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VSF: Simple, Efficient, and Effective Negative Guidance in Few-Step Image Generation Models By Value Sign Flip

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Image GenerationDiffusion ModelsNegative Guidance
Abstract:

We introduce Value Sign Flip (VSF), a simple and efficient method for incorporating negative prompt guidance in few-step (1-8 steps) diffusion and flow-matching image generation models. Unlike existing approaches such as classifier-free guidance (CFG), NASA, and NAG, VSF dynamically suppresses undesired content by flipping the sign of attention values from negative prompts. Our method requires only a small computational overhead and integrates effectively with MMDiT-style architectures such as Stable Diffusion 3.5 Turbo and Flux Schnell, as well as cross-attention-based models like Wan. We validate VSF on challenging datasets with complex prompt pairs and demonstrate superior performance in both static image and video generation tasks. Experimental results on our proposed dataset NegGenBench show that VSF significantly improves negative prompt adherence (reaching 0.420 negative score for quality settings and 0.545 for strong settings) compared to prior methods in few-step models, which scored 0.320-0.380 negative score, and even CFG in non-few-step models (scored 0.300 negative score), while maintaining competitive image quality and positive prompt adherence. Our method is also a suppressed generate-then-edit pipeline, while also having a much faster runtime. Code, ComfyUI node, and dataset will be released. Videos generated are in the Supplementary Material.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces Value Sign Flip (VSF), a method for negative prompt guidance in few-step diffusion models by flipping attention values from negative prompts. According to the taxonomy, it resides in the 'Attention-Based Negative Guidance' leaf alongside two sibling papers (Normalized Attention Guidance variants). This leaf contains only three papers total, indicating a relatively sparse but focused research direction within the broader field of negative guidance mechanisms. The taxonomy shows thirteen papers across the entire survey, suggesting VSF operates in a moderately explored niche rather than a saturated area.

The taxonomy reveals that VSF's immediate neighbors include attention-space manipulation methods, while sibling branches pursue one-step specialized techniques and CFG-based approaches. The broader 'Negative Guidance Mechanisms' parent category excludes distillation and inversion methods, which occupy separate branches (Few-Step Diffusion Distillation, Diffusion Model Inversion). VSF's attention-based approach contrasts with distillation-focused methods like Adversarial Diffusion Distillation and Score Identity Distillation, which prioritize sampling speed over explicit negative control. This positioning suggests VSF targets interpretable, modular guidance rather than end-to-end model compression.

Among thirty candidates examined, no contributions were clearly refuted. The VSF method itself examined ten candidates with zero refutable overlaps, as did the NegGenBench dataset and the fine-tuned VLM evaluator (each ten candidates, zero refutations). This limited search scope—thirty papers total, not hundreds—means the analysis captures top semantic matches and immediate citations but cannot claim exhaustive coverage. The zero-refutation result across all three contributions suggests either genuine novelty within the examined set or that highly overlapping prior work lies outside the top-thirty retrieval window.

Based on the limited search scope, VSF appears to occupy a distinct position within attention-based negative guidance, with no direct prior work identified among the thirty candidates examined. The sparse taxonomy leaf (three papers) and zero refutations across contributions suggest potential novelty, though the analysis acknowledges it cannot rule out relevant work beyond the top-K semantic matches. The method's integration with MMDiT architectures and video generation may further differentiate it from existing attention manipulation techniques.

Taxonomy

Core-task Taxonomy Papers
13
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: negative prompt guidance in few-step diffusion models. The field addresses how to steer diffusion-based image generation away from undesired concepts while maintaining efficiency in sampling. The taxonomy reveals several main branches: Negative Guidance Mechanisms explore how to incorporate negative prompts into the denoising process, often through attention manipulation or classifier-free guidance variants; Few-Step Diffusion Distillation focuses on compressing multi-step diffusion into rapid samplers via distillation techniques such as Adversarial Diffusion Distillation[1] and Score Identity Distillation[9]; Diffusion Model Inversion and Editing investigates methods like Renoise[5] and Precise Diffusion Inversion[13] for reconstructing or editing images; Domain-Specific Diffusion Applications tailor diffusion models to specialized tasks; and Adversarial Robustness in Text-to-Image Models examines vulnerabilities and defenses, including works like Guardt2i[7] and Transstratal Attack[12]. These branches collectively span the spectrum from foundational sampling strategies to practical deployment concerns. Within Negative Guidance Mechanisms, a particularly active line of work centers on attention-based approaches that modulate cross-attention maps to suppress unwanted features. Value Sign Flip[0] sits squarely in this cluster, proposing a simple yet effective technique to invert attention values for negative prompts. It shares conceptual ground with Normalized Attention Guidance[2][10], which also refines attention weighting to balance positive and negative cues, though the normalization strategy differs in detail. Meanwhile, distillation-focused methods like Supercharged One-step[3] and Full Trajectory Alignment[6] prioritize speed over fine-grained negative control, highlighting a trade-off between sampling efficiency and the expressiveness of guidance. The original paper's emphasis on attention manipulation places it closer to interpretable, modular guidance techniques rather than end-to-end distillation, offering a complementary path for practitioners who need explicit negative steering without retraining entire models.

Claimed Contributions

Value Sign Flip (VSF) method for negative prompt guidance

VSF is a novel technique that dynamically suppresses undesired content by flipping the sign of attention values from negative prompts. Unlike prior methods such as CFG, NASA, and NAG, VSF adaptively adjusts guidance strength based on current presence of negative concepts, requiring only small computational overhead and integrating with MMDiT-style and cross-attention architectures.

10 retrieved papers
NegGenBench dataset of challenging prompt pairs

The authors created NegGenBench, a benchmark dataset containing 200 intentionally difficult positive-negative prompt pairs where negative prompts typically describe essential components of objects mentioned in positive prompts. The dataset includes evaluation questions for assessing both main object presence and negative element absence.

10 retrieved papers
Fine-tuned VLM for negation-aware evaluation

The authors collected and labeled generated images from VSF, NAG, and NASA methods, then fine-tuned a vision-language model (Qwen-2.5-VL) on this data to create a better evaluation tool for assessing negative prompt adherence in future research.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Value Sign Flip (VSF) method for negative prompt guidance

VSF is a novel technique that dynamically suppresses undesired content by flipping the sign of attention values from negative prompts. Unlike prior methods such as CFG, NASA, and NAG, VSF adaptively adjusts guidance strength based on current presence of negative concepts, requiring only small computational overhead and integrating with MMDiT-style and cross-attention architectures.

Contribution

NegGenBench dataset of challenging prompt pairs

The authors created NegGenBench, a benchmark dataset containing 200 intentionally difficult positive-negative prompt pairs where negative prompts typically describe essential components of objects mentioned in positive prompts. The dataset includes evaluation questions for assessing both main object presence and negative element absence.

Contribution

Fine-tuned VLM for negation-aware evaluation

The authors collected and labeled generated images from VSF, NAG, and NASA methods, then fine-tuned a vision-language model (Qwen-2.5-VL) on this data to create a better evaluation tool for assessing negative prompt adherence in future research.

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