Nucleus-Image: Sparse MoE for Image Generation

Chandan Akiti; Ajay Modukuri; Murali Nandan Nagarapu; Gunavardhan Akiti; Haozhe Liu

cs.CVApr 14, 2026

Nucleus-Image: Sparse MoE for Image Generation

Authors: Chandan Akiti, Ajay Modukuri, Murali Nandan Nagarapu, Gunavardhan Akiti, Haozhe Liu

Abstract

We present Nucleus-Image, a text-to-image generation model that establishes a new Pareto frontier in quality-versus-efficiency by matching or exceeding leading models on GenEval, DPG-Bench, and OneIG-Bench while activating only approximately 2B parameters per forward pass. Nucleus-Image employs a sparse mixture-of-experts (MoE) diffusion transformer architecture with Expert-Choice Routing that scales total model capacity to 17B parameters across 64 routed experts per layer. We adopt a streamlined architecture optimized for inference efficiency by excluding text tokens from the transformer backbone entirely and using joint attention that enables text KV sharing across timesteps. To improve routing stability when using timestep modulation, we introduce a decoupled routing design that separates timestep-aware expert assignment from timestep-conditioned expert computation. We construct a large-scale training corpus of 1.5B high-quality training pairs spanning 700M unique images through multi-stage filtering, deduplication, aesthetic tiering, and caption curation. Training follows a progressive resolution curriculum (256 to 512 to 1024) with multi-aspect-ratio bucketing at every stage, coupled with progressive sparsification of the expert capacity factor. We adopt the Muon optimizer and share our parameter grouping recipe tailored for diffusion models with timestep modulation. Nucleus-Image demonstrates that sparse MoE scaling is a highly effective path to high-quality image generation, reaching the performance of models with significantly larger active parameter budgets at a fraction of the inference cost. These results are achieved without post-training optimization of any kind: no reinforcement learning, no direct preference optimization, and no human preference tuning. We release the training recipe, making Nucleus-Image the first fully open-source MoE diffusion model at this quality.

Figures & tables

Panel of Figure 1 — Figure 1: Nucleus-Image generations of human subjects and portraits, spanning diverse cultures, ages, and artistic styles. From expressive character studies to fine-grained close-ups with intricate skin texture and detail.

Panel of Figure 2 — Figure 2: Nucleus-Image generations spanning fantasy, surrealism, animation, and the natural world.

Panel of Figure 3 — Figure 3: Nucleus-Image generations across product photography, architecture, typography, food, and world culture demonstrating versatility in commercial, conceptual, and everyday imagery.

Figure 4: Overall performance computed as the average of GenEval, DPG-Bench, and OneIG-Bench benchmark scores

Figure 5: Dataset retention across the data pipeline. Block height indicates retained corpus size, and ring markers indicate retained fraction relative to the raw corpus.

Field	Type	Purpose
id	string	Stable record identifier for tracing and debugging.
media_path	string	Content-addressed locator in object storage.
width , height	int	Original geometry; used for aspect-ratio bucketing and validation.
captions	list[string]	Candidate captions at multiple granularities.
caption_sources	list[string]	Provenance tag per caption, aligned with captions .
caption_lengths	list[int]	Token counts per caption.

Table 1: Core training-facing metadata schema.

Figure 6: Representative quality tiers for real images. Real-image samples are ranked using aesthetic scoring together with downstream quality signals, then grouped into the shared A1-A5 quality taxonomy used by the sampler.

Figure 7: Representative quality tiers for synthetic images. Synthetic samples bypass aesthetic scoring and are assigned to the same A1-A5 taxonomy using non-aesthetic quality heuristics

Figure 8: Average caption length across quality tiers and episodic buckets. Higher quality tiers generally carry longer captions, and later buckets trend toward richer textual supervision.

Figure 9: Joint view of static quality tiers and episodic buckets. Columns denote quality tiers A1-A5 and rows denote curriculum buckets B1-B8, illustrating that episodic bucketing acts as a second control axis layered on top of quality tiering.

Figure 10: Overview of the Nucleus-Image architecture. Left : A single transformer block consisting of cross-attention (GQA), modulation, and MoE layers, repeated 32 times. The first 3 blocks use dense FFN with hidden size 2,048 instead of MoE. Right : The expert-choice MoE module with 64 routed experts and a shared expert, where the router selects tokens based on the unmodulated input and timestep.

Specification	Value
Total parameters	17B
Active parameters	~2B
Layers	32
Hidden dimension	2048
Attention heads (Q / KV)	16 / 4
Head dimension	128

Table 2: Nucleus-Image architectural specifications.

Layer Index	Capacity Factor	Effective Experts per Token
3–4	4.0	~4
5–31	2.0	~2

Table 3: Per-layer capacity factor schedule (1024-resolution stage).

Resolution Stage	Capacity Factor	Rationale
256	8.0 (uniform)	Dense gradients: only 256 image tokens across 64 experts
512	4.0 (uniform)	Moderate sparsity as representations mature
1024	4.0 / 2.0 (per-layer)	Full progressive schedule (Table 3 )

Table 4: Capacity factor across training resolution stages.

Loss component	Coefficient	Stage
Rectified flow (MSE)	1.0	All phases
Z-loss	$5\times 10^{-7}$	All phases
Orthogonal loss	$1\times 10^{-7}$	All phases (direct weight update)
Wavelet loss	0.1	High-resolution fine-tuning

Table 5: Loss components and coefficients.

Group	Parameters	Optimizer	$\eta$	$\beta_{1}/\beta_{2}$ or $\mu$	$\lambda$
Muon	Attention & expert FFN weights	Muon	$1\times 10^{-4}$	$\mu=0.95$	0.01
AdamW	Patch embed, modulation, output head	AdamW	$1\times 10^{-4}$	0.9 / 0.95	0.01
No-decay	Biases, norms, router gates	AdamW	$1\times 10^{-4}$	0.9 / 0.95	0

Table 6: Optimizer groups and hyperparameters.

Stage	Base Resolution	Global Batch Size	Capacity Factor	Notes
1	256	4,096	8.0 (uniform)	Dense gradients; multiple aspect ratios
2	512	1,024	4.0 (uniform)	Moderate sparsity; larger token sequences
3	1024	256	4.0 / 2.0 (per-layer)	Full progressive schedule; wavelet loss enabled

Table 7: Multi-stage training curriculum. Each stage trains with multiple aspect ratios derived from its base resolution. The capacity factor is progressively reduced as expert specialization matures.

Parameter	Value
Hidden dimension	2048
Number of layers	32
Attention heads (Q / KV)	16 / 4
Number of experts	64 + shared
Experts per token (effective)	~2
Expert hidden dimension	1344

Table 8: Model and training hyperparameters.

Model	Single	Two	Count	Colors	Position	Attr.	Overall
Show-o [ 44 ]	0.95	0.52	0.49	0.82	0.11	0.28	0.53
PixArt- $\alpha$ [ 45 ]	0.98	0.50	0.44	0.80	0.08	0.07	0.48
Emu3-Gen [ 46 ]	0.98	0.71	0.34	0.81	0.17	0.21	0.54
SD3 Medium [ 27 ]	0.98	0.74	0.63	0.67	0.34	0.36	0.62
FLUX.1 Dev [ 40 ]	0.98	0.81	0.74	0.79	0.22	0.45	0.66
SD3.5 Large [ 27 ]	0.98	0.89	0.73	0.83	0.34	0.47	0.71

Table 9: GenEval results.

Figure 11: GenEval overall scores for top-performing models. Nucleus-Image matches Qwen-Image at 0.87 and leads all models on spatial position understanding.

Figure 12: DPG-Bench overall scores for top-performing models. Nucleus-Image achieves the highest overall score of 88.79, leading in entity, attribute, and other subcategories.

Model	Global	Entity	Attribute	Relation	Other	Overall
SD v1.5 [ 36 ]	74.63	74.23	75.39	73.49	67.81	63.18
PixArt- $\alpha$ [ 45 ]	74.97	79.32	78.60	82.57	76.96	71.11
Lumina-Next [ 52 ]	82.82	88.65	86.44	80.53	81.82	74.63
SDXL [ 53 ]	83.27	82.43	80.91	86.76	80.41	74.65
Playground v2.5 [ 54 ]	83.06	82.59	81.20	84.08	83.50	75.47
HunyuanDiT [ 55 ]	84.59	80.59	88.01	74.36	86.41	78.87

Table 10: DPG-Bench results.

Figure 13: OneIG-Bench overall scores for top-performing models. Nucleus-Image scores 0.522, surpassing Imagen4 and Recraft V3, with particularly strong style and text rendering performance.

Model	Alignment	Text	Reasoning	Style	Diversity	Overall
Janus-Pro [ 48 ]	0.553	0.001	0.139	0.276	0.365	0.267
BLIP3-o [ 59 ]	0.711	0.013	0.223	0.361	0.229	0.307
Show-o2-1.5B [ 60 ]	0.798	0.002	0.219	0.317	0.186	0.304
Show-o2-7B [ 60 ]	0.817	0.002	0.226	0.317	0.177	0.308
SD 1.5 [ 36 ]	0.565	0.010	0.207	0.383	0.429	0.319
SDXL [ 53 ]	0.688	0.009	0.237	0.332	0.296	0.316

Table 11: OneIG-Bench (EN) results.

Benchmark	Score (0–100)	Configuration
GenEval	87.00	$1024\times 1024$
DPG-Bench	88.79	$1024\times 1024$
OneIG-Bench	52.20	$1024\times 1024$
Overall performance (avg. of normalized 3)	76.00	—

Table 12: Nucleus-Image benchmark summary.

Panel of Figure 14 — Figure 14: Expert allocation and diversity across three domains. Each column shows a different generation: stylized text rendering (left), photorealistic scene composition (center), and portrait photography (right). Top: generated images. Middle: normalized expert allocation aggregated over all 29 MoE layers and 50 denoising steps—bright regions attract more expert capacity. Bottom: expert diversity, measuring the number of unique dominant experts per spatial position across all timesteps. Across all three domains the router concentrates computation on semantically salient structures (letterforms, helicopter fuselage, facial features) while assigning fewer experts to uniform backgrounds. Foreground regions exhibit lower diversity (stable expert specialization), whereas backgrounds show higher diversity (fluctuating, uncommitted assignments).

Panel of Figure 15 — Figure 15: Timestep evolution of expert allocation at layer 17. Each row shows the number-of-experts-per-token heatmap overlaid on the generated image, sampled at 10 evenly spaced denoising steps (left to right, top to bottom: steps 0, 5, 11, 17, 22, 27, 33, 38, 44, 49). Early steps exhibit diffuse, spatially unstructured allocation; mid-steps develop coarse semantic structure; late steps produce the sharpest, most content-aligned allocation maps.

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