From Pixels, Without Pre-training: Joint Generative and Self-Supervised Representation Learning in One Model

Vicente Balmaseda; Ching-Long Lin; Tianbao Yang

cs.LGOct 5, 2026

From Pixels, Without Pre-training: Joint Generative and Self-Supervised Representation Learning in One Model

Authors: Vicente Balmaseda, Ching-Long Lin, Tianbao Yang

Organizations: Department of Computer Science and Engineering Texas A&M University College Station, TX, USA · Mechanical Engineering University of Iowa Iowa City, IA, USA

Abstract

Strong image generation models are conditioned on class labels, aligned to frozen pretrained encoders, or built on separately trained autoencoders. While effective, generation then depends on supervision or pretraining: labels must be annotated, and encoders or autoencoders pretrained for the target domain. We study joint generative and self-supervised representation learning in a single model, enabling self-conditioned generation without labels or pretrained models. This is challenging because the objectives are mismatched: contrastive learning consumes clean augmented views and favors coarse, invariant semantics, while flow matching consumes noisy images and must preserve the fine detail and spatial layout that contrastive learning discards. We propose SCION (Self-conditioned Generation on Self-supervised representation), whose core is a single pixel-space encoder conditioned on the flow timestep and an embedding. For representation learning, this conditioning embedding is a learned global vector shared across images, with the encoder's [CLS] token yielding the semantic representation trained by the contrastive loss. For generative training, the conditioning embedding is the image's own [CLS] representation, while patch tokens pass through a decoder to predict the image. To sample without a reference image at inference, we jointly learn a prior over the embedding. Gradient-norm balancing and stop-gradient mechanisms enable joint optimization in one run. SCION is self-supervised and self-contained, with no labels or pretrained models. On ImageNet 256x256, with the JiT-B recipe and no representation guidance, SCION reaches 8.92 FID, surpassing class-unconditional iREPA, which aligns to pretrained DINOv2 (46.44), and RCG, which conditions on it (14.27). With JiT-L, SCION achieves 5.89 FID without guidance and 3.47 with representation guidance, outperforming RCG with the ADM recipe (6.24).

Figures & tables

	four S requirements				capabilities
Method	Self-cont.	Self-sup.	Single-mod.	Single-stage	Repr.	Synth.
Pixel-space generation
JiT ( Li and He, 2026 )	✓	✗	–	✓	✗	✓
JiT $+$ REPA ( Yu et al., 2025 )	✗	✗	✗	✗	✗	✓
JiT $+$ Dispersive ( Wang and He, 2025 )	✓	✗	✓	✓	✗	✓
Semantic conditions

Table 1 : Comparison with related works under the four S requirements. Extended discussion on related works is presented in Appendix A . Self-contained means no pretrained component; self-supervised means no label supervision anywhere in the pipeline; single-model means that the representation is produced by the generator’s own network; and single-stage means that all components are learned jointly from scratch. Capabilities report a learned and evaluated Repr esentation and unconditional Synth esis with no source image to encode. All pipelines are as published, except REPA and Dispersive, which we combine with the pixel-space JiT recipe.

Table 2

Table 3

Appendix figures & tables12 assets

Supplementary material from the paper’s appendix.

Appendix

	four S requirements				capabilities
Method	Self-cont.	Self-sup.	Single-mod.	Single-stage	Repr.	Synth.
Generative recipes
DiT / SiT ( Peebles and Xie, 2023 ; Ma et al., 2024 )	✗	✗	–	✗	✗	✓
JiT ( Li and He, 2026 )	✓	✗	–	✓	✗	✓
JiT $+$ REPA ( Yu et al., 2025 )	✗	✗	✗	✗	✗	✓
JiT $+$ Dispersive ( Wang and He, 2025 )	✓	✗	✓	✓	✗	✓

Appendix

Table 6 : Comparison under the four S requirements , extending Table 1 to the comparable systems tabulated in this appendix. Self-contained means no pretrained component, including a latent autoencoder or tokenizer; self-supervised means no label supervision anywhere in the pipeline, including through pretrained components; single-model means that the representation is produced by the generator’s own network; and single-stage means that all components are learned jointly from scratch. Capabilities report a learned and evaluated Repr esentation and unconditional Synth esis with no source image to encode. All pipelines are as published, except REPA and Dispersive, which we combine with the JiT recipe removing the latent model dependency.

			generation					representation
Rung	views	$\lambda_{CL}$	FID $\downarrow$	sFID $\downarrow$	IS $\uparrow$	Prec. $\uparrow$	Rec. $\uparrow$	Acc. $\uparrow$	transfer-8 $\uparrow$
0. flow matching alone (JiT-B/16 unconditional)
	$1+0$	—	61.17	9.05	17.53	.384	.559	10.65	26.19
1. add contrastive learning: which views?
1i	$3+0$	0.025	59.08	9.25	17.96	.385	.571	33.74	50.93
1ii	$0+3$	0.025	70.46	16.89	15.18	.237	.625	65.30	77.36

Appendix

Table 7 : Detailed ablation ladder. All rows use the JiT-B/16 architecture under the same recipe for

200

epochs and unconditional setting. Rungs 0 to 3 isolate the joint-training recipe before representation self-conditioning and the learned prior. Rung 4 adds both components, and rung 5 adds the conditioned-branch consistency loss

{\mathcal{L}}_{\mathrm{CBC}}

to complete SCION. Rung 0 is our unconditional flow-matching reproduction. views gives the number of original and augmented inputs, respectively; bal. denotes gradient-norm balancing, which rescales the contrastive weight online.

		Acc. $\uparrow$			transfer-8 $\uparrow$
Method	views	$200$	$400$	$600$	$200$	$400$	$600$
CL only	$2$ augmented	70.48	73.61	74.84	77.04	78.88	77.09
CL only	$1$ original $+$ $2$ augmented	71.76	74.15	74.98	77.68	78.66	76.61
SCION (ours)	$1$ original $+$ $2$ augmented	71.32	72.35	72.61	79.73	79.89	79.87

Appendix

Table 8 : Representation quality throughout training. We compare SCION with capacity-matched contrastive-only controls trained from scratch. All rows use the same

8

-layer encoder; the controls have no generative objective and are independently tuned for contrastive learning. During inference, the models use approximately the same number of parameters,

57.2

M for SCION and

57.1

M for the controls.

Method	FID $\downarrow$	IS $\uparrow$	Acc. $\uparrow$	transfer-8 $\uparrow$
JiT	61.17	17.5	10.65	26.19
JiT $+$ Dispersive	58.68	18.0	8.88	24.06
JiT $+$ REPA	53.07	23.0	63.94	73.59
JiT $+$ iREPA	48.43	24.8	65.81	76.37
RCG (JiT; $400$ total)	14.27	93.4	6.54	30.28
SCION (ours)	10.10	101.9	71.32	79.73

Appendix

Table 9: Controlled class-unconditional comparison at 200 epochs. All methods use the same JiT-B/16 flow-matching recipe on ImageNet (

256\times 256

). The RCG row is a reference: its 200-epoch generator is preceded by a frozen 200-epoch prior stage, for

400

total epoch-equivalents.

	dependencies					generation
Method	Lat.	Ext.	Lab.	External data	Multi-stage	Params	FID $\downarrow$	IS $\uparrow$
latent-space
DiT-B/2 ∗ ( Peebles and Xie, 2023 )	SD-VAE	—	✓	✓	✓	179M	69.3	—
DiT-XL/2 ∗ ( Peebles and Xie, 2023 )	SD-VAE	—	✓	✓	✓	724M	44.6	—
ReDi (DiT-B/2) ( Kouzelis et al., 2025 )	SD-VAE	DINOv2	✓	✓	✓	179M	51.7	—
ReDi (DiT-B/2) (RG) ( Kouzelis et al., 2025 )	SD-VAE	DINOv2	✓	✓	✓	179M	47.3	—

Appendix

Table 10 : Unconditional ImageNet-1K ( Deng et al., 2009 )

256\times 256

generation systems and their dependencies. FID and IS as reported by the corresponding work. Lat. names the pretrained VQGAN tokenizer ( Esser et al., 2021 ) or latent autoencoder, SD-VAE a or an LDM autoencoder (LDM-AE) ( Rombach et al., 2022 ) , and Ext. any other pretrained model. Lab. marks label supervision anywhere in the pipeline, including the supervised perceptual loss ( Zhang et al., 2018 ) inherited by VQGAN and SD-VAE. External data marks a dependency trained on data beyond ImageNet-1K. In particular, SD-VAE uses OpenImages ( Krasin et al., 2017 ) and, per its model card, is fine-tuned on LAION-Aesthetics ( Schuhmann et al., 2022 ) and an unreleased LAION-Humans subset, DINOv2 uses LVD-142M ( Oquab et al., 2024 ) , and CLIP uses WIT-400M ( Radford et al., 2021 ) . Multi-stage marks more than one separately trained component. Params count all inference-time models, including a latent autoencoder or tokenizer where applicable.

\dagger

retrieves real images at sampling. ∗ denotes DiT values reported by ReDi and ‡ values reported by RCG. (CFG) and (RG) denote classifier-free and representation guidance, respectively. Training budgets are not matched. SCION (B/16) is reported at

800

epochs and SCION (L/16) at

400

epochs.

conditioning	reference	Prec. $\uparrow$	Rec. $\uparrow$
prior $e_{\omega}$	conditioning set	.718	.524
prior $e_{\omega}$	disjoint set	.722	.510
oracle repr. †	conditioning set	.645	.828
oracle repr. †	disjoint set	.626	.664

Appendix

Table 11 : Oracle conditioning against the learned prior. SCION (B/16) at

400

epochs, unguided,

50

K samples per arm. The oracle arm (

\dagger

, semi-parametric) replaces the prior sample with the representation of a real training image, one sample per representation. Each arm is scored against

10

K images from the conditioning set and from a disjoint set of training images. With the same generator, real representations give higher recall and prior samples higher precision.

	SCION (B/16)	SCION (L/16)
Architecture
Depth: total / enc. / dec.	12, 8, 4	24, 16, 8
Hidden dim	768	1024
Heads	12	16
In-context start block	4	8
[CLS] normalization	identity	BatchNorm

Appendix

Table 12 : Main hyperparameters for the reported SCION (B/16) and SCION (L/16) ImageNet-1K

256\times 256

runs. Rows spanning both columns are identical at the two scales. The baselines share this recipe apart from the objective and, for REPA and iREPA, the

4/8

encoder–decoder split, following their published split; see Appendix E . Guidance follows Ho and Salimans (2021) on the interval of Kynkäänniemi et al. (2024) .

Component	Symbol	Input	Output
Shared encoder	$f_{\theta}$	$({\bm{x}}_{t}\mid t,{\bm{c}})\in\mathbb{R}^{3\times H\times W}\times[0,1]\times\mathbb{R}^{d}$	$({\bm{e}},{\bm{h}})\in\mathbb{R}^{d}\times\mathbb{R}^{T\times d}$
Noisy image, timestep and conditioning $\mapsto$ global embedding ${\bm{e}}$ and patch tokens ${\bm{h}}$ .
Generative decoder	$g_{\phi}$	$({\bm{h}}\mid t,{\bm{c}})\in\mathbb{R}^{T\times d}\times[0,1]\times\mathbb{R}^{d}$	${\bm{x}}\in\mathbb{R}^{3\times H\times W}$
Patch tokens, timestep and conditioning $\mapsto$ predicted clean image.
Contrastive projection	$h_{\psi}$	${\bm{e}}\in\mathbb{R}^{d}$	${\bm{z}}\in\mathbb{R}^{d_{z}}$
Global embedding $\mapsto$ the space where ${\mathcal{L}}_{\mathrm{CL}}$ is optimized.

Appendix

Table 13 : Components of the shared model.

T

is the number of patch tokens,

d

the hidden width,

d_{z}

the contrastive projection dimension, and

{\bm{c}}

the conditioning vector of Section 3.1 .

Config	Value
Input / output width	representation dimension $d$
Hidden width $C$	808
Residual blocks $N$	14
Block SwiGLU width	2155
Timestep features	sinusoidal, width 256
Timestep embedding	808, added inside each block

Appendix

Table 14 : Prior architecture. The residual-MLP backbone of

e_{\omega}

, shared by both scales; its input and output width follows the backbone, so the parameter count is the B/16 one.

Panel of Figure 4 — Figure 4 : Preserving semantics while varying image details. Each pair shows a real reference and a generation conditioned on the representation extracted by SCION itself. Recognizable subjects and scene types persist across changes in pose, color, markings, background, and structure. The generator starts from image noise and bypasses the learned prior. All examples use SCION (JiT-L/16) at $400$ epochs with representation guidance.

Panel of Figure 5 — Figure 5 : Interpolation in the learned conditioning space. Real references appear at the outer edges. All five interior images are generated from interpolated representations, with $\alpha=0$ and $1$ using the endpoint representations directly. The first five rows connect images from the same ImageNet class, while the last four connect different classes. Image noise is fixed within each row. All paths use spherical interpolation, and SCION (JiT-L/16) at $400$ epochs with representation guidance.

Panel of Figure 6 — Figure 6 : Flower-bud representation interpolations. These selected paths illustrate representation changes rather than a recorded or predicted growth sequence. Flower names for the right-hand references follow their ImageNet class labels. Real photographs flank six generated images at $\alpha=0,0.2,0.4,0.6,0.8,1$ , including generated endpoint reconstructions. All paths use spherical interpolation of the representations, fixed noise within each row, and SCION (JiT-L/16) at $400$ epochs with representation guidance.

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Representation by Design in Generation: Cross-View Class-Token Alignment in Diffusion Transformers

Xiaoyu Wu, Yifei Wang, Chen Wei

Carnegie Mellon University · Rice University

Diffusion Transformers Diffusion Models Fréchet Inception Distance Semantic Representations

Open paper AskPDF arXiv

Generative and representation learning remain asymmetrically connected: semantic representations are used to improve diffusion generation, whereas the models' own representations are often treated as a by-product of synthesis. We ask whether diffusion models can instead be trained to learn substantially stronger semantic representations without sacrificing generation quality. SelfFlow takes a step in this direction by introducing self-supervised patch alignment into flow matching, but its main gains remain in faster convergence and improved generation. Inspired by DINO and iBOT, we extend this framework with cross-view class-token alignment to further strengthen semantic representations. Specifically, we form two independently noised, dual-timestep observations of each image and align each student class-token representation with the stop-gradient EMA-teacher target from the other observation. This objective is optimized jointly with the inherited flow-matching and local patch objectives. Notably, although the additional objective acts only on the class token, it strengthens both class-token and patch representations. Compared with a matched two-view baseline, ImageNet linear-probing accuracy improves by 9.4% using the class token and 10.1% using mean-pooled patch tokens, while frozen-backbone VOC2012 segmentation improves by 3.6 mIoU. These representation gains are achieved while maintaining comparable ImageNet generation FID. In text-to-image training, the same objective also improves generation FID, reducing it from 2.52 to 2.37 at matched checkpoints. Our results show that representation need not remain a by-product of generation or merely a tool for improving it: it can be directly optimized as a first-class capability of diffusion pretraining alongside generation.

Abstract

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