PixelDense: Dense Prediction as Representation Alignment for Pixel Diffusion

Lehan Yang; Daiqing Qi; Wenhao Zhang; Avery Li; Yiqing Yang; Yifan Li; Yu Kong; Haitian Zheng

cs.CVSep 30, 2026

PixelDense: Dense Prediction as Representation Alignment for Pixel Diffusion

Authors: Lehan Yang, Daiqing Qi, Wenhao Zhang, Avery Li, Yiqing Yang, Yifan Li, Yu Kong, Haitian Zheng, +4 more

Organizations: University of Virginia · Michigan State University · Adobe · Arcade AI

Abstract

Representation alignment (REPA) accelerates diffusion transformer training, but its alignment targets are almost exclusively semantic encoders such as DINOv2 and CLIP. Recent analysis points to spatial structure, not global semantics, as the carrier of the alignment effect, yet dense-prediction foundation models trained to predict that structure remain overlooked as REPA targets. In pixel-space diffusion, SAM2, Depth Anything v2, and Metric3D v2 each outperform the DINOv2-only GenEval baseline, with the two geometric teachers leading the segmentation teacher. A flat sum of all four teachers, however, lands below the best single geometric teacher, as semantic and geometric gradients compete for one denoiser projection. We introduce PixelDense, which routes DINOv2 and SAM2 through a semantic projection stream, routes Depth Anything v2 and Metric3D v2 through a geometric projection stream, and adds a weight-space orthogonality penalty that keeps the two streams in disjoint subspaces. All four teachers are frozen during training and dropped at inference. Applied to PixelGen and DeCo with a single recipe, PixelDense improves GenEval, DPG-Bench, and HPS v2.1, raises PixelGen-XXL's GenEval Overall from 0.7927 to 0.8093, and beats every single-teacher and unfactored multi-teacher variant. In partial-noise reconstruction, independent panoptic, depth, and surface-normal probes show up to 53.1% PQ gain and 36.0% depth AbsRel reduction at $τ=0.5$ across COCO and Flickr30K. From random initialization, PixelDense also reaches the baseline's peak GenEval 1.23x faster. In SDEdit editing on PIE-Bench, PixelDense keeps more of the source background and layout at every edit strength, raising background PSNR by up to 2.2 dB.

Figures & tables

Figure 2 : PixelDense factors dense-prediction supervision into a semantic stream and a geometric stream. The pixel diffusion denoiser $f_{\theta}$ takes the noisy image $\mathbf{x}_{t}$ and a Qwen3 text embedding $y$ , and reads its block- $8$ token state into two parallel projections, a $D_{s}=768$ semantic projection $W_{\text{sem}}$ aligned to frozen DINOv2 and SAM2 features, and a $D_{g}=1024$ geometric projection $W_{\text{geo}}$ aligned to frozen Depth Anything v2 and Metric3D v2 features. A weight-space orthogonality penalty $\mathcal{L}_{\text{orth}}$ on the rows of $W_{\text{sem}}$ and $W_{\text{geo}}$ keeps the two streams from collapsing into a shared subspace of the denoiser feature. Only the denoiser, the projections, and the per-teacher heads receive gradients. The four dense teachers are frozen during training and dropped at inference, so the deployed model is the original three-channel RGB generator with its original sampler.

Model	Type	#Params	GenEval $\uparrow$							DPG $\uparrow$	HPS $\uparrow$
Model	Type	#Params	Sing. Obj.	Two Obj.	Count.	Colors	Attr.	Pos.	Overall	DPG $\uparrow$	HPS $\uparrow$
SDXL [ 40 ]	latent	2.6B	98.00	74.00	39.00	85.00	23.00	15.00	0.55	74.7	–
SD3 [ 6 ]	latent	8B	98.00	84.00	66.00	74.00	43.00	40.00	0.68	–	–
PixelDiT (1024 res) [ 62 ]	pixel	1.3B	100.00	94.00	70.00	90.00	65.00	53.00	0.78	83.7	0.250
PixelFlow [ 5 ]	pixel	0.9B	–	–	–	–	–	–	0.60	77.9	–
PixelGen [ 35 ]	pixel	1.1B	99.00	88.00	59.00	90.00	70.00	70.00	0.79	-	0.281

Table 1 : PixelDense lifts compositional alignment over the matched PixelGen fine-tune at no inference-time cost. GenEval [ 9 ] , DPG-Bench [ 17 ] , and HPS v2.1 [ 54 ] on the released PixelGen-XXL [ 35 ] backbone at

512\times 512

. Our rows use the

10{,}000

-step, effective-batch-

256

2\times

H200 recipe of Section 4.1 . Higher is better for every metric. Bold marks PixelDense entries that improve over the matched PixelGen fine-tune.

Figure 3 : Qualitative geometry preservation comparison between PixelGen and PixelDense. We show noised/reconstructed images and pseudo labels for segmentation, depth, and normals. PixelGen shows more ghosting and structural distortions, while PixelDense better preserves GT geometry.

Dataset	Reference	$\tau$	OneFormer PQ $\uparrow$		OneFormer mIoU $\uparrow$		Depth AbsRel $\downarrow$		Normal err. ( ∘ ) $\downarrow$
Dataset	Reference	$\tau$	PixelGen	PixelDense	PixelGen	PixelDense	PixelGen	PixelDense	PixelGen	PixelDense
COCO val	Real panoptic	0.5	23.23	31.43	37.67	45.47	–	–	–	–
		0.7	15.70	21.31	30.64	36.59	–	–	–	–
		0.9	7.99	9.64	22.19	24.26	–	–	–	–
	Pseudo orig.	0.5	29.53	41.16	38.70	47.99	1.58/1.06	1.06/0.68	21.80	17.84
		0.7	18.93	26.51	30.73	37.32	2.09/1.54	1.63/1.12	26.90	23.48

Table 2 : Partial-noise reconstruction shows stronger geometry preservation for PixelDense. PixelGen and PixelDense reconstruct

\hat{\mathbf{x}}_{0}

from

\mathbf{x}_{t}=(1-\tau)\mathbf{x}_{0}+\tau\varepsilon

at three noise levels. OneFormer PQ/mIoU are averaged over Swin-L and DiNAT-L COCO panoptic models; Depth cells report mean/median AbsRel after per-image affine fitting; normal cells report mean angular error in degrees. Pseudo references are obtained by applying the predictor to the original image after the shared resize and center crop. Higher is better for PQ and mIoU; lower is better for depth and normals.

Table 5

Figure 4 : PixelDense converges faster on GenEval from random initialization.

Model	GenEval $\uparrow$							DPG $\uparrow$
Model	Sing. Obj.	Two Obj.	Count.	Colors	Attr.	Pos.	Overall	DPG $\uparrow$
DeCo T2I, released checkpoint [ 34 ]	100.00	92.00	72.00	91.00	79.00	80.00	0.8600	81.4
DeCo + fine-tune	99.38	94.44	73.75	93.62	79.00	77.00	0.8620	81.4
PixelDense	100.00	93.43	74.38	93.62	80.00	80.00	0.8690	81.8

Table 5 : The PixelDense recipe transfers to the DeCo backbone without retuning. The teachers, losses, hyperparameters, and checkpoint-selection rule used on PixelGen-XXL are applied unchanged to DeCo [ 34 ] at

512\times 512

. Higher is better for every metric, and bold marks PixelDense entries that improve over the matched DeCo fine-tune.

Background, outside the edit mask
Metric	Model	Noise level $\tau$
Metric	Model	0.3	0.4	0.5	0.6	0.7	0.8	0.9
LPIPS $\downarrow$	PixelGen	0.22	0.24	0.26	0.28	0.30	0.32	0.35
LPIPS $\downarrow$	PixelDense	0.19	0.21	0.23	0.24	0.26	0.29	0.32
PSNR (dB) $\uparrow$	PixelGen	26.07	24.72	23.27	21.76	20.17	18.19	15.57
PSNR (dB) $\uparrow$	PixelDense	28.00	26.74	25.39	23.92	22.30	20.23	17.10

Table 6 : PixelDense keeps more of the source image in SDEdit editing on PIE-Bench. Only the checkpoint differs between the two models. Background metrics are computed outside the edit mask, and FID is computed against the source images. Bold marks PixelDense entries that improve over PixelGen.

Figure 5 : Qualitative comparison between PixelGen and our PixelDense on text-to-image generation. PixelDense better preserves object counts, spatial relations, color binding, and scene geometry than PixelGen, yielding stronger prompt alignment and visual coherence.

Appendix figures & tables13 assets

Supplementary material from the paper’s appendix.

Appendix

Teacher	Backbone	Tap	Feature dim.	Group
DINOv2 [ 37 ]	ViT-B/14 patch tokens	last layer	$768$	semantic
SAM2 [ 43 ]	Hiera-L image encoder	last layer	$256$	semantic
Depth Anything v2 [ 60 ]	ViT-L encoder	layer $17$	$1024$	geometric
Metric3D v2 [ 16 ]	ViT-L encoder	layer $17$	$1024$	geometric

Appendix

Table 7 : Frozen dense-prediction teachers used by PixelDense. All teachers operate on the clean image

\mathbf{r}

512\times 512

, are frozen during training, and are dropped at inference. Feature dimension is the per-token width before normalization. Group is the semantic-versus-geometric assignment used by Section 3.3 .

Figure 6 : Semantic and geometric teachers light up complementary spatial structure on the same image. The leftmost column shows the RGB input, and the next four columns show the per-patch RMS magnitude of spatially normalized features from DINOv2, SAM2, Depth Anything v2, and Metric3D v2, with yellow marking high response. DINOv2 fires on object regions and SAM2 fires on instance boundaries, while Depth Anything v2 traces depth-ordered silhouettes and Metric3D v2 traces surface-normal layout.

Run	Pearson $\uparrow$	$\Delta_{\text{cos}}$ $\downarrow$	Pearson $-\,\Delta_{\text{cos}}$ $\uparrow$	Mean red. $\uparrow$	Sem. red.	Geo. red.	Sem./Geo. gap $\downarrow$	Reduction CV $\downarrow$
Naive sum, equal weight	0.9057	0.7430	0.1627	0.7213	0.7573	0.6854	0.0719	0.0973
PixelDense	0.9083	0.5966	0.3116	0.7340	0.7337	0.7343	0.0006	0.0629

Appendix

Table 8 : PixelDense preserves long-term co-improvement across teachers while breaking the short-term lockstep that the naive sum imposes. Run-level coupling and balance diagnostics on the four cosine alignment losses logged every

50

optimizer steps for

10{,}000

steps, giving

200

points per run. Pearson is the off-diagonal mean of the

4\times 4

cross-loss correlation on those

200

points.

\Delta_{\text{cos}}

is the off-diagonal mean of the cosine on the

199

consecutive step-to-step loss deltas. Pearson

-\,\Delta_{\text{cos}}

isolates long-term co-improvement from short-term lockstep. Mean reduction is the average per-teacher relative loss reduction between the first and last ten logged points. Sem. and Geo. reduction restrict that average to the two semantic teachers and the two geometric teachers, and Sem./Geo. gap is their absolute difference. Reduction CV is the across-teacher coefficient of variation of the four reductions. Higher Pearson, higher Pearson

-\,\Delta_{\text{cos}}

, higher mean reduction, lower Sem./Geo. gap, and lower CV are better.

Naive sum, equal weight, off-diagonal mean $0.7430$
	DINOv2	SAM2	DA2	M3D
DINOv2	1.0000	0.7287	0.7120	0.6950
SAM2	0.7287	1.0000	0.6799	0.6671
DA2	0.7120	0.6799	1.0000	0.9756
M3D	0.6950	0.6671	0.9756	1.0000
PixelDense, off-diagonal mean $0.5966$

Appendix

Table 9 : Step-to-step loss-delta cosine matrices on the four cosine alignment losses, naive sum versus PixelDense. Each off-diagonal entry is the cosine of step-to-step deltas of the two named per-teacher losses, computed on the

199

consecutive deltas of the

200

logged points per run. The top sub-block reports the naive equal-weight sum, and the bottom sub-block reports PixelDense. The within-geometric Depth Anything v2 against Metric3D pair stays near

0.95

under both objectives, reflecting the shared 3D structure of the two geometric teachers. The other five off-diagonal entries collapse only under PixelDense, with the largest drop on the within-semantic DINOv2 against SAM2 pair.

Run	DINOv2	SAM2	DA2	M3D	Mean
Naive sum, equal weight	83.10%	68.35%	72.81%	64.26%	72.13%
PixelDense	78.49%	68.26%	77.49%	69.37%	73.40%

Appendix

Table 10 : Per-teacher relative loss reduction over

10{,}000

training steps, naive sum versus PixelDense. The reduction is computed between the first ten and last ten logged points of the

200

-point training log per run. Higher values indicate that more of a teacher’s signal has been absorbed. Under the naive sum, Metric3D is the slowest teacher in the bank at

64.26\%

and DINOv2 is the fastest at

83.10\%

. PixelDense narrows the spread to within

10

points across the four teachers and raises the mean from

72.13\%

73.40\%

Dataset	Reference	$\tau$	PQ ${}_{\text{All}}$ $\uparrow$		PQ ${}_{\text{Th}}$ $\uparrow$		PQ ${}_{\text{St}}$ $\uparrow$		mIoU $\uparrow$
Dataset	Reference	$\tau$	PixelGen	PixelDense	PixelGen	PixelDense	PixelGen	PixelDense	PixelGen	PixelDense
COCO val	Real panoptic	0.5	23.25	31.54	26.32	35.96	18.62	24.86	37.53	45.36
		0.7	15.69	21.31	17.16	23.93	13.48	17.37	30.61	36.49
		0.9	8.02	9.72	7.92	9.96	8.17	9.36	22.16	24.32
	Pseudo orig.	0.5	29.72	41.33	31.90	44.39	26.43	36.71	38.62	47.79
		0.7	19.01	26.65	19.95	28.14	17.59	24.39	30.76	37.38

Appendix

Table 11 : OneFormer Swin-L panoptic scores per dataset, reference type, and noise fraction. Reconstructions of

\hat{\mathbf{x}}_{0}

are scored against the indicated reference at

512\times 512

. PQ is reported on All, Things, and Stuff. Higher is better, and bold marks the PixelDense column.

Dataset	Reference	$\tau$	PQ ${}_{\text{All}}$ $\uparrow$		PQ ${}_{\text{Th}}$ $\uparrow$		PQ ${}_{\text{St}}$ $\uparrow$		mIoU $\uparrow$
Dataset	Reference	$\tau$	PixelGen	PixelDense	PixelGen	PixelDense	PixelGen	PixelDense	PixelGen	PixelDense
COCO val	Real panoptic	0.5	23.21	31.32	26.06	35.68	18.91	24.75	37.80	45.59
		0.7	15.70	21.31	17.07	23.63	13.65	17.80	30.67	36.70
		0.9	7.97	9.57	7.90	9.67	8.08	9.42	22.22	24.20
	Pseudo orig.	0.5	29.34	40.99	31.23	43.66	26.49	36.95	38.77	48.19
		0.7	18.86	26.37	19.66	27.57	17.65	24.55	30.69	37.26

Appendix

Table 12 : OneFormer DiNAT-L panoptic scores per dataset, reference type, and noise fraction. Same protocol as Table 11 , with DiNAT-L replacing Swin-L. The two backbones agree to within

0.005

on PQ and mIoU per cell.

Dataset	$\tau$	Mean AbsRel $\downarrow$		Median AbsRel $\downarrow$
Dataset	$\tau$	PixelGen	PixelDense	PixelGen	PixelDense
COCO val	0.5	1.5805	1.0639	1.0559	0.6752
	0.7	2.0919	1.6286	1.5393	1.1179
	0.9	2.8269	2.6105	2.2362	2.0290
Flickr30K	0.5	1.6061	1.0257	1.1443	0.6770
	0.7	2.2186	1.6639	1.7021	1.2144

Appendix

Table 13 : Marigold v1.1 depth AbsRel against pseudo references, mean and median. Mean and median per-image AbsRel after closed-form per-image scale and shift fitting, lower is better. The median AbsRel agrees with the mean on every cell, so the comparison is not driven by a few badly aligned outliers.

Dataset	$\tau$	Mean ( ∘ ) $\downarrow$		Median per-image ( ∘ ) $\downarrow$		Mean of per-image medians ( ∘ ) $\downarrow$
Dataset	$\tau$	PixelGen	PixelDense	PixelGen	PixelDense	PixelGen	PixelDense
COCO val	0.5	21.80	17.84	20.79	16.48	15.25	11.76
	0.7	26.90	23.48	26.61	22.80	19.97	16.50
	0.9	33.20	31.94	33.70	32.36	26.50	24.93
Flickr30K	0.5	22.45	18.25	21.03	16.88	16.27	12.26
	0.7	28.12	24.62	27.55	23.59	21.88	17.84

Appendix

Table 14 : Marigold v1.1 surface-normal angular error against pseudo references, mean and per-image-median statistics. Mean pixelwise angular error in degrees, median per-image angular error, and mean of per-image medians. Lower is better. Per-image medians track the means on every cell, so the comparison is not driven by tail pixels.

Figure 7 : Additional PixelDense samples on the PixelGen-XXL backbone at $512\times 512$ . Object-centered photographs, painterly portraits, atmospheric landscapes, and stylized fantasy scenes. The dense teachers are dropped at inference, and sampling matches the protocol of Section 4.1 .

Figure 8 : Additional partial-noise reconstruction comparisons between PixelGen and PixelDense. Each row shows the noised image at the indicated $\tau$ , the two reconstructions, and the panoptic, depth, and surface-normal probes from OneFormer [ 18 ] and Marigold [ 20 ] . PixelDense reconstructions track object boundaries, depth ordering, and surface orientation more faithfully than PixelGen at every $\tau$ .

Model	Anime	Concept-art	Paintings	Photo	Average
PixelGen-XXL T2I, released checkpoint	0.292	0.278	0.269	0.284	0.281
DeCo T2I, released checkpoint	0.285	0.267	0.259	0.277	0.272
PixelGen + DINOv2-only fine-tune	0.290	0.277	0.268	0.285	0.280
PixelGen + PixelDense, ours	0.292	0.280	0.272	0.284	0.282

Appendix

Table 15 : HPS v2.1 per-style breakdown across the four prompt categories of Wu et al. [54] . Each split contains

800

prompts, and the rightmost column averages the four splits. Higher is better, and bold marks PixelDense entries that improve over the matched PixelGen fine-tune.

Role	Asset	License
Pixel backbone	PixelGen-XXL [ 35 ]	Apache 2.0 use
Pixel backbone	DeCo-T2I [ 34 ]	Apache 2.0 use
Semantic teacher	DINOv2 [ 37 ]	Apache 2.0
Semantic teacher	SAM2 [ 43 ]	Apache 2.0
Geometric teacher	Depth Anything v2 [ 60 ]	Apache 2.0
Geometric teacher	Metric3D v2 [ 16 ]	BSD 2-Clause

Appendix

Table 16 : Public assets used in this paper, with the license under which each is released. All assets are used without modification. The training corpus and evaluation prompt sets are used through their official releases.

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Open paper AskPDF arXiv

Recent advances in pixel-space diffusion models have narrowed the image quality gap with latent-space diffusion, but still converge more slowly and lag behind in final image quality. We argue that a key reason is the lack of an explicit representation prior: unlike latent diffusion, which usually denoises in a compact and structured latent space, pixel diffusion needs to learn denoising-friendly representations and pixel generation simultaneously from raw RGB space. To address this problem, we propose PixelDiT2, an end-to-end pixel-space diffusion model designed to decouple representation learning from pixel generation without introducing an autoencoder or latent reconstruction bottleneck. We propose representation grounding that uses a frozen pretrained vision foundation model to provide explicit per-patch representation guidance throughout denoising, allowing the pixel diffusion transformer to focus more on pixel generation. On ImageNet-256x256, PixelDiT2 achieves an FID of 1.46 after 600 epochs; at 512x512 resolution, PixelDiT2 achieves an FID of 1.48 after 680 epochs. Project page: https://pixeldit.github.io/pixeldit2/

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