Title: HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance
Venue: NeurIPS 2025
Accepted: yes
Keywords: high-resolution generation

text-to-image generation
Abstract: Text-to-image (T2I) diffusion/flow models have drawn considerable attention recently due to their remarkable ability to deliver flexible visual creations. Still, high-resolution image synthesis presents formidable challenges due to the scarcity and complexity of high-resolution content. Recent approaches have investigated training-free strategies to enable high-resolution image synthesis with pre-trained models. However, these techniques often struggle with generating high-quality visuals and tend to exhibit artifacts or low-fidelity details, as they typically rely solely on the endpoint of the low-resolution sampling trajectory while neglecting intermediate states that are critical for preserving structure and synthesizing finer detail. To this end,  we present HiFlow, a training-free and model-agnostic framework to unlock the resolution potential of pre-trained flow models. Specifically, HiFlow establishes a virtual reference flow within the high-resolution space that effectively captures the characteristics of low-resolution flow information, offering guidance for high-resolution generation through three key aspects: initialization alignment for low-frequency consistency, direction alignment for structure preservation, and acceleration alignment for detail fidelity. By leveraging such flow-aligned guidance, HiFlow substantially elevates the quality of high-resolution image synthesis of T2I models and demonstrates versatility across their personalized variants. Extensive experiments validate HiFlow's capability in achieving superior high-resolution image quality over state-of-the-art methods.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses adapting pre-trained image diffusion/flow models for high-resolution image generation without further fine-tuning. The paper proposes to leverage a low-resolution reference image to guide the sampling of high-resolution via alignment at multiple granularities. While prior work anchors the guidance to a single upscaled prediction of the clean image, this work introduces timestep-dependent anchors to close the distribution gap between the guidance signal and the current noisy high-res input. The alignments altogether preserves the low-frequency structures of the low-res image while generating the high-frequency details with fidelity.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. The figures illustrate qualitative effects of each component of the method well and ablation is informative of the contribution of each alignment.\n2. Baseline comparisons are comprehensive, covering both training-free and trained approaches as well as super-resolution models.\n3. Qualitative results exhibit sharp high-resolution images capturing intricate patterns and details. \n4. The effectiveness of the method when applied in conjunction with LoRA and ControlNet, quantized model setting, and across different architectures(UNet/DiT) is well demonstrated.\n\n\n**Weaknesses**\n\n1. The figure outlining the pipeline is not straightforward to follow and makes it hard to grasp the essence of the method. Arrows eventually pointing to acceleration can be misleading. Illustration can be clarified further. \n2. Although $\\alpha$ and $\\beta$ parameters follow a gradually weakening schedule according to t/$\\tau$, this choice of function and its impact on parameters and image outputs of different resolutions are not extensively discussed. Specific choices for these schedules could potentially limit the effectiveness of the guidance.\n3. More discussion about achieving fidelity with respect to the reference can be added. For instance in Fig. 5, although the palm tree and owl zoom-ins present more visual details, they are not consistent with the reference.\n\nQUESTIONS:\n1. How sensitive is the method to the weight hyperparameters used in all three alignments?\n2. How robust is the method to low-resolution reference? Would artifacts and lack of details in the reference potentially propagate or constrain the generation of high-res image?\n\nLIMITATIONS:\nYes, limitations and broader impacts are addressed in the appendix.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces HiFlow, a training-free and model-agnostic framework for high-resolution image generation using pre-trained diffusion/flow models. HiFlow addresses the challenge of generating high-quality, high-resolution images by creating a virtual reference flow in the high-resolution space. It guides the generation process through three key strategies: initialization alignment (for low-frequency consistency), direction alignment (for structure preservation), and acceleration alignment (for detail fidelity). By leveraging these flow-aligned techniques, HiFlow significantly improves the quality of high-resolution image synthesis and works effectively across various personalized T2I models. Experimental results show that HiFlow outperforms existing state-of-the-art methods in generating high-resolution images.\n\nSTRENGTHS AND WEAKNESSES:\n1. The proposed HiFlow is simple and effective, and it is very easy to understand.\n\n2. Low-frequency consistency, structure preservation, and detail fidelity are crucial for high-resolution images. The author has designed corresponding modules for each of these aspects, making the overall framework design very reasonable.\n\n3. HiFlow achieves high-quality visual results and outperforms most previous methods in quantitative evaluations.\n\n4. The acceleration visualization in Figure 3 (c) and also in Figure 4 is a bit confusing. It's not very clear what the author intends to convey with these illustrations.\n\n5. The evaluation dataset has not been specified or discussed. Additionally, there are relatively few quantitative evaluation experiments. It appears that the author has conducted ablation and comparison assessments on only a single evaluation dataset, which may not provide a comprehensive view of HiFlow's effectiveness.\n\nQUESTIONS:\nPlease see Weaknesses.\n\nThe acceleration visualizations presented in Figure 3 (c) and Figure 4 are somewhat unclear. Could the authors provide more explanation or interpretation regarding what these visualizations represent and how they support the claims about detail fidelity?\n\nAdditionally, can the three techniques proposed by HiFlow be well applied to high-resolution video generation tasks? It would be helpful if the author could briefly discuss this.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

comments: SUMMARY:
The paper presents HiFlow, a novel training-free framework for high-resolution image synthesis using pre-trained flow models. The approach is well-motivated and addresses a significant challenge in text-to-image generation. The methodology is sound, and the experimental results demonstrate clear improvements over existing methods.

STRENGTHS AND WEAKNESSES:
**Paper Strengths**

Novelty and Contribution:
1)	The introduction of virtual reference flow and flow-aligned guidance (initialization, direction, and acceleration alignment) is innovative. The idea of leveraging intermediate states of low-resolution sampling trajectories for high-resolution synthesis is compelling.
2)	The framework is model-agnostic and compatible with architectures like U-Net and DiT, enhancing its applicability.

Experimental Results: Quantitative metrics (FID, IS, CLIP score) and qualitative comparisons (Figures 5–6) convincingly show HiFlow’s superiority over baselines, including training-based methods like UltraPixel and Diffusion-4k.

**Paper Weaknesses**

Acceleration Alignment: The physical interpretation of acceleration (Eq. 10) is insightful but could be better motivated. How does aligning acceleration empirically improve detail fidelity compared to direction alignment alone?

Hyperparameters: The choice of noise-adding ratio τ and cutoff frequency D seems heuristic. A sensitivity analysis would justify these choices.

QUESTIONS:
Please refer to the paper weakness

LIMITATIONS:
The failure case in Figure E.2 (duplicated tail artifact) suggests HiFlow inherits structural flaws from low-resolution inputs. The authors should discuss mitigation strategies (e.g., post-processing or iterative refinement).
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses the problem of low quality in high-resolution image generation. The paper introduces HiFlow, which is a training-free and model-agnostic framework using pre-trained rectified flow models. In detail, HiFlow uses the low-resolution generation trajectory to construct a virtual reference flow. Then it is used to guide the high-resolution generation. There are three alignment mechanisms: initialization alignment (consistent low-frequency components), direction alignment (structural preservation), and acceleration alignment (high-fidelity detail). The method is evaluated on several benchmarks, and the experiments show that the proposed method achieves better performance compared to other baselines in terms of FID, IS, and CLIPScore.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The motivation is reasonable. Now, the low-quality issue in high-resolution image generation is important. The paper aims to improve high-resolution generation without requiring retraining compared to previous methods.\n2. The method is mode-agnostic and training-free. The method can be used in different architectures, such as DiT and U-net. It can also be applied to quantized models and personalization modules such as LoRA and ControlNet. It does not need any model-specific tuning.\n3. The method is reasonable. The paper introduces a virtual reference flow and three flow-aligned guidance to improve the high-resolution generation. The method is interpretable and effective.\n4. There are plenty of experiments in the paper. The authors conduct lots of experiments on different benchmarks and ablation studies to validate the proposed method.\n\nWeaknesses:\n1. The method heavily depends on low-resolution trajectory quality. The method points out that some previous methods are based on low-resolution image quality. However, the method assumes the low-resolution generation is structurally accurate. The artifact or mistakes in the low-resolution image have a bad influence on the high-resolution generation. \n2. The method requires high memory and computational cost. The method needs 379s on A100 to generate 4k images, which is very computationally expensive. It limits the deployment.\n\nQUESTIONS:\n1. Can the authors elaborate on how robust the virtual reference flow is to artifacts in the low-resolution generation? Is there any method to reduce the risk of error propagation?\n2. There are some acceleration sampling methods for diffusion or flow methods. Can they be used in the proposed method to accelerate the generation?\n3. Is there any detection method to prevent errors in the alignments?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n- HiFlow is a training free approach for high resolution image generation using low resolution pre-trained models.\n- Existing methods neglect the intermediate states of the low resolution sampling trajectory and rely only on the end points. This results in artifacts or low-fidelity details in the generated output.\n- HiFlow relies on a virtual reference flow within the high-resolution space that effectively captures the characteristics of low-resolution flow information. This trajectory offers guidance for high resolution generation through 1) initialization alignment for low frequency consistency, 2) direction alignment for structure preservation and 3) acceleration alignment for detail fidelity.\n- Extensive experiments show that HiFlow elevates the quality of high resolution image synthesis with the help of a well designed flow-aligned guidance.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n- HiFlow is training free and is architecture agnostic (works well with UNet or DiT family of architectures).\n- Qualitative and quantitative results show clear improvements over baselines.\n- All the experiments support the claims made in the paper.\n\n**Weaknesses**\n- HiFlow is a cascaded approach, so all intermediate resolutions have to be generated to reach high resolution (like 2k and 4k have to be generated to sample a 8k image).\n- The notations in the paper are very hard to understand. Authors use $X_{0\\leftarrow t}$ and $X_{1\\leftarrow t}$ extensively in all equations throughout the paper without spending some time discussing what that quantity is. This makes it very hard to follow the rest of the paper. How can we extract $X_{0\\leftarrow t}$ given the prediction of the model and how does it look for a sample generation? This is very important to understand the notation and the equations in the paper.\n\nQUESTIONS:\n- How did the authors end up looking at the first order derivative of vector $v_t$ (i.e. acceleration alignment). The initialization and direction alignment are very intuitive but the acceleration alignment isn't. Please explain how the authors ended up investigating the acceleration. This can help understand the method even better.\n\nLIMITATIONS:\nyes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-06"}