Title: SplitFlow: Flow Decomposition for Inversion-Free Text-to-Image Editing
Venue: NeurIPS 2025
Accepted: yes
Keywords: Image Editing

Flow Matching

Rectified Flow
Abstract: Rectified flow models have become a $\textit{de facto}$ standard in image generation due to their stable sampling trajectories and high-fidelity outputs. Despite their strong generative capabilities, they face critical limitations in image editing tasks: inaccurate inversion processes for mapping real images back into the latent space, and gradient entanglement issues during editing often result in outputs that do not faithfully reflect the target prompt. Recent efforts have attempted to directly map source and target distributions via ODE-based approaches without inversion; however, these methods still yield suboptimal editing quality. In this work, we propose a flow decomposition-and-aggregation framework built upon an inversion-free formulation to address these limitations. Specifically, we semantically decompose the target prompt into multiple sub-prompts, compute an independent flow for each, and aggregate them to form a unified editing trajectory. While we empirically observe that decomposing the original flow enhances diversity in the target space, generating semantically aligned outputs still requires consistent guidance toward the full target prompt. To this end, we design a projection and soft-aggregation mechanism for flow, inspired by gradient conflict resolution in multi-task learning. This approach adaptively weights the sub-target velocity fields, suppressing semantic redundancy while emphasizing distinct directions, thereby preserving both diversity and consistency in the final edited output. Experimental results demonstrate that our method outperforms existing zero-shot editing approaches in terms of semantic fidelity and attribute disentanglement. The code is available at https://github.com/Harvard-AI-and-Robotics-Lab/SplitFlow.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors identified that text-based image editing methods struggle with long complicated text prompts. To improve this aspect, they propose SplitFlow. Specifically, they propose to use an LLM to decompose the edit prompt into several edit prompts, performing inversion-free editing path for each of them (based on FlowEdit) following by composing these paths together. The result is a single edited image that is loyal to the text prompt and preserves the background.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n\nA new method for text-based image editing, suited for long editing prompts.  \n\nAchieves good results compared to baselines.  \n\nAblation study that justifies the design choices.  \n\nWeaknesses:  \n\nNot enough visual results, especially for long, detailed text prompts (see also questions).  \n\nLong run time - requires multiple editing paths and using an LLM.  \n\nAdditional hyperparameters that affect the results.\n\nQUESTIONS:\nI didn’t find any reference to the fidelity enhanced version mentioned in Tab. 1 and how it is different from the regular version. This is especially important as this version achieves the best or second-best results in structure preservation.  \n\nThis paper subject is text-based image editing, yet it provides only a few visual examples (only 7 examples in the main text, only 15 overall with the SM). The authors should provide more visual results, especially for long and detailed text prompts, where their method is supposed to excel. In addition, I encourage the authors to add to the SM visualization of the ablation process and hyperparameters effect. These images lead to intuition regarding the method's strengths and weaknesses.   \n\nRuntime should be mentioned in the main text as it is a major weakness of the proposed method. \n\nThe authors didn’t explore how their method works in extreme cases. For example, very long and detailed prompts that can be naturally decomposed into 5 or more sub-prompts. In addition, when the editing prompt is very simple (change the cat into a dog) will the LLM successfully recognize this simplicity and will not decompose the prompt (and then SplitFlow will behave as FlowEdit)? If the LLM will still decompose the prompt, will SplitFlow outperform FlowEdit? (Again, specifically for simple edits such as changing the cat into a dog and nothing else).  \n\nMinor: \n\nIn line 26 the authors cite many editing works as diffusion/flow models instead of citing diffusion/flow models works such as [A1, A2, A3] and Refs [1, 13, 16].  \n\nThe claim made in lines 30-34 about “precise inversion allows near-perfect reconstruction” may be reduced from “fundamental” to “empirical”, because as far as I know this is not something that has been proved theoretically. If the authors know of such proof, they should cite it (I didn’t find it in Ref [2]). In addition, in FlowEdit [11] and previously in EF [A4] the authors also discussed the shortcomings of perfect inversion (for generated images) compared to other editing methods.   \n\n“To our knowledge, our work is the first to incorporate flow decomposition and aggregation into the inversion-free editing framework.” [89-91].  I am not familiar with works regarding flow decomposition and aggregation in text-based image editing (with or without inversion). If the authors know such works, they should cite them, if they don’t know they should write something like: “To the best of our knowledge, our work is the first to incorporate flow decomposition and aggregation into text-based image editing framework.”  \n\nIn line 232 the authors should remove the word “training” as FlowEdit  (Ref [11]) and the proposed SplitFlow are training free methods.  \n\nSome of the information in Sec. 5 is unnecessarily repetitive (lines 247, 241).  \n\nComparison with diffusion models is not necessary (especially as the authors don’t mention it in the text) and could be moved to SM.  \n\nRefs [13, 14] and [16, 17] are the same.  \n\nFigure 2 is not clear. \n\nThe authors should add a graph that illustrates text adherence versus structure preservation (like EF [A4], PnP [24], FlowEdit [11]) to illustrate the empirical tradeoff between these two requirements.  \n\nAn interesting baseline the authors may want to consider is comparing their method to methods that perform chain of edits (such as [A5]).  These methods try to achieve different goal, however there are some similarities as both methods are supposed to excel with long and complicated text prompts (chain of edits methods do it multiple steps, where SplitFlow do it in one step).  \n\n[A1] Sohl-Dickstein, Jascha, et al. \"Deep unsupervised learning using nonequilibrium thermodynamics.\" International conference on machine learning. pmlr, 2015. \n\n[A2] Ho, Jonathan, Ajay Jain, and Pieter Abbeel. \"Denoising diffusion probabilistic models.\" Advances in neural information processing systems 33 (2020): 6840-6851. \n\n[A3] Song, Yang, et al. \"Score-Based Generative Modeling through Stochastic Differential Equations.\" International Conference on Learning Representations. \n\n[A4] Huberman-Spiegelglas, Inbar, Vladimir Kulikov, and Tomer Michaeli. \"An edit friendly ddpm noise space: Inversion and manipulations.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024. \n\n[A5] Zhou, Zijun, et al. \"Multi-turn Consistent Image Editing.\" arXiv preprint arXiv:2505.04320 (2025).\n\nLIMITATIONS:\nHow does the method work with large deviation from the source image? FlowEdit struggles with such edits, does SplitFlow inherit this limitation?", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes the SplitFlow framework to address two critical issues in flow-based text-to-image editing: imperfect image inversion and gradient entanglement. SplitFlow employs an innovative \"flow decomposition-and-aggregation\" strategy: it first utilizes LLMs to decompose complex target prompts into multiple simpler sub-prompts, then computes independent editing flows for each sub-prompt, and finally intelligently fuses these sub-flows into a unified editing trajectory through projection and weighting mechanisms inspired by multi-task learning (LTP and VFA modules). This inversion-free approach demonstrates superior performance compared to existing state-of-the-art methods on the PIE-Bench benchmark in terms of semantic fidelity, attribute disentanglement, and background preservation.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n• **Novel semantic flow decomposition approach**: This is the first work to introduce semantic flow decomposition for text-based image editing, directly addressing the gradient entanglement problem by decomposing complex editing tasks into independent sub-flows.\n\n• **Creative integration of LLMs for image editing**: The use of large language models to guide image editing through prompt decomposition represents an innovative cross-modal approach that bridges language understanding and image generation.\n\n• **Comprehensive experimental validation**: The paper provides thorough evaluation on PIE-Bench benchmark with multiple metrics, demonstrating clear improvements in reconstruction error, prompt alignment, and background preservation compared to state-of-the-art baselines including FlowEdit, FTEdit, and FireFlow.\n\n• **Effective gradient conflict resolution mechanism**: The adaptation of multi-task learning's gradient projection technique (PCGrad/Grad-Surgery) to the generative model domain through LTP and VFA modules is technically sound and well-motivated.\n\n**Weaknesses:**\n\n• **Heavy reliance on LLM decomposition quality without robustness analysis**: The method's performance critically depends on the quality of prompt decomposition by the LLM, yet the paper lacks analysis of failure cases when the LLM produces imperfect splits (e.g., merging attributes or generating unnecessary sub-prompts). No quantitative analysis is provided regarding sensitivity to decomposition quality.\n\n• **Limited computational efficiency analysis**: While the paper mentions computing flows for multiple sub-prompts adds overhead (approximately 48 vs 33 steps), it lacks detailed runtime comparisons or memory usage analysis compared to simpler baselines like FlowEdit.\n\n• **Inadequate explanation of the \"fidelity-enhanced\" variant**: Table 1 shows dramatic improvements for the fidelity-enhanced version with only brief explanation (\"noise interpolation is not applied when forming x_t^src\"), but this significant performance gain deserves more detailed technical exposition in the method section.\n\n• **Lack of statistical significance testing**: All experiments are conducted with fixed seeds without error bars or statistical significance analysis, making it difficult to assess the reliability of relatively modest improvements in some metrics (e.g., CLIP Similarity).\n\nQUESTIONS:\nPlease refer to the Weaknesses part.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nSplitFlow presents an inversion-free framework for text-guided image editing in rectified flow models that addresses \"gradient entanglement\" issues in the setting of complex, multi-attribute target-edit prompts. The method decomposes the target edit prompts into sub-prompts using an LLM, computes independent editing flows for each sub-prompt, and then aggregates them through two mechanisms: Latent Trajectory Projection to ensure global semantic alignment, and Velocity Field Aggregation to adaptively weight sub-flows based on their directional consistency. The approach shows qualitative improvement over existing baselines in multi concept editing settings. On the PIE benchmark it shows a some improvement in some of the background preservation and structure preservation metrics, and in text alignment.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n(1) Well-motivated problem: The paper tackles multi-concept editing in rectified flow models, addressing gradient entanglement issues that arise when target prompts contain multiple semantic attributes - a practically relevant and challenging problem.\n(2) The decomposition-aggregation approach makes conceptual sense, breaking down complex editing tasks into manageable sub-flows that can be independently computed and then combined.\n(3) Empirical results demonstrate improvements over existing methods in terms of text alignment. \n(4) Comprehensive evaluation --  appropriate baselines and relevant evaluation metrics that indeed suggest SplitFlow offers the best background-preservation and editing alignment trade-off. \n\nWeaknesses:\n\n(1) Computational overhead: The method requires computing N independent flows plus aggregation steps, significantly increasing inference time but lacks detailed analysis of the time-quality trade-off compared to baselines.\n\nQUESTIONS:\n(1) Gradient entanglement assumption: The paper assumes that \"entangled gradients can degrade edit quality\" when handling multi-attribute prompts, but why couldn't stronger text alignment in future models handle this directly? The assumption may not hold as text-image alignment capabilities improve.\n(2) Cost-benefit analysis: What is the actual computational overhead in terms of wall-clock time and memory usage? Is the performance gain worth the increased computational cost, especially for practical applications?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper focuses on the limitations of rectified flow models in image editing tasks, namely inaccurate inversion processes and gradient entanglement issues. To address these, the authors propose a flow decomposition-and-aggregation framework. The approach semantically decomposes the target prompt into sub-prompts, computes independent flows for each, and aggregates them. A projection and soft-aggregation mechanism is designed to adaptively weight sub-target velocity fields, suppressing redundancy and emphasizing distinct directions. Experiments show the method outperforms existing zero-shot editing approaches in semantic fidelity and attribute disentanglement.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths ###\n\n- This paper focuses on the limitations of rectified flow models in image editing tasks, and proposes a valid solution to address inaccurate inversion processes and gradient entanglement issues\n\n- Experiments show the method outperforms existing zero-shot editing approaches in semantic fidelity and attribute disentanglement.\n\n### Weaknesses ###\n\n- The major issue of this paper is the lack of theoretical analysis for the proposed flow decomposition and why it can handle gradient conflict.\n\n- Through multiple sub-target flow generations, the inference time would be largely increased.\n\n- In Table 2, it seems that only averaging the sub-flows can obtain much better results than the proposed method in terms of the structure and background preservation.\n\n- The paper's writing requires refinement. For instance, while the notation $x_t$ is familiar to experts in the field, the authors should explicitly define its meaning, particularly for $x^{tgt}_t$ and $x^{src}_t$ in Equation 4, instead of deferring the explanation to Equation 7.\n\nQUESTIONS:\n- What is the latency of the proposed method, and how does it perform compared to other methods?\n\n- What is the fidelity-enhanced setting?\n\n- What is the performance of applying the proposed method to other base models, such as SD 3.5, Flux, etc?\n\nLIMITATIONS:\n- The major issue of this paper is the lack of theoretical analysis for the proposed flow decomposition.\n\n- Through multiple sub-target flow generations, the inference time would be largely increased.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}