Title: Towards a Golden Classifier-Free Guidance Path via Foresight Fixed Point Iterations
Venue: NeurIPS 2025
Accepted: yes
Keywords: Classifier-free guidance

Diffusion models

Fixed point iteration
Abstract: Classifier-Free Guidance (CFG) is an essential component of text-to-image diffusion models, and understanding and advancing its operational mechanisms remains a central focus of research. Existing approaches stem from divergent theoretical interpretations, thereby limiting the design space and obscuring key design choices. To address this, we propose a unified perspective that reframes conditional guidance as fixed point iterations, seeking to identify a golden path where latents produce consistent outputs under both conditional and unconditional generation. We demonstrate that CFG and its variants constitute a special case of single-step short-interval iteration, which is theoretically proven to exhibit inefficiency. To this end, we introduce Foresight Guidance (FSG), which prioritizes solving longer-interval subproblems in early diffusion stages with increased iterations. Extensive experiments across diverse datasets and model architectures validate the superiority of FSG over state-of-the-art methods in both image quality and computational efficiency. Our work offers novel perspectives for conditional guidance and unlocks the potential of adaptive design.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes improve diffusion CFG sample quality by using fixed point iteration to align unconditional and conditional score outputs during generation. The authors identify four design spaces, consistency interval, fixed point operator, guidance strength, and number of iterations. In Section 3.2, the authors propose Foresight Guidance (FSG), which is a collection of choices for each design space. It is shown that FSG outperforms CFG, CFG++, Z-Sampling, and Resampling on a variety of benchmark tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths and weaknesses are denoted [S#] and [W#], respectively.\n\n**Quality**\n- [S1] Overall, the paper is well-written with clear notations, comprehensive baselines, and thorough ablations. I had no problem understanding the paper.\n- [W1] The paper lacks experiments on canonical tasks such as ImageNet generation. I believe results which demonstrate that FSG works on e.g., CIFAR10 or ImageNet class-conditional generation will make the paper much stronger.\n- [W2] The paper also lacks diversity metrics such as the Vendi score [1]. I believe the authors should compare quality-diversity trade-off compared to other CFG-based methods to verify whether FSG truly improves the pareto frontier.\n\n**Clarity**\n\nI found some parts of the paper to be written imprecisely, or stated without reference, proof, or analysis. See below.\n\n- [W1] Lines 96 - 98, \"when $f^c_{t \\rightarrow 0}(x_t)$ and $f^u_{t \\rightarrow 0}(x_t)$ differ significantly, the model tends to generate conditional-aligned structures at the expense of texture detail and aesthetic quality. Conversely, when they align, the model balances both conditional alignment and generation quality\". I believe supporting this claim with appropriate evidence is crucial for this paper, since this intuition serves as the basis of the proposed method.\n- [W2] Lines 99 - 100, \"we therefore aim to calibrate $x\\_t$ toward $\\hat{x}\\_t$ by solving $f^u_{t \\rightarrow 0}(x_t) = f^c_{t \\rightarrow 0}(x_t), x_t \\in \\mathcal{M}\\_t$ where $\\mathcal{M}\\_t$ denotes the data manifold at time $t$.\" To my understanding, this paper only solves $f^u\\_{t \\rightarrow 0}(x_t) = f^c_{t \\rightarrow 0}(x_t)$ without the constraint $x_t \\in \\mathcal{M}\\_t$. Indeed, as written at line 251, the authors admit that \"excessive iterations risk divergence from the data manifold\". I believe that the authors should clarify the proposed method does not solve $f^u\\_{t \\rightarrow 0}(x_t) = f^c\\_{t \\rightarrow 0}(x_t), x\\_t \\in \\mathcal{M}\\_t$ but solves $f^u_{t \\rightarrow 0}(x_t) = f^c_{t \\rightarrow 0}(x_t)$.\n- [W3] Lines 127 - 128, \"approximating $f^{u_{(-1)}}\\_{t+1 \\rightarrow t}$ by $f^u_{t \\rightarrow t+1}$ or $n_{t \\rightarrow t+1}$ ...\". Is $n_{t \\rightarrow t+1}$ a valid approximation of $f^{u_{(-1)}}\\_{t+1 \\rightarrow t}$? One is stochastic and the other is deterministic.\n\n**Significance**\n- [S1] FSG outperforms CFG, CFG++, Z-Sampling, and Resampling on a variety of benchmark tasks on popular models such as SDXL.\n\n**Originality**\n- [S1] Interpretation of CFG as a specific instance of FPI is novel and interesting.\n- [W1] The proposed method, FSG, is a collection of rather incremental engineering techniques. Specifically, it is a collection of reverse diffusion interval adjustment, FPI, and CFG++ scheduler.\n\n[1] The Vendi Score: A Diversity Evaluation Metric for Machine Learning, TMLR, 2023\n\nQUESTIONS:\nI am willing to raise the score to *borderline accept* if the authors provide appropriate explanation for the concerns in **Quality** and **Clarity**.\n\nLIMITATIONS:\nThe authors discuss the limitations of the work in Appendix D.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-17"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper reframes classifier-free guidance (CFG) for text-to-image diffusion as a fixed-point calibration problem. at each timestep, the latent should lie on a “golden path” where unconditional and conditional denoising trajectories coincide. Existing guidance methods are shown to be single-step, short-sighted fixed-point iterations.  Based on this unified fixed-point view, the authors introduce Foresight Guidance (FSG), which 1- decomposes the diffusion trajectory into fewer, longer intervals, and 2- allocates multiple forward–backward iterations early in the process.  A theoretical bound predicts that such balanced sub-problem sizing minimizes the conditional–unconditional score gap under a fixed compute budget. Empirically, the authors show FSG outperforms several baselines in prompt fidelity, Human Preference Score, ImageReward and Aesthetic score.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths:\n1. Casting diverse guidance methods as fixed-point iterations. This clarifies design choices.\n2.  FSG is derived from a clear theoretical analysis predicting why larger intervals help under limited compute.\n3. Extensive evaluation on prompt fidelity and image rewards. \n4. FSG outperforms the baselines in almost all considered metrics and settings.\n\n---\n### Weaknesses:\n1. Image diversity metrics are not reported. The focus of the paper is on alignment and image quality and loss of sample diversity from stronger early calibration is not measured.\n2. Wall clock time are not reported. Wall-clock time analysis is paramount to understanding the overhead of extra forward backward passes, and gives reader a clear picture of the paper's practical contributions.\n3. (Minor) Several hyper-parameters are introduced which require tuning.\n4. (Minor) Qualitative failure cases could be added for when too many iterations are performed, with a discussion on limitations.\n\nQUESTIONS:\n1. What is the exact wall clock time of FSG compared to the baseline in different NFE settings?\n2. Can we learn the interval schedule?\n3. Can you provide FID values for the experiments? I'm worried about mode collapse.\n4. Have you tried empirically verifying the convergence rate in synthetic settings?\n\nOverall I think this paper makes meaningful contributions to controlled generation and will increase my score if the authors provide answers to the questions above.\n\nLIMITATIONS:\nEven if diversity decreases (if that’s why FID was omitted) acknowledging this limitation and discussing its impact would significantly improve the paper’s honesty and transparency.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
The paper proposed a unified framework based on fixed-point iteration that includes variants of classifier-free guidance as special cases, and outlines the design choices under this framework. The author also provides theoretical studies on how to balance the trade-off between the effectiveness and efficiency of iterative denoising image generation. Based on the observation, the author propose the foresight guidance, a new design combination under the framework that decomposes the iterative generation into longer but fewer subproblems. Various ablation and experiments on multiple benchmarks validate the effectiveness of the proposed method.

STRENGTHS AND WEAKNESSES:
Strength:
1. The paper is generally well-written.
2. The fixed-point iteration view allows clean decomposition of the design spaces for methods based on classifier-free guidance.
3. A theoretically grounded decomposition of iterative denoising generation into subproblems with longer intervals.
4. The experiments are thorough and convincing, investigating multiple controlled scenarios on different benchmarks.

Weakness:
1. Table 1 lacks necessary details in the main text to be fully understood. On top of this, while the proposed method fits into the fixed-point framework, the reasoning behind choosing the specific fixed point operator and scheduler is unclear.

QUESTIONS:
1. It would be good to desribe the reasoning behind specific design choices such as the fixed point operaters for the foresight guidance.
2. It would be good to make Table 1 easier to understand.

LIMITATIONS:
No limitation in the main section.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a unified framework for understanding Classifier-Free Guidance (CFG) in text-to-image diffusion models by reframing conditional generation as a fixed-point iteration problem. The objective is to identify a \"golden path\" where conditional and unconditional generation processes are consistent. The authors demonstrate that existing methods like CFG are special cases of inefficient, single-step \"short-sighted\" iterations. To address this, they propose Foresight Guidance (FSG), a novel method that solves longer-interval subproblems with multiple iterations, prioritizing the early stages of diffusion. Extensive experiments across multiple datasets and models are presented to show that FSG outperforms state-of-the-art methods in image quality and prompt alignment.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n- **A Novel and Unifying Conceptual Framework (Originality/Quality):** A key strength of the paper is its novel reframing of conditional guidance. The appendix provides a detailed mathematical unification of several guidance methods (CFG, CFG++, etc.), offering a new theoretical perspective. This presents a useful and systematic framework for analyzing and designing guidance mechanisms.\n- **Comprehensive Empirical Validation (Significance):** The proposed FSG method demonstrates strong performance, outperforming baselines across a range of settings. The consistent improvements across these settings suggest the method's robustness and general applicability.\n- **Theoretical Grounding (Quality/Originality):** The paper's claims are supported by a theoretical analysis in Appendix B. Theorem 1 provides a motivation, grounded in optimization theory, for why the proposed FSG strategy could be more effective than existing approaches under a finite computational budget.\n\n**Weaknesses:**\n\n- **Insufficient Analysis of Computational Efficiency (Clarity/Significance):** The paper's efficiency claims are based on the Number of Function Evaluations (NFE). This metric is insufficient for a fair comparison, as the computational work encapsulated by a single \"function evaluation\" differs substantially between FSG and the baselines. For instance, an FSG iteration involves expensive ODE solves, which is not directly comparable to a single UNet pass in CFG. A more direct comparison of wall-clock time or total UNet calls is necessary to substantiate the claims of improved efficiency.\n- **High Hyperparameter Complexity (Clarity):** The proposed method introduces considerable hyperparameter complexity. As detailed in Appendix C.1, FSG relies on a manually-designed, heuristic schedule of intervals, interval lengths, and iteration counts. This may hinder practical adoption and reproducibility compared to the relative simplicity of standard CFG.\n- **The \"Golden Path\" Concept Lacks Rigorous Formulation (Significance):** While motivationally effective, the conceptual foundation of the \"golden path\" remains more metaphorical than rigorously defined. The paper does not fully characterize the properties of this path (e.g., existence, uniqueness), which limits its utility as a formal theoretical tool. (As mentioned in Appendix D)\n\nQUESTIONS:\n- **On Computational Overhead:** Could the authors provide a more direct analysis of the computational costs? A comparison of wall-clock time and total UNet evaluations per generated image would be crucial for a complete assessment of the method's practical efficiency.\n- **On the Golden Path Proxy:** Figure 2b provides valuable empirical evidence, showing that FSG reduces the single-step _prediction gap_ ($||ε_c(x_t) - ε_u(x_t)||$). However, the paper defines the ideal golden path based on the consistency of the _full remaining trajectory_ ($f^c_{t→0}(x_t) = f^u_{t→0}(x_t)$). Could the authors comment on how well minimizing the single-step prediction gap serves as a proxy for achieving full-path consistency? Is it possible for this proxy to be a poor indicator, for instance, in cases where small, consistent errors in prediction accumulate to cause a large divergence in the final generated images?\n- **On the Contraction Assumption:** The theoretical analysis relies on the assumption that the fixed-point operator `F` is a contraction (Assumption 4). Could the authors provide further justification or empirical evidence for this assumption? This appears to be a very strong condition for an operator whose application involves composing two ODE solves, each making repeated calls to a deep neural network.\n- **On Hyperparameter Selection:** The appendix notes that the FSG schedule is chosen heuristically. How sensitive is the method's performance to these choices?\n\nLIMITATIONS:\nThe authors appropriately acknowledge key limitations in Appendix D.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-03"}