finer detail. To this end, we present HiFlow, a training-fiee frree and model-agnostic framework unlock the resolution potential of pre-traines flow models. Specifically, HiFlow establishes a virtual reference flow within the high-resolution space that effectively captures the characteristics of low-resolution flow information, offering guidance for high-resolution generation through three key aspects: initialization alignment for low-frequency consistency, direction alignment for structure preservation, and acceleration alignment for detail fidelity. By leveraging such flow-aligned guidance, HiFlow substantially elevates the quality of high nigh-resolution image synthesis of T21 models and demonstrates versatility across their personalized variants. Extensive experiments validate HiFlow's 's capability in achieving superior high-resolution image quality over state-of-the-an methods. Our code is available at HiFlow Repo
Introduction
The text-to-image (T2I) diffusion/flow models 45
40
38
40
46
30
50
28, 53
54
which allow for flexible content creation from textual prompts, have recently achieved a landmark advancement. Despite considerable improvements, existing T2I models are typically confined to restricted resolution (c.g.. 1024 x 1024) and experience notable quality decline and even structural breakdown when attempting to generate higher-resolution images, as illustrated in Fig. Such shortcoming limits the utility and diminishes their appeal to contemporary artistic and commercial applications. in which detail and precision are paramount.
51 G3 57 56 suggest fine-tuning T2I models on nigher-resolution samples to enhance the adaptability to large-scale images. Nevertheless, this straightforward approach entails significant costs, primarily the burden of high-resolution image collection and the necessity for model-specific funnnng Therefore, recent studies have investigated
As initial efforts, several methods [6 21 35
26, 42 13, 49) to harness the inherent potential of pre-trained T2I models in high- resolution image synthesis. However, the majority of these 25 involve manipulating the internal features within models, exhibiting restricted transferability across architectures, such as applying methods tailored for U-Net architecture in DiT-based models. Another line of research [1, 26 49 [3] suggests fusing the upsampled low- resolution images into the denoising target during reso-tesolution synthesis for structure guidance. Despite simplicity, these methods merely align high- resolutoolttion images predicted at different time steps with the upsampled low-resolution image from the final step as single guidance anchor, risking the introduction of artifacts due to distribution discrepancy, as shown in Fig. 3 (b). Moreover, they primarily emphasize structural guidance, leaving the potential of deriil-oriented cues underexplored, resulting in suboptimal detail fidelity in the outputs, as illustrated in (d).
training-free strategies 17 12
23 25 600 29)
methods [17 22 60
42
In this work, we introduce a novel training-free and model-agnostic generation framework, termed HiFlow, which is designed to advance high-resolution T2I synthesis of Rectified Flow models and can be seamlessly extended to diffusion models by modifying the denoising scheduler. Specifically, HiFlow involves a cascade generation paradigm: First, a virtual reference flow is constructed in the high-resolution space based on the step-wise estimated clean samples of the low-resolution sampling flow. Then, during high-resolution synthesizing, the reference flow offers guidance in sampling initialization, denoising direction, and moving acceleration, aiding in achieving consistent low-frequency patterns, preserving structural features, and maintaining high-fidelity details, respec- tively. Such flow-aligned guidance from the sampling trajectory facilitates better merging of the structure synthesized at the low-resolution scale and the details synthesized at the high-resolution

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2004 2004
4006 4025
1:24 2014
3012 3002
FLUX (DiT/Flow)
SD3 Medium (DiT/Flow)
SDXL (U-Net/Diffusion)
Figure 2: T21 models suffer significant quality degradation in high-resolution image generation.
scale, facilitating superior visual quality, Furthermore, HiFlow exhibits broad generalizability across U-Net and DIT' architectures, owing to its independence from internal model characteristics. Ex- tensive experiments demonstrate that the proposed method surpasses state-of-the baselines on the latest Rectified Flow T21 model. The main contributions of this paper are summarized as fol- lows: (i) We propose HiFlow, a novel training-free and model-agnostic framework unlock the resolution potential of pre-trained Rectified Flow models, which constructs virtual reference flow derived from low-resolution sampling trajectories to enable high-resolution synthesis; (ii) The con- structed virtual reference flow provides flow-aligned guidance in terms of initialization, direction, and acceleration, thus promoting now-frequency consistency, structural preservation, and detail fidelity, respectively; and (iii) Comprehensive experiments validate HiFlow's superiority over state-of-the-ee art competitors and highlight its flexibility and versatility in various applications including LoRA [22]. ControlNet [59, and Quantization 31
Related work
2.1 Text-to-image generation.
Text-to-image (T2I) synthesis 45
28 has witnessed significant ad- which have demonstrated remarkable capabilities in producing high-quality images based on given textual prompts. Denoising Diffusion Probabilistic Models (DDPM) 19 and Guided Diffusion (9] showcased the potential of diffu- sion processes to generate high-fidelity images. Subsequently, the introduction of latent space diffusion
10
38
46
30
50
vancements with the advent of diffusion models 2
B3
64
45 marked revolutionary advancement the field, which significantly reduced computa- tional demands and enabled more efficient training, giving rise to pioneering models such as Stable Diffusion and Stable Diffusion XL [1]. Recently, the integration of transformer-ben based architec- tures (5] 15 65 28 into diffusion models has also led to improvements in both image quality and computational efficiency. this work, we primarily construct our method upon Flux. 1.0-dev (28). an advanced Rectified Flow T2I backbone renowned for its superior generation quality.
2.2 High-resolution image generation.
High-resolution image generation with diffusion models has gained increasing popularity in recent years. Several studies [16 21 35
63 57 [66] have proposed training or fine-tuning existing T2I diffusion models to enhance their capability for high-resolution image generation. However, remains challenging task due the scarcity of high-resolution training data and the substantial computational resources required for modeling such data. Another line of researchs [58] 11 32 52
44
51
employs super-resolution techniques upscale the resolution of generated low-resolution images. Nevertheless, the quality of generated images via this approach heavily depends on the initial quality the super-resolution model. Recent efforts
of the www-resouuionn images and the performance
have focused on training- ffree strategies [17] 12 23 I 25 60) 29 26 42 13 49 (55) that modify the
inference strategies of diffusion models for rew-uusotttion generation. For instance, HiDiffusion [60] suggests reshaping features in the outermost blocks of the U-Net architecture to match the training size deeper blocks. DiffuseHigh (26] upscales the low-resolution generation result and re-denoises
it under the structural guidance from the Discrete Wavelet Transform. I-Max [3] projects the high-
resolution flow into the low-resolution space, enabling training-fre free high-resolution image generation with flow models. Many existing training-free methods 17
25 are inherently entangled with model-specific internal features, limiting their generalizability across different architectures.
23 60
42
Others 12 26 49
[3] lack effective guidance throughout the high-resolution generation process,
3

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FID between Eu:; and
Stngle Anchon Presitur Worke)
Time dependent Anchor (HFlow)
Single Ancher (Preriuu Warks)
Time dependent Ancher (HFlow)
FID
Comparinon difinest diection
w/o Acc Aligmmnt (Previous Works)
Acc Algennen (HaFlow)
w/o Ace Algrennt (Prewious Warlss)
w ALe Aligmment (HiFlo))
Accoleation visualization
(d) Effects of scce lcoelentionn aligtment detail symbhesis
Figure 3: Observations. (a) Distribution discrepancy between predicted clean sample Xo- and clean sample Xo. (b) Comparison with constant and time-dependent direction guidance. The former exhibits artifacts, the latter demonstrates better structure preservation. (c) Visualization of acceleration. (d) Effect of acceleration alignment, validating its role facilitating high-fidelity details generation.
including exhibiting distributional discrepancy in the selection of structure guidance anchors and the neglect of detail synthesis guidance, resulting in artifact emergence and decreased detail fidelity.
Method
3.1 Preliminaries
Flow Matching 34 and Rectified Flow (36 aim at streamlining the formulation of Ordinary Differ- ential Equation (ODE) models by establishing a linear transition between two distinct distributions. Consider clean image samples X, ~ No and Gaussian noise X1 mi Rectified Flow delineates linear trajectory from X to Xo. with the intermediate state X, defined as:
X, = =Xxx + (1-t)Xo:
(1)
in which & [0, 1] denotes continuous time interval. By taking the derivative of both sides of Eq. its linear progression gives the following equation:
d.x; (X1 Xo)dt.
During the denoising phase, given X₁ and time t, a neural network vy is introduced to estimate the vector of flow, i.e., X, Xo), which can be expressed as:
ve vo (Xe,, t) X Xo-r,
in which e represents optional control signs such as textual or image prompts, and X1-- and Xue-t denote the predicted noisy and clear component within X,, respectively. Further derivations regarding X1+( and Xo-e are provided in the appendix. Since X, and are known, from Eq. U,(X,, t, c) is essentially determined by the predicted clean component Xaess i.e.
to (xe,t, X, - X--Xo-t
(4)
The progressive denoising of flow models can be formulated follows:
X Xt, + vo(,,, L4, () 42),
(5)
in which the movement t, from to indicates the trajectory of X,, from gaussian noise X; to clean sample Xg. During denoising, the predicted (o(X, t, determines the denoising direction at each time f4 In the following sections, it is used to denote up (X,,,, t, c) for simplicity.
3.2 Virtual reference flow
Rectified flow models have shown great promise in advancing high-quality image generations. Yet, these models experience critical quality drop when attempting high-resolution synthesizing, as illustrated in Fig. 2 To this end, previous works [12 26 49 [3] typically leverage the synthesized

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Low pess filter
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Inter
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Specific Operation
High
X
Hiigh
High
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High 1-11
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Initialization Alignment
Aligrment
Alignment
Alignment
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Overview of HiFlow. HiFlow constructs reference flow from low-resolution sampling trajectory to offer guidance for high-resolution generation in initialization, direction, and acceleration.
Figure 4:
low-resolution sample X! for guidance by fusing its upsampled variant into predicted clean sample xhighh modify the denoising direction. However, for given flow models, it observed that there are ignored distribution discrepancies between Xp-+ at different and Xo. and these discrepancies increase significantly as approaches 1, as depicted Fig. 3 (a). Therefore, the fusion between upsampled X/""w (single guidance anchor) and hhighh may cause artifacts and thus lead to sub-optimal results, as shown in Fig. (b). To this end, in this work, virtual reference flow is constructed in the high-resolution space that can can fully characterize the information of the low-resolution sampling trajectory. Specifically, for reference flow, its predicted clean image 0 at time is defined as:
(X (x(() LE [0,1]
which di ((x)))))) is the upsampled X by interpolation function d(.). Meanwhile, the noisy image X of the reference flow at time is defined as a virtual noisy image in high-resolution space. For each time the vector of reference flow (Xree X x// (X7et + Xlow ())()).. Indeed, the reference flow employs virtual X to construct imaginary sampling trajectory in high- high-resolution space that can produce the upsampled low-rcsolution image d(x(.)) Such a virtual reference flow acts as bridge connecting low now-resolution and high-resolution sampling trajectories, facilitating guided high-resolution synthesis with enhanced structure and fidelity.
3.3 Flow-aligned guidance
Given that T21 diffusion/flow models prioritize low-frequency structure components before synthesiz-
ing high-frequency details (50 27), we follow the cascade generation pipeline 112 26 42 i.e., first
synthesize low-resolution image and mapping its sampling trajectory to high-resolution space to
5

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obtain reference flow. This reference flow is then used for flow- low-guided high-resolution generation, which is analyzed from the following three aspects. The pipeline of HiFlow illustrated in Fig. +
Initialization alignment. For a given virtual reference flow, the sampling of high-resolution genera- tion starts from the noisy variant of X 0 which can be expressed as:
X X = x
high + (1 0e-7
in which x is the sampling initialization of high-resolution generation, X xhiigh is gaussian noise, and 7 E (0,1) is noise addition ratio. Such initialization alignment allows skipping the early stage in high-resolution generation, thereby maintaining the consistency of high-resolution results and low-resolution images in low-frequency components, while also facilitating higher inference speeds.
Direction alignment. While initialization alignment ensures low-frequency consistency at the beginning of high-resolution generation, such structural information may be destroyed at subsequent denoising steps due to the limited stability of models in synthesizing high-resolution content (as shown in Fig. 2), risking broken structures. Therefore, direction alignment is designed for structural preservation, which is achieved by modifying the denoising direction based on reference flow, i.c.
Xhe Xb + a, (F(((x))))) @ C(D))
F(F(( high @ C(D))),
(8)
in which F and F denote 2D Fast Fourier Transform and its inverse operation, respectively, L-) denotes butterworth low-pass filter and D is the normalized cutoff frequency, and ax is the direction guidance weight. Essentially, Eq. 8 replaces the wwwfrreuency component of X-high with that in Xp-., thus rejecting the updating on low-frequency structures when synthesizing high- tigh-frequency details. Unlike previous work [26 12 49] that aligns with a single guidance anchor Φ ((X))"), Eq.
manipulates X by using time-dependent Xgef such fusion between samples from the same t helps avoid artifacts caused by distribution discrepancy (see Fig. (a)), facilitating better quality.
Acceleration alignment. Although the above strategies allow for rich detail generation while maintaining structure, the fidelity of synthesized details risks dropping in some cases, in which unrealistic contents appear, such repetitive patterns and abnormal textures, as shown in Fig. d). This issue primarily arises from the constrained capability of generation models in synthesizing high-resolution content. To this end, acceleration alignment is proposed to enhance detail fidelity by aligning the synthesized content of the high-resolution flow with the reference flow at each time t. Specifically, the acceleration, which is defined the second-order derivative of movement X,, and also denotes the first-order derivation of vector it, can be expressed as:
d²x, du: Ut Ut dt dt
(9)
f tz Furthermore, based on Eq. 4 and Eq. 5 the acceleration in Eq. can be simplified as follows:
(Xe.. - Norm Eo (Xe, - -Xo+e.)
4:11 4; Nx + (ti-1 (X,, -oome) - 1, No - tx_, Noomt) 4-1t((1111 1.)
(10)
Xao- Xo
4:-1
1--1 -4
can be concluded from Eq. 10 that the acceleration depicts the variation in the predicted clean sample Xo-t between adjacent time f, with time-dependent term 1/t. As shown in Fig. 3 (c), the acceleration primarily captures texture and contour information while also indicating the sequence of content synthesis at each f, i.e., it showcases what content the model is responsible for adding at different t. Therefore, we propose aligning the acceleration of high-resolution generation with that of the reference flow to synchronize the model's 's preference for content synthesis order, enabling guided detail synthesis both in content and timing. Mathematically. acceleration alignment is modeled as:
high 0t
high du Br (e
high at
(11)
in which 3, is the acceleration guidance weight. Substituting Eq.
P into Eq.
II
we have
high
ligh
high
high
high
high
0,
1/2
1/
1/
U/e
+ Bel
(12)
t 1 1
t
t
t;
f ti
1 1
t

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Furthermore, Eq. 12 can be simplified into the following form:
high high 4 1 + B. (1
high
+ high)
(13)
Subsequently, the obtained Thigh based on Eq. 13 is plugged into the ee Eq. tion alignment. The detailed discussion and analysis provided in the appendix.
59.500 to facilitate accelera-
4 Experiments
4.1 Implementation details
Experimental settings. If not specified, the generated images are based on Flux. (tux.1.04e 1.0-dev [28] an advanced open-sourced Rectified Flow model based DiT architecture. The sampling steps as 30, the noise-adding ratio 7 in initialization alignment set as [0.6, 0.3, 0.3] for 1K - 2K - 3K - 4K cascade generation, and the normalized cutoff frequency is set as 0.4. The guidance weights in direction/acceleration alignment are set as = 3r = t/T for gradually weakening control. All experiments are conducted on single NVIDIA A00 GPU.
The compared methods encompass DemoFusion [12]. DiffuseHigh [26]. I-Max 13 and an image super-resolution method, BSRGAN (58). For fair comparison, the training-free free methods are tested on the same Flux model according to their official implementations.
Baselines.
Evaluation. We collect 1K high-quality captions across various scenarios for diverse image gen- eration. The CLIP
4 score used to assess the prompt-following capability, Frechet Inception Distance [18 (FID) and Inception Score [77 (IS) are reported measure image quality, in which FID calculated between generated images and 10K real high-quality images (with at least 1024 x 1024 resolution) sourced from LAION- LiihN-iigh-aae Resolution 48) Furthermore, the patch-version FID patch and 1Spach are calculated based on local image patches to quantify the quality of synthesized details.
Table 1: Quantitative comparison with other baselines. The best result is highlighted bold,
while the second-best result is underlined. indicates methods adapted from U-Net architecture.
Resolution (height width)
Method
FID
FiDpatch 4
IS
patch
CLIP Score
DemoFusion*
12
56.07
51.69
27.23
13.48
35.05
DiffuseHigh* 26
61.62
50.25
26.76
13.10
34.83
2048 2048 (2K)
I-Max 13
57.57
54.56
28.84
12.07
34.96
Flux + BSRGAN
58
60.25
52.06
25.85
13.39
35.34
HiFlow (Ours)
55,39
47.70
28.67
13.86
35.32
DemoFusion 12
56.72
49.48
21.17
8.49
35.27
High 26
62.01
50.98
20.60
8.09
34.98
4096 x 4096 (4K)
I-Max 13
53.27
52.93
22.21
7.65
35.05
Flux + BSRGAN
58
59.53
54.12
19.32
8.87
35.37
HiFlow (Ours)
52.55
45.01
24.62
9.73
35.40
Comparison to state-of-the-art methods
Qualitative comparison. As shown in Fig. 5 while DemoFusion and DiffuseHigh are capable of preserving the overall image structure, they often synthesize low-fidelity details and suffer from reduced contrast, primarily due to insufficient guidance in high-resolution detail synthesis. I-Max sometimes produces blurred results because it uses upsampled low-resolution images as the ideal projected flow endpoints, leading to overly limited detail creation. Although BSRGAN, an image super-resolution method, enhances image clarity to some extent, it struggles to synthesize finer details. In comparison, the proposed HiFlow consistently yiclds aesthetically pleasing and semantically coherent outcomes. More visual results are provided in the appendix. Additionally, it observed that HiFlow demonstrates the potential for correcting local artifacts in now-resolution images in the subsequent high-resolution generation process, as illustrated in Fig.
Quantitative evaluation. The quantitative results are presented in Tab. It is observed that the proposed HiFlow achieves competitive performance in terms both image quality (FID, FID IS and IS (Sacch) and image-text alignment (CL.P score) under different resolutions, validating its stable and superior performance high-resolution image generation. Moreover, as presented Tab. HiFlow surpasses all its training-fire free competitors in inference speed, offering higher efficiency.

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Figure 5: Visual comparison of synthesized 2K and 4K images. HiFlow yields high-resolution images characterized by high-fidelity details and coherent structure. Best viewed zoomed in.
4.3
Ablation study
HiFlow performs guided high-resolution generation by aligning its flow with the reference flow in three aspects: initialization (A,), direction (Aq), and acceleration (A.). As can be observed Fig. 7 initialization alignment helps avoid semantic incorrectness facilitating low-frequeet www-freuenccy consistency with low-resolution images. Furthermore, direction alignment enhances structure preservation in the generation process by suppressing the updating in the low-frequency component. Moreover, acceleration alignment contributes to high-fidelity detail synthesizing, eliminating the production of repetitive patterns in the garden, shown in the realistic woman's face and the appearance of her clothing. Quantitative results of the ablation study are shown in Tab. 3
4.4 Applications
Application on customization T2I models. Customization T2I models, which allow tailored generation to match users' specific requirements, have recently garnered increasing attention. As illustrated Fig. (a)-(b), the proposed HiFlow is compatible with various customization modules (LoRA 22 and ControlNet (59)), thus advancing personalized high-resolution image generation.

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Low Resolution Artifacts
breallane www.lonnteiaeeeeccccc
Details
Figure 6: HiFlow corrects local artifacts in the low-resolution result.
Av Agg An
-Ag. An
-Aa
HiFlow (Ours)
Figure 7: Ablation experiments in 2K and 4K resolution. Best viewed zoomed in.
Application on quantized T2I models. Given substantial rise computational complexity introduced by advanced T21 models, model quantization techniques [8] 31 explored to decrease computing resource requirements. As shown in Fig. 8 (c), the training-free and model-agnostic attributes of HiFlow allow it to be directly employed with the 4-bit version of Flux (quantized by SVDQuant (31), thereby substantially accelerating high-resolution image generation.
61 62] have been widely
Application on U-Net based T2I models. U-Net-based T2I models, such SD1.5 45 and SDXL 41], constitute pivotal parts of T2I diffusion models and have been extensively developed by the community. As depicted in Fig. (d), the integration of HiFlow and SDXL [1 enables the synthesis of realistic high- resoluionn images, demonstrating its broad applications.
Table 2: Comparison latency.
Table 3: Quantitative results of ablation study.
Resolution
Method
Latency (anc.)
Method
FID
18
DemeFusion
106
Ag. Asi-A A
67.87
71.79
23.47
9.16
33.81
2045
DiffuseHigh 26
59
2048
-A. AA
58.26
54.22
27.46
12.58
34.47
[+]
HiKlow (Ours)
56
HiFlow (Ours)
55.39
47.70
28.67
13,86
35.32
DemoFusion" DiffuseHighh
972
A
234.38
198.21
9.33
3.31
11.42
10962
I-Max [19
735
40962
56.20
55.36
48.
51.79
22.78
19.85
6.51
8.77
35.44
HiFlow (Ours)
379
HiFlow (Ours)
52.55
45.01
24.62
9.73
35.40
5
Conclusion
present HiFlow, a tuning-free and model-agnostic framework enabling pre-trained Rectified Flow models to generate high-resolution images with high fidelity and rich details. HiFlow involves novel cascade generation paradigm: (i) virtual reference flow constructed based on the step-wise
predicted clean samples of the cow-resolution sampling trajectory; (ii) the high-resolution flow is

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Baak Aath Wwww HOW
(SVDQuam)
66
(SOXL)
2412
Figure 8: Versatile applications of HiFlow.
aligned with the reference flow via flow-aligned guidance, which encompasses three aspects: initial- ization alignment for wwwfrrquency consistency, direction alignment for structure preservation, and acceleration alignment for detail fidelity. Extensive experiments demonstrate that HiFlow surpasses state-of-the-arr methods and highlight its broad applicability across different model architectures.
Acknowledgement
This project is funded in part by Shanghai Artificial Intelligence Laboratory, Shanghai Innovation Institute, the National Key R&D Program of China 00022D001002010 the Centre for Perceptual and Interactive Intelligence (CPII) Ltd under the Innovation and Technology Commission (ITC)'s InnoHK. Dahua Lin is a PI of CPII under the InnoHK.
10

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Appendix
In the appendix, we present additional implementation details (Section A). additional qualitative results (Section[)). text prompts for image generation in both the main paper and appendix (Section[)) more discussion on acceleration alignment (Section D) further derivations regarding I and Xu-c (Section pseudo code of flow-aligned guidance (Section[E). the limitations of our method (Section G as well as its broader impacts (Section HD. as supplement to the main paper.
A Additional implementation details
To enhance the Rectified Flow model's stability when generalizing to extrapolated resolutions, we adopt inference techniques suggested by previous works [39]24]] [3] the high-resolution generation process, including NTK-aware scaled RoPE [39], balancing the entropy shift of self olffattention [24] and balancing the image/text sequence length ratio for MMDiT [13.
B Additional qualitative results
We present more visual results of HiFlow integrated with various LoRA models at 4096 x 4096 resolution in Fig. HH.4 Fig. H..5) Fig. H.6 Fig. H.7 Fig. H.8 and Fig. H..9 along with generated results
using the same prompts and different random seeds (0/1/2) at 4096 x 4096 resolution in Fig. H.10
Moreover, additional results of HiFlow on SVDQuant 31 and SDXL 41 shown in Fig.
C
Text prompts
Text prompts used to generate images in this paper are provided Tab. H.I
Tab. H.2 and Tab. H.3
D Discussion on acceleration alignment
In this section, we provide deeper analysis on acceleration alignment. Given that Rectified Flow models are based on the premise that the noisy latent variable X, transitions linearly between between the noise distribution and the clean image distribution it is logical to introduce an acceleration term drawn from physics account for non-linear dynamics during the denoising process:
dXx, a. dt df2 Ut
dv, dxx,
(14)
where U2 the denoising direction predicted by the model (the first derivative of X, with respect to t), and a, denotes the acceleration component (the first derivative of e with respect to t, the second derivative of X2 with respect t). The above definition consistent with previous works 7 that introduced acceleration in image editing tasks. To explore the practical implication of ag Eq. is substituted into the definition of acceleration in Eq. 14
du' 01 dl
N4.
(X,, Xu++, 1) - 2.2 (X₁, Xoets)
(15)
4-11
4-11 -
Furthermore, based on Eq. X through the following relation:
4 and Eq. 5 we are able to express X in terms of X... No and
X XX,, + De, (t:-1 t() X,, + X., Xoo (ti-1 - -1) Xor + 4-11 (X,, X.e
t;
t,
(16)
By plugging Eq. 16 into Eq. 15 Xt,,, is eliminated and the expression for a, can be simplified as:
Qt (,-1 (Xoes, + h-1 (Xe, - Xoo - Not-ten) - i, (X,, Xoe-,)) b
4 t;
Xo-1i-1 Xo+ti
(17)
1: 4--1 - 4i
dXort
dt
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From Eq. 17 it is concluded that the acceleration term essentially depicts the first order derivative of the predicted clean sample Xpe= with respect multiplied by a deeeddeenddent factor - Therefore, the acceleration in the denoising process of Rectified Flow models primarily captures the variance image details, including what details should be synthesized at each timestep and how these details should be synthesized for better fidelity, especially in the late denoising stage when the model focuses on the generation of fine image details. Fig. D. provides a visualization of the acceleration across different timesteps, and the results consistent with our analysis.
0.6
t= 0.3
t= t=1.1
Result
Figure D.I: Visualization of acceleration at different timesteps.
The above discussion indicates that acceleration characterizes the model's ordering, rhythm, and preferences in synthesizing image details, while the reference flow reflects how this information, learned at the training resolution, manifests in the high-resolution space. To this end, the acceleration alignment in Eq. [ is introduced to guide the acceleration high-resolution generation the form of -lassifier-free guidance 220] for better detail fidelity.
E Further derivations regarding X1c and Xoc
In this paper, Xot- denotes the predicted/estimated clean component of the current noisy latent X₁+ while Xi++ represents its corresponding noise component at timestep t. For rectified flow models, the model output Xx, f, c) stands for the flow vector/velocity (we simplicity). Given the model output U and the current noisy latent X₃ at timestep t, the estimated clean sample Xo.e and its corresponding estimated noise X XIe- be obtained by:
to denote vy (X,,,,, t, for
X Xx D,t,
(18)
X = X₁ + 1z(1 1).
(19)
visualization example of Xo-ts X1e- and X, during the denoising process is presented in Fig. E.2
F Pseudo code of flow-eligned guidance
In this section, we present the pseudo code for each component of the proposed flow-aligned guidance, detailed in Algorithm [
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Timesteps (Decay Schedule)
X
x
X;
Figure E.2: Visualization of Xu-t, X1+-1 and X, at different timesteps.
Algorithm Flow-Alignes Guidance
Inttialisation Aligunent
def Inirialization Aliganent (raffrrrrrrffaeeeeeea boise_adding_ratae
noise X_0_ref
moise Randlocise(X_0 (X_0_ref_0__eeeeeeeee r_r.ee
shape)
X_O_high
(X_0 __0e, noise,
return X_0_bigh
Direction Aligument def Pirection_Aligmae wew.lu.eeeelliio.... flov, reference_flos alphaa):
X_0_high/ regh_resolution. cttpeeonttoooorrrttttttt... creedtttclean..ammlaat
X_0_ref reference_ floxxpredict. cleeentttcleeeeeeeeeeee caaeeet_ceeeenaapeleeee stmples[t]
X_0_high x_0_bigh [t] (LozFreq((X___re) LoxFFee(11
return X_O_high
Acceleration Aliganent
def Acceleration_Aligeee Alliiion_Aiimmmntttt (tg.gmmmtttigh..rrrttttteeeeeee reaiighreeeeutten_fiii flox, reference_fles flov, t, betas):
v_t_high high_reeeouuton.c
wlw. vectore(t]
v.t.high.prev = rewoorreeelliinnttte wtteiiioonnooofl.. wwtp:/oooo..ooocctteooo veoo_rectora[: 1]
v_t_ref reference_fle flww_vectoro((t)
v_t_ref_prev reference_ flow. flou_
1]
v_t_high v_t_high batas[t] (v_t_raf v_t_ref prev v_t_bigh v.t.highh.per) v_t_bigh
Notice that Initialization Aligunent perforned outside the sampler def ponpler_wwit__Foo_AAoe Aut
Guidance (eiiuh_rroluution_fooo reference_floz, t, alphas, betas); t_thighh heaggrrrelluiionneee flov. flow. (lov_vectors(1)
Direction Aligament
high_resclutionn_ flww. www.ppreeicccciieee wwt.meeeeeeeeeeeeeee samplee [t] BetinateCleamSempleee (t__te(vt___t))
x_0_high Direction.Aligument thightttththgrreettttttooo
reference_floge alphas)
Acceleration Alignnent
[t] (1.
v_t_high Accalerattoo_A11ge Aiiginnnnnnntttttteeeeeeenieeiee gegp_resalutton_llle reference_flox t, betas)
Sample with alignad high righ-ressttion flow
next_noiey_latert FLowfaatchingSanpeer (hiigg/ssnpllerrhh.eeeeeeeeee resis_rrsolutio_floe. www..ooiey.aaeeeteeeeeeeeeeee v_t_high,
return next_noisy_latemn latent
G Limitation and discussion
Despite the advancements of HiFlow in enhancing high-resolution image generation, it faces certain constraints. As training-free method, the generation capability of HiFlow is highly dependent on the quality of the reference flow, which provides flow-aligned guidance in each sampling step. Therefore, the structural irregularities within the reference flow might compromise the quality of the final production. As shown in Fig. G.3, the low-resolution image of tiger exhibits duplicated tail, and this structural anomaly is preserved in the high-resolution generation result. Fortunately, given HiFlow's ss training-free and model-agnostic nature, such flaws can be effectively mitigated by (i) integrating HiFlow into a stronger backbone model, (ii) using ControlNet [59] to introduce external control conditions for controllable generation (see Fig. g (b) in the main paper), (iii)
23

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applying inference-ting scaling techniques [37 to search for better low-res oww-rsolutionn trajectory for the subsequent high-rews nighlresouution generation, bypassing the need for fine-tuning or adjustment.
Flux. 1.Odev 1024 x 1024
HiFlow 4096 4096
Figure G.3: Failure case of HiFlow.
H Broader impacts
The introduction of HiFlow, novel training-free framework for high-resolution image generation, brings with it range of societal impacts, offering significant benefits while also posing notable risks.
the beneficial side, HiFlow's ability to produce detailed, high-resolution images directly from textual prompts without requiring model retraining opens up exciting possibilities across multiple domains. creative industries, such as design, marketing, and digital media, professionals can rapidly prototype visuals, enhance visual storytelling, and generate production-reade assets with minimal cost and effort. The high resolution and quality of the outputs make them suitable for both conceptual and final-use purposes. In education and scientific communication, HiFlow can assist instructors and researchers in generating accurate visual aids tailored to specific topics, enriching the learning experience especially in subjects that rely precise imagery, like anatomy, architecture, or environmental science.
However, this powerful capability also introduces challenges. The ease and accessibility of generating photorealistic images without any fine-tuning may lead to the creation and spread deceptive or harmful content, including synthetic news photos, manipulated evidence, or deepfake creation. Without robust safeguards, such misuse could contribute to misinformation, digital fraud, or the erosion of trust in visual media.
In conclusion, while HiFlow marks a significant milestone in the realm of high-resolution image generation, its responsible use requires the establishment of ethical standards, transparent practices, and public awareness. As the technology continues to advance, collaboration among developers, regulators, and society at large will be essential to ensure that its benefits are maximized while minimizing potential harms. By fostering accountability and encouraging the development of verification mechanisms, we are able to guide the use of HiFlow toward constructive and trustworthy applications.
24

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Figure H.4: Visual results of HiFlow at 4096 4096 resolution.
25

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Figure H.5; Visual results of HiFlow at 4096 4096 resolution.
26

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brealksynn
Figure H.6: Visual results of HiFlow at 4096 4096 resolution.
27

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Figure H.7: Visual results of HiFlow at 4096 4096 resolution.
28

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Figure H.8: Visual results of HiFlow at 4096 4096 resolution.
29

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Figure H.9: Visual results of HiFlow at 4096 4096 resolution.
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Seed = O
Seed = 1
Seed = 2
Figure H.10: Generated results using same prompts and different seeds at 4096 x 4096 resolution.
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SVD-Q 1024 2048
SVD-Q 2048 2048
Coca-Cola
SVD-Q
SVD-Q 2048 1024
2049 1024
SVD 2048 2048
SbXL 4096 4096
SDXL 4096 4096
Figure H.l1: More visual results of HiFlow on SVDQuant and SDXL.
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Table H.1: The image generation prompts for each figure are listed sequentially, following the order from left to right and top to bottom. (Table 1/3)
Figure
Text Prompt
Knight holding kitten in flower garden: A knight in full plate armor stands amidst blooming flowers, gently cradling a tabby kitten in their gauntleted hands. Sunlight filters through the foliage, creating a warm, dappled light. The kitten looks up at the knight with a curious expression. Focus on the contrast between the hard armor and the soft fur. Photorcalistic style with a touch of fantasy.
The image depicts detailed and dynamic illustration of Gundam, specifically Gundam Barbatos from the anime Mobile Suit Gundam: Iron- Blooded Orphans. This powerful mecha is portrayed in a closc-up action shor, emphasizing its imposing and bartle-wom appearance. Gundam Barbatos features a distinct design with white and blue color scheme, accented by gold red details. The mecha's 's head particularly striking. with prominent yellow V-fin crest that is characteristic of many Gundam designs. Its eyes glow a vibrant green, udding to the intensity and life-like presence of the machine. The head unit also has a faceplate that suggests a fierce and determined expression, fitting for a battle-hardened warrior. The armor plating on B4RB4TOS appears worn and weathered, with visible scratches and damage. indicating that it has seen many battles. The chest area reinforced with blue armor, and the overall structure of the Gundam is muscular and robust, reflecting its strength and combat capabilities. In the background, there are blurred elements that suggest a chaotic buttlefield, with dark, smoky skies and hints of fire or explosions, adding to the dramattc atmosphere the scene. The lighting and shading in the image and realism. while also highlighting the metallic surfaces of the Gundam. Overall. the image captures the essence of Gundam Barbutos as a powerful and relentless war machine, ready to engage in fierce combat. The detailed rendering and dynamic composition emphasize the Gundam's role as a central figure in the struggle depicted in Iron-Blooded Orphans.
expertly done, creating sense of depth
A breathtaking mountain landscape featuring towering peuks with snow caps under serene blue sky. The scene captures the carly morning light, custing soft shudows and illuminating the majestic Tetons. A trunquil river runs through, reflecting the mountains and surrounding evergreen trees, creating mirror-like like effect. The composition emphusizes depth. framing the mountains with cluster of pines the left and gentle frost covering the ground. The cool color palette evokes sense of calm and tranquility, enhanced by the crisp air and delicate mist lingering over the water. The scene embodies peaceful wilderness atmosphere, reminiscent of fine art photography.
A mouthwatering photograph of well-plated gournet burger and French fries, with glass of cola with ice cubes in the background
Portrait of a bear as a roman general in a roman city-state, with a helmet, decorative, fantasy environment, detailed, sharp, clear,
A whimsical village scene is nestled within enormous teacup, where winding cobblestone streets and quaint cottages create a surreal microcosm reminiscent of dreamlike landscapes. From high vantage point, the viewer looks down on the diminutive inhabitants going about their day-to-day activities, contrasting the juxtaposition of innocence and chaos in this fantastical setting. Soft, ethereal lighting envelops the scene, creating atmosphere of tranquility as ordinary life intertwines with elements of fantasy and absurdity to weave a captivating visual narrative that invites the audience into extraordinary world where the commonplace becomes surreal.
Figure 1
Super detailed, half-body portrait of a heautiful young girl with flawless skin and golden hair, wearing an ancient and opulent castle hull with ornate decorations and grand architecture
elegant ball gown. Posed in the background. Bright, soft light illuminates her face. Clear, perfect, and detailed face with brilliant blue eyes, full red lips, a friendly smile, pale skin with freckles, and vivid colors.
A fluffy, round- British Shorthair cat with big expressive eyes, sitting calmly on wooden desk cozy study room. The background features a bookshelf filled with vintage books and decorative ohjects. Soft, natural lighting enhances the cozy, nostalgic atmosphere. A blood-red Ferrari SF90 parked on sain-soaked city street at night, reflecting neon lights from nearby buildings. The wet pavement glistens, and the car's smooth curves are highlighted by the ambient glow of the urban environment.
A professional photograph of a quirky squirce with a fiery red mohawk, energetically playing a whimsical drum set in vihrant autumn forest. The drum kit is entirely imagined, with bass drums made from hollowed, oversized chestnuts that have been polished to gleaming finish. The snare drum is crafted from woven basket of intertwined twigs and leaves, producing crisp, carthy sound. Cymbals are formed from large, dry maple leaves, their edges curled and textured, hanging on thin branch stands. Smaller acorns serve as tom-toms, and the hi-hat composed of overlapping oak leaves. delicately stackod on a twig frame. Every element of the drum set organic, blending scamlessly into the forest floor, which blanketed in a rich layer of autumn leaves, as the squirrel's paws expertly tap the instruments. filling the scene with the imagined thythm of nature.
Wukong, wearing the armor of the Monkey King, holding his weapon with a serious expression, inside an abandoned traditional Chinese temple.
A close-up of blooming peony, with layers of soft, pink petals, delicate fragrance, and dewdrops glistening in the carly morning light.
A futuristic robotic unicom with chrome-plat plated horn and ncon- -oo-ggnoing hooves, shredding a halfpipe in post-apocalyptt skatepark. Sparks fly from its hooves, and graffiti-covered ruins in the background in electric blue spray paint. Dynamic motion blur, ultra-realistic metallic textures, vibrant cyberpunk colors. Mad Max meets Lisa Frank acsthetic.
A tiny astronaut hatching from an egg on the moon.
Figure
A lighthouse standing tall against crashing waves.
Toddy bear walking down Avenuc, bcautiful sunset, close up, high definition, 4k.
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Table H.2: The image generation prompts for each figure are listed sequentially, following the order from left to right and top to bottom. (Table 2/3)
Figure
Text Prompt
A pair of navy blue Converse shoes displayed on the table.
A highly detailed oil painting of tiger.
Figure
3
A woman in pink T-shirt.
Small cottage near the lake, summer.
A cule and adorable fluffy puppy wearing a witch hat in halloween autumn evening forest, falling autumn leaves, brown acoms on the ground, halloween pumpkins spiderwebs, bats, witch's broom.
A majestic, metallic giraffe walking through futuristic city park, its long neck towering above the plants and creatures.
Figure 5
A city at night, illuminated by ncon lights, with cars zooming between towering skyscrapers, and a giant
holographic owl in the sky.
A mystical elf with emerald green eyes and a crown of leaves, standing in a forest glade bathed in soft moonlight.
A painting of brooklyn new york 1940 storefronts, by John Kay, highly textured, rich colour and detail, ballard, deep colour's, 's, style of raymond swanland, trio, oill painting, 768, well worn, displayed, detailed 4k oil painting, glenn harr, textured oil canvas, looking cute.
Figure f
A gardener lending colorful, blooming garden, oil painting by Van Gogh.
Figure
Portrait of luxurious model in a plush coat.
Furina from Genshin Impucc sitting gracefully lavish dining tuble inside grand pulace hall. The setting is elegant and upulent, with towering arched windows letting in soft afternoon sunlight. The table is set for a classic English afternoon tea delicate porcelain teacups, a silver teapot, and a multi-ticn tielitttieeddde tray filled with culorful macurons, scones with clotted cream and jam, finger sandwiches, and petits fours. Furina ornate. cettoian-innspire outfit with lace gloves. looking both noble and contemplative. Crystul chandeliers hang overhead, casting a warm glow, and the palace decor is rich with gold accents and deep velvel drapes. Wukong sits cross-legged in deep meditation atop the blazing peuks of Plame Mountain. Surrounded by rivens of molten lava and pillars of rising smoke, his golden fur glows softly in the fiery light. His staff rests beside him, half-buried in scorched stone. Despite the searing heal and roaring fames around him, his expression is calm and focused, as if in perfect harmony with the chaos. The sky above is a storm of red and orange, casting dramatic hackdrop to his solitary training.
un
A close-up portrait of a young woman with flawless skin, vibrant red lipstick, and wavy brown hair, wearing vintage floral dress and standing in front of blooming garden.
Character of lion in style of saiyan, mafia, gangsta, city lightshss background, Hyper detailed, hyper realistic, unreal engiie ue5, egi 3d, cinematic shot, 8k.
Figure 8
A jellyfish dances in the sea, against backdrop of coral and scaweed.
A product image of an iPhone standing upright in forest. The iPhone's 's screen shows view of the forest with a dramatic lightning strike captured in the background. The surrounding forest is lush und dense with trees, and the sky above is stormy and dark, with the lightning illuminating the scene. The hackground clear and detailed, highlighting the contrast hetween the natural elements and the advanced technology of the phone. This image emphasizes the iPhone's 's high-quality display and camera capabilitics, idcal for use in a tech blog or product design portfolio.
futuristic astronaut exploring an alien planet, with detailed spacesuit and a landscape strange plants and rock formations.
A cat next to a stack of pancakes in a living room, best quality, extremely detailed, 8k.
A frozen tundra with glowing ice spires and northen lights.
Steampunk makeup, in the style of vray tracing, colorful impasto, dark cyan and amber makeup. Rich colourful plumes. Victorian style.
Cinematic photo of delicious chocolate icceream.
A hamster piloting tiny hot air balloon.
Figure
D.I
A Samoyed wearing a sunglasses, sticking out its longue, dsir image, 8k,
Figure E2
A potato ustronaut in spacesuit is watering a rose plant on the moon. It looks happy, Cyberpunk style.
Figure
G.3
A tiger is playing football.
Floating islands connected by waterfalls in sunset sky.
Summer landscape, vivid colors, a work of art, grotesque, Mysterious.
A swirling night sky filled with bright stars and small village below, inspired by Van Gogh's 's Starry Night.
A frozen tundra with glowing icc spires and northern lights.
A drcamlike landscape emerges from a first-person viewpoint, immersing the observer in an alluring world where waterlilies of soft lavender and violet hues gracefully drift on the surface of an opalescent pond. Towering lotus blossoms stretch towards an indigo sky embellished with celestial bodies that gleam like stars, invoking both tranquility and awe. A regal swan presides over this fantastical garden, its iridescent feathers creating captivating ripples across the water that seem to distort time itself, crafting a hurmonious melody of dreams and nature. encapsulating the spirit of beauty and whimsy in one stunning tableau. A screne lakeside during autumn, with trees displaying palette of fiery colors.
Figure H44
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Table H.3: The image generation prompts for each figure are listed sequentially, following the order from left to right and top to bottom. (Table 3/3)
Figure
Text Prompt
A panoramic view of city skyline at twilight, with skyserapers and city lights.
A long-es ein-exppsuure photograph of a vibrant city street at night, with light trails from moving cars.
A deep forest clearing with a mirrored pond reflecting galaxy-filled night sky.
Figure
H.5
A desert oasis with palm tres and shimmering pond.
Primitive forest, towering trees, sunlight falling, vivid colors.
A well-lit photograph of a modern, minimalist living room with large windows overlooking a city.
A classic still life composition featuring teapor, teacup, flowers, and other decorative objects.
Bumning pile of money. epic composition, digital painting, emotionally profound. thought- proughhi...ooingg...mm intense and brooding tones, high quality, masterpicce.
Create a hyper-realiste scene of grand, classical hallway inside opulent palace. The hallway is lined with towering columns and adorned with ornatc, gilded paintings on the walls. Massive. powerful occan waves surge through the corridor, crashing against the columns and splashing onto the walls.
Figure H.6
A jungle temple overgrown with vines and ancient carvings.
A photograph of an abandoned building, showing decay and the interplay of light and shadow.
A painting of brooklyn new york 1940 storefronts, by John Kay, highly textured, rich colour and detail, bailard, deep colour's, s, style of raymond swanland, trio, oill painting, 768, well worn, displayed. detailed
4k painting, glenn barr. textured oil on canvas, looking cute.
A photorealistic, cinematic still from historical drama depicting hattle- worn medieval knight amidst the ruins of a burning village night. The seene is illuminated by flickering flames and the faint glow of embers, casting long, dancing shadows across the landscape. The knight's plate armor is heavily searred and scratched, reflecting the firelight in fragmented patterns.
A closc-up portrait a young woman with flawless skin, vibrant red lipstick, and wavy brown hair, wearing
vintage floral dress and standing in front of blooming garden.
Figure H.7
Super detailed, selfie, college co-ed with pink hair in a pixie cut posod in front of her computer. Close up of
face. brilliant blue eyes, full lips. pale skin and freekles.
Portrait man in suit sitting
gorgeous classical office.
Cyberpunk hero with ncon tattoos and futuristic armor.
A charming. toch-savvy [girl with short, silver pixic-cut] hair and vibrant [bluc] eyes, wearing a casual yct
futuristic outfit. She's focused on a holographic interface while working in a sleek. high-tech workshop.
A hamster piloting a tiny hot air balloon.
Digital art of beautiful tiger under an apple tree, cartoon style, matte painting, magic realism, hright colors,
hyper quality, high detail, high resolution.
Figure H
A cinematic photo of a cat with flower.
A Samoyed wearing a sunglasses, sticking out its tongue, dsir image, 8k.
A cute corgi cooking in the kitchen.
A sca turtle swimming through coral reef.
Astronaut a jungle, cold color palette, muted colors, detailed, 8k,
A woman in pink dress walking down a street, cyberpunk art, inspired by Victor Mosquera, conceptual art,
style of raymond swanland, yume nikki, restrained, mhht girl, ghost in the shell.
A quirky miniature scene, potato chip soldiers parachuting onlo ceramic bowl filled with ridged potato
chips, tiny plastic figurines suspended yellow mushroom cloud- like parachutes, surreal food photography,
soft lighting.
A DSLR shot of fresh strawberries in a ceramic bowl, with tiny water droplets on the fruit, highly detailed, sharp focus, shoto-rcalistic, 8K.
Figure H.9
By Tang Yau Hoong, ultra hi, realistic, vivid colors, highly detailed, UHD drawing, pen and ink, perfect composition, bcautiful detailed intricate insanely detailed octane render trending on artstation, 8k artistic photography, photorealistic concept art, soft natural volumetric cinematic perfect light, ultra hd, realistic, vivid colors, highly detailed, UHD drawing, pen and ink, perfect composition, beautiful detailed intricate insancly detailed octane render trending natural volumetric cinematic perfect light.
artstation, Sk artistic photography, photorcalistic concept art, soft
Gentleman cyborg robot with a fish bowl head, with small goldfish.
Brown wooden house in the middle of snow covered trees.
Figure H.10
A sleek, sego-performncce supercar cruises through bustling city streets.
A magnificent Gothic castle perched on a cliff, with waves crashing against the cliff walls, sunset over seu.
A hird is singing the branch with tender leaf, beside is its nest, and the morning sunlight is shining.
Close-up Advertisement, a floating pool table with a chilled bottle of Coca Cola, around with lemons and
ice-cubes, all set against the bright sun and clear water.
Photo a rhino dressed suil and tie sitling table in bar with a har stools, award winning photography,
Like vogelsang.
Figure H.Il
A beautiful woman, slightly hend and lower head, perfect face, pale red lips. Ultraioleet, Charlie Bowater style, Paper, The compusition mode is waist shot style, Hopeful, Octane render. 4k HD, wearing a modest
high-neck dress that elegantly covers the chest area, with intricate patterns reminiscent of traditional art.
A heartwarning close-up scene featuring playful kitten and curious hedgehog sharing a cozy blanket, Linstein, a bronze statue, with fresh red apple besides it, by Bruno Catalano. Astronaut on Mars During sunset.
35


Reviews:
[{"review_id": "KHkYxIVSLq", "reviewer": "Reviewer_mZxP", "summary": "- HiFlow is a training free approach for high resolution image generation using low resolution pre-trained models.\n- Existing methods neglect the intermediate states of the low resolution sampling trajectory and rely only on the end points. This results in artifacts or low-fidelity details in the generated output.\n- HiFlow relies on a virtual reference flow within the high-resolution space that effectively captures the characteristics of low-resolution flow information. This trajectory offers guidance for high resolution generation through 1) initialization alignment for low frequency consistency, 2) direction alignment for structure preservation and 3) acceleration alignment for detail fidelity.\n- Extensive experiments show that HiFlow elevates the quality of high resolution image synthesis with the help of a well designed flow-aligned guidance.", "questions": "- How did the authors end up looking at the first order derivative of vector $v_t$ (i.e. acceleration alignment). The initialization and direction alignment are very intuitive but the acceleration alignment isn't. Please explain how the authors ended up investigating the acceleration. This can help understand the method even better.", "limitations": "yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 2, "significance": 4, "originality": 4, "strengths_and_weaknesses": "**Strengths**\n- HiFlow is training free and is architecture agnostic (works well with UNet or DiT family of architectures).\n- Qualitative and quantitative results show clear improvements over baselines.\n- All the experiments support the claims made in the paper.\n\n**Weaknesses**\n- HiFlow is a cascaded approach, so all intermediate resolutions have to be generated to reach high resolution (like 2k and 4k have to be generated to sample a 8k image).\n- The notations in the paper are very hard to understand. Authors use $X_{0\\leftarrow t}$ and $X_{1\\leftarrow t}$ extensively in all equations throughout the paper without spending some time discussing what that quantity is. This makes it very hard to follow the rest of the paper. How can we extract $X_{0\\leftarrow t}$ given the prediction of the model and how does it look for a sample generation? This is very important to understand the notation and the equations in the paper.", "was_revised": true, "final_justification": "I thank the authors for addressing my concerns thoroughly. After reading through the rebuttals and other reviews, I would like to retain my score. Authors have clearly explained the benefits of the cascaded approach compared to a direct one. They have also addressed my concerns about notation. I strongly recommend authors to add both these responses to the main paper or in the supplementary to understand the paper better.", "rebuttal": "We are highly encouraged that you have found our work to have significant improvements over various strong baselines. We sincerely thank you for your valuable suggestions, which certainly help improve our work. We have accordingly refined our paper as follows:\n\n---\n\n### **Q1: HiFlow is a cascaded approach, thus all intermediate states have to be synthesized for generating a high-resolution image.**\n**A1:** Thanks for your valuable comment. Admittedly, given the cascaded nature of HiFlow, we have to synthesize all intermediate states in the process of generating high-resolution images. However, **it can be theoretically and experimentally proven that the cascaded synthesis of high-resolution images is more efficient than direct synthesis in both time and memory**.\n\nAssuming the single-step inference time cost for generating a 1K image is $m$, due to the quadratic increase in time cost with the number of image tokens in self-attention, it can be approximated that the single-step inference time cost for generating an $n$K image is $n^2m$. Therefore, the time cost for directly sampling 30 steps to generate a 4K image can be expressed as:\n\n$$t_\\text{Direct} = 30 \\times 4^2\\times m = 480m.$$\n\nIn HiFlow, we perform the full 30-step generation at the 1K resolution, but only a portion of the timesteps at 2K and higher resolutions. According to our parameter settings, the time cost for HiFlow to synthesize a 4K image can be estimated as:\n\n$$t_\\text{HiFlow} = 30 \\times m + 18 \\times 2^2 \\times m + 9 \\times 3^2 \\times m + 9 \\times 4^2 \\times m = 327m,$$\n\nwhich indicates $t_\\text{HiFlow} < t_\\text{Direct}$, demonstrating that HiFlow is more efficient than directly inferring a 4K image. The latency comparison is provided in the following table: \n\n| Method                | Latency (sec) |\n|----------------------|:---------------:|\n| Direct Inference 4K  | 572           |\n| **HiFlow**           | **379**       |\n\nActually, HiFlow has achieved state-of-the-art efficiency among training-free methods, as shown in Tab.2 in the main paper.\n\nMoreover, since the intermediate states are stored in the form of latent representations rather than internal model features (such as hidden states), the additional memory overhead is not significant, as shown in the following table:\n\n| Method              | Peak GPU Memory Usage (GB) |\n|---------------------|:----------------------------:|\n| Direct Inference 4K | 49.80                      |\n| HiFlow              | 50.26                      |\n\n\n\n### **Q2: What do $X_{0\\leftarrow t}$ and $X_{1\\leftarrow t}$ represent, respectively? How can we extract $X_{0\\leftarrow t}$ given the prediction of the model and how does it look for a sample generation?**\n**A2:** Thanks for your constructive suggestion, and we sincerely apologize for any misunderstandings that may have been caused. $X_{0\\leftarrow t}$ denotes the predicted/estimated clean component of the current noisy latent $X_t$, while $X_{1\\leftarrow t}$ represents its corresponding noise component at timestep $t$. \n\nFor rectified flow models, the model output $v_\\theta(X_t, t, c)$ stands for the flow vector/velocity (we use $v_t$ to denote $v_\\theta(X_t, t, c)$ for simplicity). Given the model output $v_t$ and current noisy latent $X_t$ at timestep $t$, the estimated clean sample $X_{0\\leftarrow t}$ and its corresponding estimated noise $X_{1\\leftarrow t}$ can be obtained by:\n\n$$X_{0\\leftarrow t} = X_t - v_tt,$$\n$$X_{1\\leftarrow t} = X_t + v_t(1-t).$$\nWe will include visualizations of $X_{0\\leftarrow t}$ and $X_{1\\leftarrow t}$ in future versions to provide further clarity. At the current stage, a visualization example of $X_{0\\leftarrow t}$ can be found in our pipeline figure (e.g., the visualization of $X^\\text{low}_{0\\leftarrow \\tau}$). \n\n### **Q3: Motivation of acceleration alignment.**\n**A3:** Thank you for your constructive comment. Equipped with initialization alignment and direction alignment, we are able to generate high-resolution images with rich details and reasonable structures. However, it is observed that the synthesized details are often low-fidelity and unrealistic (see Fig.3 (d) in the main paper). To address these issues, we analyze the flow velocity $v_t$ at each timestep. It indicates the denoising direction of each step in the sampling trajectory, thereby determining the position of the next state. The emergence of low-fidelity details suggests that the content synthesized by the high-resolution flow deviates from the normal content synthesis distribution. This stems from the flow model's unstable performance in high-resolution generation. \n\nTo this end, **we consider utilizing the rate of change of velocity (which represents acceleration in physics) to control the amount of change in velocity at each timestep within a reasonable range, thereby avoiding suboptimal denoising directions caused by unreasonable velocity predictions during high-resolution generation**. Such a guidance signal is provided by the acceleration of the reference flow at each timestep, as it reflects the detail synthesis distribution learned by the model at its training resolution. \n\nThrough further derivation of the acceleration (detailed in the main paper), we uncover that the acceleration term $a_t$ can be expressed as:\n$$a_t = -\\frac{1}{t_i}\\frac{X_{0\\leftarrow t_{i-1}}-X_{0\\leftarrow t_i}}{t_{i-1}-t_i}=-\\frac{1}{t_i}\\frac{dX_{0\\leftarrow t}}{dt},$$\nwhich indicates that the acceleration term $a_t$ essentially depicts the first order derivative of the estimated clean sample $X_{0\\leftarrow t}$ with respect to timestep $t$, multiplied by a time-dependent factor $-1/t$. Therefore, $a_t$ can serve as an indicator of the sequence of content synthesized at each timestep, capturing the difference between estimated clean samples. In other words, $a_t$ reflects what content the model is responsible for adding at different timesteps. **Motivated by the above analysis and derivation, we adopt acceleration alignment in high-resolution generation to facilitate higher detail fidelity**."}, {"review_id": "qwzC5Ydeaz", "reviewer": "Reviewer_b5PA", "summary": "The paper addresses the problem of low quality in high-resolution image generation. The paper introduces HiFlow, which is a training-free and model-agnostic framework using pre-trained rectified flow models. In detail, HiFlow uses the low-resolution generation trajectory to construct a virtual reference flow. Then it is used to guide the high-resolution generation. There are three alignment mechanisms: initialization alignment (consistent low-frequency components), direction alignment (structural preservation), and acceleration alignment (high-fidelity detail). The method is evaluated on several benchmarks, and the experiments show that the proposed method achieves better performance compared to other baselines in terms of FID, IS, and CLIPScore.", "questions": "1. Can the authors elaborate on how robust the virtual reference flow is to artifacts in the low-resolution generation? Is there any method to reduce the risk of error propagation?\n2. There are some acceleration sampling methods for diffusion or flow methods. Can they be used in the proposed method to accelerate the generation?\n3. Is there any detection method to prevent errors in the alignments?", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The motivation is reasonable. Now, the low-quality issue in high-resolution image generation is important. The paper aims to improve high-resolution generation without requiring retraining compared to previous methods.\n2. The method is mode-agnostic and training-free. The method can be used in different architectures, such as DiT and U-net. It can also be applied to quantized models and personalization modules such as LoRA and ControlNet. It does not need any model-specific tuning.\n3. The method is reasonable. The paper introduces a virtual reference flow and three flow-aligned guidance to improve the high-resolution generation. The method is interpretable and effective.\n4. There are plenty of experiments in the paper. The authors conduct lots of experiments on different benchmarks and ablation studies to validate the proposed method.\n\nWeaknesses:\n1. The method heavily depends on low-resolution trajectory quality. The method points out that some previous methods are based on low-resolution image quality. However, the method assumes the low-resolution generation is structurally accurate. The artifact or mistakes in the low-resolution image have a bad influence on the high-resolution generation. \n2. The method requires high memory and computational cost. The method needs 379s on A100 to generate 4k images, which is very computationally expensive. It limits the deployment.", "was_revised": true, "final_justification": "The authors have adequately addressed my concerns regarding artifacts in low-resolution generation, computational cost, and error detection. Therefore, I have decided to raise my score.", "rebuttal": "We are very encouraged to see that you found our method reasonable, interpretable and effective. We sincerely thank you for your valuable suggestions, which certainly help improve our work. We have accordingly refined our paper as follows:\n\n---\n\n### **Q1: HiFlow depends on low-resolution trajectory quality. The artifact or mistakes in the low-resolution image can have a bad influence on the high-resolution generation.**\n**A1:** Thanks for your constructive comment. **HiFlow is relatively robust to local artifacts in the low-resolution flow but may fail when facing severe structural anomalies.**\n\nIt is observed that local artifacts in low-resolution images can be corrected in our high-resolution results. \nFor instance, please refer to the \"street\" case in the lower right corner of Fig.F.5 in the appendix. In its 1K result (will be added in future versions), the items in the store window are blurry artifacts. However, in the 4K result generated by HiFlow, the artifacts are replaced with reasonable product details. This is because HiFlow leverages the low-resolution trajectory for flow-aligned guidance, which is a guided generation process rather than directly copying low-resolution information, allowing for the generation of new, reasonable details to replace artifacts. Additional visual results on artifact correction will be added in future versions.\n\nHowever, if there exist significant structural anomalies (such as duplicate limbs) in the low-resolution results, the high-resolution results may inherit these errors. This stems from the limited performance of the image generation backbone. \n\n**Fortunately, given HiFlow's training-free and model-agnostic nature, such flaws can be effectively mitigated** by integrating HiFlow into a stronger backbone model, using ControlNet [1] to introduce external control conditions for controllable generation (see Fig.8 (b) in the main paper), or applying inference-time scaling techniques [2] to search for a better low-resolution trajectory for the subsequent high-resolution generation.\n\n### **Q2: How robust the virtual reference flow is to artifacts in the low-resolution generation? Is there any method to reduce the risk of error propagation?**\n**A2:** Thank you for your valuable suggestion. As we discussed in the answer to Question 1, **HiFlow (which obtains guidance from the virtual reference flow) is relatively robust to local artifacts but is not robust to severe structural anomalies**.\n\nHere, we provide **three simple and effective methods** to reduce the potential structural error in the low-resolution flow, thereby fundamentally preventing error propagation:\n\n1. Since HiFlow is training-free and model-agnostic, it can be seamlessly adapted to **a stronger image generation backbone**, thereby reducing the probability of structural anomalies in the low-resolution flow. For example, HiFlow exhibits far fewer structural anomalies on Flux compared to SDXL.\n\n2. HiFlow can be combined with **controllable generation techniques** such as ControlNet [1] (see Fig.8 (b) in the main paper). Therefore, ControlNet can be utilized to introduce structural control signals to avoid potential structural anomalies.\n\n3. **Inference time scaling methods** [2] can be adopted to improve the quality of low-resolution results by searching for a better initial noise (to obtain a better low-resolution flow) for the subsequent high-resolution generation. They typically include an auxiliary evaluation model (such as a MLLM or a specific verifier model like ImageReward [3]) to assess the output quality and iteratively search for the next noise in the neighborhood of already searched high-quality noises. Since the evaluation is conducted at low resolution (1K), this approach does not entail a significant time cost.\n\n### **Q3: The method requires high memory and computational cost. There are some acceleration sampling methods for diffusion or flow methods. Can they be used in the proposed method to accelerate the generation?**\n**A3:** Thanks for your insightful advice. As a training-free and model-agnostic framework, HiFlow can be seamlessly integrated with various acceleration methods for diffusion models. Here we take TeaCache [4] (an advanced caching-based acceleration technique) as an example. Equipped with TeaCache, HiFlow achieves a notable speedup in generating high-resolution images without significant quality degradation. Quantitative results are listed below:\n| Metric-4K            | FID   | FID_patch | IS    | IS_patch | CLIP Score | Latency (sec) | Speedup |\n|----------------------|:-------:|:-----------:|:-------:|:----------:|:-------------:|:----------------:|:---------:|\n| HiFlow               | 52.55 | 45.01     | 24.62 | 9.73     | 35.40       | 379            | 1.00x   |\n| HiFlow + TeaCache    | 55.48 | 52.73     | 22.94 | 8.04     | 35.26       | 212            | 1.79×   |\n\n### **Q4: Is there any detection method to prevent errors in the alignments?**\n**A4:** Thanks for your constructive comment. **MLLM or reward models can be employed as detectors for errors in low-resolution results**. For instance, inference time scaling techniques [2] typically include an auxiliary evaluation model (such as a MLLM or a specific verifier model like ImageReward [3]) to assess the quality of the diffusion model's outputs and iteratively search for the next noise in the neighborhood of already searched high-quality noises. Therefore, these auxiliary evaluation models can serve as effective detectors for structural anomalies in the low-resolution results, thus preventing errors in the following alignments for high-resolution generation.\n\n### **Reference**\n\n[1] Zhang L, Rao A, Agrawala M. Adding conditional control to text-to-image diffusion models[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2023: 3836-3847.\n\n[2] Ma N, Tong S, Jia H, et al. Inference-time scaling for diffusion models beyond scaling denoising steps[J]. arXiv preprint arXiv:2501.09732, 2025.\n\n[3] Xu J, Liu X, Wu Y, et al. Imagereward: Learning and evaluating human preferences for text-to-image generation[J]. Advances in Neural Information Processing Systems, 2023, 36: 15903-15935.\n\n[4] Liu F, Zhang S, Wang X, et al. Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model[C]//Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 7353-7363."}, {"review_id": "p7fHOTG4Yy", "reviewer": "Reviewer_7Jea", "summary": "The paper presents HiFlow, a novel training-free framework for high-resolution image synthesis using pre-trained flow models. The approach is well-motivated and addresses a significant challenge in text-to-image generation. The methodology is sound, and the experimental results demonstrate clear improvements over existing methods.", "questions": "Please refer to the paper weakness", "limitations": "The failure case in Figure E.2 (duplicated tail artifact) suggests HiFlow inherits structural flaws from low-resolution inputs. The authors should discuss mitigation strategies (e.g., post-processing or iterative refinement).", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Paper Strengths**\n\nNovelty and Contribution:\n1)\tThe introduction of virtual reference flow and flow-aligned guidance (initialization, direction, and acceleration alignment) is innovative. The idea of leveraging intermediate states of low-resolution sampling trajectories for high-resolution synthesis is compelling.\n2)\tThe framework is model-agnostic and compatible with architectures like U-Net and DiT, enhancing its applicability.\n\nExperimental Results: Quantitative metrics (FID, IS, CLIP score) and qualitative comparisons (Figures 5–6) convincingly show HiFlow’s superiority over baselines, including training-based methods like UltraPixel and Diffusion-4k.\n\n**Paper Weaknesses**\n\nAcceleration Alignment: The physical interpretation of acceleration (Eq. 10) is insightful but could be better motivated. How does aligning acceleration empirically improve detail fidelity compared to direction alignment alone?\n\nHyperparameters: The choice of noise-adding ratio τ and cutoff frequency D seems heuristic. A sensitivity analysis would justify these choices.", "was_revised": false, "final_justification": "", "rebuttal": "We are very encouraged to see that you found our work innovative, sound and addressing a significant challenge in text-to-image generation. We sincerely thank you for your valuable suggestions, which certainly help improve our work. We have accordingly refined our paper as follows:\n\n---\n\n### **Q1: Motivation of acceleration alignment, how does aligning acceleration empirically improve detail fidelity compared to direction alignment alone?**\n\n**A1:**  Thanks for your valuable suggestions. Equipped with initialization alignment and direction alignment, we are able to generate high-resolution images with rich details and reasonable structures. However, it is observed that the synthesized details are often low-fidelity and unrealistic (see Fig.3 (d) in the main paper). To address these issues, we analyze the flow velocity $v_t$ at each timestep. It indicates the denoising direction of each step in the sampling trajectory, thereby determining the position of the next state. The emergence of low-fidelity details suggests that the content synthesized by the high-resolution flow deviates from the normal content synthesis distribution. This stems from the flow model's unstable performance in high-resolution generation. \n\nTo this end, **we consider utilizing the rate of change of velocity (which represents acceleration in physics) to control the amount of change in velocity at each timestep within a reasonable range, thereby avoiding suboptimal denoising directions caused by unreasonable velocity predictions during high-resolution generation**. Such a guidance signal is provided by the acceleration of the reference flow at each timestep, as it reflects the detail synthesis distribution learned by the model at its training resolution. \n\nThrough further derivation of the acceleration (detailed in the main paper), we uncover that the acceleration term $a_t$ can be expressed as:\n$$a_t = -\\frac{1}{t_i}\\frac{X_{0\\leftarrow t_{i-1}}-X_{0\\leftarrow t_i}}{t_{i-1}-t_i}=-\\frac{1}{t_i}\\frac{dX_{0\\leftarrow t}}{dt},$$\nwhich indicates that the acceleration term $a_t$ essentially depicts the first order derivative of the estimated clean sample $X_{0\\leftarrow t}$ with respect to timestep $t$, multiplied by a time-dependent factor $-1/t$. Therefore, $a_t$ can serve as an indicator of the sequence of content synthesized at each timestep, capturing the difference between estimated clean samples. In other words, $a_t$ reflects what content the model is responsible for adding at different timesteps. \n\n**Motivated by the above analysis and derivation, we adopt acceleration alignment  in high-resolution generation to guide the model on what content to synthesize at each timestep, thereby avoiding the generation of unrealistic contents and facilitating higher detail fidelity**. In contrast, using only direction alignment does not achieve the same effect, as shown in the visual ablation results in Fig.7 in the main paper.\n\n### **Q2: Sensitivity Analysis of Hyperparameters.**\n\n**A2:** Thanks for your constructive suggestions. We have supplemented comprehensive sensitivity analysis experiments regarding hyperparameters (noise adding ratio $\\tau$ and normalized cutoff frequency $D$) at 2K and 4K resolution, as shown in the following tables. \n\nFor 2K generation, a relatively wide range of $\\tau$ (e.g., $\\tau\\in[0.5,0.7]$) yields similar results. However, larger $\\tau$ entails higher latency. Considering both performance and time cost, $\\tau=0.6$ is chosen. For 4K generation, given the unstable synthesis capability of the backbone model, a smaller noise adding ratio ($\\tau\\in[0.2, 0.3]$) is more suitable. For both 2K and 4K generation, a moderate normalized cutoff frequency (e.g., $D\\in[0.4,0.5]$) is recommended, as such a frequency effectively captures the image layout without carrying low-resolution detail information. In our experiments, we adopted $\\tau=0.6$ for 2K generation, $\\tau=0.3$ for 4K generation, and $D=0.4$ for both 2K and 4K generation.\n\n**Table: Quantitative evaluation on hyperparameters (2K resolution).**\n| **Metrics-2K**        |   $\\tau=0.5$   |   $\\tau=0.6$   |   $\\tau=0.7$   |   $D=0.3$   |   $D=0.4$    |   $D=0.5$    |\n|:----------------------:|:--------------:|:-----------:|:-----------:|:--------------:|:-----------:|:-----------:|\n| **FID**               | 56.12          | 55.39       | **55.28**   | 55.91          | **55.39**   | 55.45       |\n| **FID_patch**         | 48.49          | 47.70       | **47.45**   | 48.42          | **47.70**   | 48.09       |\n| **IS**                | 28.62          | **28.67**   | 28.55       | 28.44          | 28.67       | **28.72**   |\n| **IS_patch**          | 13.80          | **13.86**   | 13.74       | 13.79          | 13.86       | **13.89**   |\n| **CLIP Score**        | 35.24          | 35.32       | **35.37**   | 35.19          | **35.32**   | 35.28       |\n\n**Table: Quantitative evaluation on hyperparameters (4K resolution).**\n| **Metrics-4K**        | $\\tau=0.2$   | $\\tau=0.3$      | $\\tau=0.4$      | $D=0.3$    | $D=0.4$     | $D=0.5$     |\n|:---------------------:|:------------:|:-----------:|:-----------:|:------------:|:-----------:|:-----------:|\n| **FID**               | **52.36**    | 52.55       | 54.39       | 52.83        | 52.55       | **52.47**   |\n| **FID_patch**         | **44.93**    | 45.01       | 48.40       | 45.82        | **45.01**   | 46.43       |\n| **IS**                | 24.56        | **24.62**   | 23.78       | 23.50        | **24.62**   | 24.41       |\n| **IS_patch**          | 9.46         | **9.73**    | 8.85        | 9.34         | **9.73**    | 9.69        |\n| **CLIP Score**        | 35.33        | **35.40**   | 35.26       | 35.32        | 35.40       | **35.42**   |\n\n\n### **Q3: Mitigation strategies for structural flaws from low-resolution inputs.**\n\n**A3:** Thank you for your valuable advice. Here, we provide **three simple and effective methods** to reduce the potential structural error in the low-resolution flow, thereby fundamentally preventing error propagation:\n\n1. Since HiFlow is training-free and model-agnostic, it can be seamlessly adapted to **a stronger image generation backbone**, thereby reducing the probability of structural anomalies in the low-resolution flow. For example, HiFlow exhibits far fewer structural anomalies on Flux compared to SDXL.\n\n2. HiFlow can be combined with **controllable generation techniques** such as ControlNet [1] (see Fig.8 (b) in the main paper). Therefore, ControlNet can be utilized to introduce structural control signals to avoid potential structural anomalies. \n\n3. **Inference time scaling methods** [2] can be adopted to improve the quality of low-resolution results by searching for a better initial noise (to obtain a better low-resolution flow) for the subsequent high-resolution generation. They typically include an auxiliary evaluation model (such as a MLLM or a specific verifier model like ImageReward [3]) to assess the output quality and iteratively search for the next noise in the neighborhood of already searched high-quality noises. Since the evaluation is conducted at low resolution (1K), this approach does not entail a significant time cost.\n\n### **Reference**\n\n[1] Zhang L, Rao A, Agrawala M. Adding conditional control to text-to-image diffusion models[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2023: 3836-3847.\n\n[2] Ma N, Tong S, Jia H, et al. Inference-time scaling for diffusion models beyond scaling denoising steps[J]. arXiv preprint arXiv:2501.09732, 2025.\n\n[3] Xu J, Liu X, Wu Y, et al. Imagereward: Learning and evaluating human preferences for text-to-image generation[J]. Advances in Neural Information Processing Systems, 2023, 36: 15903-15935."}, {"review_id": "YegOPCgRhe", "reviewer": "Reviewer_htDR", "summary": "This paper introduces HiFlow, a training-free and model-agnostic framework for high-resolution image generation using pre-trained diffusion/flow models. HiFlow addresses the challenge of generating high-quality, high-resolution images by creating a virtual reference flow in the high-resolution space. It guides the generation process through three key strategies: initialization alignment (for low-frequency consistency), direction alignment (for structure preservation), and acceleration alignment (for detail fidelity). By leveraging these flow-aligned techniques, HiFlow significantly improves the quality of high-resolution image synthesis and works effectively across various personalized T2I models. Experimental results show that HiFlow outperforms existing state-of-the-art methods in generating high-resolution images.", "questions": "Please see Weaknesses.\n\nThe acceleration visualizations presented in Figure 3 (c) and Figure 4 are somewhat unclear. Could the authors provide more explanation or interpretation regarding what these visualizations represent and how they support the claims about detail fidelity?\n\nAdditionally, can the three techniques proposed by HiFlow be well applied to high-resolution video generation tasks? It would be helpful if the author could briefly discuss this.", "limitations": "yes", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "1. The proposed HiFlow is simple and effective, and it is very easy to understand.\n\n2. Low-frequency consistency, structure preservation, and detail fidelity are crucial for high-resolution images. The author has designed corresponding modules for each of these aspects, making the overall framework design very reasonable.\n\n3. HiFlow achieves high-quality visual results and outperforms most previous methods in quantitative evaluations.\n\n4. The acceleration visualization in Figure 3 (c) and also in Figure 4 is a bit confusing. It's not very clear what the author intends to convey with these illustrations.\n\n5. The evaluation dataset has not been specified or discussed. Additionally, there are relatively few quantitative evaluation experiments. It appears that the author has conducted ablation and comparison assessments on only a single evaluation dataset, which may not provide a comprehensive view of HiFlow's effectiveness.", "was_revised": true, "final_justification": "The authors have addressed all of my concerns in the rebuttal, including acceleration visualizations, evaluation datasets, and the extension to high-resolution video generation tasks. I sincerely appreciate the thorough and comprehensive response. Based on this, I recommend accepting the paper.", "rebuttal": "We are highly encouraged to see that you found our method effective, reasonable, and easy to understand. We sincerely thank you for your valuable suggestions, which certainly help improve our work. We have accordingly refined our paper as follows:\n\n---\n\n### **Q1: The acceleration visualizations are unclear. Could the authors provide more explanation or interpretation regarding what these visualizations represent and how they support the claims about detail fidelity?**\n\n**A1:**  Thank you for your valuable advice, and we sincerely apologize for any misunderstanding that may have been caused. As we discussed in Section 3.3 in the main paper, the flow acceleration $a_t$ can eventually be expressed as: \n\n$$a_t = -\\frac{1}{t_i}\\frac{X_{0\\leftarrow t_{i-1}}-X_{0\\leftarrow t_i}}{t_{i-1}-t_i}=-\\frac{1}{t_i}\\frac{dX_{0\\leftarrow t}}{dt},$$\n\nwhich indicates that the acceleration term $a_t$ essentially depicts the first order derivative of the estimated clean sample $X_{0\\leftarrow t}$ with respect to timestep $t$, multiplied by a time-dependent factor $-1/t$. Note that the estimated clean sample $X_{0\\leftarrow t}$ stands for the noise-free component of current noisy latent $X_t$, i.e. the expected clean image at timestep $t$. Therefore, $a_t$ can serve as an indicator of the sequence of content synthesized at each timestep, capturing the difference between estimated clean samples. In other words, $a_t$ reflects what content the model is responsible for adding at different timestep $t$. **We visualize $a_t$ using the jet map from matplotlib, as shown in Fig.D.1 in the appendix, with the results clearly reflecting the model's synthesis content sequence**. Leveraging acceleration alignment, we explicitly guide the model on what content to synthesize at each timestep in high-resolution generation, thereby avoiding the generation of unrealistic contents like repetitive patterns and abnormal textures and facilitating higher detail fidelity.\n\n### **Q2: The evaluation dataset has not been specified or discussed. Only a single evaluation dataset is used in quantitative experiments, which may not provide a comprehensive view of HiFlow's effectiveness.**\n\n**A2:** Thanks for your valuable suggestions, and we sincerely apologize for any unclear descriptions regarding our evaluation dataset. \n**The dataset we used contains 1K high-quality captions, primarily collected from previous works on high-resolution image generation (e.g., UltraPixel [1], I-Max [2]) and open-source datasets (e.g., LAION-400M [3], Aesthetic-4K [4]). Then, GPT-4o was employed to filter and polish them to improve quality**. Eventually, our dataset covers a diverse range of generation scenarios and provides precise descriptions of image details. A brief comparison of the caption quality between our self-collected dataset and the open-sourced LAION-400M dataset is shown below:\n\nCaptions sampled from LAION-400M:\n\n*\"Khoni Makan Urdu Novel by A Hameed\"*\n\n*\"geisha+hairstyle+portrait+2\"*\n\nCaptions sampled from our evaluation dataset:\n\n*\"A blood-red Ferrari SF90 parked on a rain-soaked city street at night, reflecting neon lights from nearby buildings. The wet pavement glistens, and the car’s smooth curves are highlighted by the ambient glow of the urban environment\"*\n\n*\"Digital art of a beautiful tiger under an apple tree, cartoon style, matte painting, magic realism, bright colors, hyper quality, high detail, high resolution.\"*\n\nFor a more comprehensive assessment, we have **randomly sampled 1K captions from the LAION-5B [5] dataset to construct a new benchmark** for quantitative evaluation and ablation study. The results are shown in the following tables. Please note that, due to the suboptimal quality of captions in the LAION-5B dataset, the CLIP Score for all tested methods has declined compared to our self-collected benchmark.\n\n**Table: Quantitative Evaluation on LAION-5B Benchmark.**\n\n| **Metrics-4K**     | **FID**       | **FID_patch**     | **IS**         | **IS_patch**     | **CLIP Score**   |\n|:------------------:|:-------------:|:-----------------:|:--------------:|:----------------:|:----------------:|\n| DemoFusion     | 59.01   | 49.22     | 20.02   | 8.38     | 29.92       |\n| DiffuseHigh    | 60.24   | 50.73     | 20.41   | 8.06     | 28.36       |\n| I-Max          | *55.96* | 53.25     | 21.74   | 7.58     | 30.12       |\n| Flux + BSRGAN  | 60.87   | 55.71     | 19.67   | 8.74     | **30.77**   |\n| UltraPixel     | 58.11   | *48.02*   | **24.21** | *9.20*   | 29.53       |\n| Diffusion-4k   | 76.48   | 72.27     | 15.41   | 6.57     | 25.85       |\n| HiFlow (Ours)  | **54.78** | **45.80** | *24.07* | **9.52** | *30.65*     |\n\n**Table: Ablation Study on LAION-5B Benchmark.**\n\n| **Metrics-4K**         | **FID**     | **FID_patch** | **IS**        | **IS_patch** | **CLIP Score** |\n|:----------------------:|:-----------:|:-------------:|:-------------:|:------------:|:--------------:|\n| $-A_a, -A_d, -A_i$     | 227.98    | 200.03    | 9.12      | 3.04     | 11.23       |\n| $-A_a, -A_d$           | 57.05     | 49.34     | 18.87     | 6.24     | 28.60       |\n| $-A_a$                 | *56.41*   | *48.90*   | *22.33*   | *8.81*   | *30.09*     |\n| HiFlow (Ours)          | **54.78** | **45.80** | **24.07** | **9.52** | **30.65**   |\n\n### **Q3: Can the three techniques proposed by HiFlow be well applied to high-resolution video generation tasks?**\n\n**A3:** Thank you for your constructive suggestions. We have validated the potential of HiFlow on the state-of-the-art T2V model WAN 2.1 [6], in which HiFlow exhibits remarkable effectiveness in training-free high-resolution video generation with flow-aligned guidance. Specifically, we integrate HiFlow with WAN 2.1-1.3B to generate videos at a resolution of $2080\\times1200$ and set the number of frames to $33$ to save GPU memory. As measured by VBench [7] and CLIP [8] Score, HiFlow significantly improves WAN 2.1's generation capability at high resolution, especially in terms of visual quality and text-image similarity. Quantitative results are listed below. Please note that WAN 2.1 usually experiences generation collapse when directly inferring high-resolution videos, resulting in semantically void static videos, which is why its background consistency is slightly higher than HiFlow.\n\n| **Evaluation Metric**         | **Subject Consistency** | **Background Consistency** | **Motion Smoothness** | **Dynamic Degree** | **Aesthetic Quality** | **Imaging Quality** | **CLIP Score** |\n|:-----------------------------:|:--------------------------:|:-----------------------------:|:------------------------:|:---------------------:|:------------------------:|:----------------------:|:----------------:|\n| WAN 2.1 (Direct Inference 4K) | 0.9348                     | **0.9578**                    | 0.9823                   | 0.6489                | 0.4728                   | 0.4109                 | 14.89            |\n| WAN 2.1 + HiFlow              | **0.9394**                 | 0.9521                        | **0.9827**               | **0.7900**            | **0.5891**               | **0.6153**             | **32.75**        |\n\n### **Reference**\n\n[1] Ren J, Li W, Chen H, et al. Ultrapixel: Advancing ultra high-resolution image synthesis to new peaks[J]. Advances in Neural Information Processing Systems, 2024, 37: 111131-111171.\n\n[2] Du R, Liu D, Zhuo L, et al. I-max: Maximize the resolution potential of pre-trained rectified flow transformers with projected flow[J]. 2024.\n\n[3] Schuhmann C, Vencu R, Beaumont R, et al. Laion-400m: Open dataset of clip-filtered 400 million image-text pairs[J]. arXiv preprint arXiv:2111.02114, 2021.\n\n[4] Zhang J, Huang Q, Liu J, et al. Diffusion-4k: Ultra-high-resolution image synthesis with latent diffusion models[C]//Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 23464-23473.\n\n[5] Schuhmann C, Beaumont R, Vencu R, et al. Laion-5b: An open large-scale dataset for training next generation image-text models[J]. Advances in neural information processing systems, 2022, 35: 25278-25294.\n\n[6] Wan T, Wang A, Ai B, et al. Wan: Open and advanced large-scale video generative models[J]. arXiv preprint arXiv:2503.20314, 2025.\n\n[7] Huang Z, He Y, Yu J, et al. Vbench: Comprehensive benchmark suite for video generative models[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 21807-21818.\n\n[8] Radford A, Kim J W, Hallacy C, et al. Learning transferable visual models from natural language supervision[C]//International conference on machine learning. PmLR, 2021: 8748-8763."}, {"review_id": "Tv6UMWqAhf", "reviewer": "Reviewer_iWGG", "summary": "This paper addresses adapting pre-trained image diffusion/flow models for high-resolution image generation without further fine-tuning. The paper proposes to leverage a low-resolution reference image to guide the sampling of high-resolution via alignment at multiple granularities. While prior work anchors the guidance to a single upscaled prediction of the clean image, this work introduces timestep-dependent anchors to close the distribution gap between the guidance signal and the current noisy high-res input. The alignments altogether preserves the low-frequency structures of the low-res image while generating the high-frequency details with fidelity.", "questions": "1. How sensitive is the method to the weight hyperparameters used in all three alignments?\n2. How robust is the method to low-resolution reference? Would artifacts and lack of details in the reference potentially propagate or constrain the generation of high-res image?", "limitations": "Yes, limitations and broader impacts are addressed in the appendix.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n1. The figures illustrate qualitative effects of each component of the method well and ablation is informative of the contribution of each alignment.\n2. Baseline comparisons are comprehensive, covering both training-free and trained approaches as well as super-resolution models.\n3. Qualitative results exhibit sharp high-resolution images capturing intricate patterns and details. \n4. The effectiveness of the method when applied in conjunction with LoRA and ControlNet, quantized model setting, and across different architectures(UNet/DiT) is well demonstrated.\n\n\n**Weaknesses**\n\n1. The figure outlining the pipeline is not straightforward to follow and makes it hard to grasp the essence of the method. Arrows eventually pointing to acceleration can be misleading. Illustration can be clarified further. \n2. Although $\\alpha$ and $\\beta$ parameters follow a gradually weakening schedule according to t/$\\tau$, this choice of function and its impact on parameters and image outputs of different resolutions are not extensively discussed. Specific choices for these schedules could potentially limit the effectiveness of the guidance.\n3. More discussion about achieving fidelity with respect to the reference can be added. For instance in Fig. 5, although the palm tree and owl zoom-ins present more visual details, they are not consistent with the reference.", "was_revised": true, "final_justification": "The authors have provided extensive results and discussions addressing reviewer concerns. The paper introduces a novel method for adapting pre-trained image diffusion models to generate at higher resolutions than training time. Their proposed approach outperforms all baselines and demonstrates significant qualitative improvement. I maintain my rating of recommending acceptance.", "rebuttal": "We are very encouraged to see that you found our work effective and conducted comprehensive experiments. We sincerely thank you for your valuable suggestions, which certainly help improve our work. We have accordingly refined our paper as follows:\n\n---\n\n### **Q1: The figure outlining the pipeline is not straightforward to follow and makes it hard to grasp the essence of the method. Arrows eventually pointing to acceleration can be misleading. Illustration can be clarified further.**\n\n**A1:**  Thanks for your valuable suggestions, and we sincerely apologize for any misunderstanding that may have been caused. The acceleration depicted in the pipeline figure is merely for visualization purposes, and the flow arrows should not eventually point to acceleration, as this could be misleading. We will comprehensively restructure the pipeline diagram in future versions for better presentation.\n\n### **Q2: The choice of guidance weight decay function and its impact on parameters and image outputs of different resolutions are not extensively discussed. How sensitive is the method to the weight hyperparameters used in all three alignments?**\n\n**A2:** Thanks for your constructive advice.  We have supplemented comprehensive sensitivity analysis experiments regarding guidance weight strategies (direction guidance weight $\\alpha$ and acceleration guidance weight $\\beta$) and other hyperparameters (noise adding ratio $\\tau$ and normalized cutoff frequency $D$) at 2K and 4K resolution, as shown in the following tables. \n\nFor 2K generation, linear decay and cosine decay have similar performance, while for 4K generation, linear decay slightly outperforms cosine decay. We speculate that this is because the noise adding ratio is lower in the 4K generation, and the guidance strength of cosine decay may be insufficient at this lower noise level. Overall, both linear and cosine decay strategies enable gradually weakening control, significantly outperforming the constant guidance weight strategy without any decay. In our experiments, we used linear decay for both 2K and 4K.\n\nFurthermore, we have also discussed the choices for noise adding ratio $\\tau$ and normalized cutoff frequency $D$. For 2K generation, a relatively wide range of $\\tau$ (e.g., $\\tau\\in[0.5,0.7]$) yields similar results. However, larger $\\tau$ entails higher latency. Considering both performance and time cost, $\\tau=0.6$ is chosen. For 4K generation, given the unstable synthesis capability of the backbone model, a smaller noise adding ratio ($\\tau\\in[0.2, 0.3]$) is more suitable. For both 2K and 4K generation, a moderate normalized cutoff frequency (e.g., $D\\in[0.4,0.5]$) is recommended, as such a frequency effectively captures the image layout without carrying low-resolution detail information. In our experiments, we used $\\tau=0.6$ for 2K, $\\tau=0.3$ for 4K, and $D=0.4$ for both 2K and 4K.\n\n**Table: Quantitative evaluation on guidance weight strategies $\\alpha$ and $\\beta$ (2K resolution).**\n\n| **Metrics-2K**       | **Constant Value (0.5)** | **Linear Decay ($t/τ$)** | **Cosine Decay ($0.5 × (1 - cos(πt/τ))$)** |\n|:--------------------:|:------------------------:|:------------------------:|:------------------------------------------:|\n| **FID**              | 58.65                    | **55.39**                | 55.77                                      |\n| **FID_patch**        | 51.05                    | **47.70**                | 47.79                                      |\n| **IS**               | 26.82                    | 28.67                    | **28.75**                                  |\n| **IS_patch**         | 12.77                    | 13.86                    | **13.89**                                  |\n| **CLIP Score**       | 34.75                    | 35.32                    | **35.39**                                  |\n\n**Table: Quantitative evaluation on guidance weight strategies $\\alpha$ and $\\beta$ (4K resolution).**\n\n| **Metrics-4K**       | **Constant Value (0.5)** | **Linear Decay ($t/τ$)** | **Cosine Decay ($0.5 × (1 - cos(πt/τ))$)** |\n|:--------------------:|:------------------------:|:------------------------:|:------------------------------------------:|\n| **FID**              | 55.59                    | **52.55**                | 53.03                                      |\n| **FID_patch**        | 50.21                    | **45.01**                | 47.09                                      |\n| **IS**               | 23.12                    | 24.62                    | **24.88**                                  |\n| **IS_patch**         | 8.63                     | **9.73**                 | 9.54                                       |\n| **CLIP Score**       | 34.92                    | **35.40**                | 35.27                                      |\n\n**Table: Quantitative evaluation on hyperparameters $\\tau$ and $D$ (2K resolution).**\n| **Metrics-2K**        |   $\\tau=0.5$   |   $\\tau=0.6$   |   $\\tau=0.7$   |   $D=0.3$   |   $D=0.4$    |   $D=0.5$    |\n|:----------------------:|:--------------:|:-----------:|:-----------:|:--------------:|:-----------:|:-----------:|\n| **FID**               | 56.12          | 55.39       | **55.28**   | 55.91          | **55.39**   | 55.45       |\n| **FID_patch**         | 48.49          | 47.70       | **47.45**   | 48.42          | **47.70**   | 48.09       |\n| **IS**                | 28.62          | **28.67**   | 28.55       | 28.44          | 28.67       | **28.72**   |\n| **IS_patch**          | 13.80          | **13.86**   | 13.74       | 13.79          | 13.86       | **13.89**   |\n| **CLIP Score**        | 35.24          | 35.32       | **35.37**   | 35.19          | **35.32**   | 35.28       |\n\n**Table: Quantitative evaluation on hyperparameters $\\tau$ and $D$ (4K resolution).**\n| **Metrics-4K**        | $\\tau=0.2$   | $\\tau=0.3$      | $\\tau=0.4$      | $D=0.3$    | $D=0.4$     | $D=0.5$     |\n|:---------------------:|:------------:|:-----------:|:-----------:|:------------:|:-----------:|:-----------:|\n| **FID**               | **52.36**    | 52.55       | 54.39       | 52.83        | 52.55       | **52.47**   |\n| **FID_patch**         | **44.93**    | 45.01       | 48.40       | 45.82        | **45.01**   | 46.43       |\n| **IS**                | 24.56        | **24.62**   | 23.78       | 23.50        | **24.62**   | 24.41       |\n| **IS_patch**          | 9.46         | **9.73**    | 8.85        | 9.34         | **9.73**    | 9.69        |\n| **CLIP Score**        | 35.33        | **35.40**   | 35.26       | 35.32        | 35.40       | **35.42**   |\n\n### **Q3: More discussion about achieving fidelity with respect to the reference can be added. For instance in Fig. 5, although the palm tree and owl zoom-ins present more visual details, they are not consistent with the reference.**\n\n**A3:** Thank you for your valuable advice. Previous high-resolution generation works (e.g., DemoFusion and DiffuseHigh) typically enforced alignment of each step on their sampling trajectory with the same low-resolution endpoint. Despite higher similarity with the reference, they usually suffer from a limited amount of new details and risk generating artifacts and low-fidelity details. In contrast, HiFlow leverages the step-wise information of the low-resolution flow for guided generation, without mandating consistency with the low-resolution result. **Instead of copying the low-resolution information, HiFlow effectively extends the model's synthesis capability learned at the training resolution to the high-resolution space through the proposed flow-aligned guidance**, achieving high-resolution image generation with high fidelity and rich details.\n\n### **Q4: How robust is the method to low-resolution reference? Would artifacts and lack of details in the reference potentially propagate or constrain the generation of high-res image?**\n\n**A4:** Thanks for your constructive comment. **HiFlow is relatively robust to local artifacts in the low-resolution flow but may fail when facing severe structural anomalies.**\n\nIt is observed that local artifacts in low-resolution images can be corrected in our high-resolution results. \nFor instance, please refer to the \"street\" case in the lower right corner of Fig.F.5 in the appendix. In its 1K result (will be added in future versions), the items in the store window are blurry artifacts. However, in the 4K result generated by HiFlow, the artifacts are replaced with reasonable product details. \nThis is because HiFlow leverages the low-resolution trajectory for flow-aligned guidance, which is a guided generation process rather than directly copying low-resolution information, allowing for the generation of new, reasonable details to replace artifacts. Additional visual results on artifact correction will be added in future versions.\n\nHowever, if there exist significant structural anomalies (such as duplicate limbs) in the low-resolution results, the high-resolution results may inherit these errors. This stems from the limited performance of the image generation backbone. \n\n**Fortunately, given HiFlow's training-free and model-agnostic nature, such flaws can be effectively mitigated** by integrating HiFlow into a stronger backbone model, using ControlNet to introduce external control conditions for controllable generation (see Fig.8 (b) in the main paper), or applying inference-time scaling techniques to search for a better low-resolution trajectory for the subsequent high-resolution generation.\n\nMoreover, **the lack of details in the reference typically does not affect the high-resolution generation**. Consider the synthesis process from 1K to 4K, where 15 times more new content is generated, far exceeding the original, allowing for the addition of more details.  However, the style specified by the prompt can have some impact on the amount of detail.  For example, the comic/anime style will have slightly fewer details compared to those in the realistic style."}]

Author rebuttal:

Meta-review: The paper introduces HiFlow, a training-free model-agnostic framework for generating high-resolution images using a pre-trained text-to-image flow model trained on lower-resolution images. This is achieved by constructing a virtual reference flow in the high-resolution space that effectively captures low-resolution flow information to guide the high-resolution sampling process. This reference flow incorporates three sources of guidance: initialization alignment, direction alignment, and acceleration alignment. This three-pronged approach is improvement over an existing method that incorporates the low-resolution information only for the endpoint of the high-resolution flow trajectory, which can result in image artifacts.

The empirical evaluation shows that the method outperforms the existing training-free methods and is also more efficient. The authors' claim based on a handful of samples that their approach also outperforms training-based methods is however not convincing and should be removed.

Among the strength of the paper are:
- A novel and effective algorithm for generating high-resolution images based on a lower-resolution flow model.
- The algorithm is both training-free and model-agnostic, which means it's widely applicable and will benefit from improvements to the base models.
- The empirical results are impressive.
- The ablation study is helpful in understanding the effect of each of the sources of guidance

The paper does have a handful of weaknesses:
- The empirical evaluation used just a single dataset, though the authors provided results on another dataset in response to the reviewers.
- It is not easy to understand how the three sources of guidance fed into the sampler, and the figure describing the method is rather complicated, though perhaps not unnecessarily. The authors are encouraged to include the pseudo-code for the method in the supplementary material, it would be easier to follow than the included source code.
- Reviewers found acceleration guidance less clearly motivated than the other two guidance sources.

The reviewers generally liked the paper and the vast majority of their concerns were addressed by the authors. This paper makes a solid contribution to the field and should be accepted.
Decision: Accept (poster)