arxiv: 2605.08835 · v1 · submitted 2026-05-09 · 💻 cs.AI

Recognition: no theorem link

SynerDiff: Synergetic Continuous Batching for Fast and Parallel Diffusion Model Inference

Ziqi Zhou , Peng Yang , Yuxin Liang , Mingliu Liu , Jia Lu

Authors on Pith no claims yet

Pith reviewed 2026-05-12 02:26 UTC · model grok-4.3

classification 💻 cs.AI

keywords diffusion modelscontinuous batchinginference servingUNetVAElatency optimizationthroughput scaling

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The pith

SynerDiff resolves UNet-VAE contention in diffusion serving to deliver 1.6 times higher throughput and up to 78.7 percent lower latencies.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces SynerDiff as a continuous batching system that coordinates UNet and VAE operations at two levels. At the intra-concurrency level it applies VAE Chunking and Adaptive Skip-CFG to cut resource contention inside each task. At the inter-concurrency level a threshold-aware scheduler and feedback controller adjust how many sequences run together, keeping UNet busy while trimming VAE wait times. The result is higher overall throughput and shorter end-to-end latencies without measurable loss in image quality. A sympathetic reader would care because these gains directly expand the number of users that can be served on the same hardware.

Core claim

SynerDiff builds continuous batching on intra-inter level synergy: VAE Chunking and Adaptive Skip-CFG prune component-specific bottlenecks at the intra level, while a threshold-aware scheduler plans concurrent sequences and a feedback controller tunes the threshold according to queue load, jointly raising throughput 1.6 times and cutting average and P99 E2E latencies by up to 78.7 percent while preserving image fidelity.

What carries the argument

The intra-inter level synergy of SynerDiff, which uses VAE Chunking plus Adaptive Skip-CFG inside tasks and a threshold-aware scheduler with feedback controller across tasks.

If this is right

Diffusion serving clusters can process more concurrent image-generation requests on the same GPUs.
User-perceived response times for AI content tools become shorter even under bursty demand.
Multi-stage generative pipelines gain a template for balancing encoder and decoder stages.
Resource provisioning for diffusion-based services can be reduced while meeting the same latency targets.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same contention-mitigation pattern may transfer to other generative models that alternate heavy and light compute stages.
Energy use per generated image could drop in proportion to the throughput gain if the techniques scale to larger clusters.
Hardware vendors might expose finer-grained scheduling hooks once software schedulers demonstrate clear value from them.

Load-bearing premise

VAE Chunking and Adaptive Skip-CFG cut contention without any drop in image fidelity, and the scheduler plus controller can keep UNet throughput high across changing loads without adding new overhead.

What would settle it

Measure FID scores and P99 tail latency while running increasing numbers of simultaneous diffusion tasks; if either metric falls below the reported baseline the central claim fails.

Figures

Figures reproduced from arXiv: 2605.08835 by Jia Lu, Mingliu Liu, Peng Yang, Yuxin Liang, Ziqi Zhou.

**Figure 1.** Figure 1: Latency spikes in concurrent execution of UNet and VAE. Coarse Fine Ours Scheduling Strategy 0 50 100 150 200 250 300 350 Concurrent Latency (ms) (8,6) 262.6 (1,1) 46.3 (1,1) (1,1) (1,1) (1,1) (1,1) (2,0) 44.2 (2,2) 87.4 (3,2) 99.4 (3,2) 0 50 100 150 200 Avg. VAE Latency (ms) Avg. VAE Latency [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

**Figure 3.** Figure 3: Throughput vs. Batch Size for UNet and VAE components. 200 300 400 500 600 Arithmetic Intensity (FLOPs/Byte) 30 50 100 200 400 Performance (TFLOPs/s) MEM BOUND TRANSITION ZONE COMPUTATION BOUND Roofline 5090 Roofline H800 SDv1.5 (5090) SDv2.1 (5090) SDXL (5090) SDv1.5 (H800) SDv2.1 (H800) SDXL (H800) VAE (5090) VAE (H800) [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗

**Figure 5.** Figure 5: Structural decomposition of the VAE decoder. [PITH_FULL_IMAGE:figures/full_fig_p003_5.png] view at source ↗

**Figure 6.** Figure 6: The workflow of SynerDiff. The global scheduler orchestrates concurrency by assigning execution sequences, Skip-CFG statuses, and VAE Chunking granularities to be implemented for precise local execution. Finally, the proposed SynerDiff architecture is model-agnostic and applicable to various UNet-based DM serving systems. B. Intra-Concurrency Coordination To mitigate latency spikes caused by severe resourc… view at source ↗

**Figure 7.** Figure 7: Comparative analysis of concurrent scheduling strategies. [PITH_FULL_IMAGE:figures/full_fig_p004_7.png] view at source ↗

**Figure 11.** Figure 11: System throughput analysis. = 0.8 = 1.5 = 2.3 = 3.0 Burst Workload Scenarios 10 0 10 1 10 2 10 3 10 4 Poisson Latency (s) Poisson Arrival Burst Arrival Methods Diffuser DynBatch InstGenIE Ours P99 Avg 0 100 200 300 400 500 600 700 800 Burst Latency (s) [PITH_FULL_IMAGE:figures/full_fig_p006_11.png] view at source ↗

read the original abstract

The expansion of Artificial Intelligence-generated content service requires diffusion model serving to simultaneously achieve high throughput and low task end-to-end (E2E) latency. However, existing continuous batching methods suffer from severe resource contention during UNet-VAE concurrency, leading to latency spikes. Furthermore, concurrent multi-task scheduling entails a trade-off between UNet throughput and VAE latency across varying scheduling strategies. To address these, we propose SynerDiff, an efficient continuous batching system built on intra-inter level synergy. At the intra-concurrency level, SynerDiff alleviates resource contention by pruning component-specific resource bottlenecks via VAE Chunking and Adaptive Skip-CFG. At the inter-concurrency level, leveraging components' differential sensitivity to scheduling granularities, a threshold-aware scheduler plans concurrent sequences and tunes intra-concurrency decisions to minimize VAE latency while maintaining UNet within high-throughput threshold. Additionally, a feedback controller dynamically adjusts this threshold based on queue loads to boost system capacity ceiling. Experimental results show that, SynerDiff improves throughput by 1.6$\times$ and decreases both average E2E and P99 tail latencies by up to 78.7\%, compared to benchmarks while guaranteeing high image fidelity.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SynerDiff combines VAE chunking, adaptive CFG skipping, and load-aware scheduling to ease UNet-VAE contention in diffusion serving, delivering claimed throughput and latency gains that rest on the experiments rather than the design alone.

read the letter

Hi, SynerDiff's main contribution is a continuous batching system for diffusion models that handles UNet and VAE contention better than prior approaches. It uses VAE Chunking and Adaptive Skip-CFG to cut down on internal resource fights, then adds a threshold-aware scheduler that plans task concurrency and a feedback controller to adjust based on queue loads. This setup reportedly delivers 1.6 times higher throughput and slashes average and tail latencies by as much as 78.7 percent, all while keeping image quality high. The paper does a good job laying out the problem of latency spikes from concurrent UNet-VAE execution and the scheduling trade-offs. Their intra-inter level synergy makes sense as a way to balance throughput and latency dynamically. The adaptive elements seem tailored to diffusion's specific compute patterns, which is an improvement over generic batching methods. On the experimental side, they compare against benchmarks and show gains, which is solid for this kind of work. The focus on both E2E and P99 metrics is practical. That said, without seeing the full details on the test setups, model sizes, or statistical analysis, it's tough to gauge how general the improvements are. The claim of no fidelity loss relies on whatever metrics they used, and any overhead from the new components isn't broken out. The high-throughput threshold is mentioned as tunable, but its sensitivity isn't explored much in the summary. This paper is for systems researchers and practitioners dealing with deploying large generative models at scale. Someone optimizing inference servers would find the scheduling ideas relevant. It should go to peer review so the community can check the implementation and results thoroughly.

Referee Report

0 major / 4 minor

Summary. The paper proposes SynerDiff, a continuous batching system for diffusion model serving that targets UNet-VAE resource contention and scheduling trade-offs. At the intra-concurrency level, it introduces VAE Chunking and Adaptive Skip-CFG to prune bottlenecks. At the inter-concurrency level, a threshold-aware scheduler plans sequences and tunes decisions to keep UNet above a high-throughput threshold while minimizing VAE latency, augmented by a feedback controller that adjusts the threshold based on queue loads. Experiments claim 1.6× throughput gains and up to 78.7% reductions in average E2E and P99 latencies versus baselines, with preserved image fidelity.

Significance. If the experimental claims hold under rigorous benchmarking, the work offers a practical advance in high-throughput, low-latency serving of diffusion models for AIGC workloads. The intra-inter synergy design directly addresses contention points that existing continuous batching overlooks, and the explicit mechanisms (chunking, skip-CFG, threshold planning, feedback) provide a concrete, implementable path to better resource utilization without fidelity loss.

minor comments (4)

The abstract and introduction should explicitly state the baseline systems used for the 1.6× and 78.7% comparisons (e.g., specific continuous batching variants or frameworks) and report error bars or statistical significance for the latency and throughput numbers.
Clarify the definition and tuning procedure for the 'high-throughput threshold' parameter; if it is workload-dependent, describe how it is set or learned in the experimental sections.
Include a dedicated ablation study isolating the contribution of VAE Chunking versus Adaptive Skip-CFG versus the scheduler/controller to the overall gains.
Add a limitations or future-work paragraph discussing potential overheads of the feedback controller under extreme load spikes or very large batch sizes.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive summary of SynerDiff, recognition of its practical contributions to diffusion model serving, and recommendation for minor revision. No specific major comments were raised in the report.

Circularity Check

0 steps flagged

No significant circularity; claims rest on experimental benchmarks

full rationale

The paper describes a systems engineering contribution: intra-level optimizations (VAE Chunking, Adaptive Skip-CFG) and inter-level scheduling (threshold-aware planner + feedback controller) to reduce UNet-VAE contention in continuous batching for diffusion models. All performance numbers (1.6× throughput, up to 78.7% latency reduction) are presented as outcomes of concrete implementation choices measured against external baselines. No equations, fitted parameters, uniqueness theorems, or self-citations are invoked as load-bearing derivations; the argument chain is therefore self-contained against external benchmarks and contains none of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on domain assumptions about component sensitivities and resource contention in inference serving; no new physical entities are postulated and free parameters such as the throughput threshold are dynamically managed but not fully specified.

free parameters (1)

high-throughput threshold
Used by the scheduler to keep UNet in high-throughput regime; value is tuned and adjusted by feedback controller but no specific fitting process or values given in abstract.

axioms (1)

domain assumption UNet and VAE components exhibit differential sensitivity to scheduling granularities
Invoked to justify planning concurrent sequences and tuning intra-concurrency decisions at the inter-concurrency level.

pith-pipeline@v0.9.0 · 5524 in / 1374 out tokens · 65927 ms · 2026-05-12T02:26:37.674288+00:00 · methodology

discussion (0)

Reference graph

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