AReA: Adaptive Receptive-Field Attention for Efficient High-Resolution Diffusion Transformers

Javad Rajabi; Koorosh Roohi; David B. Lindell; Babak Taati

cs.CVOct 12, 2026

AReA: Adaptive Receptive-Field Attention for Efficient High-Resolution Diffusion Transformers

Authors: Javad Rajabi, Koorosh Roohi, David B. Lindell, Babak Taati

Organizations: University of Toronto · Vector Institute

Abstract

Generating images beyond the training resolution of Diffusion Transformers (DiTs) remains challenging, as the number of tokens grows rapidly with resolution, full attention becomes expensive, and outputs often suffer from structural distortions and degraded detail. We identify uniform full attention as the common source of both problems. Attending to all tokens at every step causes quadratic cost and exposes the model to more tokens and relative distances beyond the training range, pushing the model out of distribution. We observe that attention's effective receptive field narrows during denoising, from global in early steps that form structure to local in later steps that refine details. We propose AReA, a training-free attention mechanism whose receptive field evolves from coarse to fine, following the denoising progression. In early steps, AReA applies cross-scale coarse attention over downsampled keys and values, capturing global context, while in later steps it applies local attention within progressively shrinking windows. Without additional parameters or training, experiments show that AReA consistently outperforms state-of-the-art training-free baselines across target resolutions and different models, while also being faster and more efficient.

Figures & tables

Figure 1: High-Resolution Generation. AReA enables training-free and efficient high resolution generation across FLUX.1, Qwen-Image, FLUX.2, and Ideogram 4. Best viewed zoomed-in.

Figure 2: Failure Modes at High Resolution . FLUX.1 at 4096 × 4096. Full attention keeps the model’s intended subject but collapses the layout, while RoPE-based methods keep a layout but fragment, lose, or distort the subject. AReA preserves both and renders fine details.

Figure 3: Attention Locality as Denoising Progresses. Vanilla FLUX.1 ( $1024\times 1024$ ), averaged across heads, blocks, and 24 prompts. Left : attention-weighted query–key distance. Over all keys it shrinks only $1.4\times$ , but over dominant keys ( $\geq 1/4$ max attention) it shrinks $5.5\times$ : late in denoising, the most important keys lie close to the query. Right : attention maps of a center query at early, middle, and late steps (top 60% of mass).

Figure 4: Pipeline of AReA. AReA follows the coarse-to-fine progression of denoising, switching from Phase I to Phase II at noise level $\sigma^{\star}$ . In Phase I, full-resolution queries attend to keys and values pooled to one token per $f\times f$ block, with $f$ ramped over steps, and positioned on a grid matching the training resolution. In Phase II, attention is restricted to progressively shrinking local windows, and those over smooth regions of the DWT map are further split to reduce cost.

Figure 5: Qualitative comparisons across resolutions. AReA eliminates structural distortions and subject duplication seen in baseline methods, achieving superior fidelity and prompt alignment.

Figure 6: Generation latency vs. resolution. AReA is significantly faster than full-attention baselines across all resolutions, reaching

3.2\times

and

2.9\times

acceleration at

6144^{2}

on FLUX.1 and Qwen-Image, respectively.

		FLUX.1							Qwen-Image
Res.	Method	Latency(s) $\downarrow$	ImageReward $\uparrow$	Aesthetic $\uparrow$	CLIP $\uparrow$	PickScore $\uparrow$	FID $\downarrow$	FID ${}_{p}\downarrow$	Latency(s) $\downarrow$	ImageReward $\uparrow$	Aesthetic $\uparrow$	CLIP $\uparrow$	PickScore $\uparrow$	FID $\downarrow$	FID ${}_{p}\downarrow$
2048 $\times$ 4096	Base	97	-0.8696	5.91	26.49	20.41	171.4	67.4	274	-0.5186	5.96	27.25	20.83	167.6	57.8
	UltraImage	89	-0.1474	6.25	28.11	21.28	173.3	79.0	301	0.4448	5.79	28.19	21.07	161.7	62.6
	DyPE	97	0.5927	6.30	28.43	22.15	156.5	79.9	274	0.5769	6.12	28.71	21.64	159.0	74.7
	SigMa	103	0.6727	6.14	28.07	22.01	152.5	63.9	275	0.6155	6.22	28.87	22.03	152.5	63.0
	SEGA	98	0.7863	6.28	28.95	22.47	153.9	76.1	276	1.2467	6.42	29.89	23.10	146.1	69.6

Table 1: Comparison of AReA against state-of-the-art baselines on FLUX.1 and Qwen-Image across different resolutions and aspect ratios on Aesthetic-4K. Bold : best; underline : second-best.

Method	IR $\uparrow$	Aes. $\uparrow$	CLIP $\uparrow$	Pick $\uparrow$	HPS $\uparrow$	IS $\uparrow$
Base	-1.4649	5.12	23.36	19.04	0.232	7.20
UltraImage	-0.0277	5.77	28.26	20.46	0.269	10.17
DyPE	0.3280	5.83	29.35	20.94	0.266	10.93
SigMa	0.0627	5.63	27.91	20.54	0.258	10.70
SEGA	0.4422	5.82	29.63	21.18	0.270	11.99
AReA	0.6001	5.87	29.89	21.44	0.275	13.90

Table 2: Comparison on FLUX.1 at

4096\times 4096

on HRGen-1K. Bold : best; underline : second-best.

Figure 7: Qualitative ablation on FLUX.1 at $\mathbf{4096\times 4096}$ . Without Phase I, windows diverge and disrupt global layout. Without Phase II, the image lacks fine texture and remains blurry. Mean pooling degrades overall image quality (compared in Table 4 ), while fixed windows without shrinking cause seams and content fragmentation compared to default AReA.

	Method	Latency(s) $\downarrow$	ImageReward $\uparrow$	Aesthetic $\uparrow$	MUSIQ $\uparrow$	PickScore $\uparrow$	HPS $\uparrow$	CLIP $\uparrow$	FID $\downarrow$
Phase I	No Phase I (full attention)	201	-0.41	5.85	38.06	20.73	0.264	26.63	171.42
	Flat $f=2$	165	1.23	6.27	46.67	23.03	0.294	29.13	152.08
	Flat $f=4$ (no ramp)	142	1.27	6.38	44.72	23.17	0.292	29.63	154.21
Phase II	No Phase II (full attention)	248	1.10	6.35	40.67	22.71	0.286	28.78	155.44
	Fixed windows ( $w=64$ )	123	1.26	6.43	45.26	23.12	0.296	29.78	152.15
	w/o Global-KV bridge	133	1.32	6.25	45.38	23.25	0.294	29.11	161.25

Table 3: Ablation study on FLUX.1 at

4096\times 4096

on Aesthetic-4K. Within each phase, the first row replaces the whole phase with full attention; the rows below change one component.

Pooling	IR $\uparrow$	Aes. $\uparrow$	MUSIQ $\uparrow$	Pick $\uparrow$	HPS $\uparrow$	CLIP $\uparrow$	FID $\downarrow$
Mean	1.23	6.30	44.93	22.84	0.289	29.28	152.78
Random	1.25	6.35	45.59	23.22	0.293	29.37	152.53
Medoid	1.26	6.31	44.04	23.19	0.293	29.19	151.82
Center †	1.32	6.37	45.22	23.31	0.297	29.71	148.50

Table 4: Phase I pooling strategy on FLUX.1 (

4096\times 4096

, Aesthetic-4K). † Default.

Appendix figures & tables16 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 8: Effect of the Phase I pooling schedule on FLUX.1 at $4096\times 4096$ . A fixed $f=4$ forms the layout on a $64\times 64$ pooled grid, which matches the training resolution but is too coarse to separate objects, so the sheep merge into a single blurred mass. A fixed $f=2$ forms the layout on a $128\times 128$ grid, twice the training resolution, producing scattered and repeated subjects. Decreasing $f$ from $4$ to $2$ first sets the layout at the training resolution and then refines it on the finer grid, giving separated subjects and a coherent scene.

Figure 9: Effect of the Phase I pooling strategy on FLUX.1 at $4096\times 4096$ . Mean pooling averages away the components that distinguish keys within a block, blurring fine detail such as armor ornamentation and facial features. Medoid selection keeps a real token but favors the most typical one in each block, partially inheriting this loss. Random selection preserves more detail but depends on the seed. Center pooling (default) uses a fixed, content-free token per block and gives the sharpest and most coherent results.

Figure 10: Effect of switching phases too early or too late. An early switch starts Phase II before the structure has settled, degrading the final result. A late switch leaves too few Phase II steps to add fine details, so the final image lacks detail.

Quality
	Too early	Stable					Too late
Switch step $s$ ( $\sigma$ )	2 (0.98)	4 (0.95)	6 (0.92)	8 (0.89)	10 (0.85)	12 (0.81)	14 (0.76)	16 (0.70)	18 (0.64)	20 (0.56)	22 (0.46)
ImageReward $\uparrow$	0.77	1.22	1.18	1.25	1.24	1.25	1.20	1.22	1.21	1.19	1.18
Aesthetic $\uparrow$	6.03	6.20	6.22	6.23	6.15	6.16	6.21	6.20	6.18	6.16	6.08
CLIP-IQA $\uparrow$	0.685	0.712	0.705	0.705	0.696	0.686	0.671	0.658	0.628	0.579	0.528
Alignment

Appendix

Table 5: Ablation of the phase switch step

s

(Phase I

\rightarrow

Phase II) on FLUX.1 at 4096

\times

4096 resolution on 20 prompts from Aesthetic-4K. The noise level

\sigma

at the switch is given in parentheses.

Figure 11: Window size follows the locality of the model’s attention. AReA’s window size at

4096^{2}

and the model’s attention range at its native resolution of

1024^{2}

, computed over keys receiving at least a quarter of the maximum attention (blue) and over all keys (orange). All curves are shown relative to their values at the start of Phase II. In Phase I, every query attends to pooled keys from the whole image, so no window is shown. In Phase II, the window size follows a similar trend to the strongest attention, while attention over all keys remains broad.

Figure 12: AReA Across Denoising on FLUX.1 at 4096 × 4096 . Top row : Phase I steps with pooled keys and values (insets show the pixelation effect). Other rows : Phase II steps, each showing the current image (upper half) and its DWT detail map (lower half), with base windows in red, overlap regions hatched, and DWT splits in orange.

Figure 13: Effect of the Global-KV Bridge and Halo . Left : without the global-KV bridge, each window is blind to content elsewhere on the canvas and paints extra copies of the subject. With the bridge, windows see distinctive tokens from the rest of the grid and the duplicates disappear. Right : without the Halo, tokens at window edges lack context from across the boundary, leaving seams in smooth regions such as the sky.

Figure 14: Key budget per denoising step. Number of image keys each query attends to on FLUX.1 at

4096\times 4096

, averaged over 20 prompts (shading shows the range). Top : In Phase I, the pooled keys match the token count of the native

1024^{2}

resolution for

f=4

and four times that for

f=2

. In Phase II, the windows shrink from about 32K keys to the native 4K, and full AReA stays within about 30% of plain windowing. No query attends to more than about 40K of the 64K image keys at any step. Bottom : Change in keys per query relative to plain windows. The global-KV bridge adds a roughly constant share of about 25%, the halo takes a growing share as windows shrink, and DWT splits remove up to 35% late in Phase II.

Figure 15: Comparison with Multi-Stage Methods on FLUX.1 at $\mathbf{4096\times 4096}$ . Multi-stage methods and AReA-Bootstrap inherit the composition of the same base-resolution image and differ mainly in local detail. AReA with Phase I (default) sets its own layout at the target resolution and renders finer detail (green insets). ScaleDiff introduces repetitive content in low-detail regions (red insets).

Method	Latency (s) $\downarrow$	ImageReward $\uparrow$	Aesthetic $\uparrow$	MUSIQ $\uparrow$	CLIP-IQA $\uparrow$	ARNIQA $\uparrow$	CLIP $\uparrow$	HPS $\uparrow$	FID $\downarrow$	FID ${}_{p}\downarrow$
HiFlow	182	1.1710	6.3147	38.674	0.6561	0.4811	29.3	0.2951	153.0	59.0
I-Max	440	1.1716	6.3043	41.086	0.6838	0.5013	29.2	0.2956	154.8	60.1
ScaleDiff	135	1.2339	6.2785	44.496	0.7066	0.5663	29.2	0.2935	152.5	73.7
AReA-Bootstrap	126	1.2658	6.3402	43.224	0.7027	0.5479	29.4	0.2955	152.0	61.2
AReA	151	1.3250	6.3717	45.217	0.7132	0.5942	29.7	0.2969	148.5	58.2

Appendix

Table 6: Comparison with multi-stage methods on FLUX.1 at 4096×4096 on Aesthetic-4K. Bold : best; underline : second-best.

Figure 16: Out-of-Distribution Exposure and Its Consequence on FLUX.1. Left : distance and token-count exposure at

4096^{2}

, averaged over denoising and scaled so that

0

is the model at its native

1024^{2}

and

1

is the vanilla model at

4096^{2}

. RoPE-modifying methods (like SEGA) remove only the distance cause, while AReA reduces both. Right : FID of each method’s outputs to the model’s own native-resolution outputs; lower is closer to the model’s intended and trained distribution. AReA stays lowest and flat as resolution grows.

Figure 17: Statistics of HRGen-1K . left : Distribution of prompt lengths, grouped into short (

\leq 15

words), medium (16-40 words), and long (

>40

words) prompts. Right : Share of prompts containing common attributes, estimated via keyword matching. Attributes are not mutually exclusive.

Figure 18: Additional Comparison on FLUX.1 at $\mathbf{4096\times 4096}$ on HRGen-1K . Existing methods warp scenes into symmetric, fisheye-like layouts, reduce object-centric prompts to zoomed-in texture, miscount objects, and garble rendered text. AReA preserves scene composition, object counts (highlighted in red), and legible text.

Figure 19: Comparison on Qwen-Image at Non-Square Resolutions . Top : $2048\times 4096$ . Bottom : $6144\times 2048$ . Full attention misplaces the subject on the tall canvas and duplicates it on the wide one. DyPE stretches the subject and introduces artifacts, and SEGA duplicates the subject in the wide setting. AReA keeps a single, well-proportioned subject in both aspect ratios.

Figure 20: Comparison on Qwen-Image at $\mathbf{5120\times 5120}$ . Full attention blurs the scene and repeats subjects. SEGA keeps the layout but corrupts the details with high-frequency artifacts. AReA produces clean faces and the prompted number of subjects.

Figure 21: Comparison on Ideogram 4 at $\mathbf{6144\times 6144}$ . Full attention completely collapses, while SEGA fails to keep the layout and add fine detail. AReA excels in both structure and fine detail, showing that it transfers to a different backbone without modification.

Diffusion Transformer

Diffusion Transformers use attention-based networks to generate and transform images, video, audio, and other structured data through iterative denoising.

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SEGA: Spectral-Energy Guided Attention for Resolution Extrapolation in Diffusion Transformers

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University of Toronto · Vector Institute

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Open paper AskPDF arXiv

Diffusion transformers (DiTs) have emerged as a dominant architecture for text-to-image generation, yet their performance drops when generating at resolutions beyond their training range. Existing training-free approaches mitigate this by modifying inference-time attention behavior, often through Rotary Position Embeddings (RoPE) extrapolation combined with attention scaling. However, these strategies apply a uniform and content-agnostic scaling across RoPE components with distinct frequency characteristics, inducing a trade-off between preserving global structure and recovering fine detail. We introduce SEGA, a training-free method that dynamically scales attention across RoPE components according to the latent's spatial-frequency structure at each denoising step. This adaptive scaling improves both structural coherence and fine-detail fidelity. Experiments show that SEGA consistently improves high-resolution synthesis across multiple target resolutions, outperforming state-of-the-art training-free baselines.

Abstract

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