Sphere Encoder 2

Kaiyu Yue; Sean McLeish; Ruchit Rawal; Brian Bartoldson; Menglin Jia; Tom Goldstein

cs.CVOct 1, 2026

Sphere Encoder 2

Authors: Kaiyu Yue, Sean McLeish, Ruchit Rawal, Brian Bartoldson, Menglin Jia, Tom Goldstein

Organizations: University of Maryland · Lawrence Livermore National Laboratory · Cornell University

Abstract

Sphere Encoder is an autoencoder that generates images by decoding random points from a high-dimensional latent sphere. We identify two limitations of the original formulation that reduce its generation quality. First, random points concentrate near the equator relative to the pole on an encoded latent, but the training rotation never reaches this region, leaving a gap that limits one-step generation. Second, training for generation with pixel-wise reconstruction loss encourages the decoder to average over plausible images, producing blurry images that lack high-frequency details. We present Sphere Encoder 2 to address both limitations, substantially improving image generation quality while maintaining the speed and simplicity of a autoencoder. Models are released at \href{https://github.com/kaiyuyue/sphere2}{github.com/kaiyuyue/sphere2}.

Figures & tables

Figure 1: Curated 1-step generation without CFG on ImageNet $512\times 512$ by Sphere Encoder 2.

Figure 2: Sphere Encoder vs. Sphere Encoder 2. Uncurated generation on ImageNet $256\times 256$ from Sphere Encoder ( Yue et al., 2026 ) (top) and Sphere Encoder 2 (bottom) with same classes.

Figure 3: Sphere Encoder 2. The encoder $\mathcal{E}$ maps an image to a clean latent $\hat{\mathbf{z}}(0^{\circ})$ on the sphere $\mathbb{S}$ , which is then rotated toward a random direction by an angle $\alpha\in[0,90^{\circ}]$ in Eq. 4 . The cutoff angle $\alpha_{\mathrm{cutoff}}$ splits the arc into a reconstruction regime ( $\alpha\leqslant\alpha_{\mathrm{cutoff}}$ ) and a generation regime ( $\alpha\geqslant\alpha_{\mathrm{cutoff}}$ ). The decoder $\mathcal{D}$ serves both: near the pole it reconstructs the input, and at the equator $\hat{\mathbf{z}}(90^{\circ})=\mathcal{F}(\mathbf{e})$ it generates a new image from a random latent in Eq. 2 .

Figure 5: Cutoff angle

\alpha_{\mathrm{cutoff}}

, which splits the rotation arc into the reconstruction and generation regimes. Generation improves as the cutoff moves toward the equator until

70^{\circ}

Figure 6: Reconstruction and generation regimes at different angles from $0^{\circ}$ to $90^{\circ}$ on Oxford Flowers $256\times 256$ . On the same rotation arc with $\alpha_{\mathrm{cutoff}}=70^{\circ}$ , the decoder reconstructs the input below the cutoff and generates new images above it.

Figure 7: Score matching loss. Fake features from the frozen extractor $\phi$ are denoised by the real and fake score models (each a two-layer transformer EDM denoiser ( Karras et al., 2022 ) ), and their difference gives the direction $\mathbf{g}$ that pulls fake features toward real ones. During transport, both score models are frozen and run once each; only the decoder $\mathcal{D}$ is updated by $\mathbf{g}$ through $\mathcal{L}_{\text{score}}$ .

Model	Params	Guid.	NFE	FDr 6 $\downarrow$ @ $K$ steps				gFID $\downarrow$ @ $K$ steps
Model	Params	Guid.	NFE	1	2	4	6	1	2	4	6
Image Size $256\times 256$
Sphere ( 2026 )	948M	CFG	$K\times 2$	10.59	5.44	3.96	3.92	25.12	14.08	11.25	10.63
Sphere Latent ( 2026 )	130M	CFG	$K\times 2$	–	–	–	–	–	12.22	8.61	7.85
Sphere2-B	159M	–	$K$	2.67	2.23	2.17	2.19	7.62	7.45	7.50	7.55
Sphere2-L	487M	–	$K$	2.19	1.85	1.82	1.83	9.36	8.33	7.98	7.93

Table 1: Main comparison on Oxford Flowers . Compared with Sphere Encoder ( Yue et al., 2026 ) and Sphere Latent Encoder ( Do et al., 2026 ) . Params for Sphere2 cover both encoder and decoder; for latent-space models they cover the generator only. Sphere2 has no CFG in sampling.

Figure 8: Uncurated 4-step generation without CFG on Oxford Flowers $512\times 512$ by Sphere2-L.

Model	Params	Guid.	Total GFLOPs		$256\times 256$		$512\times 512$
Model	Params	Guid.	GFLOPs $\times$	NFE	FDr 6 $\downarrow$	gFID $\downarrow$	FDr 6 $\downarrow$	gFID $\downarrow$
Latent space
SiT-XL/2 ( 2024 )	675M	CFG	119 $\times$	$250\times 2$	8.44	2.06	–	2.62
SiT-XL/2 + REPA ( 2025 )	675M	CFG	119 $\times$	$250\times 2$	5.45	1.42	–	2.08
LightningDiT-XL/2 ( 2025 )	675M	CFG	119 $\times$	$250\times 2$	4.57	1.35	–	–
DDT-XL/2 ( 2026b )	675M	CFG	119 $\times$	$250\times 2$	5.70	1.26	–	1.28

Table 2: Main comparison on ImageNet

256\times 256

and

512\times 512

. Params and GFLOPs for Sphere2 cover both encoder and decoder; for latent-space models they cover the generator only and exclude the VAE decoder. GFLOPs are measured for a single forward, so Total GFLOPs

=

GFLOPs

\times

NFE. Guidance: CFG is classifier-free guidance applied at sampling, so NFE is doubled; AG is AutoGuidance ( Karras et al., 2024 ) , which doubles NFE with a smaller guiding model; CFG ∗ denotes guidance folded into the training target for single-forward sampling. Among pixel-space models, taking each model at its best setting, the best, second and third FDr 6 per resolution are shaded 00 , 00 and 00 . † marks Sphere2 models trained with the

\mathcal{L}_{\mathrm{FD\text{-}lite}}

loss in Sec. B.1 .

Figure 9: Generation quality vs. sampling compute on ImageNet

256\times 256

among pixel-space models. Circles are pixel-space baselines and stars are Sphere2 at 1, 4 (without CFG) and

4\times 2

NFE (with CFG), joined per model. The halo area around each marker is proportional to parameters.

Model	Sampler	Guid.	bsz	img/s $\uparrow$	NFE	ms/NFE	50K imgs (min) $\downarrow$
Sphere2-L	loop $\times$ 1	–	1	53.4	1	18.7	16
	loop $\times$ 2	CFG	1	14.5	4 + 2 enc.	17.3	58
	loop $\times$ 4	CFG	1	6.1	8 + 6 enc.	20.7	137
	loop $\times$ 1	–	16	424.8	1	2.4	2
	loop $\times$ 2	CFG	16	145.4	4 + 2 enc.	1.7	6
	loop $\times$ 4	CFG	16	62.8	8 + 6 enc.	2.0	14

Table 3: Sampling speed at

512\times 512

on a single NVIDIA H100. Timings are end-to-end, averaged over runs after warmup (std below

1\%

), at batch size 1 and 16. For Sphere2, “loop

\times K

” denotes

K

sampling steps of Alg. 1 . NFE counts decoder forwards; “

+\,m

enc.” adds the encoder forwards used by spherical CFG, and “

+\,49

guide” the guiding-model forwards of AutoGuidance. Sphere2 and JiT run in bf16, pMF and RAE-DiT in fp32, following their official implementations.

Figure 10: Curated 2-step generation without CFG on ImageNet $512\times 512$ by Sphere Encoder 2.

Figure 11: Uncurated samples on ImageNet $512\times 512$ . Left: RAE-DiT ${}^{\text{DH}}$ -XL/2 with $\text{NFE}=50\times 2$ and $\text{Guidance}=1.5$ . Right: Sphere2-L with $\text{NFE}=4$ and no CFG. Best viewed zoomed in.

Figure 12: Uncurated samples on ImageNet $512\times 512$ . Left: JiT-L/32 with $\text{NFE}=50\times 4$ and $\text{CFG}=2.5$ . Right: Sphere2-L with $\text{NFE}=2$ and no CFG. Best viewed zoomed in.

Figure 13: Uncurated samples on ImageNet $512\times 512$ . Left: pMF-L/32 with $\text{NFE}=1$ and $\text{CFG}=7.5$ . Right: Sphere2-L with $\text{NFE}=1$ and no CFG. Best viewed zoomed in.

Appendix figures & tables18 assets

Supplementary material from the paper’s appendix.

Appendix

Figure A14: Class interpolation on ImageNet $512\times 512$ by Sphere2-L with 4-step sampling and no CFG. Each row fixes the random point and slerps between two class embeddings, from the left class to the right one. Rows are ordered from similars pairs at the top to dramatic pairs at the bottom.

Figure A15: Interpolation from non-ImageNet images by Sphere2-L trained on ImageNet $512\times 512$ . The first column shows input images outside the ImageNet classes: a raccoon, a matcha cake, and grapes. The second and third columns decode the clean latent of each input conditioned on $\alpha=0^{\circ}$ and $\alpha=85^{\circ}$ , respectively. The remaining columns unroll the refinement loop of Alg. 1 , which carries the input toward a target class: raccoon to red panda (class 387), matcha cake to espresso (class 967), and grapes to indigo bunting (class 14).

Sphere2-B
Steps $K$	$\mathcal{L}_{\mathrm{FD\text{-}lite}}$	gFID $\downarrow$	FDr 6 $\downarrow$	FDr 6 $\downarrow$ per feature space
Steps $K$	$\mathcal{L}_{\mathrm{FD\text{-}lite}}$	gFID $\downarrow$	FDr 6 $\downarrow$	Incep.	ConvNeXt	DINOv2	MAE	SigLIP	CLIP
	-	5.20	14.53	3.49	2.99	13.18	15.08	28.01	24.42
1	✓	3.43	12.76	2.30	2.59	11.55	13.64	24.41	22.03
	-	4.72	9.48	3.17	2.67	8.83	10.80	16.09	15.35
2	✓	2.69	8.43	1.81	2.36	7.43	9.62	14.83	14.51

Appendix

Table A4: Effect of

\mathcal{L}_{\mathrm{FD\text{-}lite}}

per feature space on ImageNet

256\times 256

. FDr 6 at

K

sampling steps, trained with and without

\mathcal{L}_{\mathrm{FD\text{-}lite}}

, broken down into its six feature spaces.

Figure A16: Effect of

\mathcal{L}_{\mathrm{FD\text{-}lite}}

for Sphere2-L on ImageNet

256\times 256

. Left and middle: FID and FDr 6 with and without the loss at different sampling steps. Right: relative FDr reduction in each of the six feature spaces.

Figure A17: Distribution shift is invisible but measurable. 4-step samples on ImageNet $256\times 256$ from Sphere2-B trained with and without $\mathcal{L}_{\mathrm{FD\text{-}lite}}$ are visually indistinguishable, yet the loss aligns their feature statistics with those of real data in the Inception space where gFID is measured. More uncurated samples for side by side comparison are in Fig. A18 for 1 step and Fig. A19 for 4 steps.

Figure A18: Uncurated samples on ImageNet $256\times 256$ . Left: Sphere2-B without $\mathcal{L}_{\mathrm{FD\text{-}lite}}$ and $\mathrm{NFE}=1$ . Right: Sphere2-B with $\mathcal{L}_{\mathrm{FD\text{-}lite}}$ and $\mathrm{NFE}=1$ .

Figure A19: Uncurated samples on ImageNet $256\times 256$ . Left: Sphere2-B without $\mathcal{L}_{\mathrm{FD\text{-}lite}}$ and $\mathrm{NFE}=4$ . Right: Sphere2-B with $\mathcal{L}_{\mathrm{FD\text{-}lite}}$ and $\mathrm{NFE}=4$ .

Figure A20: Angle distribution in training. Left: sampling curves of

\alpha

for different

k

. Middle and right: generation quality of Sphere2-B on Oxford Flowers

256\times 256

for each

k

. Uniform sampling (

k=0

) is the best choice.

Figure A21: Sampling angle $\alpha$ with 1-step generation using Sphere2-B trained on ImageNet $256\times 256$ . Sampling angle decides the generation diversity by forcing the decoder away from the mean.

Model	$\alpha$	Steps	Metric	Guidance angle $\gamma$ , $256\times 256$								Guidance angle $\gamma$ , $512\times 512$
Model	$\alpha$	Steps	Metric	$0^{\circ}$	$5^{\circ}$	$10^{\circ}$	$15^{\circ}$	$20^{\circ}$	$30^{\circ}$	$40^{\circ}$	$50^{\circ}$	$0^{\circ}$	$5^{\circ}$	$10^{\circ}$	$15^{\circ}$	$20^{\circ}$	$30^{\circ}$	$40^{\circ}$	$50^{\circ}$
Sphere2-B	$85.5^{\circ}$	2	FDr 6 $\downarrow$	9.48	9.03	8.77	8.63	8.55	8.55	8.67	8.89	10.28	9.85	9.56	9.45	9.33	9.26	9.35	9.52
		2	gFID $\downarrow$	4.72	5.30	5.80	6.30	6.78	7.66	8.34	9.04	5.64	6.11	6.46	7.06	7.46	8.24	9.16	9.81
		4	FDr 6 $\downarrow$	8.58	8.08	7.89	7.83	7.91	8.20	8.65	9.17	9.35	8.92	8.69	8.61	8.66	8.91	9.33	9.85
		4	gFID $\downarrow$	4.89	5.58	6.33	7.00	7.66	8.66	9.54	10.02	6.04	6.62	7.36	7.94	8.50	9.54	10.51	11.23
Sphere2-L	$84^{\circ}$	2	FDr 6 $\downarrow$	6.42	6.31	6.28	6.30	6.39	6.62	6.94	7.32	7.50	7.37	7.28	7.30	7.31	7.44	7.61	7.84

Appendix

Table A6: Guidance angle

\gamma

of spherical CFG on ImageNet

256\times 256

and

512\times 512

. Sampling angle

\alpha

is fixed per model, listed as

256/512

when it differs between resolutions.

Figure A23: Guidance angle

\gamma

of spherical CFG at 4 sampling steps on ImageNet

256\times 256

and

512\times 512

in Tab. A6 . The hollow marker on each curve is the FDr 6 -optimal

\gamma

, which the CFG rows of Tab. 2 use. Sampling angle

\alpha

85.5^{\circ}

for Sphere2-B and

84^{\circ}

for Sphere2-L at both resolutions. See Fig. A24 for generated images along the

\gamma

sweep.

Figure A24: Spherical CFG for 2-step generation on ImageNet $256\times 256$ with Sphere2-B. Each row is one random point decoded at increasing guidance angle $\gamma$ . Guidance fixes the bad structures and strengthen the class semantics.

Encoder	$d=N\times D$	Compression ratio	FDr 6 $\downarrow$			gFID $\downarrow$
Encoder	$d=N\times D$	Compression ratio	1	2	4	1	2	4
DINOv3-small	$16^{2}\times 384$	2	3.92	3.14	3.04	10.29	8.24	7.51
DINOv3-small	$16^{2}\times 256$	3	4.53	3.77	3.61	17.67	13.64	12.27
DINOv3-base	$16^{2}\times 768$	1	4.28	3.58	3.51	11.47	9.28	8.69
DINOv3-base	$16^{2}\times 384$	2	5.17	4.48	4.46	18.18	15.67	15.78
SigLIP2-base	$16^{2}\times 768$	1	5.82	4.37	4.18	15.15	13.73	15.15

Appendix

Table A7: Frozen latent space of the encoder

\mathcal{E}

with different pretrained encoders and latent dimensions on Oxford-Flowers

256\times 256

Feature $\phi$	Layers	Params	FDr 6 $\downarrow$		gFID $\downarrow$
Feature $\phi$	Layers	Params	1	4	1	4
DINO-S/8	5 11	25.19M	6.16	4.63	24.86	17.17
	3 7 11	37.79M	5.95	4.49	27.23	17.68
	5 8 11	37.79M	6.20	4.72	24.28	17.80
	2 5 8 11	50.38M	6.48	5.12	27.20	20.28
ConvNeXt V2-N	1 2	24.16M	6.51	4.88	26.80	18.60

Appendix

Table A8: Feature extractor

\phi

and its layers used by the score loss on Oxford Flowers

256\times 256

. Each score model is a two-layer transformer with width 384. Params counts total parameters of all score models. Params of DINO-S/8 is 21.67M, and ConvNeXt V2-N fcmae is 14.98M.

Feat. $\phi$	Transport $(q_{\mathrm{mean}},q_{\mathrm{std}})$	FDr 6 $\downarrow$		gFID $\downarrow$
Feat. $\phi$	Transport $(q_{\mathrm{mean}},q_{\mathrm{std}})$	1	4	1	4
DINO-S/8	$(-1.2,1.0)$	4.54	3.93	13.69	10.89
	$(-1.5,1.0)$	4.57	3.94	13.35	10.54
	$(-1.8,1.0)$	4.61	3.96	13.19	10.37
	$(-1.8,0.8)$	4.97	4.28	14.46	11.02
	$(-1.2,1.0)$	3.82	3.09	11.40	8.28

Appendix

Table A9: Sigma distribution for transport procedure of the score loss on Flowers

256\times 256

architecture
	Sphere2-B	Sphere2-L
encoder $\mathcal{E}$	DINOv3-small (frozen, 29M)
latent	$N=16\times 16$ , $D=384$ , $d=98{,}304$
decoder $\mathcal{D}$	ViT-B	ViT-L
depth	12	24
hidden dim	768	1024

Appendix

Table A10: Configurations of experiments. Values in a merged cell are shared by both models.

Model	timm identifier	Arch.	Dim	Input	Objective	Pooling
Inception-v3 ( Szegedy et al., 2016 )	inception_v3 (torch-fidelity)	CNN	2048	299	supervised	global avg pool
ConvNeXt-v2 ( Woo et al., 2023 )	convnextv2_base.fcmae_ft_in22k_in1k	CNN	1024	224	self-supervised	global avg pool
MAE ( He et al., 2022 )	vit_large_patch16_224.mae	ViT	1024	224	reconstructive	CLS token
DINOv2 ( Oquab et al., 2024 )	vit_large_patch14_dinov2.lvd142m	ViT	1024	256	contrastive	CLS token
SigLIP2 ( Tschannen et al., 2025 )	vit_so400m_patch16_siglip_256.v2_webli	ViT	1152	224	vision-language	CLS token
CLIP ( Radford et al., 2021 )	vit_large_patch14_clip_224.openai	ViT	1024	256	vision-language	CLS token

Appendix

Table A11: Representation models of FDr 6 at

256\times 256

. The set follows the setup of FDr 6 ( Yang et al., 2026 ) , which is originally for ImageNet

256\times 256

Model	timm identifier	Arch.	Dim	Input	Objective	Pooling
Inception-v3 ( Szegedy et al., 2016 )	inception_v3 (torch-fidelity)	CNN	2048	299	supervised	global avg pool
ConvNeXt-v2 ( Woo et al., 2023 )	convnextv2_base.fcmae_ft_in22k_in1k_384	CNN	1024	384	self-supervised	global avg pool
MAE ( He et al., 2022 )	vit_large_patch16_224.mae	ViT	1024	224	reconstructive	CLS token
DINOv2 ( Oquab et al., 2024 )	vit_large_patch14_dinov2.lvd142m	ViT	1024	518	contrastive	CLS token
SigLIP2 ( Tschannen et al., 2025 )	vit_so400m_patch16_siglip_512.v2_webli	ViT	1152	512	vision-language	CLS token
CLIP ( Radford et al., 2021 )	vit_large_patch14_clip_336.openai	ViT	1024	336	vision-language	CLS token

Appendix

Table A12: Representation models of FDr 6 at

512\times 512

. Each family uses the highest-resolution checkpoint. Inception-v3 and MAE have no such checkpoint and keep the ones from Tab. A11 .

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Few-step image generation has seen rapid progress, with consistency and meanflow-based methods significantly reducing the number of sampling steps. Despite their low inference cost, these approaches often suffer from training instability and limited scalability. Sphere Encoder is a recent alternative that produces high-quality images in only a few steps; however, it requires repeated transitions between the pixel space and latent space during inference while jointly optimizing reconstruction and generation within a single architecture. This design leads to computational inefficiency and objective conflict between reconstruction and generation. To address these limitations, we decouple the framework into a fixed pretrained image encoder and a separate latent denoising model trained entirely in a spherical latent space. Our approach eliminates repeated pixel-space operations during training and inference, improving efficiency and allowing reconstruction and generation to specialize independently. On Animal-Faces, Oxford-Flowers and ImageNet-1K datasets, our method significantly outperforms Sphere Encoder in both generation quality and inference speed, while achieving competitive results against strong few-step and multi-step baselines.

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