FreCaS: Efficient Higher-Resolution Image Generation via Frequency-aware Cascaded Sampling
Authors: Zhengqiang ZHANG
Ruihuang Li
Lei Zhang
Venue: ICLR 2025 Poster (2025)
Keywords: generative models
diffusion models
training-free
Primary area: generative models
TL;DR: A frequency-aware cascaded sampling framework for higher-resolution image synthesis using pre-trained diffusion models.

Abstract: While image generation with diffusion models has achieved a great success, generating images of higher resolution than the training size remains a challenging task due to the high computational cost. Current methods typically perform the entire sampling process at full resolution and process all frequency components simultaneously, contradicting with the inherent coarse-to-fine nature of latent diffusion models and wasting computations on processing premature high-frequency details at early diffusion stages. To address this issue, we introduce an efficient $\textbf{Fre}$quency-aware $\textbf{Ca}$scaded $\textbf{S}$ampling framework, $\textbf{FreCaS}$ in short, for higher-resolution image generation. FreCaS decomposes the sampling process into cascaded stages with gradually increased resolutions, progressively expanding frequency bands and refining the corresponding details. We propose an innovative frequency-aware classifier-free guidance (FA-CFG) strategy to assign different guidance strengths for different frequency components, directing the diffusion model to add new details in the expanded frequency domain of each stage. Additionally, we fuse the cross-attention maps of previous and current stages to avoid synthesizing unfaithful layouts. Experiments demonstrate that FreCaS significantly outperforms state-of-the-art methods in image quality and generation speed. In particular, FreCaS is about 2.86$\times$ and 6.07$\times$ faster than ScaleCrafter and DemoFusion in generating a 2048$\times$2048 image using a pretrained SDXL model and achieves an $\text{FID}_b$ improvement of 11.6 and 3.7, respectively. FreCaS can be easily extended to more complex models such as SD3. The source code of FreCaS can be found at https://github.com/xtudbxk/FreCaS.

Published as a conference paper at ICLR 2025
FRECAS: EFFICIENT HIGHER-RESOLUTION IMAGE GENERATION VIA FREQUENCY-AWARE
CASCADED SAMPLING
Zhengqiang Zhang-?, Ruihuang Li', Lei Zhang 1.2
'Te Hong Kong Polytechnic University zhengqi ang. zhange connect. polyu.hk, csizhang@comp. polpaaangeeomppppyy..u.. edu. edu.hk. hk *Corresponding author
2OPPO Research Institute
ABSTRACT
While image generation with diffusion models has achieved a great success, gen- erating images of higher resolution than the training size remains a challenging task due to the high computational cost. Current methods typically perform the entire sampling process at full resolution and process all frequency components simultaneously, contradicting with the inherent coarse-to- nature of latent dif- fusion models and wasting computations processing premature high-frequency details at early diffusion stages. To address this issue, we introduce an efficient F Frequency-awe aware Cascaded Sampling framework, FreCaS in short, for higher- resolution image generation. FreCaS decomposes the sampling process into cas- caded stages with gradually increased resolutions, progressively expanding fre- quency bands and refining the corresponding details. We propose an innova- tive frequency-aware classifier-free guidance (FA-CFG) strategy assign differ- ent guidance strengths for different frequency components, directing the diffu- sion model to add new details in the expanded frequency domain of each stage. Additionally, we fuse the cross-attention maps of previous and current stages to avoid synthesizing unfaithful layouts. Experiments demonstrate that FreCaS sig- nificantly outperforms state-of-the-arr methods in image quality and generation speed. In particular, FreCaS about 2.86x and 6.07 faster than ScaleCrafter and DemoFusion in generating 2048 x 2048 image using pre-trained SDXL model and achieves an FID, improvement 11.6 and 3.7, respectively. FreCaS can be easily extended to more complex models such SD3. The source code of com/ xt wttp///xxxxx///eacacc
FreCaS can be found at /rttps://gitee
INTRODUCATION
In recent years, diffusion models, such as Imagen (Saharia et al. 2022). SDXL (Poell et al. 2023), )
PixelArt-a (Chen al., 2023) and SD3 Esser et (2024), have achieved remarkable success in generating high-quality natural images. However, these models face challenges in generating very high resolution images due to the increased complexity in high-dimensional space. Though efficient diffuuion models, including ADM (Dhariwal & Nicho] 2021). CascadedDM (Ho et al. 2022) and LDM (Rombach et al., 2022). have been developed, the computational burden of training diffusion models from scratch for high-resolution image generation remains substantial. As a result, popular diffusion models, such as SDXL (Podell et al., 2023) and SD3 (Esser al. 2024), primarily focus on generating 1024 1024 resolution images. It is thus increasingly attractive to explore training- free strategies for generating images at higher resolutions, such as 2048 x 2048 and 4096 x 4096, using pre-trained diffusion models.
MultiDiflusion (Bar-Tal et al. 2023) is among the first works to synthesize higher-resolution images using pre-trained diffusion models. However, it suffers from issues such as object duplication, which largely reduces the image quality. To address these issues, Jin et al. (2024) proposed to manually adjust the scale of entropy in the attention operations. He et al. (2023) and Huang et al. (2024) attempted to enlarge the receptive field by replacing the original convolutional layers with strided ones, while Zhang al. (203) explicitly resizes the intermediate feature maps match the train-

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Published as a conference paper at ICLR 2025
ing size. Du et al. (2024) and Lin et al. (2024) took different strategy by generating reference image at the base resolution and then using it to guide the whole sampling process at higher resolu- tions. Despite the great advancements, these methods still suffer from significant inference latency, hindering their broader applications real world.
In this paper, we propose efficient Frequency-awar Cascaded Sampling framework, namely FreCaS, for training-free higher-resolution image generation. Our proposed FreCaS framework is based on the observation that latent diffusion models exhibit coarse-to-fine generation manner in the frequency domain. In other words, they first generate low-frequency contents in early diffusion stages and gradually generate gigher-frrquencee details later stages. Leveraging this insight, we images through multiple stages of increased resolutions, progressively synthesizing details of increased frequencies. FreCaS avoids unnecessary computations during the early diffusion stages as high-frequency details are not yet required.
generate
In the latent space, the image representation expands its frequency range as the resolution increases. To encourage detail generation within the expanded frequency band, we introduce novel frequency- aware classifier-free guidance (FA-CFG) strategy, which prioritizes newly introduced frequency components by assigning them higher guidance strengths in the sampling process. Specifically, we decompose both unconditional and conditional denoising scores into two parts: low-frequency component, which captures content from earlier stages, and frequneyy component, which cor- responds to the newly increased frequency band. FA-CFG applies the classifier-free guidance to different frequency components with different strengths, and outputs the final denoising score by combining the adjusted components. The FA-CFG strategy can synthesize much clear details while maintaining computational efficiency. Additionally, to alleviate the issue of unfaithful layouts, such as duplicated objects mentioned in Jin et al. (2024), we reuse the cross-attention maps (CA-maps) from the previous stage, which helps maintaiiing consistency in image structure across different stages and ensuring more faithful object representations.
In summary, our main contributions are follows:
We propose FreCaS, an efficient frequency-awand cascaded sampling framework for training-free higher-resolution image generation. FreCaS leverages the coarse-to-fine ture of the latent diffusion process, thereby reducing unnecessary computations associated with processing premature high-frequency details.
We design a novel FA-CFG strategy, which assigns different guidance strengths to compo- nents of different frequencies. This strategy enables FreCaS to focus on generating contents of newly introduced frequencies in each stage, and hence synthesize clearer details. In ad- dition, fuse the CA-maps of previous stage and current stage maintain consistent image layouts across stages.
We demonstrate the efficiency and effectiveness of FreCaS through extensive experiments conducted on various pretrained diffusion models, including SD2.1, SDXL and SD3, vali- dating its broad applicability and versatility.
2 RELATED WORKS
2.1 DIFFUSION MODELS
Diffusion models have gained significant attentions due to their abilities to generate high-quality natural images. Ho et (2020) pioneered the use of a variance-preservingg diffusion process to bridge the gap from natural images to pure noises. Dhariwal & Nichol (202T) exploited various net- work architectures and achieved superior image quality than contemporaneous GAN models. Ho & Salimans (2022) introduced a novel classifier-free guidance strategy that attains both generated im- age quality and diversity. However, the substantial model complexity makes high-resolution image synthesis challenging. Ho et al. (2022) proposed novel cascaded framework that progressively in- creases image resolutions, |Rombach et al. (2022) performed the diffusion process in the latent space of pre-trained autoencoder, enabling high- resoluton image synthesis with reduced computational cost. (Esser et al. 2024) presented SD3, which employs the rectified flow matching (Lipman et al. 2022 Liu et al.) 2022 at the latent space and demonstrates superior performance. Despite the great progress, it still requires substantial efforts to train a high-resolution diffusion model from scratch. Therefore, training-free higher-resolution image synthesis attracts increasing attentions.
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2.2 TRAINING-FREE HIGHER- RESOLUTION IMAGE SYNTHESIS
A few methods have been developed to leverage pre-trained diffusion models to generate images of higher resolutions than the training size. MultiDiffusion (Bar-Tal al. (023) is among the first methods to bind multiple diffusion processes into one unified framework and generates seam- less higher-resolution images. However, the results exhibit unreasonable image structures such as duplicated objects. AttnEntropy (Jin et al., 2024) alleviates this problem by re-normalizing the en- tropy of attention blocks during sampling. On the other hand, ScaleCrafter (He et al., 2023) and FouriScale (Huang et al. 2024) expand the receptive fields of pre-trained networks to match higher inference resolutions, thereby demonstrating improved image quality. HiDiffusion (Zhang et al.) 2023) dynamically adjusts the feature sizes to match the training dimensions. DemoDiffusion (Du et al. 2024) and AceDiffusion (Lin et al., (024) first generate a reference image at standard resolu- tions and then use this image guide the generation of images higher resolutions. Despite their success, the above mentioned approaches neglect the coarse-to- nature of image generation and generate image contents of all frequencies simultaneously, resulting in long inference latency and limiting their broader applications.
To address this issue, we propose an efficient FreCaS framework for training-free higher-resolution image synthesis. FreCaS divides the entire sampling process into stages of increasing resolutions, gradually synthesizing components of different frequency bands, thereby reducing the unnecessary computation of handling premature high-frequency details in early sampling stages. It is worth not- ing that DemoFusion (Du et al. 2024) and ResMaster (Shi et al., 2024) also employ a cascaded sampling scheme. However, there exist fundamental differences between FreCaS and them: De- moFusion and ResMaster perform a complete diffusion process at each resolution, whereas FreCaS transitions the diffusion from low to high resolutions in just one process. This distinction makes our method significantly more efficient than them while achieving better image quality.
3
METHOD
This section presents the details of the proposed FreCaS framework, which leverages the coarse-to- fine nature latent diffusion models and constructs a frequency-aware cascaded sampling strategy to progressively refine high-frequency details. We first introduce the notations and concepts that form the basis our approach (see Section 3.T.. Then, we delve into the key components of our method: FreCaS framework (see Section 3.2) FA-CFG strategy (see Section 3.3). and CA-maps
re-utilization (see Section 3.4
3.1 PRELIMINARIES
Diffusion models. Diffusion models (Ho et al. 2020 Dhariwal & Nichol 2021) transform complex image distributions into the Gaussian distribution, and vice versa. They gradually inject Gaussian noises into the image samples, and then use reverse process to remove noises from them, achieving image generation. Most recent diffusion models operate in the latent space and utilize discrete timestep sampling process to synthesize images. Specifically, for T T-step sampling process, latent noise øT is drawn from standard Gaussian distribution, and then iteratively refined through a few denoising steps until converged to the clean signal latent 2g. Finally, the natural image x: is decoded from øo using a decoder D. The whole process can be written as follows:
ZT ~ (0,1) - ZT
2
20 - D(zg).
For each denoising step, current works typically adopt the classifier-free guidance (CFG) (Ho & Salimans| 2022 to improve image quality. It predicts unconditional denoising score c.ne and conditional denoising score e.. The final denoising score is obtained via a simple ixtra-interpolatioo process as é (1 w) Eune + w Ec. where w denotes the guidance strength.
Resolution and frequency range. The resolution a latent z determines its sampling fre- quency (Rissanen et al., 2023). thereby influencing its frequency domain characteristics. Specifi- cally, if a latent of unit length has a resolution of s x 8, its sampling frequency fa can be defined as the number of samples per unit length, which is The Nyquist frequency is then obtained as L. 5. Therefore, the frequency of the latent ranges from [0, ÿ1 Reducing its resolution to
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power spocteal densiy (PSD)
PSD of latents
100
100
PSD of moise parts
100
300
PSD of signal parts
Freq.
106
10
10
10
105
10
High Freq.
(a) Z000
(b) 2600
(c) 2300
(d) Z0
Figure 1: From (a) to (d). the sub-figures show the PSD curves of latents 2900. 2600. 2300 and Zo of SDXL, respectively. One can see that the energy of synthesized clean signals (the red slashed regions) first emerges in the low-frequency band and gradually expands to high-frequency band.
8 x 8 narrows the frequency range to [0. )1 As a result, higher resolutions capture a broader frequency domain, while lower resolutions lead to narrower frequency spectrum.
3.2
FREQUENCY-AWARE CASCADED SAMPLING
Pixel space diffusion models exhibit a coarse-to carse-to-finee fine behavior in the image synthesis process (Rissa-) nen et al. [2023 Teng et al. 2024). In this section, we show that such a behavior is also exhibited for latent diffusion models during the sampling process, which inspires us to develop frequency-avee cascaded sampling framework for generating higher-resolution images.
PSD curves in latent space. The power spectral density (PSD) is a powerful tool for analyzing the energy distribution of signals along the frequency spectrum. Rissanen al. (2023) and Teng et al.
2024) have utilized PSD to study the behaviour of intermediate states in the pixel diffusion process. Here, compute the PSD of the latent signals over collection of 100 natural images using the pre-trained SDXL model (Podell et al., 2023). [ the PSD curves of 2900. #600. 2300 and 20. The solid line denotes the PSD curve of intermediate noise corrupted latent, while the dashed line represents the PSD) of Gaussian noise corrupted into the latent. The inner area between the two curves (marked with red slashes) indicates the energy of clean signal latent being synthesized. One can see that the clean image signals emerge from the low-frequency band (see 2900 and =600) and gradually expand to the high-frequency band (see 2300 and zo) during the sampling process. These observations confirm the coarse-to-fine nature of image synthesis in the latent diffusion process, where www-frequecy content is generated first, followed by figh-frequency details.
F Framework of FreCaS. Based the above observation, developed an efficient FreCaS frame- work to progressively generate image contents of higher frequency bands, reducing unnecessary computations in processing premature nigh-frequency details in early diffusion stages. As shown in Figure our FreCaS divides the entire T-step sampling process into N N+1 stages of increasing resolutions. The initial stage performs the sampling process at the default training size so with frequency range of [0, #1. Each of the subsequent stages increases the sampling size to its prede- cessor, gradually expanding the frequency domain. At the final stage, the latent reaches the target resolution s, achieving full frequency range from 0 to SN
Specifically, we begin with a pure noise latent 27"h stage sp, and iteratively perform reverse sam- pling until obtaining the last latent in this stage, denoted by z Next, we transition 2 to the first latent, denoted by zz), in next stage, as illustrated by the blue dashed arrow Figure([al This pro- cedure is repeated until the latent feature reaches the target size, resulting in = The final image æ obtained by applying the decoder to =0 so that æ = D(= With such a sampling pipeline, FreCaS ensures gradual refinement of details across coarse-to-fine scales, ultimately producing high-quality and high-resolution image with minimum computations.
For the transition between two adjacent stages, perform five steps to convert the last latent of previous stage z S1-1
to the first latent of next stage z
denoise
decode #1-1
interpolate
difluse
(2)
20
where "denoise" " and "diffuse" are standard diffusion operations, "decode" and "encode" are per- formed using the decoder and encoder, respectively, and "interpolation" adjusts the resolutions using
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PSD alozg
Stage
CA-maps
FA-CRG
7
D
Stage
alneg
Stage
PSD curves
CA-maps
FA-CFG
Freq. hands of previous stages
FF
+1
Expanded Freq. bands
PSD Fenj
Stage
CFG
Sample
#
L
process
The first/last state # of stage maps reuse
(a) FreCaS Framework
J-wh
Cine
E
Σt
Uncond
Eunc
c
uth.
High Freq,
E-wa
text
Cond Net
Ec
cune
E!
Freq. part
ui
e!
CFG
Frog. decompose
(b) FA-CFG
Freq. combine
Figure 2: (a) The overall framework of FreCaS. The entire T-step sampling process is divided into N + stages of increasing resolutions and expanding frequency bands. FreCaS starts the sampling process at the training size and obtains the last latent zī at that stage. Then, FreCaS continues the sampling from the first latent = at the next stage with a larger resolution and expanded frequency domain. This procedure is repeated until the final latent 2
stage N is obtained. A decoder is then used to generate the final image. (b) FA-CFG strategy. We separate the original denoising scores into frequency and figh-frequency components and assign a higher CFG strength the high-frequency part. The two parts are then combined to obtain the final denoising score é.
the bilinear interpolation. To determine the timestep x we follow previous works (Hoogeboom
al. 2023 Chen 2023 Gu al. 2023 Teng et al. 2024 keep the signal-to-noise ratio (SNR)
equivalence between z
and
Please refer to Appendix for more details.
3.3 FA-CFG STRATEGY
Our FreCaS framework progressively transitions the latents to stages with higher resolutions and ex- tended high-frequency bands. To ensure that the diffusion models focus on generating contents of newly introduced frequencies, we propose a novel FA-CFG strategy. which assigns higher guid- ance strength the new frequency components. In FreCaS, upon transitioning to stage s,, the latent increases its resolution from 8;-1 to the higher resolution s;, thereby expanding the frequency band from [O, - to [O, & J. This inspires us divide the latents into two components: low-frequency component ranging from [0, #:-1 and fighffreueeccy component covering the frequency interval (#i_1 J. The former preserves the generated contents from previous stages, whereas the latter is reserved for the contents to be generated in this stage. Our goal is to encourage the diffusion models to generate natural details and textures in the newly expanded frequency band.
To achieve the above mentioned goal, we propose to perform CFG on the two frequency- -ware parts with different guidance strengths. The entire process illustrated Figure ([b).. First, we obtain the unconditional denoising score eune and conditional denoising score e, using the pre-trained diffusion network. Then, we split the scores into www-freuency part and a high-frequency part. The former is extracted by downsampling the scores and then resizing them back, while the latter is the residual by subtracting the low- www-freuenccy part from the original denoising scores. Subsequently, apply the CFG strategy to the two parts with different weights. Specifically, for the fow-frequency

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part, we assign the normal guidance strength wi, while for the high-frequency part, we use much higher weight wh to prioritize content generation in this frequency band. The final denoising score is obtained by summing up the two parts. This process can be expressed as:
= E" = (1-w))- é'un + wi e + wn) eine + wn E
(3)
where é' and et are the low- wwwffrequency and sigh-frequeney parts of é, respectively. Similarly, elme eunor c and e follow the same notation.
3.4 CA-MAPS REUTILIZATION
When applied higher resolutions, pre-trained diffusion models often present unreasonable image structures, such as duplicated objects. To address this issue, we propose to reuse the CA-maps from the previous stage to maintain layout consistency across stages. The CA-maps represent attention weights from cross-attention interactions between spatial features and textual embeddings, effec- tively capturing the semantic layout the generated images. Specifically, we average the CA-maps of all gross-ateention blocks when predicting z = at stage 8;-1- After transitioning to stage 8;, we replace the current CA-maps of each cross attsntittnnionn block using its linear interpolation with the averaged CA-maps M as follows:
M (1- (1-we) M," + We ML 91-1
where Mt is the CA-maps at step of stage 8;. In this way, FreCaS can effectively maintain content consistency and prevent unexpected objects or textures during higher-resolution image generation.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Implementation details. We evaluate FreCaS on three widely-used pre-trained diffusion models: SD2.1 (Rombach et al. 2022), SDXL. (Podell et al., 2023) and SD3 (Esser et al. 2024). The sizes of generated images
x4 and x 16 the original training size. Specifically, 1024x 1024 and 2048 x 2048 for SD2. I, while 2048 x 2048 and 4096 x 4096 for SDXL. For SD3, we only generate images of 2048 2048 due to the GPU memory limitation. We randomly select 10K, 5K, and 1K prompts from the LAION5B aesthetic subset for generating images of 1024x 1024, 2048 and 4096 4096, respectively. We follow the default settings and perform a 50-step sampling process with DDIM sampler for SD2.1 and SDXL, and perform a 28-step sampling process with a flow matching based Buler solver for SD3. For x4 experiments, we employ two sampling stages at the training size and target size, respectively. For x 16 experiments, we employ three sampling stages at the training size, 4x training size and 16x training size, respectively. More details can be found in Appendix B.
generate images of
Evaluation metrics. We employ the Fréchet Inception Distance (FID) (Heusel et al. 2017) and In- ception Score (IS) (Salimans et al.) 2016) to measure the quality of generated images. Following He jet al.] (2023). we also employ FID, as the metric, which is computed on the samples of training size and target size. As suggested by Du et al. (2024), report FID, and IS,, which compute the metrics at patch level, to better evaluate the image details. The CLIP score (Radford et al., (021) utilized to measure the text prompt aligmment of generated images. As in previous works (Zhang let al. (023). we measure the model latency on a single NVIDIA A100 GPU with a batch size of We generate five images and report the averaged latency of the last three images for all methods.
Moreover, we conduct a user study and employ the non-reference image quality assessment metrics to further evaluate our FreCaS. Please refer to Appendix C for the details.
4.2
EXPERIMENTS ON SD2.1 AND SDXL
For experiments on SD2.1, 1, we compare FreCaS with DirectInference. MultiDiffusion (Bar- Tal et al.) 2023 AutnEntropy (Jin et al.] 2024), ScaleCrafter (He et al.) 2023), FouriScale (Huang et al, 2024 and HiDiffusion (Zhang et al. 2023). For experiments on SDXL, we compare with Di- Infernfeee AttnEntropy, ScaleCrafter, FouriScale, HiDiffusion, AccDiffusion (Lin et al., 2024) and DemoFusion (Du et al.] (024).. We further compare FreCaS with rrining-basee methods (Pixart-Sigmi (Chen et al. 2024) and UltraPixel (Ren et al. 2024)) and super-resoution methods (ESRGAN (Wang 2021 and SUPIR (Yu et al. 2024 in Appendix D.

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Table 1: Experiments x4 and 16 generation of SD2.1 and SDXL. "DO" means "duplicated object", which indicates whether the method takes the duplicated object problem into consideration. "SpeedUP" denotes the efficiency speed-up indicate the best and second ones among all methods that consider the duplicated object problem.
the DirectInference baseline. The red and blue
Methods
DO
FID
FID.
FID,
IS?
IS,1
CLIP SCORE
Latency(s) SpeedUP
Directinference
X
31.07
34.54
23.84
15.00
17.26
32.01
5.50
MultiDiffusion
X 21.05
22.44
14.68
17.46
18.29
32.49
120.21
0.046x
AtinEntropy
V 28.33
30.63
21.34
15.67
17.71
32.28
5.56
0.99x
ScaleCrafter
V 16.65
13.18
22.44
17.42
16.29
32.88
6.36
0.86x
FouriScale
V 19.01
15.33
23.26
17.11
15.57
32.92
11.06
0.50x
HiDiffusion
V 19.95
16.21
25.26
17.13
16.12
32.37
3.57
1.54x
SD2.1
Ours
V 16.38
13.14
21.23
17.55
16.04
32.33
2.56
2.16x
DirectInference
X 124.5
128.3
50.23
8.84
15.30
27.67
49.27
1x
MultiDiffusion
X 67.44
74.15
15.28
8.75
18.82
31.14
926.33
0.05
AttnEntropy
122.6
127.6
46.52
9.31
16.25
28.33
49.33
1.00x
x16
ScaleCrafter
V 34.47
34.55
57.47
13.02
12.12
31.44
92.86
0.53x
FouriScalc
34.17
34.13
58.01
12.79
13.15
31.68
90.13
0.55x
HiDiffusion
V 33.15
34.17
70.58
13.49
11.87
31.09
18.22
2.70x
Ours
V 19.95
20.11
43.71
15.22
13.74
31.92
13.35
3.69
Directlaference
X 39.15
43.83
29.71
11.52
14.60
32.51
34.10
1x
AttnEntropy
36.54
41.30
27.67
11.69
15.04
32.71
34.36
0.99
ScaleCrafter
V 22.76
24.23
23.17
14.10
14.97
32.70
39.64
0.86x
FouriScale
V 26.44
26.88
27.24
13.97
14.44
32.90
66.18
0.52x
HiDiffusion
V 21.67
20.69
21.80
15.56
15.93
32.62
18.38
1.86x
AccDiffusion
19.87
17.62
21.11
17.07
16.15
32.66
102.46
0.33
DemoFusion
V 18.77
16.33
18.77
17.10
17.21
33.16
83.95
0.41x
SDXL
Ours
V 16.48
12.63
17.91
17.18
17.31
33.28
13.84
2.46x
Directlnference
X 145.4
151.3
62.39
6.41
11.66
28.24
312.36
1x
AtunEntropy
142.1
148.9
60.54
6.46
12.44
28.46
312.46
1.00x
ScaleCrafter
71.49
75.11
73.21
8.68
9.81
30.76
560.91
0.56x
x16
FouriScale
98.01
77.63
84.05
8.00
9,41
30.78
534.08
0.58x
HiDiffusion
81.48
83.41
120.1
9.79
9.56
29.18
101.59
3,07
AccDiffusion
50.47
48.15
46.07
12.11
11.75
32.26
763.23
0.41
DemoFusion
47.80
44.54
35.52
12.38
13.82
33.03
649.25
0.48
Ours
42.75
40.63
39.82
12.68
14.16
33.03
85.87
3.64
Quantitative results. Table presents quantitative comparisons for x4 and x x16 generation be- that FreCaS not only outperforms other methods
tween FreCaS and its competitors. We synthesized image quality but also exhibits significantly faster inference speed. In specific, FreCaS achieves the best FID scores all experiments over the other methods. In terms of the IS metric, FreCaS performs the best in most cases, only slightly lagging behind DemoFusion on the 16 experiments of SDXL. (Note that DirectInference and MultiDiffusion occasionally achieve higher FID, and IS scores because they disregard the is- sue of duplicated objects.) For CLIP score, FreCaS obtains the best results on 3 out of the 4 cases, except for the less challenging x4 generation with SD
SD2.1 and SDXL, achieving clear advantages
While having superior image quality metrics, FreCaS demonstrates impressive efficiency. shows more than 2x speedup over DirectInference on x4 generation experiments, and shows more than 3.6x speedup on the x 16 generation experiments. DemoFusion, which overall the second best method in terms of image quality, is significantly slower than FreCaS. Its latency is about 6x and 7.5x longer than FreCas on x4 and x16 experiments, respectively. On the other hand, HiDiffusion, which is the second faster method, sacrifices image quality for speed. For example, the x x16 experiment with SD2.1, HiDiffusion achieves a latency of 18.22s but its FID, score 34.17. 17. In contrast, FreCaS is faster (13.35s) and has much better FID, score 120.11).

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X on SD2.1
Direcilnference
MuliDiifuusion
AutnEntropy
ScaleCrafter
HiDiffusion
FreCaStours)
x SD2.1
Directlaference
MultDDiffusia
AttnEntropy
ScaloCrafter
HiDiffusion
FreCaStours)
Xx4 om SDXL
ScaleCrafter
FouriScale
HiDifTusion
AccDiflusion
DemoFusion
FreCaStours)
TOKYO
X on SDXL
TOK Yo
ScaleCrufter
FouriScale
IrDiffusion
AccDiffusion
DemoFusion
FreCaStours)
Figure 3: Visual comparison x4 and 166 experiments of SD>2.1 and SDXL. From top bottom, the prompts used the four groups of examples are: 1. "A cosmic traveler, floating in zero gravity, spacesuit reflecting the Earth below, stars twinkling in the distance." 2. "A fierce Viking, hand, leading a raid, the longship slicing through the waves." 3. "A bustling flower market, stalls filled with bouquets, the air thick with fragrance, people selecting their favorites." 4. "Tokyo Japan Retro Skyline, Airplane, Railroad Train, Moon etc. Modern Postcard". Zoom-in for better view.
in
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FID,
PID
FID,
FID,
RID.
(8) M4 7.5,Mc N5
(b) 9/
3g,
(c) W/ 35, 7.5
Figure 4: Ablation studies w/ and in FA-CFG strategy and we in CA-maps reutilization.
Wt 7.5
Wi 7.5
W| 7.5
W/ = 7.5
W| 7.5 Wh.
Wy
Wh 7.5
W/ 25
35
Figure 5: Visual results of adjusting шy, in the FA-CFG strategy. From top to bottom, the prompts are "Eccentric Shaggy Woman with Pet Little Puppy" and "Rabat Painting Mdina Poppies Malta by Richard Harpum", respectively.
Qualitative results. Figure illustrates visual comparisons between FreCaS and competitive ap- proaches. From top to bottom are four groups of examples, presenting the results of x4 generation of SD2.1, x16 generation of SD2.1, x4 generation of SDXL, and generation of SDXL, re- spectively. In each group, the top row shows the generated images, while the bottom row shows the zoomed region for better observation. From F we can see that FreCaS effectively synthesizes the described contents while maintaining coherent scene structure. DirectInference, MultiDiffu- sion and AttnEntropy often produce duplicated objects, such as the many astronauts and warriors. ScaleCrafter and HiDiffusion achieve reasonable image contents in experiments of SD2. but gener- ate unnatural layouts in the experiments SDXL, such as the excessive flowers on the ceiling in x experiment. Our FreCaS consistently maintains coherent image contents and layout in experiments of both SD2.1 and SDXL. AceDiffusion and DemoFusion also achieve natural image contents, but FreCaS generates clearer details such the flowers and trains. Please refer to Appendix for more visual results, including images with other aspect ratios.
4.3
EXPERIMENTS ON SD3
SD3 (Esser et al. 2024) adopts rather different network architecture from SD2.1 and SDXL, and many existing methods cannot be applied. We can only compare FreCaS with DirectInference and DemoDiffusion Du et al. (2024). Due to page limitation, please refer to Appendix for the results.
4.4 ABLATION STUDIES
In this section, we conduct ablation studies on x4 experiments of SDXL to investigate the effective- ness and settings of our cascaded framework, FA-CFG and CA-maps strategies.
Effectiveness of each component. We conduct series of ablation studies to better demonstrate the effectiveness of each component of FreCaS, including the cascaded sampling framework, FA-CFG and CA-maps reutilization strategies. Please refer Appendix G for more details.
FA-CFG strategy. The FA-CFG strategy aims to guide the model to generate content within the expanded frequency band. To achieve this. FA-CFG introduces two parameters, wy and wy. to adjust the guidance strength on the low and high frequency components, respectively. When w; = wh, the FA-CFG strategy degenerates to the conventional CFG approach. We conduct series of

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Published as a conference paper at ICLR 2025
Wc 0.0 Figure 6: Visual results adjusting we in CA-maps reutilization. The prompt is "Blueberries and Strawberries Art Print".
We 0.6
We 1.0
experiments to explore the optimal settings of the two parameters. First, we fix wi at 7.5 and vary wp. The results are shown in Fa). We observe that as wp increases from 1.0 45, the FID, and FID, metrics initially decrease, indicating improved image quality. However, as wh becomes too high, the metrics begin deteriorate. The sweet spot lies between 25 and 35, achieving a low FID, of nearly 12.65 and a low FID,, of 17.91. We then fix Wi at 35 and vary Wy. The results are presented in Figure 4b). Reducing wy below 7.5 leads to a slight increase in FID, from 17.91 to 18.06, whereas increasing wy over 7.5 deteriorates FID, from 12.81 to 13.00. Compared to wh, adjusting wi brings much smaller effects on those two metrics. Thus, we set wi to 7.5 for experiments SD2.1 and SDXL, and set it to 7.0 for SD3.
Figure provides visual examples of adjusting w/e Increasing w/ enhances the sharpness of details, such clearer hair strands and more vivid flower petals. However, excessively high value of wy, (e.g., 45) will introduce artifacts, as highlighted by the red boxes in the figure. This underscores the importance of selecting appropriate we, value to strike balance between detail enhancement and artifact suppression. Based on these findings, we set wy to 7.5 and wh to 35 yields favorable outcomes in most of the cases.
CA-maps re-utilization. To evaluate the effect of weight we in the re-utilization of CA-maps, we conduct an ablation study by varying we from to The results are shown in Figure4c) Increasing we continuously decreases FID, but increases FIDp, indicating an improvement on the image layout but a drop on image details. To balance between the two metrics, we set w, 0.6. visual example is shown in Figure 6 We see that this setting leads to clearer textures on strawberry compared to we 1.0 and prevents the unreasonable surface of the blueberry w, 0.0.
Inference schedule. FreCaS uses two factors to adjust the inference schedule. The first one is the count of additional stages N. The second factor the timestep of last latent in each stage. We conduct experiments the selection of these two factors. The details can be found in Appendix G. Based on results, we set L to 200, and set N to for x experiments and for x 16 experiments.
5 CONCLUSION
We developed a highly efficient Frequency-aware Cascaded Sampling framework, namely Fre- CaS, for training-free higher-resolution image generation. FreCaS leveraged the coarse-to-fine na- ture of latent diffusion process, reducing unnecessary computations in processing premature high- frequency details. Specifically, we divided the entire sampling process into several stages having increasing resolutions and expanding frequency bands, progressively generating image contents of higher frequency details. We presented a Frequency-Aware Classifier- Free Guidance (FA-CFG) strategy to enable diffusion models effectively adding details of the expanded frequencies, leading to clearer textures. In addition, we fused the cross-attention maps of previous stages and currett one to maintain consistent image layouts across stages. FreCaS demonstrated advantages pre- vious methods in both image quality and efficiency. In particular, with SDXL, it can generate a high quality 4096 4096 resolution image in 86 seconds an A 100 GPU.
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Reviews:
[{"review_id": "sorEPxFRz7", "reviewer": "Reviewer_27vZ", "summary": "This paper introduces FreCaS, a training-free approach for high-resolution image generation based on a diffusion model. FreCaS progressively upscales images using Frequency-Aligned Classifier-Free Guidance, which guides the diffusion model to incorporate new details within the expanded frequency domain. Experimental results show that FreCaS outperforms existing training-free methods in both efficiency and performance. Furthermore, the approach can be seamlessly integrated with advanced models, such as SD3.", "questions": "1. In Figure 6, the weight of the cross-attention map appears to influence image details and textures (e.g., the blueberry's surface) rather than the layout, which remains stable as the weight varies from 0 to 1. This observation seems to contradict the claim in Section 3.4, which states that cross-attention maps capture the semantic layout by representing attention weights from interactions between spatial features and textual embeddings. Could you clarify whether cross-attention maps primarily control fine-grained details and textures, or if they indeed play a more critical role in determining the semantic layout as suggested earlier?\n2. What are the GPU memory requirements for each of the compared methods and for the proposed FreCaS method?", "limitations": "", "rating": 5, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The proposed method is simple and useful. FreCaS leverages the latent space of lower resolutions, thereby reducing computational costs in the initial stages of higher-resolution generation. By efficiently reusing low-frequency information from lower-resolution outputs, it streamlines the generation process. This simplicity and effectiveness make FreCaS adaptable to other diffusion-based text-to-image models.\n2. The ablation study is extensive and thorough, covering critical factors such as inference steps at each resolution stage, adjustments in CFG weights, and reuse of cross-attention maps.", "weaknesses": "1. The coarse-to-fine approach and frequency domain enhancement are commonly applied in training-free high-resolution image generation methods, such as the latest HiPrompt and ResMaster. Although these methods do not use CFG, combining such techniques in this paper does not provide particularly new insights.\n2. Generating higher-resolution images can be approached through various methods. This paper focuses primarily on training-free comparisons, but including comparisons with training-based methods, such as Pixart-Sigma or UltraPixel, or super-resolution methods like ESRGAN and SUPIR, would provide a more comprehensive analysis.\n3. Training-free methods typically yield low success rates, often requiring tens of attempts to obtain a single satisfactory result. I am not convinced that training-free methods offer a viable solution for high-resolution image generation, especially given that training-based methods are generally faster, more reliable, and stable. Additionally, some training-based methods do not require significant computational resources.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2Z8uKrtGMQ", "reviewer": "Reviewer_z3AD", "summary": "The paper proposes an efficient frequency-aware cascaded sampling framework designed to tackle the high computational cost of high-resolution image generation. It breaks down the sampling process into progressive stages, expanding frequency bands and refining details step-by-step. Using frequency-aware classifier-free guidance (FA-CFG) and cross-stage attention fusion, FreCaS improves both image quality and generation speed, outperforming state-of-the-art methods like ScaleCrafter and DemoFusion in generating high-resolution images.", "questions": "See the weekness part.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Assigns different guidance strengths to components of different frequencies to achieve more finegrained control.\n2. Good effiency compared with existing works.", "weaknesses": "1. Missing FID which is an important metric and widely adopted by exsiting works. The lacking of FID makes it difficult to comprehensively judge its effectiveness.\n2. The cascade framework is widely adopted by the exisitng works. It is also necessary to introduce FID into the ablation study to validate the effectiveness of the main contributions of this work.\n3. Some improvements are marginal, e.g., the results on SDXL.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "bqtYXuNGkR", "reviewer": "Reviewer_N5Ek", "summary": "This paper introduces an efficient frequency-aware cascaded sampling framework designed for tuning-free higher-resolution generation. The framework breaks down the sampling process into multiple stages, gradually increasing resolution at each stage. Additionally, it presents a frequency-aware classifier-free guidance approach that applies varying guidance strengths to different frequency components.\nExperimental results demonstrate the effectiveness and efficiency of proposed method over various settings.", "questions": "Please refer to weakness part.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* This paper proposes a higher-resolution generation method that achieves better performance and faster sampling speeds.\n* The paper is well-written, with a clear and straightforward motivation.", "weaknesses": "* Some evaluation metrics are missing. The authors only provided FID_b and FID_p, but did not include the FID score between the generated and real image sets, which is the most widely used metric for this task.\n\n* Most of the 4K comparison results are in non-realistic styles, such as painting styles. More comparisons in realistic styles need to be provided.\n\n* The cascade pipeline for high-resolution generation has been utilized in several previous works, such as DemoFusion and FouriScale, making the contribution here relatively marginal as it primarily builds on this existing approach. Therefore, a comprehensive evaluation is necessary to demonstrate the improvement brought by the addition of fa-cfg and the F step calculation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Aob0bg4uRT", "reviewer": "Reviewer_Sncf", "summary": "The authors introduce an efficient Frequency-aware Cascaded Sampling (FreCaS) framework for higher resolution image generation. FreCaS decomposes the sampling process into cascaded stages with gradually increased resolutions, progressively expanding frequency bands and refining the corresponding details. Additionally, the authors fuse the cross-attention maps of previous and current stages to\navoid synthesizing unfaithful layouts. Experiments demonstrate that FreCaS outperforms state-of-the-art methods in image quality and generation speed.", "questions": "1. In addition to metrics like FID and IS, more comprehensive image quality assessment indicators, such as human subjective evaluation, could be considered to assess the quality of generated images more thoroughly.\n2. The experimental part may need to further explore the performance of FreCaS in real-world application scenarios, such as different image contents, styles, and complexities.\n3. Although FreCaS improves efficiency, the demand for computational resources remains high when generating high-resolution images.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The  FreCaS is faster than ScaleCrafter and DemoFusion in generating a 2048×2048 image.\n2. The  FreCaS introduces a FA-CFG strategy that assigns different guidance strengths to different frequency components.\n3. The  FreCaS can be extended to more complex models, validating its broad applicability and versatility.", "weaknesses": "1.  FreCaS relies on several hyperparameters (e.g., w_l, w_h, w_c) that may need careful adjustment to achieve optimal results, which may increase the complexity of experiments.\n2. Although FreCaS can be extended to more complex models, its performance may be limited by the pre-trained models.\n3. This idea is somewhat similar to FreeU and FreeEnhance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)