FlowCast: Trajectory Forecasting for Scalable Zero-Cost Speculative Flow Matching
Authors: Divya Jyoti Bajpai
Shubham Agarwal
Apoorv Saxena
Kuldeep Kulkarni
Subrata Mitra
Manjesh Kumar Hanawal
Venue: ICLR 2026 Poster (2026)
Keywords: Flow Matching
Speculative Decoding
Inference Acceleration
Training-Free
Generative Models
Zero-Cost Drafts
Parallel Verification
Adaptive Sampling
Primary area: generative models
TL;DR: FlowCast accelerates Flow Matching inference via training-free speculative generation, using constant-velocity forecasting to skip redundant steps, achieving >2.5× speedup without quality loss.

Abstract: Flow Matching (FM) has recently emerged as a powerful approach for high-quality visual generation. However, their prohibitively slow inference due to a large number of denoising steps limits their potential use in real-time or interactive applications. Existing acceleration methods, like distillation, truncation, or consistency training, either degrade quality, incur costly retraining, or lack generalization. We propose FlowCast, a training-free speculative generation framework that accelerates inference by exploiting  the fact that FM models are trained to preserve constant velocity. FlowCast speculates future velocity by extrapolating current velocity without incurring additional cost, and accepts it if it is within a mean-squared error threshold. This constant-velocity forecasting allows redundant steps in stable regions to be aggressively skipped while retaining precision in complex ones. FlowCast is a plug-and-play framework that integrates seamlessly with any FM model and requires no auxiliary networks. We also present a theoretical analysis and bound the worst-case deviation between speculative and full FM trajectories. Empirical evaluations demonstrate that FlowCast achieves $>2.5\times$ speedup in image generation, video generation, and editing tasks, outperforming existing baselines with no quality loss as compared to standard full generation.

Published as a conference paper at ICLR 2026
FLOWCAST: TRAJECTORY FORECASTING FOR SCAL- ABLE ZERO-COST SPECULATIVE FLOW MATCHING
Divya Jyoti Bajpai'; Shubhaam Agarwal,; Apoorv Saxena Kuldeep Kulkarni", Subrata Mitra", Manjesh K. Hanawal
Shubham
'Indian Institute Technology Bombay
UUniversity of California, Berkeley
*Inception Labs
4 AAdobe Research India
ABSTRACT
Flow Matching (FM) has recently emerged as powerful approach for high- quality visual generation. However, their prohibitively slow inference due to a large number of denoising steps limits their potential use real-time or interac- tive applications. Existing acceleration methods, like distillation, truncation, consistency training, either degrade quality, incur costly retraining, or lack gener- alization. We propose FlowCast, training- free speculative generation framework that accelerates inference by exploiting the fact that FM models are trained to preserve constant velocity. FlowCast speculates future velocity by extrapolating current velocity without incurring additional time cost, and accepts it if it is within mean-squared error threshold. This constant-velocity forecasting allows redun- dant steps in stable regions to be aggressively skipped while retaining precision in complex ones. FlowCast is plug-and aadg--nd-payyy framework that integrates seamlessly with any FM model and requires no auxiliary networks. We also present theoret- ical analysis and bound the worst-case deviation between speculative and full FM trajectories. Empirical evaluations demonstrate that FlowCast achieves >> 2.5x speedup in image generation, video generation, and editing tasks, outperforming existing baselines with no quality loss as compared to standard full generation.
INTRODUCTION
Flow Matching (FM) Lipman al. 2022 has recently emerged powerful and principled ap- proach for generative modeling Bond-TTylor et al. 2021). ). By learning a time-dependent velocity field that continuously transforms samples from base distribution to target distribution, FM mod- els generate data by integrating an Ordinary Differential Equation (ODE). This framework offers advantages over diffusion-based methods Croitoru et al. 2023). including faster convergence, stable training, and direct control over sample trajectories.
However, despite these strengths, inference in FM models is costly and is a bottleneck Kornilov et al. (2024). ODE integration is inherently sequential- each step depends on the output of the pre- vious one making the process slow and difficult to parallelize. For high-fideliyy generation, large number of fine-graines steps are needed, pushing the latency cost higher, which makes it difficult for real-time or interactive use. As data dimensionality and model complexity increase, so does the inference burden. This issue is even more pronounced in video generation Kong et al. (2024). where models operate on high-dimensional spatio-temporal data. The sequential ODE integration compounds across frames, making efficient inference essential for long videos Liu al. (002aa).
To reduce inference latency, static reduction strategics such as distillation Luhman & Luhman (2021); Yan et al. (2024); Kornilov et al. (2024), trajectory truncation Dhariwal & Nichol (2021); Liu et al (2025a). and consistency training Yang et al. (2024); Zhang & Zhou 2025 Dao et al. (2025) have been proposed. However, these approaches often degrade output
Lu et al. (2022)
"Wrk done as part of internship at Adobe Research India. I Equal contribution, listed alphahetically.

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Published as a conference paper at ICLR 2026
resulty-rssuliing in bluny textures, semantic drift, broken spatial coherence. In the video do- main, such approaches often cause temporal flickering and motion inconsistency, which severely degrade realism despite visually plausible individual frames. Furthermore, many of these meth- ods require retraining the model with auxiliary objectives or handcrafted losses, which not only increases the engineering burden but also restricts transferability across model families. As a result, these approaches are less modular, harder to scale, and often infeasible to deploy in time-sensitive or interactive pipelines.
A promising line of work to accelerate inference in language models is Speculative Decoding (SD) Xia et al. (2024). where multiple drafts are proposed in parallel and selectively verified reduce latency. However, this idea does not directly extend image and video generative models such as Flow Matching (FM). Some of the unique challenges are: (1) Unlike in language models, no readily available draft model exists. Then, how to design such a draft model whose trajectories closely couple with the full model? (2) Performance of FM is highly sensitive to velocity output. Even a small perturbation in velocity can accumulate along the integration path, leading severe generative errors. Then how to evaluate the draft model velocity and control the trajectory drifts? We address these challenges using properties of FM trajectories.
Our Key Insight. We observe that FM trajectories are typically locally smooth and slowly varying Lipman et al. (2022), especially in well-trained models. Leveraging this property, propose speculative framework FlowCast where the model's own past velocity predictions act as a zero-cost draft for future steps. These drafts selectively validated using mean-squared error check: if accurate within the limits of a threshold, we skip recomputation entirely; if not, we fall back to the backbone. This mechanism introduces negligible overhead, requires no retraining or auxiliary models, and remains fully compatible with existing FM pipelines.
Why it matters. Unlike previous acceleration techniques that require retraining or trade off genera- tion fidelity, our method is plug- laiug-ad-pan and-play, adapts to the complexity of the trajectories, and leverages the inherent smoothness of the FM. In regions with stable dynamics, it skips steps aggressively to accelerate inference, while in complex regions it falls back to fine-grained integration for accuracy. This design enables significant speedups without compromising output quality. A key advantage is that the approach is entirely training-free and readily applicable to existing methods, allowing even current acceleration strategies to be further enhanced.
FlowCast is task-agnostic: it applies seamlessly to image generation, image editing, and video gen- cration, where it ensures faithful, high-quality edits. This broad applicability highlights speculative generation unified framework for accelerating generative models. We compare the trajectories of the FlowCast and those obtained from the full ODE to develop a theoretical bound on their devi- ations. The resulting error bound guarantees the stability and fidelity of our method, ensuring that speculative generation remains close to the original FM solution while enabling faster inference.
In summary, major contributions are:
We introduce a speculative generation framework viz. FlowCast for flow-matching- -base genera-
tive models that enables adaptive, partially parallel inference.
FlowCast uses zero-cost drafts from the model's 's own velocity predictions, avoiding retraining and preserving trajectory fidelity.
We derive a theoretical error bound that formally characterizes how much the speculative trajec- tory can deviate from full ODE integration.
Our method achieves substantial inference acceleration (> 2.5x) while maintaining sample qual- ity, outperforming existing baselines especially in multi-turn editing tasks.
2 RELATED WORKS
Flow Matching Lipman et al. 2022 Dao et al. (2023); Labs et al.] (2025); Deng et al. (0025) has recently emerged as strong alternative to diffusion models Croitoru et al. (2023) Xing et al. 2024);] Yang et al. 2023);; ; Zhu et al. 2023), providing a deterministic mapping between noise and data. This determinism makes flow-based models appealing for applications such as image inversion
Deng et al. (2024). editing Wang et al. (204)), and video generation Kong et al. (2024); /Wn et al. 22025) as it reduces randomness and enables faster sampling with fewer neural function evaluations.
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Multimodal extensions like FlowTokk He al. (2025) compress text and images into shared token spaces for faster inference, and FLUX.1 Labs Labs et al. (2025) shows trrge-scale flows can rival diffusion models at lower cost. For video, Pyramidal Flow Matching Jin al. (2024) reduces compute via hierarchical generation.
However, iterative sampling remains major bottleneck in these models, restricting real-time and in- teractive deployment. To accelerate inference, prior work has focused on retraining-basse solutions Lee ) Bartosh et al. (2024). Distillation methods Song et al. (2023); Luhman Luhman
2021
Liu et al. [ Kornilov et al. (2024); Salimans & Ho] (2022) like InstaFlow Liu et al.
2023 LeDiFlow Zwick al. (2025) and Diff2Flow SSchusterbauer al. (2025) transfer knowl-
edge from diffusion priors for one- or ftw-step sampling, while PeRFlow Yan et al. (2024) straight-
ens trajectories with piecewise rectified flows. Methods used for sampling modification Dhariwal & Nichol (2021): Lu et al. (2022); [Shaul et al. (023) such as TeaCache [iu et al. (2025a) skip redun-
dant steps by getting informed by the timestep-emheddingg and consistency- approaches Yang
al. (2024); Zhang & Zhou (2025); Haber et al. (2025) Dao al. (2025) combine adversarial and consistency losses for high-quality few-step generation.
Our work accelerate the inference process using the speculative decoding framework making the FM more suitable for real-time and interactive applications. contrast to the existing methods, our method do not require any retraining, distillation, trajectory straightening, method offers a fully plug-and-play solution for the existing flow models. It operates dynamically inference time by speculating future steps and verifying them the fly. This design delivers substantial speedup, while preserving the model's original generative quality.
step skipping. Our
SETUP
In this section, we provide the foundational formulation of Flow Matching (FM) and Speculative Decoding (SD) frameworks.
3.1 SPECULATIVE DECODING FRAMEWORK
SD parallelizable inference strategy designed to accelerate autoregressive generation, partieu- larly in large language models (LLMs), without retraining or sacrificing output quality. The frame- work leverages the observation that evaluating a sequence of tokens in parallel is only marginally slower than evaluating single token, enabling partial parallelism during decoding.
Let My denote the target (slow but accurate) model, and Mg denote a draft model (faster but less accurate). In SD, the objective is to accelerate token generations from Mp by verifying the specu- lative drafts generated by MM. The verification can be done at a higher speed than the generation, thus achieving speedup. Given imput with i tokens 1
(21, E2..., I,). SD has two phases:
The draft model Mg generates (speculative) tokens autoregressively: drail drall ~ My(- Z1.). This phase is fast due to the small size and shallow ar- (1 #421 ,..,,, chitecture of Mg.
1) Drafting Phase:
2) Verification Phase: The full model Mp evaluates the entire speculative sequence in a single uua forward pass, computing: Mp(#.4k #1 )) for = I....,K, K. These predicted distributions compared against the draft tokens assess validity (e.g.. via cross-entropy top-k agreement). draft #111.... ...., (tt 1 is accepted. Accepted tokens are then passed again to the draft model to generate new drafts based updated context.
Let be the first index where Mp's 's prediction deviates significantly from the draft. Then the prefix
draft
This speculative verification cycle continues iteratively until a full sequence produced. The method improves throughput without modifying My. and is particularly effective when the draft model's 'ss outputs closely align with the target model.
Under the assumption that Mg is sufficiently aligned with MM. the speculative decoding process preserves output quality while accelerating generation. Moreover, this approach avoids retraining and can be applied any pair (My Mp) where My is reasonably well-calibrated.
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Adapting speculative decoding to image generation is nontrivial, as drafting valid future states in continuous, high-dimensional latent spaces far more complex than next-token prediction in lan- guage models. Drafts must maintain spatial and temporal consistency in predicted noise velocity, which is difficult without a trained surrogate. Moreover, small trajectory errors can accumulate under ODE/SDE integration, causing semantic drift, structural distortions, misalignment with conditioning. These compounding effects make speculative decoding fragile in visual domains, ne- cessitating drafting and verification strategies that explicitly respect the geometry and stability of the underlying dynamics.
3.2 FLOW MATCHING OVERVIEW
Let and Ti be two smooth probability densities over Rd,, representing the source and target distributions, respectively. The goal of Flow Matching (FM) to construct a deterministic and continuous mapping that transports samples from To to Ti through time-indexed trajectory gov- ermed by an ordinary differential equation (ODE). Specifically, FM defines the transformation using time-dependent velocity field e R [0, 1] + R", yiclding the following initial value problem:
dx.
0( t), dt
To To, t [0,
Here, E I&d denotes the state of the sample at time t, and v t) is typically parameterized by neural network trained to align the marginal density at t = 1, denoted TT, with the target distribution. The ODE defines a flow map (200) that moves points in Rd along continuous trajectories, with @ (Irp) ~ πΙ. Learning valid v(ze,) 1) that realizes this transport is the central objective FM.
Numerical Integration: In practice, the solution of the ODE must be computed numerically, as closed-form solutions are generally unavailable for learned velocity fields. The most widely used method for solving the ODE in flow matching models is the forward Euler method, due to its simplicity and computational efficiency. The time interval [o, 1] is discretized into a sequence of K points (to 0, 11 ................................ tk 1}. which may be non-uniformly spaced. Starting from Zg To, the trajectory is updated iteratively as
Fk- = Ik + Ats U(Kk, (k), A4 = 1x+1 - 44-
(2)
This discretization approximates the continuous flow by a finite sequence of steps, with each update advancing the state in the direction prescribed by the velocity field. The Fuler method is favoured in most implementations for its low computational overhead Deng et al. (2025): Labs et al. (2025).
Trajectory Smoothness: The validity and accuracy of Euler-based discretization depend on the regularity of the velocity field v(z,, t). In particular, when the ODE admits a unique solution, the discretization errors can be bounded. More importantly, empirical observations in trained FM mod- els reveal that the velocity field tends to vary gradually across time steps and sample locations Liu jet al. (2025a), particularly when trained using interpolants or synthetic velocity targets, indicating that the velocity does not fluctuate sharply between adjacent steps.
SPECULATIVE DECODING FOR FLOW MODELS
In this section, we present our core contributions: establishing connection between speculative de- coding and flow matching models, introducing our speculative generation framework, and providing theoretical error bounds that formally characterize its fidelity.
Autoregressive nature of FM: SD works well for the autoregressive models. FM process is in- herently autoregressive as each denoising step depends entirely on the output of the previous step, creating strict sequential dependency chain-much like generating tokens one by one in lan- guage model. Though, SD is used in language tasks where the autoregression is in the discrete space (e.g., tokens from discrete set), they can also be leveraged in FMs where autoregression is in the continuous space (e.g., velocity in a bounded interval).
Zero-Cost Draft Construction: To enable speculative decoding in FM models without relying a separate draft network, we propose a lightweight and self colf-contained strategy for draft generation by directly reusing the velocity predictions from the target model itself.
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Recall from Section 3.2 that FM models learn time-dependent velocity field v(ze, t) that transports samples from the source distribution Ty toward the target distribution #1 via integration of the ODE:
dz dt
c(zr,t),
20 To.
During training, the model & is typically supervised to match a reference velocity (often constant over time) along interpolated paths. This encourages the model v to vary smoothly in both space and time. Leveraging this smoothness, we propose FlowCast, where the velocity is kept constant to extrapolate not just the immediate next state, but the entire remaining trajectory. resulting zero- cost draft trajectories. Each step of the trajectories is verified by the full model in parallel, and either accept them or apply corrections. FlowCast has three components: Drafting, Verification, and Correction. Its pseudo-code is given in Algorithm |
Drafting:
Given the current state It, at step L, define the speculative trajectory as:
Algorithm FlowCast
Require: Initial state Tio timesteps {4 thresh- old e, model
B1,+ = It, + e( (4) At (4)
1: Compute initial velocity: Uto + v to)
for k = L,..., and k > i. Here de- notes the number of remaining time steps to be traversed complete the trajectory, and Atx = ft- - t,. This effectively reuses the same velocity vector to lincarly extrap- olate the remaining path.
2: Predict next state: It, f- Tio + (11 40) 40)
3: Set current index m <- 0
4: repeat
5: (Drafting): Generate speculative states:
Tia- + I'm (tx 1 ttmmmm) vk = 300mmm11,, mm+1........
This method introduces no auxiliary mod- els or additional computational cost during drafting. While simple, it leverages the in- ductive bias of FM models to generate co- herent trajectories, particularly domains where velocity evolution is smooth.
6: (Parallel Verification): For each ks
I,..., K, compute:
oa - vi (x), ex O-SSE Utd)
7:
Find first index j > m such that ej >c if rejection (i.e., e₁ I E for all k) then
Verification: We employ a lightweight yet principled mechanism to verify the valid- ity of each drafted state. For every ex- trapolated point Tz+Ar, we compare the reused velocity v(z,, 1) with the model's own velocity prediction at the extrapolated point and step, i.c., v(īc+Ar. + At). the mean squared error (MSE) between the two remains below a pre-defined threshold
8:
9:
Accept full draft (5t, kkm=+1 set m <- K
10:
else
11:
(Correction): Accept draft (Te, 35-1 kk=++1 It, + Æty-1 1 U1y-1 (13 tj- f -1 Recompute velocity: Vt... + V Temm bm)
12:
13:
14:
end if
15: until m = K
c, the draft is accepted, i.e.,
(5) Correction: If the criterion is violated at given step, the draft is rejected, and all subsequent drafts are discarded. A new trajectory is then re-initiated starting from the rejection time step. using fresh velocity predictions from the model as per Equation 4
MSB(u(Ia, (), (1, At)) <.
This verification strategy is efficient and directly aligned with the FM formulation, where the ve- locity field encodes the generative dynamics. Unlike traditional methods that assess validity in data or latent space, our velocity-bar basedd criterion operates in function space, making it more sensitive to inconsistencies in local dynamics while remaining inexpensive compute. This ensures the inte- gration path remains faithful to the learned flow without unnecessary recomputation.
The working procedure of FlowCast illustrated in Figure T eration process, where the FM model is invoked sequentially in each step of the trajectory. The right side shows the FlowCast process for a trajectory generated from the drafting step. All the speculatively generated drafts in the trajectory the regular FM generation, the output of the model in each step depends only on the output of the previous step. We use this fact verify the correctness of the drafts. In particular, if the output of the FM model at step matches that the speculated draft at step + 1 within the MSE threshold, the speculated draft at k + 1 is accepted, else rejected. This verification is performed efficiently in
The left part shows the normal gen-
processed in parallel through the FM model. In
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t
1
f
Flow Matching Model
Flow Matching Model
Verter
T
Vertier
Venteer
t
T
X
Normal Generation
Speculative Generation
Comparison normal vs. speculative image generation: The left side is the normal iter- ative process of generation in FM model. The right side is our approach which introduces interme- diate speculative drafts that are rapidly proposed and verified in parallel by the backbone, enabling faster sampling while preserving quality.
Figure I:
single forward pass. In Figure 1 drafts are accepted up to step 3, but the draft at step is rejected. Once draft is rejected at step, all later drafts are discarded, and the trajectory is computed from the last accepted step.
4.1 ANALYSIS
In this subsection, we bound the deviations in the draft trajectories and that of the full model. We then provide a bound on the MSE threshold to guarantee the worse case deviations.
Ip, where v is Lipschitz in x with constant M. Assume: 1) [t)l I N for all t E [0, 1]. 2) ll 8e Be (z, t)ll I M for all (æ, 3) forward Fuler scheme with step size h is run for steps, where speculative velocities are accepted in a fraction p [0, 1] of the steps and satisfy [],( (x) o( fx)l /e. Then the global discretization error Eg = [|x(t)) - xxll can be bounded as
Lemma 4.1. Let xxt) be the solution of da dt v(x, t), x(0)
M
||x(tz) xxll S
(NN + 2pv/).
2M
Here, the first term
M1 hN represents the standard Fuler discretization error, while the second 2M
term MM pvee quantifies the additional deviation introduced by speculative updates. The proof of this lemma is given in Appendix A.I This lemma leads us to the theorem stated below.
Theorem 4.2. Let qu > 0 denote a user-specified tolerance on the maximum admissible deviation from full speclapecllative) generation trajectory. Then, for the speculative error || (i.e., the deviation introduced by speculative generation) to remain bounded by qd, is sufficient to choose the speculative threshold such that
MM
Qd 2A
A
M
Using this result, we can formally control the worst-case deviation between speculative and full trajectories by setting an appropriate Theorem (.22 serves as principled mechanism tune c, ensuring that trajectory deviation through speculative steps does not exceed quality budget. The proof of the Theorem 4.2 can found Appendix A.2
EXPERIMENTS
Below we provide details of our experimental setup and results. We begin with dataset descriptions.
Text-to-image generation: We GenEval dataset Ghosh et al. (2023) consisting 553 prompts targeting compositional reasoning, with categories evaluating object co- occurrence, spatial positioning, color binding, and object count.

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BAGEL
FLUX
Method
Overall
CLIPIQA
Speedup t
Overall f
CLIPIQA f
Speedup
Full Model
50 steps
0.78
0.84
1.00x
0.65
0.83
1.0x
25 steps
0.77
0.82
2.0x
0.64
0.80
2.00
10 steps
0.73
0.75
5.0x
0.57
0.59
5.00x
steps
0.57
0.40
10.0x
0.44
0.43
10.0x
Static Baselines
InstaFlow PerFlow TeaCache
0,33
0.70
50.0x
0.58
0.79
5.0x
0.75
0.80
1.8x
0.64
0.81
1.84x
Ours: Speculative Decoding
FlowCast-50
0.78
0.83
2.5x
0.65
0.83
2.4x
FlowCast-25
0.77
0.82
4.1x
0.64
0.80
4.2x
FlowCast-IDe 10
0.73
0.74
7.8x
0.57
0.60
7.6x
FlowCast-5
0.57
0.38
13.0x
0.43
0,45
12.8x
Table 1: Comparison of BAGEL and FLUX Models on Image Generation. We report Overall GenEval, CLIPIQA image quality scores, and Speedup relative to the 50-step full generation.
Image editing: We adopt the GEdit benchmark Liu et al. (2025b), which provides 606 real-world editing instructions spanning object manipulation, color modification, etc.
Multi-turn editing: We further construct auxiliary dataset based EditBench, where GPT-4. |Achiam et al. (2023) generates three incremental edits followed by three reverse edits restoring the original image. Reconstruction fidelity is quantified via PSNR between the final output and the original.
Video generation: We evaluate the VBench dataset Huang et al. (022)), sampling 80 prompts by uniformly drawing 5 from each the 16 evaluation dimensions, ensuring balanced coverage across motion, persistence, camera control, and scene composition.
Baselines: We compare approach against the following baselines:
Full Generation. This the standard sampling procedure, where the model executes the entire denoising trajectory without any acceleration. It provides the upper bound in terms of fidelity and serves as the reference point for all accelerated methods.
TeaCache. Liu et al. (225a) It speeds up generation by caching intermediate representations and reusing them across timesteps, guided by timestep embeddings. By eliminating computations, achieves faster inference, the cost of fidelity degradation due approximation in reused states. MostaFlow[Lin et al. (2023) A wow-matching-biee based sampler trained for extremely fast generation (down to single step) Stable-Diffuse mable-Difffusion--1.. model. While efficient, sacrifices fidelity com- pared to full sampling. We evaluate InstaFlow on the released caabee-ifffusionvy 1.5 weights.
PeRFlow (Piecewise Rectified Flow). PeRFlow Yan et al. (2024) straightens the diffusion trajec- tory via piecewise-linen rectification over segmented timesteps, enabling few-step generation with strong quality-efficiency tradeoffs on Stable-Diffusiin-- model. We evaluate PerFlow on the re- leased Stable-Diffusion-xy checkpoints.
FlowCast (Ours). Our approach accelerates inference by allowing a lightweight draft process to propose multiple future steps, which are then verified in parallel by the full model. We denote this as FlowCast-n, where n represents the number of speculative steps.
Our method is complementary to existing acceleration techniques such PeRFlow and TeaCache, further reducing redundant steps even in frameworks already optimized for efficiency. The corre- sponding results reported in Tables and
For all baselines, we adopt the official hyperparameters provided in their codebases. In Table ! TeaCache is applied released, and in Figure 2 we further evaluate it across different timesteps

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Semantic Consistency
Perceptual Quality (G,PO)
Overall (0
70
Score (BAGZL)
Stape
Staps
Mtrps
Stept
Score (PLUM)
Score (50 (Stpp-1-22
Steps
Speedup
Full Model
TeaCache
FlowCrst
Figure 2: GEdit Scores across three models: BAGEL, FLUX, and Step-Edit, Each subfigure reports semantic consistency (G (G_SC), perceptual quality (G_PQ), and overall score (G_O) versus speedup.
Vbench
BRISQUE
Aul Model
TesCache
Cact
Figure 3: Results on Hunyuan Video model where we report the Vbench score and specifically for quality we use the BRISQUE metrics that assess the quality of individual frames.
emphasize its plug-and-play flexibility. For the choice of in FlowCast, from Theorem 4.2 we have found that value of E [0.01, 0.02] for image generation (all models), E [0.07, 0.08 for image editing (all models), e [0.001, 0.002] for video generation were giving high speedups with similar performance as the full model. An ablation study over across multiple steps can be found in the Appendix
Models. To demonstrate the versatility of our approach, evaluate across multiple state-of-ther the-trt art models: for image generation, BAGEL Deng et al. 2025), Flux- KonteKo Labs et al. (2025), and PeRFlow Yan al. (2024); for image editing, BAGEL, Konteett, and Step-IX-Edi Liu et al. 2025b): and for video generation, Hunyuan Video Kong et al. (0024) are used.
5.1
RESULTS
Image Generation: Table I reports results on the GenEval benchmark with BAGEL and FLUX, and Table 4 shows additional evaluation on the optimized PerFlow model. GenEval scores cap- ture semantic dimensions (detailed in Tables 2 while fidelity assessed via CLIPIQA. Static
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BAGEL
50 stops
steps
Our method
Prompt photo a iran below an arplane.
FLUX
50 stepe
25
mathod staps
Prompt: A photo of call phona left of tannia racket.
Parflo
10 stops
stops
Our method 5
Prampt: photo of zebra below brocooll.
Figure 4: Speculative step reduction maintains image quality across models, whereas direct step reduction leads to noticeable degradation.
reduction methods (e.g., fixed step truncation) cause significant quality loss, whereas our adaptive approach achieves the lowest quality drop and preserves semantic alignment.
Image Editing: Figure presents GEdit results with BAGEL, FLUX, and Step-IX-Edit, reporting G.SC (semantic consistency), G PQ (perceptual quality), and G O (overall editability). Static step reduction degrades both editability and quality, while our adaptive strategy consistently matches full-step generation. Multi-iteration results are shown in Figure and discussed in Appendix A.3
Video Generation: In Figure 3 we extend our analysis to video generation using the Hunyuan- Video model. Performance is reported with the vBench metric, which evaluates temporal coher- ence, background stability, flicker artifacts, etc. We also report the frame-wise quality score using the BRISQUE metric. Static reduction methods introduce visible temporal inconsistencies and mo- tion artifacts, whereas FlowCast scheme maintains high-quality, temporally stable videos.
Qualitative analysis: We qualitatively compare static and adaptive step reduction across tasks. In image generation (Figure(4), static reduction often introduces semantic inconsistencies and artifacts, whereas our adaptive method skips only redundant steps, producing outputs closely aligned with full-step generations. For editing (Figures 5 [. static reduction yields incomplete or unintended edits and compounds residual noise, while our method preserves semantic accuracy and visual fi- delity-event even in multi-turn settings. In video generation (Figure (.). static reduction causes temporal ruptures and frame drops, whereas FaowCast maintains smoothness and fidelity.
Summary: Across diverse tasks masks-image generation, editing, multi-iteration editing, and video ieteraation-xistttii acceleration techniques consistently suffer from loss of fidelity and alignment, highlighting their limited generality. In contrast, FlowCast emerges as universal adaptive frame- work for accelerating flow-based models that adapts to the generation complexity (see Appendix A.5) Unlike heuristic truncation, which causes performance drops, FlowCast leverages model- informed verification to skip only redundant updates. This ensures that outputs remain indistin- guishable from full-step generation while achieving substantial speedups. These results establish FlowCast as the preferred solution for efficient, reliable, and broadly transferable step reduction across modalities.

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HELLS KITCHEN
EIS HEAVEN
BAGEL
HEAVEN
HEAVEN
stops
steps
Image
Change
FLUX
SC sleps
sleps
Our maliad- stapp
Reference Imagge
Prorpt Replecs the paintrrash the fouugrounnd persoria harrd with babry ginall.
Ship Ski--1 11--51
30 steps
steps
mellodd
baby lerge
Figure 5: Speculative step reduction maintains edit fidelity and consistency better than direct step reduction.
CONCLUSION
We introduced an adaptive inference strategy that accelerates generation by eliminating truly re- dundant computation. Our approach extends speculative decoding with zero-cost drafts obtained directly from prior velocity predictions, which are then verified in parallel through lightweight mean-squas equan-s error criterion. Beyond its simplicity, we establish theoretical guarantees on the devi- ation from the original trajectory, ensuring both reliability and rigor. Extensive experiments across diverse models and tasks demonstrate that our method achieves consistent speedups without sacrific- ing performance, highlighting its potential The main limitation of FlowCast its dependence on parallel draft evaluations, which require ade- quate compute; cutting drafts reduces overhead but also reduces speedup.
general framework for faster and reliable inference.
7 ETHICS AND REPRODUCIBILITY STATEMENT
We have read and adhere to the Code of Ethics as detailed out for this conference. To the best of our knowledge, this is our original work and all related work has been appropriately cited. All authors have contributed towards this work and are responsible for it's content. There no implicit use of Large Language Models (LLMs), except for the cases where these models are already part of our technical method, and in cases where the use has already been disclosed.
work. We publicly available code, models, and datasets. Additionally, we intend to provide the source code using which the results of this work be reproduced. The results from related works are taken from their official manuscripts and/or official source repositories (as separately specified for each such work).
We describe detailed algorithm to reproduce
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A APPENDIX
A.1 PROOF OF LEMMA
Proof. Define the error at step lo + 1 as
Ex+1 == x((x+1) - Ik+1.
Using the Fuler update,
rk-1 = Ię + h weed (rks tx); where ueed is either vo or the speculative velocity e when accepted.
By Taylor expansion of stt) around 4s. there exists some vir E (/k, (x+1) such that
h² x" (v))
x(tx+1) x(tx) h (tx) + 2
Therefore,
h?2 x²"(v) - (xx + heweed (Ix
|E
x(th) + ttt( +
2
ht2
Eg + h (1lvv((((taa (x) -
used (Ik, 1x)) + 2 xmb))
+ h?
E + h (1g)) tx) vo t4)l + h 10 tx) uused (Ikitz) tx)
22N. N.
By the Lipschitz continuity of og in x. [1 tx) - uo (xx, tx)ll S M| - =xll = M Exll.
Also, by assumption, at steps where the speculative velocity is accepted, | (x) (( [+)l S Ve.
Accounting for the fraction p of such steps, [uo tx) - v uxed ( (x), S SPVE.
Combining the above,
h² | S |xll + EM] hpve+
122 -N= -1 (1+) hpve+ N.
2
2
Let
= h-hhp + h²
N.
2
The recursion becomes
| |l| S (1+ (1+hM)|EE A.
Unrolling this recursion with initial error Eo = 0:
k-1 (1 + hM)'
(1 + hM)² -1 (1 1+MM)* A
E <
(1 + hM) -1 hM
(1 + hM)* -1
(NN + 2pvE).
2M
For hM < is well-known that
(1 + hM)* s nM Msx
Hence,
Me -1 (A.N + 2pVc).
|| <
2M
Since t S 1, for the entire integration interval,
M
E <
(h. + 2pVC).
2M
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A.2
PROOF OF THEOREM 4.2
From Lemma 4.
we know that after full generation (i.e., at b 1), the total error is bounded by
M
|
S
(hN + 2pve).
2M
This upper bound can be decomposed into two contributions:
MM
eM -
KN
M DVE
2M
discretization
speculative deviation
The first term corresponds to the inherent discretization error the full generation process, whereas the second term quantifies the additional deviation induced by speculative generation. To ensure that the speculative deviation does not exceed the admissible tolerance Qa. suffices to require
e
PVE S qa.
M
Rearranging, we obtain the sufficient condition
M
Qd
=
2A
M
Hence, the deviation introduced by speculative generation remains within the prescribed tolerance qd. completing the proof.
A.3 MULTI-ITERATION EDITING
Figure 7 evaluates the robustness of multi-step editing, where a sequence of edits is applied and subsequently reverted to reconstruct the original image. To quantify reconstruction fidelity, report I.PISS, SSIM, and PSNR between the restored and ground-truth inputs.
The motivation for this setup is that diffusion-based editing models typically inject small amount of noise into the image during each edit. While this noise often imperceptible for single edit, accumulate significantly when multiple sequential edits performed. In such scenarios, naive acceleration strategies- as uniformly reducing the number of steps-tend to amplify the compounded noise, degrading reconstruction quality.
This phenomenon is illustrated in Figure [I Although the full 50-step process introduces some noise, it still preserves most of the structural and semantic consistency of the original input. By contrast, a direct reduction to 25 steps produces noticeably different outputs, with the discrepancies compounding as more edits are applied.
Our method avoids this pitfall. By selectively eliminating only truly redundant steps, it achieves substantial reduction inference time while maintaining reconstruction quality comparable to the full model. This suggests that our approach does not compound emror across sequential edits, making it more reliable and stable solution for practical editing workflows.
A.4 ABLATION
In Figure we report the trade-off between speedup and reconstruction quality on the multi-turn edit dataset. The curves demonstrate that increasing the value of results in higher computational speedup but at the expense of degraded perceptual quality, as measured by PSNR. Distinct colors in the figure denote different values, thereby disentangling the contribution of this parameter to the observed trade-offs. This representation enables systematic selection of hyperparameters: for given target speedup, one identify the step size and that jointly yield the most favorable balance between efficiency and fidelity. For instance, as shown in Figure achieving a 3x speedup can be optimally realized with 50 sampling steps and 0.07, which preserves quality while significantly reducing inference cost. Thus, the figure provides empirical evidence for principled hyperparameter selection in speculative decoding under efficiency constraints.
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A.5 ADAPTIVENESS OF FLOWCAST
In Figure TO we highlight the adaptive nature of FlowCast. When the input prompt corresponds to relatively simple generation, the method accepts larger proportion of draft proposals, leading to a substantial speedup. In contrast, for more complex prompts, fewer drafts are accepted, resulting in reduced speedup but higher reefeity-rfllecinngg the need for finer refinements in challenging cases. This trend is consistently observed across all tasks considered in our study, including image genera- tion, image editing, and video generation. Such behavior demonstrates that FlowCast automatically tailors the trade-off between efficiency and quality to the complexity of the input, making it flexible and broadly applicable framework for diverse generative scenarios.
A.6 COMPARISON OF BASELINE METHODS
Direct Reduction. A naive alternative is simply truncate the diffusion process by reducing the number of steps. However, this static reduction assumes that the velocity at removed step can be approximated by reusing the previous update. Such oversimplification discards valuable inter- mediate information and leads to noticeable quality degradation. In contrast, FlowCast does not discard steps blindly. It adaptively identifies which steps are genuinely redundant by leveraging model outputs, ensuring faster inference without sacrificing quality.
TeaCache accelerates sampling by fitting a polynomial over timestep-mose moduulaed noisy inputs to decide which steps cache. While simple, this approach suffers from two key drawbacks: 1) It requires model- and task-specific calibration for the polynomial fit. 2) not truly adaptive once a target speedup is fixed, TeaCache repeatedly applies the same caching pattern (e.g., alter- nating steps for 2x speedup), independent of prompt complexity. In our experiments (50 videos and 100 diverse image generations), consistently observed this repetitive caching behavior, showing its limited ability to handle prompt-specific difficulty.
TeaCache.
Ad Advantages of FlowCast. Unlike static approaches, FlowCast is genuinely adaptive. It learns the-fly during inference, tailoring its speedup to the complexity of generation. For simpler prompts, it aggressively reduces model calls to maximize efficiency; for harder prompts, it retains more steps to safeguard fidelity. This dynamic adjustment enables FlowCast to consistently achieve a superior efficieccyquuliiyy trade-off compared to both direct reduction and TeaCache.
A.7 BOUND TIGHTNESS
we compare the theoretical error bound derived in 4.1 with the empirically observed error. For very small values of e, the theoretical bound closely matches the empirical error, indicating that the bound is tight in the low-tolerance regime. As increases, we begin to observe slight increase in the gap between the bound and the empirical value. This behavior is expected: larger allows the algorithm to accept increasingly aggressive skips, which carry higher risk of error accumulation.
In Figure 6
In other words, the bound "plays safe" by overestimating potential emror when the acceptance thresh- old is large, ensuring that the guarantee remains valid even under worst-case deviations. Thus, the slight looseness at higher c is reflection of the bound's protective nature under more error-prone conditions.
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Empll cal [X "X"
Bound
150
125
102
0.02
0.04
3. 05
0.08
3.10
240
Figure 6: Comparison of the errors made by FlowCast vs the true bound value.
LPIPS
PSAK
SSIN
25 Shece
Spendup (1)
Speedup (X
Speedup [4]
Fill Model
TesCarh
FastCatt
Figure 7: Results on the BAGEL model for multi-iteration editing, where the original image and the final image compared, and LPIPS, PSNR and SSIM metrics are compared.
steps
Epsilon
24
(=0.03
c=0.07
Full steps
g=0.05
c=0.09
100.05
1-0.10
Full
Steps
Full 5 steps
25
12
Speedup
Figure 8: Speedup vs Quality curve for the multi-turn edit dataset on the BAGEL model.
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Piomptt & dog in digging with slick and sudierity slar ruaninng.
Figure 9: Speculative step reduction ensures temporal coherence and quality, outperforming direct step reduction.
Complexity
Speedup
Complexity
Speedup
Prompt: photo
Prompt: A photo and tiger.
2.9x speedup
2. 1x speedup
Original Image
Edit Prompt: Add sunglasses.
Edit Prompt: place the man on a chair and zoom out.
3.25x speedup
2.78x speedup
Figure 10: Examples over editing and generation tasks showing adaptiveness of our method where complex generations have lower speedups and vice-versa.
50 steps
25 elepp
alecpe Sprec
Original Image
mag
Remove mug
Figure 11: An instance of the multi-turn editing task where the performance of speculative genera- tion better than normal generation with reduced steps.
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50
Mathod
Prompt: photo of yellow car and orange toothbrush.
sleps
sleps
Our Method stops
Prompt: photo of a blue cow and black keyboard.
STOP
STOP
STOP
STOP
STOP
50 steps
stops
Our Method = 16 steps
Prompt:: photo of a fork and stop sign.
steps
Method
steps
Prompt: photo a tennis racket and bicycle.
Figure 12: Multiple Generation instances using prompts.
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Method
SO
TO
CO
CL
CA
PO
Overall 1
CIJPIQA 1
Spd.
Full Model
BAGEL-50
0.99
0.90
0.81
0.85
0.59
0.54
0.78
0.84
1.0x
BAGEL 25
0.99
0.90
0.78
0.84
0.62
0.51
0.77
0.82
2,0x
BAGEL-110
0.99
0.88
0.68
0.84
0.56
0.48
0.73
0.71
5.0x
BAGEL-5
0.93
0.62
0.55
0.72
0.25
0.36
0.57
0.40
10x
Static Baselines
InstaFlow
0.85
0.19
0.20
0.65
0.04
0.02
0.33
0.70
50x
PerFlow
0.99
0.79
0.43
0.85
0.25
0.15
0.58
0.79
5.0x
TeaCache
0.99
0.89
0.78
0.83
0.58
0.52
0.76
0.80
1.8x
Speculative Decoding (Ours)
Spec-50
0.99
0.90
0.81
0.85
0.61
0.54
0.78
0.83
2.5x
Spec-25
0.99
0.91
0.77
0.84
0.62
0.51
0.77
0.82
4.1
Spec-10
0.99
0.89
0.67
0.82
0.55
0.48
0.73
0.70
7.8x
Spec-5
0.93
0.61
0.55
0.73
0.25
0.36
0.57
0.38
13.0x
Table 2: Results the BAGEL model for image generation. We report GenEval scores (SO: Single Object, TO: Two Objects, CO: Counting, CL: Color, CA: Category, PO: Position), overall score, CLIPIQA, and Speedup (Spd.). Speculative decoding achieves strong speedup while retaining quality close to the full model, outperforming static basclines.
Method
SO
TO
CO
CL
CA
PO
Overall 1
CLIPIQA f Spd. f
Full Model
FLUX-50
0.97
0.79
0.73
0.78
0.44
0.20
0.65
0.83
1.0x
FLUX-25
0.97
0.78
0.71
0.75
0.43
0.18
0.64
0.80
2.0x
FLUX-10
0.97
0.65
0.58
0.67
0.41
0.15
0.57
0.59
5.0x
FLUX-5
0.88
0.43
0.47
0.54
0.23
0.08
0.44
0.43
10x
TeaCache
0.97
0.78
0.70
0.75
0.43
0.20
0.64
0.81
1.9x
Speculative Decoding (Ours)
Spec-50
0.97
0.78
0.72
0.77
0.45
0.20
0.65
0.83
2,4x
Spec-25
0.97
0.78
0.70
0.76
0.42
0.18
0.64
0.80
4.2x
Spec-10
0.96
0.66
0.58
0.67
0.40
0.15
0.57
0.60
7.6x
Spec-5
0.87
0.42
0.46
0.53
0.24
0.08
0.43
0.47
12.8x
Table 3: Results on the FLUX model for image generation. We report GenEval scores (SO: Single Object, TO: Two Objects, CO: Counting, CL: Color, CA: Category, PO: Position), overall score, CLIPIQA, and Speedup (Spd.). Speculative decoding achieves substantial speedups while preserving performance close the full model, surpassing static baselines.
Method
So
TO
CO
CL.
CA
PO
Overall
CLIPIQA + Spd. 1
Full Model
PerFlow-10
0.99
0.79
0.37
0.88
0.21
0.15
0.57
0.86
1.0x
PerFlow-6
0.99
0.78
0.36
0.86
0.20
0.15
0.55
0.83
2.0x
PerFlow-3
0.98
0.75
0.29
0.85
0.18
0.12
0.52
0.78
3.3x
Speculative Decoding (Ours)
Spec-10 10
1.00
0.78
0.37
0.88
0.22
0.15
0.57
0.85
1.9x
Spec-6
0.99
0.77
0.37
0.87
0.21
0.15
0.55
0.82
2.7x
Spec-3
0.98
0.74
0.28
0.85
0.18
0.11
0.52
0.77
3.5x
Table 4: Results the PerFlow model for image generation. We report GenEval scores (SO: Single Object, TO: Two Objects, CO: Counting, CL.: Color, CA: Category, PO: Position), overall score, CLIPIQA, and Speedup (Spd.). Speculative decoding achieves strong speedups while retain- ing quality close to the full model.
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Method
G G.PQ G.O Spd. 1
Full Model
Step-Edit-30
7.54
7.43
6.94
1.0x
Step-Edit-15
7.55
7.31
6.89
2,0x
Step-Edit-10
7.43
7.11
6.54
3.0x
TeaCache
TeaCache-30
7.51
7,41
6.91
1.8x
TeaCache-15
7.50
7.25
6.81
2.6x
TeaCache-10
7.39
7.06
6.44
3,4x
+ FlowCast (Ours)
FlowCast-30
7.53
7.49
6.95
2.3
FlowCast-15 15
7.53
7.32
6.88
3.0x
FlowCast-10
7.42
7.09
3.6x
+ TeaCache + FlowCast
TeaCache+FlowCaat
7.52
7.41
6.90
2.4x
TeaCache+FlowCas-Il
7,48
7.28
6,85
3,0x
TeaCache++FowCaast-II 7.35
6.98
6.35
3.7x
Table 5: Quantitative comparison of acceleration strategies. We report semantic consistency (G_SC), perceptual quality (G_PQ), overall score (G_O), and speedup (Spd.). FlowCast consistently accelerates inference while maintaining quality and remains complementary to TeaCache.
Method
Latency (s)
Memory (GB)
Speedup
Full 50
33.8
48
1.00
Speculative 50
15.5
2.40x
TeaCache
18.3
48
1.84x
Full 25
17.5
48
2.00x
Speculative 25
10.2
4.20x
Full 10
7.1
48
5,00x
Speculative 10
5.38
58
7.60x
Full
3.9
10.00
Speculative 5
3.1
52
12.80
Table Memory profiling for FLUX model.
Method
Latency (s)
Memory (GB)
Speedup
Full
36.8
1x
Speculative 50
16.1
65
2.5x
TeaCache
18.3
1.8x
Full 25
19.5
29
2x
Speculative 25
10.5
4.1x
Full
7.4
5x
Speculative 10
5.59
46
7.8x
Full 5
3.8
29
10x
Speculative
3.2
36
13,0x
Table Memory profiling for BAGEL model.
21


Reviews:
[{"review_id": "cdY1sRqSPf", "reviewer": "Reviewer_1Hsg", "summary": "This manuscript proposes a FlowCast method, which is a training-free speculative generation framework accelerating inference. The proposed method is a plug-and-play framework and can be integrated into different FM models. Experimental results on different tasks demonstrate the efficiency of the proposed method.", "questions": "-\tIs the SD method limited to the FM models? Can it be applied to other image/video generating methods such as diffusion models?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "-\tThe proposed method is training-free and model-agnostic.\n-\tThe key insight is good. The method pinpoints and exploits a core property of FM—local smoothness and near-constant velocities—turning the model’s own current velocity into a “zero-cost” draft and verifying in velocity space. This aligns perfectly with FM’s dynamics, enabling aggressive step skipping in stable regions while preserving fidelity, and it explains both the simplicity and effectiveness of the approach.\n-\tSubstantial inference acceleration.", "weaknesses": "-\tIn Table 1, provide profiling of memory footprint and latency of speculative vs. existing methods.\n-\tDiscuss the failure case or limitation of the proposed method.\n-\tThe manuscript organization can be improved. Such as the Eq.5 in L244.\n-\tA more comprehensive comparison of the proposed method in term of computation complexity, memory usage and so are expected.", "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": "iIKxRANQ6B", "reviewer": "Reviewer_jE6u", "summary": "This paper introduces FlowCast, a training-free speculative generation framework designed to accelerate the notoriously slow inference process in Flow Matching (FM) models. The core idea capitalizes on the observation that FM trajectories, particularly in well-trained models, are locally smooth and can be accurately modeled as having near-constant velocity. FlowCast creates \"zero-cost drafts\" by simply extrapolating future steps using the model's most recent velocity prediction, thus eliminating the overhead of auxiliary drafting networks or time-consuming retraining. These predicted steps are then verified in parallel using a swift mean-squared error check on the velocity field. This adaptive, dynamic mechanism enables the model to aggressively skip redundant computations in stable regions while ensuring full fidelity in complex, rapidly changing areas of the generation trajectory.\n\nFlowCast is a plug-and-play solution applicable to any FM model and task, demonstrating broad versatility. A key contribution is the theoretical derivation of an error bound that formally controls the maximum deviation between the speculative trajectory and the precise full-step integration. Extensive empirical evaluations across image generation, complex image editing, and video generation confirm its effectiveness. The results show that FlowCast consistently delivers substantial speedups, typically over $2.5\\times$, while successfully preserving output quality that is indistinguishable from the original, unaccelerated generation, positioning it as a preferred method for real-time and interactive generative applications.", "questions": "- The theoretical bound in Theorem $4.2$ requires the Lipschitz constant $M$ and the probability of acceptance $p$, both of which are challenging to determine analytically for a new FM model. Can the authors discuss approaches to estimate $M$ and $N$ for a given trained FM model without resorting to extensive validation steps, or how the empirically derived $\\epsilon$ relates back to the theoretical bounds? \n\n- Figure 6 and Table 5 show that FlowCast is complementary to TeaCache. Can the authors provide a more detailed analysis, possibly through an ablation, of the combined method (TeaCache $+$ FlowCast) to explain why the gains are not simply additive, and what type of redundancy each method primarily targets within the diffusion trajectory? \n\n- Given that FlowCast relies on the ODE being locally smooth (constant velocity assumption), how does the performance scale when applied to rectified flow variants (like the one used in the PeRFlow experiment), where the velocity field is explicitly straightened to be nearly constant over the entire path?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "+ FlowCast's core mechanism uses the model's own previous velocity prediction as a draft, incurring zero additional training or computational overhead for the drafting phase.\n\n+ The framework integrates seamlessly with any existing FM model and is successfully demonstrated across image generation, image editing, multi-turn editing, and video generation tasks, showcasing its broad applicability.\n\n+ FlowCast dynamically adapts the number of steps skipped based on the local complexity of the generation trajectory (Figure 9), ensuring maximal speedup while preserving fidelity—a clear advantage over static reduction methods.\n\n+ The framework is supported by a theoretical error bound (Theorem $4.2$) which formalizes the maximum allowable deviation based on the threshold $\\epsilon$, ensuring reliability and rigor.", "weaknesses": "The main practical limitation acknowledged in the conclusion is FlowCast's dependence on adequate compute for parallel verification of drafts, where cutting drafts reduces overhead but also limits speedup. While the verification step is performed in a single forward pass for parallel drafts, the overall efficiency gain is bounded by the available parallelism. While the paper provides empirical guidance for setting the threshold $\\epsilon$ for different tasks (e.g., $\\epsilon \\in [0.01, 0.02]$ for image generation), a generalized, non-empirical approach or model-dependent estimation of the Lipschitz constant $M$ and the second derivative bound $N$ in Lemma $4.1$ would enhance the theoretical contribution's utility for arbitrary FM models. The comparison against TeaCache, although demonstrating FlowCast's superiority, focuses on TeaCache's non-adaptive nature, but a more direct quantitative comparison of the maximum quality achieved by both methods at a fixed, competitive speedup could further clarify the trade-off space.", "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": "vYkZUokJbs", "reviewer": "Reviewer_wbif", "summary": "This paper proposes FlowCast, a training-free speculative generation framework that accelerates inference by exploiting the fact that FM models are trained to preserve constant velocity. \n\nFlowCast speculates future velocity by extrapolating current velocity without incurring additional cost, FlowCast is a plug-and-play framework that integrates seamlessly with any FM model and requires no auxiliary networks.", "questions": "- How do different designs of verifier affect the performance?\n- Please compare the performance with training-based methodsl.", "limitations": "", "rating": 2, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- the paper is well-written and easy to follow\n- the method is training-free and plug-and-play", "weaknesses": "- the motivation to predict future velocity based on known ones is not fully justified. In few-step sampling the velocity actually varies a lot in adjacent steps. \n- experiments on text-to-image generation is not very comprehensive, only some cherry-picked simple cases is shown\n- the performance drop (e.g., from 0.78 to 0.57) is unacceptable. In image generation, we would prefer high-quality instead of fast-speed.", "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": "l7T4W05ZwQ", "reviewer": "Reviewer_2jF7", "summary": "The paper proposes FlowCast, a training-free speculative inference scheme for Flow Matching (FM) models. Observing that FM trajectories exhibit locally constant/slowly varying velocity, FlowCast linearly extrapolates future states using the current velocity (“zero-cost drafts”) and verifies them via a lightweight MSE check on predicted velocities. Accepted drafts allow aggressive step skipping in stable regions; rejections trigger fallback to standard updates. The authors provide a deviation bound relating the acceptance threshold to worst-case trajectory error. Experiments across image generation, image editing (incl. multi-turn), and video generation report >2.5× speedups with negligible quality loss versus full 50-step sampling, and improvements over TeaCache and other accelerators.", "questions": "1.How does FlowCast behave under deliberately adversarial schedules with rapid velocity changes ? Any failure analyses?\n\n2. What are end-to-end wall-clock gains on a single-GPU, limited-memory setting (no tensor-parallel drafts)? Please report FLOPs/latency breakdown for verification vs. baseline.\n\n3. Can the acceptance threshold ϵ be adapted online (e.g., via Lipschitz/variance estimates) to trade speed for quality automatically? Any guidance for setting ϵ per model/task?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.Training-free & plug-and-play: No distillation or auxiliary networks; applicable to diverse FM backbones and tasks.\n\n2.Clear principle & theory: A simple constant-velocity extrapolation paired with an MSE-based verifier, plus a worst-case deviation bound.\n\n3.Broad empirical coverage: Image, editing, multi-turn editing, and video; consistent speedups with small fidelity drops and fair comparisons to truncation/TeaCache.", "weaknesses": "1.Assumption sensitivity: Reliance on locally constant/slowly varying velocity may fail for sharp dynamics (e.g., complex edits, rapid motion), but stress tests are limited.\n\n2.Parallel verification cost: Speedups hinge on verifying many drafts in parallel; wall-clock gains on constrained hardware are unclear.\n\n3.Metric breadth: Quality is mostly CLIPIQA/GenEval/vBench/BRISQUE; limited human or task-specific fidelity/temporal metrics to substantiate “no quality loss.”", "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)