Title: Align Your Flow: Scaling Continuous-Time Flow Map Distillation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Generative models

diffusion models

consistency models

flow models
Abstract: Diffusion- and flow-based models have emerged as state-of-the-art generative modeling approaches, but they require many sampling steps. 
Consistency models can distill these models into efficient one-step generators; however, unlike flow- and diffusion-based methods, their performance inevitably degrades when increasing the number of steps, which we show both analytically and empirically.
Flow maps generalize these approaches by connecting any two noise levels in a single step and remain effective across all step counts. 
In this paper, we introduce two new continuous-time objectives for training flow maps, along with additional novel training techniques, generalizing existing consistency and flow matching objectives. We further demonstrate that autoguidance can improve performance, using a low-quality model for guidance during distillation, and an additional boost can be achieved by adversarial finetuning, with minimal loss in sample diversity.
We extensively validate our flow map models, called *Align Your Flow*, on challenging image generation benchmarks and achieve state-of-the-art few-step generation performance on both ImageNet 64x64 and 512x512, using small and efficient neural networks. 
Finally, we show text-to-image flow map models that outperform all existing non-adversarially trained few-step samplers in text-conditioned synthesis.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nFlow Matching and Diffusion are considered the current state-of-the-art (SOTA) method for media generation. These models learn a flow process from Gaussian noise to the data distribution by regressing the velocity field of the flow map which result in an iterative generative process. Inferencing the flow/diffusion models is compute expensive and generally require from 50 to 1000 function evaluations (NFE)  since it requires solving an ordinary differential equation. This paper suggest a new distillation method that generalize Consistency Models [1], and allows sampling with 2-4 NFE without hurting image quality. A critical contribution of the paper is that the Align Your Flow (AYF) method achieve this performance without adversarial loss. Given a trained velocity $v_t(x)$ of a flow/diffusion model,  the velocity $v_t(x)$ defines the trajectories $x_t$ (the flow process) such that $x_0\\sim p_{\\text{noise}}$ and $x_1\\sim p_{\\text{data}}$.  AYF learns the flow map between every $t<s\\in [0,1]$, $f_{\\theta}$(x,t,s) i.e., $f_{\\theta}$(x_t,t,s) = x_s$. The paper suggest two training objective to learn the flow map. A crucial point is that these losses do not require to simulate the flow during training which make them tractable. Additionally, the paper provide a few \"training tricks\" that stabilize optimization and improve performance. Finally, the paper validates the proposed method on a number of experiments, including distillation of class-conditioned image generation models and a text-to-image model.\n\n[1] Song, Yang, et al. \"Consistency models.\" (2023).\n\nSTRENGTHS AND WEAKNESSES:\n> Strengths:\n1. The paper tackles the important challenge of fast sampling for flow/diffusion models.\n2. AYF is able to distill flow/diffusion models without adversarial loss and achieve SOTA results for first time.\n3. The contribution of the paper is novel and potentially have a large impact.\n4. The paper provide an extensive comparison of the AYF method to baselines, specially on the class-conditioned image generation.\n5. The paper is well written and provide proofs in the appendix for all of its claims.\n\n> Weaknesses:\n1. The paper is not entirely self contained. For example, in line 221 it seems the authors assume the time dependency of $F_{\\theta}(x,t,s)$ is inserted through a function $c_{noise}$ without mentioning it explicitly. Took me a while to understand it is assumed the reader is familiarise with the fine details of EDM [2].\n2. Two training objectives are presented in the paper AYF-EMD, AYF-LMD in theorems 3.2, 3.3, however right after Theorem 3.3 the authors state that the AYF-LMD objective perform poorly on the image generation and subsequently all experiments on images in the main paper are done with the AYF-EMD objective. The AYF-LMD objective is not considered a baseline and does not show any advantage in the paper. Hence, it is my opinion that the AYF-LMD is not so interesting to the reader and adds an overhead for understanding the paper. I would recommend moving it to the appendix.\n3. While on the text-to-image task the reported user study shows a great advantage to AYF, the evaluation still seems lacking and not completely fair:\n  - The AYF on the FLUX [3] model is compared two baselines on the SDXL [4] and on FLUX model as well. It could certainly be that the user in general prefer images generated by the FLUX model.\n  - No comparison of AYF and a  distillation baseline that uses adversarial loss. To the best of my knowledge, these type of distillation method are still considered the SOTA. \n  - Most importantly, a comparison between the performance of a distilled AYF FLUX model and the non-distilled FLUX model is missing. Hence, the reader cannot know if to what degree of degradation the AYF distillation process causes (if any).\n\nFinally, concurrent to your work, [5] seems to achieve the same method with a loss similar to the AYF-EMD, where your $F_{\\theta}(x,t,s)$ is their \"average velocity\". They provide a different and interesting point of view on the AYF-EMD loss, and you can consider adding reference to their paper for completeness of discussion on this training objective. However, I want to stress that I acknowledge this is a concurrent work and do not count as a weaknesses or a strengths, I only provide here for the completeness of discussion.\n\n[2] Karras, Tero, et al. \"Elucidating the design space of diffusion-based generative models.\" Advances in neural information processing systems 35 (2022): 26565-26577. \n\n[3] Black Forest Labs. Flux. https://github.com/black-forest-labs/flux, 2023.\n\n[4] Podell, Dustin, et al. \"Sdxl: Improving latent diffusion models for high-resolution image synthesis.\" arXiv preprint arXiv:2307.01952 (2023).\n\n[5] Geng, Zhengyang, et al. \"Mean flows for one-step generative modeling.\" arXiv preprint arXiv:2505.13447 (2025).\n\nQUESTIONS:\nIf you can provide any of the comparisons I mentioned on weakness 3. specially the third one, i.e., comparison of AYF with the non-distilled FLUX model.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Align Your Flow (AYF), a continuous-time flow map distillation framework that addresses the limitations of consistency models in multi-step sampling. The authors theoretically prove that consistency models suffer from error accumulation, motivating the use of flow maps to generalize between noise levels. They propose two novel training objectives (AYF-EMD and AYF-LMD), integrate autoguidance for improved quality, and demonstrate adversarial finetuning with minimal diversity loss. AYF achieves state-of-the-art few-step generation on ImageNet 64×64/512×512 and outperforms non-adversarial text-to-image samplers when distilling FLUX.1.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n- AYF generalizes consistency and flow matching objectives, introducing continuous-time losses that enable effective multi-step generation. Autoguidance integration and adversarial finetuning without severe diversity loss are notable advancements.\n- AYF sets new benchmarks on ImageNet and text-to-image tasks, demonstrating superior FID metrics with fewer steps and smaller models.\n- The paper provides detailed training algorithms, ablation studies, and comparisons, enhancing reproducibility.\n\n### Weaknesses\n- The paper is not easy to follow.\n- The article does not provide experimental results on ImageNet 256, and the author does not explain the reason.\n- In the Text-to-Image task, LCM and TCD uses SDXL while AYF uses FLUX.1 [dev] as the base model, the comparison may be unfair.\n\nQUESTIONS:\nThanks for your work, I have the following questions:\n- How effective is the method proposed in the article on ImageNet 256? Is there any comparison with other methods?\n- For ImageNet 512×512, have AYF results been compared against recent works like DPM-Solver-3? Updated baselines would strengthen the significance claim.\n- In the Text-to-Image task, LCM and TCD uses SDXL while AYF uses FLUX.1 [dev] as the base model. Is this comparison fair? Since FLUX.1 [dev] performs better than SDXL, is AYF’s superior performance due to using a better baseline rather than the AYF method itself? How is the performance if the same baseline is used?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents \"Align Your Flow\" (AYF) for distilling diffusion or flow-based models into few-step samplers. This paper shows that standard consistency models (CMs) are \"flawed\" for multi-step generation. They introduce two continuous-time training objectives, termed Eulerian (AYF-EMD) and Lagrangian (AYF-LMD). To improve sample quality, the paper leverages distillation of an autoguided teacher model, incorporates several stability-enhancing training techniques, and employs a final adversarial finetuning step. The method is evaluated on ImageNet and text-to-image generation with state-of-the-art performance among non-adversarially trained few-step models.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n- Empirical results. FID scores on ImageNet 64x64 and 512x512 are highly competitive, particularly for the 2-4 steps. The method appears to achieve these results with smaller models and lower computational cost than strong baselines like sCD. This makes a compelling case for the practical utility of the proposed distillation pipeline.\n- The visualization in Figure 2 and the corresponding mathematical framing are clear and provide good intuition.\n- This paper discusses the flaws of Consistency Models in multi-step generation.\n\n## Weaknesses\n\n- The method's reliance on a collection of very specific, finely-tuned \"tricks\" (clamped warmup, specific time embeddings, etc.) may limit its generality and ease of application to new domains. \n- Some ablation studies for some of these crucial components are missing, e.g., what happens if r is not clamped, or normalization is not applied?\n- The paper introduces two objectives, AYF-EMD and AYF-LMD, but then casually dismisses the latter, stating it \"leads to overly smoothened samples\" on images. The paper offers zero analysis or intuition for this failure. Proposing two methods and admitting one doesn't work for the main application with a hand-wavy, one-sentence explanation is a significant scientific weakness. \n- Furthermore, the Regularized Tangent Warmup section reveals that the training is unstable if the warmup coefficient r reaches 1. The proposed \"fix\" of clamping r at 0.99 feels like an ad-hoc hack. This suggests that the proposed AYF-EMD objective may be inherently less stable than the sCM objective it builds upon, a critical issue that is recorded rather than investigated.\n- Line 353, \"a fact we prove analytically for the first time,\" seems not to be a sound claim. \n- It could be better to, instead of unifying every model under flow maps, which acts not informatively, introduce and discuss the formulation and training algorithms for each model definition.\n\n### Typos\n\n- \"objective objective\" in line 216.\n- ?? in line 737.\n\nQUESTIONS:\n- Why not apply the proposed methods to the largest model, like EDM-XXL? Are there any stability concerns?\n- AYF-LMD seems expensive in computation. Could you provide a cost comparison between AYF-EMD and AYF-LMD in terms of throughput or training FLOPs?\n- Why does training become unstable as the tangent warmup coefficient r approaches 1, a problem not reported for the original sCM \n- How does the lora rank change the model performance for the FLUX model?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}

comments: SUMMARY:
This paper proposes two new objectives for continuous-time Flow Map distillation, which use a consistency condition at either the beginning or the end of a denoising interval. The authors first formally prove that the generation quality of a learned Consistency Models (CMs) does not always improve with the number of sampling steps, given a simple data distribution $p_{data}=\mathcal{N}(0, c^2I)$. They then derive the two objectives, based on 1-step Euler solver and 1-step Lagrangian solver. They further introduce a bunch of training techniques including autoguidance, stabilizing time embedding with modified time parametrization, and regularized tangent warmup from sCM paper, to improve the training stability and final performance. In their experiments, the proposed method demonstrates better FID than sCD and sCT, two SOTA methods in the consistency distillation family. They further show that the proposed method enables better usage of wallclock time than these baselines. The proposed method also performs well in distilling text-to-image models such as FLUX.1.

STRENGTHS AND WEAKNESSES:
\+ This paper is well written and easy to follow. 

\+ The proof of the limitation of Consistency Models (CMs), though only on a very simple data distribution, is inspiring. 

\+ The derivation of the the two new objectives is formal. 

\+ The discussion of the use of existing techniques is helpful. 

\+ The performance is good. 

\- Directly modifying the time embedding module by supervised training a new embedder may introduce unbounded bias. 

\- Most techniques covered in Sec. 3.4 are not original.

QUESTIONS:
I wonder if the authors would like to provide a dependency analysis, theoretical or analytical, for the replacement of the embedder.

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-06