PixNerd: Pixel Neural Field Diffusion
Authors: Shuai Wang
Ziteng Gao
Chenhui Zhu
Weilin Huang
Limin Wang
Venue: ICLR 2026 Poster (2026)
Keywords: pixel diffusion model
Primary area: generative models
TL;DR: pixel diffusion transformer with neural field decoder

Abstract: The current success of diffusion transformers are built on the compressed latent space shaped by the pre-trained variational autoencoder(VAE). However, this two-stage training paradigm inevitably introduces accumulated errors and decoding artifacts. To avoid these problems, researchers return to pixel space modeling but at the cost of complicated cascade pipelines and increased token complexity.
Motivated by the simple yet effective diffusion transformer architectures on the latent space, we propose to model pixel space diffusion using a large-patch diffusion transformer and employ neural fields to decode these large patches, leading to a single-stage streamlined end-to-end solution, which we coin as pixel neural field diffusion transformer (**PixNerd**). Thanks to the efficient neural field representation in PixNerd, we achieve **1.93 FID** on ImageNet 256x256 and nearly **8x lower latency** without any complex cascade pipeline or VAE. We also extend our PixNerd framework to text-to-image applications. Our PixNerd-XXL/16 achieves a competitive 0.73 overall score on the GenEval benchmark and 80.9 overall score on the DPG benchmark.

Published as a conference paper at ICLR 2026
PIXNERD: PIXEL NEURAL FIELD DIFFUSION
Shuai Wang'
Ziteng Gao"
Chenhui Zhu'
Weilin Huang"
Limin Wang
NNanjing University
ByttDDance Seed
National University of Singapore
PixNerd Architecture (Ours)
Pixel Diffusion Models
Latent Diffusion Models
Cescade/MultScale
LangePaachhSnnnissciileeeurrr
147
VAz
Micid
Ciltatoon
Model
Diffusion
Oritaton
Transfomer
Midil
Madel
Cillaion
TUB
Predicted
MUPs weight
Paad coordinate encodings
Citcaion
Mode
MILPs
Figure I: Left: Comparison with other diffusion paradigms. Right: PixNerd architecture.
Our PixNerd is streamlined LargePatch/SingleScale pixel space diffusion model. PixNerd follows the diffu- sion transformer design, replacing the final lincar projection with neural field to model the large patch details.
ABSTRACT
The current success of diffusion transformers built the compressed latent space shaped by the pre-trained variational autoencoder (VAE). However, this two-stage training paradigm inevitably introduces accumulated errors and decod- ing artifacts. To address these issues, researchers have returned to pixel space modeling but at the cost of complicated cascade pipelines and increased token complexity. Motivated by the simple yet effective diffusion transformer archi- tectures on the latent space, we propose to model pixel space diffusion using a large-patch diffusion transformer and employ neural fields to decode these large patches, resulting in a single-stage streamlined end-to-end solution. We coin our method as pixel neural field diffusion transformer (PixNerd). Thanks to the ef- ficient neural field representation in PixNerd, we achieve 1.93 FID on ImageNet 256 x 256 and nearly 8x lower latency without any complex cascade design VAE compared with these previous pixel diffusion models. We also extend our PixNerd framework text-to-image tasks, and achieve competitive 0.73 overall score on the GenEval benchmark and 80.9 overall score on the DPG benchmark.
INTRODUCTION
The current success of diffusion transformers largely depends on variational autoencoders (VAEs) (Rombach et al.] 2022; Yao & Wang 2025: Chen et al.) 2024a). VAEs significantly re- duce the spatial dimension of raw pixels while providing a compact and nearly lossless latent space, substantially casing the learning difficulty for diffusion transformers. Operating in this compressed space, diffusion transformers can effectively learn the reverse diffusion process using small patch sizes. However, training high-quality VAEs typically requires adversarial training (Brock et al.
X Corresponding author: Limin Wang (Imwang@nju.edd

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Published as a conference paper at ICLR 2026
Figure 2: Selected 256 x 256 and 512 x 512 resolution samples. Generated from PixNerd-X1./16 trained ImageNet 256 256 resolution and ImageNet 512 512 resolution with CFG=33.
2018 Sauer al. 2022 Rombach et 2022 Yao & Wang 2025 Chen et al. 2024a) and ad-
ditional supervision (Zhang et al.) 2018))
introducing complex optimization challenges. Moreover,
this two-stage training paradigm leads accumulated errors and inevitable decoding artifacts.
To address these limitations, Leng et al. (2025) has explored joint training approaches, though these come with substantial computational costs. An alternative approach involves implementing di diffusion models directly in raw pixel space (Dhariwal & Nichol 2021] Chen et al. (025b) Teng al.) 2023).
In contrast to the success of latent diffusion transformers, progress pixel-space diffusion trans- formers proves significantly more challenging. Without the dimensionality reduction provided by VAEs, pixel diffusion transformers allocate substantially more image tokens (Chen et al., 2025b), requiring impractical computational resources when using the same patch size as latent diffusion transformers. To maintain comparable number of image tokens, pixel diffusion transformers must employ much larger patch sizes. Unfortunately, due the vastness of raw pixels, larger patches make diffusion learning particularly difficult. Chen et al. (2025b); Teng et al. (0033) have proposed cascade solutions that divide the diffusion process across different scales to reduce computational costs. However, such a cascade approach complicates both training and inference.
In contrast to the afore-mentioned methods, explore the performance upper bound using large- patch diffusion transformer raw pixels while maintaining the same token count and compa- rable computational requirements latent diffusion models. Inspired by the success of implicit neural fields in scene reconstruction (Mildenhall et al. 2021 Sitzmann et al. 2020), we propose modeling large-patch decoding using an implicit neural field in the pixel diffusion pipeline. We replace the traditional diffusion transformer's s linear projection with patch-wise implicit neural field head, leading to a novel pixel-space architecture PixNerd (Pixel Nerual Field Diffusion).
Specifically, we faithfully follow the classic diffusion transformer and design patch-wise adaptive neural field head, whose weights are predicted by the diffusion transformer's 's last hidden features. For each pixel within patch, first transform its local coordinates along with noisy pixel values into input encoding, and then predict the diffusion velocity by the neural field MLP. Our approach significantly alleviates the challenge of learning fine details with large-patel patch configurations.
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Compared to previous latent diffusion transformers and pixel-space diffusion models, our end-to- end PixNerd offers a more streamlined and efficient solution for image generative tasks. For class- conditional image generation ImageNet 256 x 256, PixNerd-XL/16 achieves competitive 1.93 FII and significantly better 4.50 sFID (indicating superior spatial structure) than others. For text- to-image generation, 1666/ achieves 73.0 the GenEval benchmark and 80.9 average score on the DPG benchmark.
Our contributions are summarized follows:
We propose new, efficient end-to-end pixel-space diffusion transformer equipped with our proposed neural field head, dubbed PixNerd.
For the glass-to-image generation, on ImageNet 256 x 256, our PixNerd-XX -L/16 achieves a competitive 1.93 FID with similar computation demands as its latent counterpart.
For the text-to-image generation, our SeANerd-XXL/IA achieves a 0.73 overall score on the GenEval benchmark and 80.9 average score on DPG benchmark.
2 RELATED WORK
Latent Diffusion Models train diffusion models on compact latent space shaped by a VAE.
Compared to raw pixel space, this latent space significantly reduces spatial dimensions, easing both
learning difficulty and computational demands (Rombach et al.] 2022 Yao Wang 2025 Chen
al.
2024a). Thus VAE has become core component modern diffusion models (Peebles & Xie
2023
Ma et al. 2024; Wang et al. 2026 Karras et al. 2024 Wang et al. 2024b [Song et al. 2025
Rombach et
2022). However, VAE training typically involves adversarial training and percep-
tual supervision, complicating the overall pipeline. Insufficient VAE training can lead to decoding
artifacts, limiting the broader applicability of diffusion generative models Zhou et al. (2024b). Ear-
lier latent diffusion models primarily focused on U-Net-like architectures. |Peebles & Xie (2023) introduced transformers into diffusion models, replacing the traditionally dominant U-Net. Ma et al. 2024) further validated the transformer architecture with linear flow diffusion.
Pixel Diffusion Models have progressed much more slowly than their latent counterparts. Due to the vastness of pixel space, learning difficulty and computational demands are typically far greater than those of latent diffusion models (Dhariwal & Nichol, 2021, Kingma Gao [2023] Hoogeboom
ct al. 2023). Current pixel-space diffusion models still rely on long residuals (Teng et al. [023] Dhariwal & Nichol 2021). limiting further scaling. Early attempts primarily split the diffusion process into chunks at different resolution scales to reduce computational burdens (Chen et al.
20256 Teng et al. 2023). [Chen et (202bb) used the same denoising model across all scales, while Teng et al. (2023) employed distinet models for each. However, this cascaded pipeline inevitably complicates both training and sampling. Additionally, training these models at isolated resolutions hinders end-to-end optimization and requires careful design. Li al. (2025a) constructed fractal generative models by recursively applying atomic generative modules. Zhai et al. (2024) introduces a Transformer- based normalizing flow capable of directly modeling and generating pixels.
Generative Models with Neural Field/Function Space leverage neural fields to enhance VAES (Chen al. 2024b; Park et al. 2024), and their generative capacity still stems from latent diffu- sion model. Gong et al. (2024) trains independent neural weights for each image before training generative model on these pre-collected weights. This remains a two-stage framework and poses non-trivial challenges for large-scale training. WWang et al. (2024c) and Wang et al. (202bb) utilized coordinate encodings to enhance diffusion model performance. Beyond diffusion- -aased generative models, GAN-based methods (Ntavelis et al.) 2022] (Skorokhodov et al., 2021 Lin et al. 2019
Karras et al. 202l also utilized neural fields or coordinate encodings.
METHOD
3.1 PRELIMINARY
Diffusion Models gradually adds 2o with Gaussian noise e to perturb the corresponding known data distribution p(æo) into simple Gaussian distribution. The discrete perturbation function of
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each t satisfies (Z If where at, 0z 0. It can also be written as Equation 1
Ft = Or + 0y6.
Moreover, as shown in eq. do log of). Anderson (982) establishes a pivotal theorem that the for- aoogge and g(t) = at df ward SDE has an equivalent reverse-time diffusion process as in Equation (3). so the generating process is equivalent to solving the diffusion SDE. Typically, diffusion models employ neural net-
2 eq.
has a forward continuous- description, where f(t)
dt
works and distinct prediction parametrization to estimate the score function V log, Pa, (æt) along
the sampling trajectory (Song et al. 2020 Karras et al. 2022
Ho et al. 2020):
dæ: f(1)a,dl + g())dw.
(2)
dx, [f(t) 9 pog 9(t)dw.
(3)
Rectified Flow Model simplifies diffusion model under the framework of Equation 2 and Equa- tion (3). Different from Ho et al.] (2020), which introduces non-linear transition scheduling, the rectified-flow floww model adopts a linear function to transform data to standard Gaussian noise. Instead of estimating the score function vx, log Pt (Zx), rectified-flow models directly learn neural net-
work vo (Ze, t) to predict the velocity field vt dx, [Xeaal c).
Diffusion Transformer was introduced into diffusion models replace the traditionally dominant UNet architecture (Peebles & Xiel 2023). Given a noisy image latent æ; as Equation ( is the condition, t is the timestep, we first partition it into non-overlapping patches, converting it into a ID sequence. These noisy patches are then processed through stacked self-attention and FFN blocks, with class label conditions incorporated via AdaLN modulation. We denote the feature of x as X,;
X, = X, + A 4, ATTENTION(X.)) X, X, + ADALN y E, FFN(X,)).
(4)
Recent architectural improvements such as SwiGLU Touvron et al. 2023a b). RoPE (Su et al, 2024]. and RMSNorm (Touvron et al. 2023a b) have been extensively validated in the research
community (Chu et al. 2024: Yao & Wang 2025 Lu et al. 2024: Gong et al. 2025 Gao et al.
2025
Liao al. 2025).
Neural Field
is usually adopted to represent a scene through MLPs that map coordinates encod-
ings signals MMildenhall et 2021 Sitzmann al. [2200] Park al. 2024 Lin al. 20e9|Chen
Chen
et al. 2024b). It has been widely applied to objects (Barron et |||2| Lombardi et al. 2019) and surface reconstruction (Wang al.) 202T: Yu al.) 2022] Wang et al. 2023a). Specifically, recall that an MLP consists of Linear, SILU, and another Linear, we regard WT as the weight for the first linear layer in ML.,, while W is for the second. If we have a neural field with a single set of MLP parameters O = {W1, 1. W2} for naive 2D scene, given the query coordinate (i, the co- ordinate will be transformed into cosine/sine encodings in Equation (6) or DCT-basis encodings in Equation 11
PE(i,.) = sin(2 cos(22mm)) sin(2"mi), mi), cos(22
siniei mj), cos((2 (6)
Then this encoding will be fed into the neural field MLP defined by 0 to extract features V (i,
(i = MPE(,
Finally, V (i,jj can be used to regress the needed value, e.g., RGB MMildenhall et al. 2021 Sitz-
mann al. 2020), density, and SDF (Yu et al. 2022
3.2 DIFFUSION TRANSFORMER WITH WISC NEURAL FIELD
While VAES significantly reduce spatial dimensions in the latent space, rendering a single linear projection sufficient for velocity modeling in latent diffusion transformers, pixel diffusion transform- ers must handle substantially larger patch sizes to maintain compu- tational parity with their latent counterparts. As shown in the right figure (Figure 3 under such conditions, a simple linear projection becomes inadequate for capturing fine details.
Figune Frod Comupe Park
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Same initialization Noise with Euler-50 solver and CFG=4.0 400k training steps
PixNerd-L/16 PixDIT-L/16
Figure 4: The visualization Comparison with PixDiT-L/16 under 400k training steps. With the help of neural field representation, our PixNerd-L/16 yields promising details and better structure.
To address the limitations of linear projection, we propose modeling patch-wise velocity decod- ing using an implicit neural field, which has been proven beneficial for fine details by Milden- hall et al.] (202T); Sitzmann
al. (2020). Formally, given the last hidden states X" (defnned in Equation (4) of the n-th patch in diffusion transformer, we predict neural field parameters 0" (WT D2x D1, W E IR D2 P= from X":
WT. W = LINEAR (SIUU(X))
(8)
To decode the pixel-wise velocity v" (i. for the pixel coordinate j) in the n-th patch feature X", where i.j E (0, K), we first encode the coordinates into encodings. These encodings (PE(i, along with the noisy pixel value ("(,,j)). are then fed into the neural field MLP to predict the velocity. To enhance performance and stabilize training, we apply row-wise normalization to neural field parameters:
WT
W
V (i,i)) MLP ())()))((PE(((.). xe" (i,j))), 0"
|T
||3l
Finally, shown in Equation (1) the pixel velocity (i, is decoded from (*(,,) via a linear projection:
vi,i,)) LINEAR (V (i
(10)
4 EXPERIMENTS
We conduct ablation studies and baseline comparison experiments on ImageNet-256 256. For class-to toss-to-image generation, we provide system-level comparisons on ImageNet-255 256 x 256, and re- port FID (Heusel et al., 2017). sFID (Nash al. 2021). IS (Salimans al., 2016). Precision, and Recall (Kynkäänniemi et al. 2019) as the main metrics. For text-to-image generation, we report results collected on the GenEval (Ghosh et al. 2023) and DPG (Hu et al. 2024) benchmarks.
4.1 COMPARISON WITH BASELINES
We conduct a baseline comparison (PixDiT and PixNerd) on ImageNet 256 256. PixDiT faithfully follows the diffusion transformers (Ma et al. 2024 Peebles & Xie 2023), using naive linear pro- jection decode the velocity field. Both PixDiT-L/16 and PixNerd-L/16 are built upon SwiGLU, RoPE2d, RMSNorm, and trained with lognorm sampling. All optimizer configurations are consis- tently aligned. Shown in Figure 3 neural field modeling significantly improves the performance at large patch size settings. As shown in Figure 5al our model consistently achieves lower velocity loss expectations and higher representation alignment similarity. We also provide visualization compar- isons with PixDiT-L/16 in Figure 4 PixNerd-L/16 trained for the same number of steps, generates better details and structures. We provide the FID5OK (without CFG) metrics of both PixDiT-L/16 and our PixNerd-1/16 in Figure 5b) PixNerd yields much better FID scores (lower is better).
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23
Trahs
100000
3:208
Toaniin Recakeenac stope))
(a) REPA Sim and Flow/attching Veclocity loss
(b) FID50K w/o CFG
Figure 5: Quantitative Comparison with PixDiT-L/16 under 400k training steps. Our PixNerd- I/16 achieves consistently lower REPA loss and flow matching loss than its diffusion transformer counterpart.
22-
36-
37-
30
328
200
230
ParateeXX
(a) Neural Field Normalization
(b) Neural Field Channels
(c) Neural Field MLPs layers
110
3.20
27
4.2
11
122
Trinaig barvtinnt mape)
(d) Coordinate-Fnnoohin
(c) Interval Guidance
(f) Sampling Solver
Figure 6: Ablations studies of PixNerd. We conduct ablation studies on class-to-image generation hench- mark ImageNer256 x 256 with PixNerd-1/16.
4.2 NEURAL FIELD HEAD DESIGN
We conduct ablation studies PixNerd-L/16, which comprises 22 transformer layers with 1024 channels. The Neural Field head is configured to have computational burden comparable to that of a two-layer transformer block, so PixNerd-L/16 has inference latency similar counterpart, PixDiT-L/16 (24 transformer layers with 1024 channels). The detailed inference and training re- source consumption can be found in the appendix.
Neural Field Normalization As illustrated in Figure 6a we evaluate different neural field nor- malization strategies. Our baseline compares three approaches: (1) normalizing only the first weight (FC1), (2) normalizing both weights (FC1/FC2), and (3) our default strategy that additionally nor- malizes the output features. Experiments demonstrate that the default strategy achieves optimal performance and convergence speed. More discussion of normalization techniques can be found in the appendix.
Neural Field MLP Channels We conduct an empirical study of different MLP channel configu- rations (36, 64, and 72 channels) shown in Figure 6bl Our experiments reveal that: (1) minimal

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Published as a conference paper at ICLR 2026
configuration of 36 channels leads noticeable performance degradation compared to 64 chan- nels; (2) while the 72-channel variant achieves marginally better results, it incurs significant com- putational overhead, including slower training speed and increased parameter count. Based on this trade-off analysis, we select 64 channels as our default configuration.
Neural Field MLP Depth We investigate the impact of neural field depth by evaluating PixNerd- L/16 with 1, 2, and 4 MLP layers, as shown in Figure [c] Our experiments demonstrate consistent performance improvements with increasing network depth. However, considering the trade-off be- tween computational efficiency (inference latency and training speed) and model performance, we establish the 2-layer configuration as our optimal default architecture.
Coordinate Encodings
We propose a novel DCT-Basis coordinate encoding following:
DCT-PE(i,,) {cos(k1i) cos(k2j), h (20
(11)
We compare the DCT-Basis coordinate encoding (defined in Equation (11) with traditional sine/- cosine encoding in Figure [2] Our DCT-based encoding achieves significantly better results than sine/cosine encoding in terms of both convergence and final outcome. We place the implementation
details of DCT-Basis encoding in the appendix.
4.3 INFERENCE SCHEDULER DESIGN
Interval Guidance Classifier-free free guidance (Peebles & Xie 2023 Ma et al. 2024 Ho & Sali- mans 2022) is commonly used technique to improve the diffusion model performance. Interval guidance (Kynkäänniemi al. 2024) is an improved cfg technique, and has been validated by recent works (Yao & Wang. 2025 Wang et al. 2026). We sweep different CFG values from 3.0 to 5.0 with a step of 0.2 find the optimal CFG scheduler of PixNerd- -L/16 (800k training steps). As shown in Figure 6e it achieves best result FIDIOK result with 3, 3.6 within the interval [0.1. 11.
Sampling Solver In Figure 6f we armed PixNerd-XX XL// with an Fuler solver and an Adams-like linear multistep solver with 2/4 orders. The Adams-2 order solver consistently achieves better results than the Euler solver with limited sampling steps. Due to the learning difficulty of pixel spaces, the Adams-4-order solver performs unstably compared the Euler and Adams2 solvers.
4.4 CLASS-TO-IMAN GENERATION
Training Details In class-to-image generation, to ensure fair comparative analysis, did not use gradient clipping and learning rate warm-up techniques. We adopt EMA with 0.9999 to stabilize the training. Our default training infrastructure consisted of 8x A100 GPUs.
Visualizations We placed the selected visual examples from FixNerd-L//L trained on Ima- geNet 256 x 256 and ImageNet 512 x 512 at Figure 2 Our ExNerd-XLL/Ie can generate images with promising details. We generate these images with CFG of 3.5 and the Euler-50 solver.
ImageNet 256 256 Benchmark We report the metrics of -iANe and PixNerd- FixNerdXXL/16 in Table Under merely 160 training epochs, PixNerd-L/16 achieves 2.64 FID, significantly better than other pixel generative models like Jetformer (Tsshannen et al.] 2024). FractalMAR (Li et al. 2025a). Further, PixNerd-XL/IK achieves 2.29 FID under 50 steps with the Euler solver. When used with Adams-2-order- solver (Adam2 for brevity),
-LLNerd-XL/16 (800k training steps) achieves 2.16 FID, comparable to DiT. With enough sampling steps and training iterations, PixNerd-XL/16 ( (1600k training steps) boosts FID to 1.93 under 100 steps. We also provide FixNerd-XL/8. which achieves 1.87 FID within 800K training iterations. Moreover, our PixNerd has much lower latency com- pared to other pixel space generative models.
4.5 TEXT-TO-IMAGE GENERATION
Data Preprocess Details For text-to-image generation, trained our model mixed dataset containing approximately 45M images from open-soureed datasets, e.g., SAM (Kiillovv et al. 2023), JournyDBB (Sun al. 2023), ImageNet-1K (Russakovsky et al. 2015). The detailed data source
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ImageNet 256x226 with CPG
Params
Epochs
NEE
Latency(s)
FID]
sFID]
1St
Pre.
Rec."
Generative
LDM-4
Rombuch et al 2022
400M + 86M
170
250x2
1
3.6
/
247.7
0.87
0.48
DIT-XL
675M
1400
250x2
2.82
2.27
4.60
278.2
0.83
SIT-XL
(Mla of 2024
675M 86M
1400
250x2
2.82
2.06
4.50
270.3
0.82
0.39
FlowDDN
Waing
2024h
618M 86M
400
250x2
f
2.00
4.33
263.1
0.82
0.58
REPA
Ya 2124
675M + 86M
800
250x2
2.82
1.42
4.70
305.7
0.80
0.64
DDT-XL
Wang 2022
675M 86M
400
250x2
2,92
1.26
4.51
310.6
0.79
0.65
MAR-L
Lrwaa 31230
479M 86M
800
/
1.78
296.0
0.81
0.60
CausalFusioo
Deng mi 20144
676M 86M
800
250x2
/
1.77
282.3
0.82
0.61
Pixel Genenative Models
ADM (Dhariwal & Nichol 2021
554M
400
250
21.3
4.59
5,13
186.7
0.82
0.52
RÓM [Tene et 2024
1.99
260.4
0.81
0.58
Jettormerr
(1 [sschammnn el th 2024
2.8B
1
/
6.64
/
0.69
0.56
FractalMAK
600
348.9
0.81
PixciFlow-XX Flow-X/4 Cheil Fil 20251
677M
320
10.1
1,98
5.83
282.1
0.81
0.60
PixDTT-L/1G (Euler-50)
458M
160
50x2
0.48
4.11
6.61
256.38 38
0.75
0.56
11 (Fuler-50)
458M
160
50x2
0.51
2.64
5.25
297
0.78
0.60
PixNerd-XL/16 (Adam2-50)
160
50x2
0.64
2.16
291
0.78
PixNerd-XI XL6/1 (Euler-100)
700M
160
100x2
1.28
2.15
4.55
297
0.79
0.59
PixNerd-XI/16 (Faler-100)
700M
320
100x2
1.28
1,93
4.50
298
0.80
0.60
PixNend XLJ8 (Fuler-100)
700M
160
100x2
1.98
1.87
4.36
298
0.79
0.61
Table 1: System performance comparison on ImageNet 256 x 256 class-conditioned generation. Our PixNerd- 6 achieves comparable results with latent diffusion models under similar computation de- mands while achieving much better results than other pixel space generative models. The detailed interval guidance configuration can be found in the appendix.
GenEval Benehmark #Params Sin.Obj. Two.Obj. Counting Colors Pos Color.Attr/ (verall
latent diffusion model
LDM (Rombach et al. 2022
1.4B
0.92
0.29
0.23
0.70
0.02
0.05
0.37
DALL-E2
4.2B
0.94
0.66
0.49
0.77
0.10
0.19
0.52
DALL-E3
0.96
0.87
0.47
0.83
0.43
0.45
0.67
Imagen
3B
SD3 (Esser et al. 2024
8B
0.98
0.84
0.66
0,74
0.40
0.43
0.68
Translusion (Zhou et al
2024a
7.3B
0.63
pixel diffusion mode!
PixelFlow- 1oW-//44 (Chen al.
2025b
882M
3B
0.60
PixelFlow "xx//41o CChnn 20256
882M
3B
0.64
HXNNed-XXL/16
1.2B + 1.7B| 0.97
0.86
0.44
0.83
0.71
0.53
0.73
Table 2: Comparsion with other text-to-image models on GenEval Benchmark. indicates prompt rewriting. Parameters consist of denoiscr+text encoder+vae. Our FixNerd-XXII/I achieves competitive per- formance compared with others under much-limite data scale (45M images).
be found in the appendix. We recaption all the images with Qwen2.5-VL-7B (Wang al. 2024a) to yield English caption descriptions of various lengths. Note that our caption results only contain English descriptions. All the images are cropped into a square shape of 256 x 256 or 512 512, we do not adopt various aspect ratio training. We leave the native resolution Wang et al. (2025) or
native aspect training (Gong et al. 2025 Gao et al. 2025
Liao et al. 2025) as future work.
Training Details We adopt Qwen3-1.71 as the text encoder. To improve the alignment of frozen
text features Fan et al. al. (2024), we jointly train several transformer layers on the frozen text features adopt SFT stage resolution 512 x 512 with the dataset released by Chen et al. (2025a). The total batch size is 1536 for 256 256 resolution pretraining and 512 for 512 512 resolution pretraining. We trained PixNerd on 256 x 256 resolution for 200K steps and trained on 512 x 512 resolution for 80K steps. The default training infrastructure consisted of 16x A100 GPUs. We use the Adams-2nd solver with 25 steps the default choice for sampling.
similar to Fan et al. (2024). To further enhance the generation quality,
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groen-drrtn ritedd water, sunligtt thera
surret paints the Gleaay,
insplied
miouge koip foreota-bi encling
aittnucced wet with genmeniio
Victorian muchinary
potterroliibrent primarics.
Figure 7: The Text-to-Image 512 512 visualization with text descriptions of different lengths and styles. Given text descriptions of different lengths and styles, PixNerd can generate promising samples with large patch size of 16. We used Adams-2nd solver with 25 steps and CFG value of 4,0 for sampling.
Solver-Stepp
4
12
20
25
Altamo Indbtte)
Adaano 2rd
Adanno
(Chineae Inoun) 5059 A.A No 12:20:3 # Poak
Adaamte 201d
TEnglish Input! Stcompunk mechanieol wol with brass gears and dowing omber eyes prowing loggy Industi city
Upponese Imput, 0
Figure 8: The Text-to-Image 512 512 visualization. armed PixNerd with different ODE solvers, eg, Euler, Adams. Also, thanks to the powerful text embedding in Qwen3 models, though only trained PixNerd with English captions, PixNerd can generate samples promising quality with other languages.

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Published as a conference paper at ICLR 2026
DPG Benchmark Attribute Relation
Model
#Params
Global Entity
Other Average
latent diffusion model
SD v2
0.86B 0.7B+ 86M
77.67
78.13
74.91
80.66
68.09
PixArt-c
0.61B + 4.7B
74.97
79.32
78.60
82.57
76.96
71.11
Playground v2
2.61 0.7B 86M
83.61
79.91
82.67
80.62
81.22
74.54
DALL-E3
90.97
89.61
88.39
90.58
89.83
83,50
vl.5
0.86B 0.7B 86M
74.63
74.23
75.39
73.49
67.81
63.18
SDXL
2.61B +1.4B 86M
83.27
82.43
80.91
86.76
80.41
74.65
pixel diffusion model PixclHlowXLLA
0.81 + 3B
77.90
-XXN11
1.2B 1.7B
80.5
87.9
87.2
72.8
80.9
Table 3: Comparsion with other text-to-image models on DPG Benchmark. Parameters consist of denoiser+text vacoder+vae. Our PixNerd-XXI/16 achieves competitive performance compared with others under much-limited data scale (45M images).
Visualizations We provided 512 x 512 visualizations with prompts provided by DouBao- 1.5- Pro in Figure 7 As illustrated in Figure 7 our PixNerd-XXL/16 is capable of generating visually compelling images from complex text prompts. Overall. the atmosphere and color tones largely accurate. Noted that we only trained PixNerd with English prompts. As shown in Figure g we generate images even with other languages like Chinese and Japanese thanks the powerful emhedding space of Qwen3 models (Wang et al. 2024a). We place more visual samples in the appendix.
Sampling Solvers We provide denoising trajectories in ii space (clean data) of different solvers in Figure 8 including Fuler, Adams-2nd, and Adams-3rd solvers. Adams-2nd solver achieves stable and fast sampling results. Thus, we take Adams-2nd as the default sampling choice.
GenEval Benchmark We provided quantity comparison on Geneval (Ghosh al., 2023 bench- mark in Table Our BxxNerd-XX1L11 achieves comparable results under enormous patch sizes and limited data scales. As shown in Table 2 our LoxNNer-XXLL/A achieves 0.73 overall score, beating Chen et al. (2025b) with significant margin.
DPG Benchmark We provided quantity comparison on DPG (Hu et al. 2024) benchmark in Table Our MokNNerdXXL/// achieves competitive results compared to latent counterparts. As shown in Table B our PixNerd-XXLL achieves 80.9 overall score, beating other pixel generation models with a significant margin.
5 CONCLUSION
In this paper, we return to pixel space diffusion with neural field. We have presented a single-scale, single-stage, efficient, end-to-end solution, termed as the pixel neural field diffusion (PixNerd). Thanks to the powerful high-frequency modeling capability the neural field head in PixNerd, we achieve very competitive results in image generation without any complex cascade design. In par- ticular, our PixNerd-XXL/I achieves competitive 0.73 overall score on the GenEval benchmark and 80.9 average score on the DPG benchmark. However, pixel-space diffusion models still do not exhibit better performance scaling than advanced latent diffusion transformer models. We leave further improvements pixel diffusion transformers future work.
ACKNOWLEDGMENTS
This work partially supported by the Basic Research Program of Jiangsu (No. BK20250009) and the Collaborative Innovation Center of Novel Software Technology and Industrialization.
REFERENCES
Brian DO Anderson. Reverse-time diffusion equation models. Stochastic Processes and their Ap- plications, 12(3)331-3326, 1982.
10

---

Published as a conference paper at ICLR 2026
Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Partin-Bruulla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF international conference computer vision, pp. 5855-5864, 2021.
Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809. 10096, 2018.
Jiuhai Chen, Zhiyang Xu, Xichen Pan, Yushi Hu, Can Qin, Tom Goldstein, Lifu Huang, Tianyi Zhou, Saining Xie, Silvio Savarese, et al. Blip3-o: A family of fully open unified multimodal training and dataset. arXiv preprint arXiv:2505.0955 2025a.
Junyu Chen, Han Cai, Junsong Chen, Enze Xie, Shang Yang, Haotian Tang, Muyang Li, Yao Lu, and Song Han. Deep compression autoencoder for efficient high-resolution diffusion models. arXiv preprint arXiv:2410.10773 2024a.
Shoufa Chen, Chongjian Ge, Shilong Zhang, Peize Sun, and Ping Luo. Pixelflow: Pixel-space generative models with flow, arXiv preprini arXiv:2504.00 079663, 2025b.
Yinbo Chen, Oliver Wang, Richard Zhang, Eli Shechtman, Xiaolong Wang, and Michael Gharbi Image neural field diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8007-8017, 2024b.
Xiangxiang Chu, Jianlin Su, Bo Zhang, and Chunhua Shen. Visionllama: unified Ilama interface for vision tasks. arXiv preprint arXiv:2403.0080 00522, 2024.
Quan Dao, Khanh Doan, Di Liu, Trung Le, and Dimitris Metaxas. Improved training technique for latent consistency models. arXiv preprint arXi::2502.01441, 2025.
Chaorui Deng, Deyao Zh, Kunchang Li, Shi Guan, and Haoqi Fan. Causal diffusion transformers for generative modeling. arXiv preprint arXiv:2412 /2095, 2024.
Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis, Advances in neural information processing systems, 34:8780-877 -7944, 2021.
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et Scaling rectified flow transformers for high-resolution image synthesis. arXiv preprint arXiv:2403..m 03206, 2024.
Lijie Fan, Tianhong Li, Siyang Qin, Yuanzhen Li, Chen Sun, Michael Rubinstein, Deqing Sun, Kaiming He, and Yonglong Tian. Fluid: Scaling autoregressive text-to-image generative models with continuous tokens. arXiv preprint arXiv:2410.1388. 2024.
Kevin Frans, Danijar Hafner, Sergey Levine, and Pieter Abbeel. One step diffusion via shortcut models. arXiv preprint arXiv:2410.1255/7 2024.
Yu Gao, Lixue Gong, Qiushan Guo, Xiaoxia Hou, Zhichao Lai, Fanshi Li, Liang Li, Xiaochen Lian, Chao Liao, Liyang Liu, et al. Seedream 3.0 technical report. arXiv preprint arXiv:2504.11 11346, 2025.
Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt. Geneval: An object-focused framework for evaluating text-to-image alignment. Advances in Neural Information Processing Systems, 36: 2023.
Lixue Gong, Xiaoxia Hou, Fanshi Li, Liang Li, Xiaochen Lian, Fei Liu, Liyang Liu, Wei Liu, Wei Lu, Yichun Shi, et al. Seedream 2.0: A native chinese-english bilingual image generation foundation model. arXiv preprint 250:02503307703, 2025.
Yifan Gong, Zheng Zhan, Yanyu Li, Yerlan Idelbayev, Andrey Zharkov, Kfir Aberman, Sergey Tulyakov, Yanzhi Wang, and Jian Ren. Efficient training with denoised neural weights. In Euro- pean Conference on Computer Vision, pp. 18 34.3. Springer, 2024.
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017.

---

Published as a conference paper at ICLR 2026
Jonathan Ho and Tim Salimans. 12XV::2207.12588, 2022.
Classifier-fre
diffusion guidance.
arXiv preprint
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-688 2020.
Emiel Hoogeboom, Jonathan Heek, and Tim Salimans. simple diffusion: End-to-end diffusion for high resolution images. In International Conference on Machine Learning, pp. 13213-13232.
PMLR, 2023.
Xiwei Hu, Rui Wang, Yixiao Fang, Bin Fu, Pei Cheng, and Gang Yu. Ella: Equip diffusion models with IIm for enhanced semantic alignment. arXiv preprini arXiv:2403.05 2024.
Allan Jabri, David Fleet, and Ting Chen. Scalable adaptive computation for iterative generation. arXiv preprini 2rXiv:2212.119999 2022.
Tero Karras, Mika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Alias-free generative adversarial networks. Advances in neural information processing systems, 34:852-863, 2021.
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion- based generative models. Advances in Neural Information Processing Systems, 35:26565-26577, 2022.
Tero Karras, Miika Aittala, Jaakko Lehtinen, Janne Hellsten, Timo Aila, and Samuli Laine. Analyz- ing and improving the training dynamics of diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 24174-24184, 2024.
Dongjun Kim,
Lai, Wei-Hsiang Liao, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, and Stefano Ermon. Pagoda: Progressive growing of one-step generator from low-resolution diffusion teacher. Advances Neural Information Processing Systems, 37:19167- 19208, 2024.
Diederik Kingma and Ruiqi Gao. Understanding diffusion objectives as the elbo with simple data augmentation. Advances in Neural Information Processing Systems, 36:65484-655 65516, 2023.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceed- ings of the IEEE/CVF international conference on computer vision, pp.
2023.
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila. Improved precision and recall metric for assessing generative models. Advances in neural information processing systems, 32, 2019.
Tuomas Kynkäänniemi, Miika Aittala, Tero Karras, Samuli Laine, Timo Aila, and Jaakko Lehtinen. Applying guidance in limited interval improves sample and distribution quality in diffusion models. arXiv preprint arXiv:2404.07724, 2024.
Xingjian Leng, Jaskirat Singh, Yunzhong Hou, Zhenchang Xing, Saining Xie, and Liang Zheng. Repa-e: Unlocking vae for end-to-end tuning with latent diffusion transformers. arXiv preprint MXXY::2504.1488 2025.
Tianhong Li, Qinyi Sun, Lijie Fan, and Kaiming He. Fractal generative models. arXiv preprint 1rX2::2502.17437. 202.
Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, and Kaiming He. Autoregressive image generation without vector quantization. Advances in Neural Information Processing Systems, 37: 56424-56445, 2025b.
Chao Liao, Liyang Liu, Xun Wang. Zhengxiong Luo, Xinyu Zhang, Wenliang Zhao, Jie Wu, Liang Li, Zhi Tian, and Weilin Huang. Mogao: An omni foundation model for interleaved multi-modal generation. arXiv preprint 275552250..05472 2025.
12

---

Published as a conference paper at ICLR 2026
Chich Hubert Lin, Chia-Cha Chang, Yu-Sheng Chen, Da-Cheng Juan, Wei Wei, and Hwann-Tzoo Chen. Coco-gan: Generation by parts via conditional coordinating Proceedings of the IEEE/CVF international conference computer vision, pp. 4512 2412-4521,2011
Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. Neural volumes: Learning dynamic renderable volumes from images. arXiv preprint 1906v:990600770 2019.
Zeyu Lu, Zidong Wang, Di Huang, Chengyue Wu, Xihui Liu, Wanli Ouyang, and Lei Bai. Fit: Flexible vision transformer for diffusion model. arXiv preprini arXiv:2402. /2376, 2024.
Nanye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Sain- ing Xie. Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers. arXiv preprint arXiv:2401.08744 2024.
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106 2021.
Charlie Nash, Jacob Menick, Sander Dieleman, and Peter W Battaglia. Generating images with sparse representations. arXiv preprint arXiv:2103.0384, 2021.
Evangelos Ntavelis, Mohamad Shahbazi, lason Kastanis, Radu Timofte, Martin Danelljan, and Luc Van Gool. Arbitrary-scale image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1 11533-11542, 2022.
Dogyun Park, Sihyeon Kim, Sojin Lee, and Hyunwoo J Kim. Ddmi: Domain-agnostic latent diffuusin models for synthesizing high-quality implicit neural representations. arXiv preprint arXiv:2401.12517. 2024.
William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4195 4205, 2023.
Yuxi Ren, Xin Xia, Yanzuo Lu, Jiacheng Zhang, Jie Wu, Pan Xie, Xing Wang, and Xuefeng Xiao. Hyper-sd: Trajectory segmented consistency model for efficient image synthesis. arXiv preprint arXiv:2404. 10044.13688 13666, 2024.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pp. 10684-10695, 2022.
Olga Russakovsky, Jia Deng. Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 15:2111252,,000
Tim Salimans, Ian Goodfellow, Wojeiech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. Advances in neural information processing systems, 29, 2016.
Axel Sauer, Katja Schwarz, and Andreas Geiger. Stylegan-xl: Scaling stylegan to large diverse datasets. In ACM SIGGRAPH 2022 conference proceedings, pp. 1-10, 2022.
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Im- plicit neural representations with periodic activation functions. Advances in neural information processing systems, 33:7462-7473, 2020.
Ivan Skorokhodov, Savva Ignatyev, and Mohamed Elhoseiny. Adversarial generation of continuous images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10753-10764, 2021.
Tianhui Song, Weixin Feng, Shuai Wang, Xubin Li, Tiezheng Ge, Bo Zheng, and Limin Wang. Dmm: Building a versatile image generation model via distillation based model merging. arXiv preprint ar04v:2504. 12364, 2025.
13

---

Published as a conference paper at ICLR 2026
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456. 2020.
Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: En- hanced transformer with rotary position embedding. Neurocomputing, 568: 127063, 2024.
Keqiang Sun, Junting Pan, Yuying Ge, Hao Li, Haodong Duan, Xiaoshi Wu, Renrui Zhang, Aojun Zhou, Zipeng Qin, Yi Wang, et al. Journeydb: A benchmark for generative image understanding. Advances in neural information processing systems, 36:49659-49678 2023.
Jiayan Teng, Wendi Zheng, Ming Ding, Wenyi Hong. Jianqiao Wangni, Zhuoyi Yang, and Jie Tang.
Relay diffusion: Unifying diffusion process across resolutions for image synthesis. arXiv preprint arXiv:2309.0333 2023.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint urXiv:2302.1 2093,,
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaci, Niko- lay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open founda- tion and fine-tuned chat models. arXiv preprint wxXiv:2307.09288 2023b.
Michael Tschannen, André Susano Pinto, and Alexander Kolesnikov. Jetformer: An autoregressive generative model of raw images and text. arXiv preprint arXiv:2411. 2024.
Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. arXiv preprint arxiv: 106. 10689, 2021.
Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, model's 's perception of the
Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing world at any resolution. arXiv preprini arXiv:2409. /2191, 2024a.
Shuai Wang, Zexian Li, Tianhui Song, Xubin Li, Tiezheng Ge, Bo Zheng, and Limin Wang. Explor- ing den-like architecture for fast image generation with arbitrary resolution. Advances in Neural Information Processing Systems, 37:87959-877 -7797,, 2024b.
Shuai Wang, Zhi Tian, Weilin Huang, and Limin Wang. Decoupled diffusion transformer. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattemn recognition, 2026.
Yiming Wang, Qin Han, Mare Habermann, Kostas Danilidiis, Christian Theobalt, and Lingjie Liu. Neus2: Fast learning of neural implicit surfaces for multi-view reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3295-3306, 2023a.
Yuyang Wang, Anurag Ranjan, Josh Susskind, and Miguel Angel Bautista. Inrflow: Flow matching for inrs in ambient space. arXiv preprint arXivv2412..3371,, 2024c.
Zhendong Wang, Yifan Jiang, Huangjic Zheng. Peihao Wang. Pengeheng He, Zhangyang Wang, Weizhu Chen, Mingyuan Zhou, et al. Patch diffusion: Faster and more data-efficient training of diffusion models. Advances neural information processing systems, 36.72137-72154, 2023b.
Zidong Wang, Lei Bai, Xiangyu Yue, Wanli Ouyang, and Yiyuan Zhang. Native-resolution image synthesis. arXiv preprint arXiv:2506.001132 2025.
Jingfeng Yao and Xinggang Wang. Reconstruction vs. generation: Taming optimization dilemma in latent diffusion models. arXiv preprint arXiv:2501.01423 2025.
Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jínwoo Shin, and Saining Xie. Representation alignment for generation: Training diffusion transformers is easier than you think. arXiv preprint arXiv:2410.06944) 2024.
Zehao Yu, Songyou Peng, Michael Niemeyer, Torsten Sattler, and Andreas Geiger. Monosdf: Ex- ploring monocular geometric cues for neural implicit surface reconstruction. Advances in neural information processing systems, 55:25018-250333 2022.
14

---

Published as a conference paper at ICLR 2026
Shuangfei Zhai, Ruixiang Zhang, Preetum Nakkiran, David Berthelot, Jiatao Gu, Huangjie Zheng, Tianrong Chen, Miguel Angel Bautista, Navdeep Jaitly, and Josh Susskind. Normalizing flows capable generative models. arXiv preprint arXiv:2412.063229 2024.
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 586-595 2018.
Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, and Omer Levy. Transfusion: Predict the next token and diffuse images with one multi-modal model. arXiv preprint arXiv:2408. 11039, 2024a.
Linqi Zhou, Stefano Ermon, and Jiaming Song. arXiv:2503.07555, 2025.
Inductive moment matching. arXiv preprint
Mingyuan Zhou, Huangjie Zheng. Zhendong Wang, Mingzhang Yin, and Hai Huang, Score identity distillation: Exponentially fast distillation of pretrained diffusion models for one-step generation. In Forty-first International Conference on Machine Learning. 2024b.
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Inference
Training
Model
NFB
image
step
Mem (GB) Speed (s/it) Mem (GB)
SiT-L/2(VAE--8)
50x2
0.51s
0.0097s
2.9
0.30
18.4
Baseline-L/16
50x2
0.48s
0.0097s
2.1
0.18
18
PixNerd-L/16
50x2
0.51s
0.010s
2.1
0.19
ADM-G
50
4.21s
0.08s
2.23
ADM-G
250
21.3s
0.08s
2.23
PixelFlow-XI.n
/
10.1s
0.084s
4.0
PixNerd-XL/16
50x2
0.65s
0.012s
3.1
0.27
33.9
Table 4: The resource consumption comparison. means the average time consumption for a single step across different stages. Our PixNerd consumes much less memory and latency (Nearly 8x fatser than other pixel diffusion models.
A APPENDIX
A.1 TRAINING AND INFERENCE COMPARSION
Transformer layer
Nerf MLP
Nerf PE
Nerf RGB out
Latency(ms)
0.54
0.49
0.25
0.17
Table 5: Latency of different components under Batch Size 2
We employ torch.compile optimize memory allocation and reduce redundant computations for both the baseline and PixNerd. As shown in Table 4 compared with latent counterparts, our PixNerd-L/16 achieves much higher training throughput without VAE latency and with similar infer- ence memory. Compared to other pixel-space diffusion models, our PixNerd consumes significantly less memory and has lower latency (nearly 8x) faster than ADM-G and PixelFlow. We also provide detailed latency of specific layers in Table 5
A.2 INTERVAL GUIDANCE DETAILS OF IMAGENET 256x256
Show in Section Among the models listed, PixDiT-L/16 and PixNerd-L/16 share identical interval guidance configurations both are trained for 160 epochs, use an interval range of 0.1 (start) 1 (end), and adopt a CFG value of 3.5. For the MoANerd-XL/Ie model, two sets of training configurations are provided: one with 160 epochs (matching the interval [0.1, 1] and CFG value 3.5 of the aforementioned two models) and another with extended 320 epochs, where the interval remains [0.1, 1] but the CFG value is adjusted to 3.0. Finally, the PixNerd-XLA model is trained for 160 epochs, uses the same interval [0.1, I] as other models, and employs a CFG value of 3.0.
A.3 PRETRAINING DATA SOURCE OF TEXT-TO-IMAGE EXPERIMENTS
For text-to-image generation, trained model mixed dataset containing approximately 45M images from open-sourced datasets. We recaption all the images with Qwen2.5-VL-7B (Wang al. 2024a) yield English caption descriptions of various lengths. Note that our caption results
Model
Epochs| Interval start Interval end CFG value
Intchs/Interval
PiXDiT-L/11
160
0.1
3.5
PixNerd-L/16
160
0.1
3.5
PixNerd-XT./16
160
0.1
3.5
PixNerd-XI/Ht
320
0.1
3.0
PixNerd-XL/8
160
0.1
3.0
Table 6: Interval Guidance Details of Inference ImageNet 256 x 256
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1024
248
Figure 9: Training-free arbitrary resolution generation. We keep the amount of tokens in PixNerd as constant as pretraining resolution, we only interpolate the neural field coordinates different resolutions yield multi-resolution images.
only contain English descriptions. All the images cropped into square shape of 256 x 256 or 512 x 512.
Here is the detailed image source:
ImageNet: http://www.imugg-net.orrg
JouneryDB: http://hhnnniieeeeedddooooooobbr
Laion(aes6+): CC12M: htt.://gg..aaacteeeuuuuuuue/ssrs
v2.4.75
SAM: htlps:/lui.melee www.:meeaacccooo mett..com/uutaeetseeeeeeetaaaaae
MidJouney23M: htwps//nnggnnniccccooaeeeeeeeeen
captioned 23m full
Because the webpage link is invalid in CC12M dataset and the Laion dataset, the downloaded images are incomplete, and only parts them were obtained.
A.4 PIXNERD w/O REPA
PixNerd is training with REPA as default configuration. We also provide ablation w/o REPA here. We trained the model for 200K steps under batch size 256 on ImageNet256.
FID
SFID
IS
Precision
Recall
w/o REPA
67.68
9.96
19.41
0.338
0.551
REPA loss weight 0.1
42.49
10.19
37.11
0.448
0.61
REPA loss weight 0.5
37.31
10.44
42.92
0.426
0.6275
Table 7: Performance metrics under different REPA configurations The default REPA loss weight achieves better performance.
A.5 ARBITRARY RESOLUTION INFERENCE
As shown in Figure 9 without any resolution adaptation fine-tuning, we can achieve arbitrary resolu- tion generation through the coordinate interpolation while keeping the amount of tokens constant as the pretraining resolution. Specifically, we sampled pretraining resolution from the given noisy image, then fed this sampled version into the transformer. This keeps the token amount in our PixN- erd as consistent as the pretraining stage. To match the velocity field with the desired resolution of the given noisy image, then interpolate the coordinates for neural field decoding accordingly.
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regictytio numelic Ruam
Hight
rncoentce silon copperde sesperdieee
with sunian braadin www.iigaaalseeeo
whot the dhate femeee enpulet to beartis Shere dyle
show pnt and vilarien Eopple dnglhee wwediieeaaeeee Bronnyy
Fulmoo
thrugh bearchess anadwwy
dian dramattc
Diser
Pagicn gave sathor pocciis cues Spercaa wwdertaggaa ragita
patieen actoot wrth cull gloiigg saral Cass aayyl with cod Mustciinse
Figure 10: The Text-to-Image 512 x 512 visualization with different initialization noises. Given
different initialization noises, PixNerd produce samples with correct semantics and diverse styles. This indicates a superior generation diversity and semantic following capability.
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PixNerd256-NFE-1
PiNNer256-NFFE22(0DE)
PiNNe1256-WVE-22SDD/CM))
Figure 11: The visual samples from Distiledd-PxNeer--LL/1... We employed CM sampler and ODE sampler for few-steps inference.
A.6 MORE VISUALIZATIONS OF PIXNERD-T21
Occasional blurry unnatural artifacts appear in certain scenarios (e.g., steampunk lab image). We posit that appropriate post-training processing could mitigate such artifacts Ren et al. (2024), and we intend to explore space post-training future work.
A.7 FEW STEP GENERATION PIXNERD.
Model
FID4
NFE/
#Params
GAN Models
StyLeGAN-XL Sauer al. PagoDA (Kim et al. 2024)
2022
2.30
1.56
Latent Space Diffiusion Models
iCT-XL/2
Dao et al. 2025)
34.24
1+VAE
84M + 675M
20.30
2+VAE
84M
675M
Shortcut-XI./ (Frans et al.
2024
10.60
1+VAE
84M + 675M
7.80
4+VAE
84M + 675M
IMM Zhou et al.
2025
8.05
1+VAE
84M + 675M
Pixel Space Diffuusion Model
Distilled-PixNerrd XL.16
6.76
700M
Distilled-PixNee -ixNerd-XL/116
4.56
700M
Table 8: Few-step Generation Performance of PixNerd. PixNerd-XI//6 achieves superior perfor- few-step generation. emploied multi-stage architecture and initialized from diffusion models.
Currently, however, our focus is on improving the upper-bound performance of pixel diffusion while maintaining latency comparable to latent diffusion models. Having said that, PixNerd ex- tends classic DiT/SiT architectures with minimal modifications enhance performance, thus main- taining compatibility with existing few-step distillation techniques. We reserve the exploration of distillation-basee enhancements for future work. With limited training and parameter tuning, we achieved an FID of 4.56 at NFE and 6.76 at NFE within 10 epochs of finetuning. We also provide visual samples from Distilled-PixNee XL/16 in Figure 11
A.8 CLASS-TO-IMAGE IMAGENET 512x512 BENCHMARK
We provide the final metrics of PixNerd-XLLL XL/16 Table 9 To validate the superiority of our PixNerd model. we take our PixNerd-XX XL//16 trained on ImageNet 256 256 as the initialization, fine-tune PixNerd-XX XL.16 ImageNet 512 x 512 for 200K steps. We adopt the aforementioned interval
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Published as a conference paper at ICLR 2026
ImageNet 512 x 512
Model
Params
FID4
sFID
IS1
Pre.f
Rec.
Latent Diffusion Models
D (Peclles
Xie
202.3
675M + 86M
3.04
5.02
240.82
0.84
0.54
SiT-X1/2 Ma et al. 2024
675M 86M
2.62
4.18
252.21
0.84
0.57
REPA-XL/Z Yu et al. 2024)
675M 86M
2.08
4.19
274.6
0.83
0.58
FlowDCN-XL -L72 Wang
2024b
608M + 86M
2.44
4.53
252.8
0.84
0.54
EDM2 (Karras et al. (022)
1.5B + 86M
1.81
DDT-XL72
[Wang ef al. 2026
675M + 86M
1.28
4.22
305.1
0.80
0.63
Pixel Diffuuion Modely
ADM-G (Dhariwal & Nichol 2021
S59M
7.72
6.57
172.71
0.87
0.42
ADM-G, ADM-U
559M
3.85
5.86
221.72
0.84
0.53
RIN Jabri 2022
320M
3,95
210
SimpleDittusion Hoogeboom et al.
2023
2B
3.54
205
PixNerd-XL/16 (10OK+CFG 3.5)
700M
2.84
5.95
245.62
0.80
0.59
PixNerd-XL/II (100K+CFG 4.5)
700M
2.56
5.50
277.80
0.80
0.60
PixNerd-XL/16 (200K+CFG 4.5)
700M
2.45
5.19
275.77
0.79
0.63
Table 9: Benchmarking class-conditional image generation ImageNet 512x512. Our PixNerd- XL/16(512 x 512) is fine-tuned from the same model trained on 256 256 resolution for 200K steps. We adopt the interval guidance with interval [0.1, 1] and CFG of 4.5. We take Euler solver with 100 steps as the default choice.
guidance Kynkäänniemi et al. 2024) and we achieved 2.45 FID, with CFG of 4.5 within the time interval (0.1, 1.0.. PixNerd-XL achieves comparable performance to other diffusion models.
A.9 THE DETAILS OF NEURAL FIELD HEAD
As shown in Equation 9 we normalize the predicted neural field parameters W1, W2, and the final feature of the neural ficld(Out) as the default configuration. Specifically, given the predicted weight matrix W W Wi
[w1, W2, uU,] RDxDDs we normalize W row-wisely as
T E RDxD:
|
class NerfBlock (nn. Module):
def _init_ (self, hidden_size_s, hidden_size_x mip_ratio=4);
super init init__
self.paran, generatorl sele:paranngeeerrtoorll an. Sequential
nn. Ln.Linerr (hidden_size_ sthidden_size_x*22nne bias= Truc),
self.nore RMSNorm (hidden sizee_nssiie___e eps=1c-6) self.mlp_ratio mlp_ratic def (self,, x, s):
batch_aize, num_x, hidden_size x.shape mip_paramsl self.param_generatoo. (eneratorl
fel paraml, fe2 paraml mip_ppramss/ paramsl.chunk (2, din=-1)
13
fcl _cl_aaraml = fcl
hidden size
fc2_paraml fo2_paraml view (batcbatch_siiee, hidden_size_xxseel mlp_ratio, hidden_size_x)
15
16
# normalize fel nermalized fcl paraml torch.nn nn. functional. wormalize f (faaaaaannn I
dim=-2)
normalize fe2 (optional for T2I Task)
19
torch, nn. functional. normalize [622
dim=-2)
mlp
res_x
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Published as a conference paper at ICLR 2026
SinCos PE
dim
dim
Figure 12: DCT PE and Classic Sin/Cos PE. DCT PE more sensitive position changes than Classic
Sin/Cos PE.
selÉ. (x)
23
X torch. bmm (x, normalizee_fel__prrtt
24
X torch. nn. functional.silu
25
X torch. norch.bmm(e (x, normalized_foo_parrae
res_x
A.10 THE DETAILS DCT ENCODINGS
We provide the feature visualization of DCT- Based position encoding and classic sin/cos position encoding in Figure [2 Shown in Figure 12 DCT PE is more sensitive to position changes. Below, we provide the detailed code implementation of DCT PE.
def (scc,,, patch_size, device, dtype):
pos_x torch. www.pannnscccc (0, 1, patch_size, device=device, dtype=
dtype)
pes_y torch. 1inspace (0, 1, patch_size, device-device, dtype- dtype)
pos_y, pes_x torch.meshgriidlpos.yy pos_x, indexing="ii")
pos_x pos_ x. reshape (-1, 1, 1)
6
yos_y = pos reshape (-1, 1, 1)
freqs torch. www.paccneceecc (0, self.max_freey self.max_freqp, dtype-
dtype, device-device)
freqs_x freqs [None, :, Nene]
10
freqs_ = freqs (None, None, :]
cceffs (1 + freqs_x freqs_y)
-1
dct_x x torch.
(pos_x freqs_x torch.pi)
13
det_y torch. cos (pos_y * freqs_y torch.pi)
14
act = (dct_x + y cceffs) view (erffs)..iew(L..
(1, -1, falf.max_freqe ** 2)
return det
A.11 THE RELATION BEHIND NEURAL FIELD NORMALIZATION AND QK-NORM
Generally, QK-Norm plays a crucial role in modern transformers. To better understand the weight normalization, we can imagine the neural field decoding as cross-attention. Coordinate encodings in local patch look like queries, while patch-wise W, is the keys, and patch-wise W, is the values. This analogy can also explain why only normalizing W, in Equation (9) delivers comparable results (in Figure (a) in limited training iterations(up to 400k steps). Our further experiments on text-to- image also reveal that only normalizing W, sufficient.
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Published as a conference paper at ICLR 2026
However, since class-to-image models typically trained with far more steps than text-to- image models (1.6M steps vs. 200k steps), we observe that normalizing only W1 leads to loss spikes in the class-to-image task. Even though these spikes do not crash down the training, we still prune to normalize all fully connected (FC) layers to ensure training stability.
A.12 DISCUSSION WITH OTHER RELATED WORKS
Several related works (Chen al. 2024b Park et al. 2024 Wang et al.) 2024c) also combine diffu- sion with neural fields. In practice however, they differ fundamentally from PixNerd. For example, INFD (Chen et al.) 2024b) and DDMI (Park et al.
2024) leverage neural fields to enhance VAEs rather than diffusion models, and their generative capacity still stems from latent diffusion model. DenoisedWeights (Gong et al. 2024) trains independent neural weights for each image before train- ing generative model these pre-collected weights. This remains two-stage framework and poses non-trivial challenges for large-scale training. INRFlow Wang et al. (202cc) and PifchDiffu- sion(Wange al. 2023b) utilize coordinate encodings to enhance diffusion model performance. Be- yond diffusion- based generative models, GAN-based methods (Ntavelis et al. 2022 Skorokhodov
al. 2021 Lin al., 2019 Karras al. 2021) also utilize neural fields coordinate encodings. PixNerd is a simple yet elegant single-stage pixel-space generative model that does not rely on
VAE. Current latent generative models inevitably cascade errors due their two-stage configura- tions. Further, high-quality VAE usually demands numerous losses supervisions, e.g., adversarial loss, LPIPS loss. In particular, adversarial loss unstable in training and tends introduce artifacts. Pixel generative model has more potential in the future, and PixNerd is simple yet elegant solution for pixel-space generative model.
A.13 LARGE LANGUAGE MODEL USAGE
We only LLM polish sentence-level writing and grammar. We confirm that the revised sen- tences fully reflect our original texts.
22


Reviews:
[{"review_id": "uJQmmx7fWI", "reviewer": "Reviewer_AqD9", "summary": "This paper introduces PixNerd, a end-to-end pixel-space diffusion model that matches the performance of SOTA latent diffusion model without relying on pretrained VAE. PixNerd achieves this by using implicit neural field to replace the traditional decoding head of DiT. PixNerd achieves competitive performance on ImageNet and text-to-image generation tasks.", "questions": "- What is the main source of additional training memory and how can this be optimized? \n- Why is DCT-Basis encoding better? What about the other popular alternatives like RoPE?\n- I notice that PixNerd (512x512) is finetuned from PixNerd(256x256) and its performance on ImageNet512 is not as impressive as PixNerd(256x256). What's the reason for not training PixNerd (512x512) from scratch? \n- Is PixNerd compatible with representation alignment techniques like REPA? Since it operates directly in pixel space, would this alignment even be more effective?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is well-written with clear structure. \n- The paper present comprehensive comparisons against current SOTA models both qualitatively and quantitatively. PixNerd matches or exceeds the performance of comparable methods on ImageNet and text-to-image tasks\n- The ablation studies are conducted systematically to evaluate each design choice.", "weaknesses": "- The training memory usage is almost doubled compared to that of latent diffusion counter part. \n- PixNerd's performance at higher resolutions (512×512) does not scale as strongly as at 256×256. For example, PixNerd is better than SiT-XL on ImageNet256 but falls behind on ImageNet512. Does this imply that the gains from the neural field head diminish at higher resolutions?\n- Minors:\n\t- The citation of Rectified flow seems missing. \n\t- Table 4 should specify that the comparison is reported on ImageNet256 for clarity.", "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": "ToGt9gdXbE", "reviewer": "Reviewer_iKuE", "summary": "The paper introduces PixNerd, a novel end-to-end pixel-space diffusion transformer that replaces the traditional linear projection head with a neural field decoder. Its main goal is to bring the efficiency and visual fidelity of latent diffusion transformers into the raw pixel domain, without relying on VAEs or multi-scale cascades that introduce complexity and latency.", "questions": "-", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "S1. Originality and Significance\n\n- I highly commend the paper’s central motivation and direction. While most recent efforts focus on reducing computational cost by operating entirely in latent space, this work takes the opposite yet equally important perspective to explore how to lower cost directly in pixel space. This inversion of the conventional design philosophy is both insightful and original, addressing a long-standing challenge in diffusion modeling.\n\nS2. Experimental Quality and Clarity\n\n- I also deeply appreciate the thorough and well-executed ablation studies. The paper demonstrates an exceptional level of experimental rigor, allowing readers to form a fair and comprehensive understanding of the proposed method’s behavior. In my view, this kind of meticulous empirical investigation exemplifies what a well-written paper should strive for.", "weaknesses": "W1. Justification for architectural necessity.\n\n- Given that recent advances in diffusion distillation (e.g., one-step or few-step distilled diffusion models) can achieve nearly identical performance to full diffusion models with drastically reduced sampling steps, it is unclear why PixNerd needs to exist as a separate model. Could the authors clarify whether PixNerd itself can serve as a teacher model for distillation? If not, then PixNerd would likely exhibit much higher latency compared to diffusion + distillation pipelines. Moreover, the claimed 8× speedup still appears slower than VAE + diffusion pipelines, which challenges the practical advantage of the method. If the authors could convincingly argue in this point, then I will flip my assessment.\n\nW2. Missing baselines and quantitative comparison.\n\n- In Table 1, it would significantly strengthen the evaluation to include representative generative baselines such as StyleGAN [1], CDM [2], Simple Diffusion [3], VDM++ [4], and PaGoDA [5].\nIn particular, the paper should provide quantitative comparisons with one-step models (either GAN or diffusion + distillation) and explicitly report latency differences to position PixNerd more clearly within the current landscape of efficient generative models.\n\n[1] Sauer, Axel, Katja Schwarz, and Andreas Geiger. \"Stylegan-xl: Scaling stylegan to large diverse datasets.\" ACM SIGGRAPH 2022 conference proceedings. 2022.\n[2] Ho, Jonathan, et al. \"Cascaded diffusion models for high fidelity image generation.\" Journal of Machine Learning Research 23.47 (2022): 1-33.\n[3] Hoogeboom, Emiel, Jonathan Heek, and Tim Salimans. \"simple diffusion: End-to-end diffusion for high resolution images.\" International Conference on Machine Learning. PMLR, 2023.\n[4] Kingma, Diederik, and Ruiqi Gao. \"Understanding diffusion objectives as the elbo with simple data augmentation.\" Advances in Neural Information Processing Systems 36 (2023): 65484-65516.\n[5] Kim, Dongjun, et al. \"Pagoda: Progressive growing of a one-step generator from a low-resolution diffusion teacher.\" Advances in Neural Information Processing Systems 37 (2024): 19167-19208.", "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": "vsbVd6CQ9C", "reviewer": "Reviewer_vPnv", "summary": "This paper argues that current latent diffusion models are limited the VAE which introduces encoding and decoding errors. This paper proposes to directly model pixel space with large patch sizes and learn neural fields to decode large patches. The new approach avoids the two-stage training process of the latent diffusion. The paper reports state-of-the-art results on ImageNet 256x256 with a FID score of 1.93 and a nearly 8x reduction in latency compared to existing methods. The authors also demonstrate the model's effectiveness in text-to-image generation, achieving competitive scores on the GenEval and DPG benchmarks.", "questions": "How does the performance of PixNerd, in terms of both image quality and latency, scale as the image resolution increases (e.g., 512x512, 1024x1024)?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "- Novel method. The proposed PixNerd architecture, which combines a large-patch diffusion transformer with a neural field decoder, is a novel and interesting approach to pixel-space diffusion modeling. \n\n- Strong results. This paper reported a competitive FID score of 1.93 on ImageNet. Moreover, the proposed framework can be applied to text-to-image generation and achieves a competitive 0.73 overall score on the GenEval benchmark and 80.9 overall score on the DPG benchmark.\n\n- Efficiency. This paper shows a significant latency reduction compared to both pixel diffusion and latent diffusion methods.", "weaknesses": "- While the results on ImageNet at 256x256 resolution are quite competitive (see Table 1), the results at 512 resolution are not so convincing (see Table 6). The authors are encouraged to explain the performance degradation. The authors are also encouraged to provide comparisons at a even higher resolution like 1024 or 768.\n\n-Unclear latency comparison. The abstract mentions an 8x latency improvement but does not specify which models were used for comparison. According to Table 1, PixNerd did not achieve 8x latency improvement compared to latent diffusion methods.", "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": "brGoioSrPY", "reviewer": "Reviewer_Aqjg", "summary": "This paper introduces PixNerd, a single-stage, end-to-end diffusion transformer that operates directly in pixel space. It aims to solve the trade-off between VAE-based latent models (which brings 2-stage training and is not elegant) and traditional pixel-space models (which are computationally expensive or rely on complex cascades).\n\nThe core technical contribution is a novel decoder. Instead of using a simple linear projection on large patches (which loses detail), the diffusion transformer's final features are used to predict the weights of a small MLP (a \"neural field head\") for each patch. This patch-specific MLP then decodes the final velocity for each pixel within that patch by taking the pixel's local coordinates as input. This approach allows the model to be computationally efficient (using large patches) while retaining high-frequency detail (via the neural field decoder).", "questions": "1. Why does PixNerd have lower latency than the traditional latent diffusion models? Any insights here?\n\n2. On text-to-image generation task, the parameters of PixNerd is 1.2B + 1.7B. What modules do these two parameters correspond to?\n\n3. For NeRF MLP depth ablation in figure 5 (c), does the total number of layers is the same? For PixNerd-L/16 with 4 MLP layers, whether it use 22 transformer layers or it use 20 transformer layers? Further, given the same total layer number, which is the best ratio for transformer layers and NeRF MLP layers? \n\n4. How about the latency comparison of transformer layers and NeRF MLP layers?\n\n5. Based on figure 5 (d) DCT-Basis is better than Sin/Cos coordinate-encoding. Any insights for this?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 2, "contribution": 3, "strengths": "This paper proposes an alternative approach besides latent diffusion models. It has several strengths: \n\n1. Elimination of VAE: The single-stage, end-to-end pipeline avoids the pre-trained VAE, removing its associated decoding artifacts and 2-stage training.\n\n2. Efficient Pixel-Space Modeling: The proposed \"neural field head\" is an effective method for combining the computational efficiency of large-patch transformers with the high-fidelity representation of neural fields.\n\n3. Strong Benchmark Performance: Achieves a 1.93 FID on ImageNet 256x256, which is state-of-the-art for pixel-space models and 3. competitive with top-tier latent-space models. It also demonstrates superior performance on text-to-image generation tasks.\n\n4. Flexible Resolution: The coordinate-based nature of the decoder allows for training-free scaling to arbitrary resolutions at inference time, a significant practical benefit.", "weaknesses": "1. New Artifacts: While VAE artifacts are removed, the authors note in the appendix (A.4) that the model can introduce its own \"blurry or unnatural artifacts.\"\n\n2. Architectural Complexity: The design trades the complexity of a VAE for the complexity of the neural field head. Ablation studies (4.2) show performance is sensitive to this head's depth, channel count, and normalization strategy.\n\n3. Potential Training Instability: The paper discusses the necessity of specific normalization strategies (Fig 5a, A.9) to \"ensure training stability\" and prevent \"loss spikes\" during long training runs, suggesting a sensitive training dynamic.\n\n4. The main insight of this paper is to use NeRF MLP Head to replace the traditional MLP Head. Does it induce some specific designs other than the original NeRF method?", "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)