# FreeDiff: Progressive Frequency Truncation for Image Editing with Diffusion Models arXiv: https://arxiv.org/abs/2404.11895 ยท HTML: https://mlsanity.com/paper/2404.11895/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. - Builds on 22 of its 24 references that are in the map. - First posted on arXiv 18 Apr 2024. ## Placed in - Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/) ## Builds on (most cited first, 22 of 22) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020) - [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022) - [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023) - [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019) - [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022) - [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021) - [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022) - [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022) - [Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation](https://mlsanity.com/paper/2211.12572/) (2022) - [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023) - [SVDiff: Compact Parameter Space for Diffusion Fine-Tuning](https://mlsanity.com/paper/2303.11305/) (2023) - [Pix2Video: Video Editing using Image Diffusion](https://mlsanity.com/paper/2303.12688/) (2023) - [An Edit Friendly DDPM Noise Space: Inversion and Manipulations](https://mlsanity.com/paper/2304.06140/) (2023) - [Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting](https://mlsanity.com/paper/2212.06909/) (2022) - [SINE: SINgle Image Editing with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2212.04489/) (2022) - [Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code](https://mlsanity.com/paper/2310.01506/) (2023) - [Effective Real Image Editing with Accelerated Iterative Diffusion Inversion](https://mlsanity.com/paper/2309.04907/) (2023) - [Scene Text Image Super-resolution based on Text-conditional Diffusion Models](https://mlsanity.com/paper/2311.09759/) (2023) ## Built on by (most cited first, 13 of 13) - [FDS: Frequency-Aware Denoising Score for Text-Guided Latent Diffusion Image Editing](https://mlsanity.com/paper/2503.19191/) (2025) - [FIA-Edit: Frequency-Interactive Attention for Efficient and High-Fidelity Inversion-Free Text-Guided Image Editing](https://mlsanity.com/paper/2511.12151/) (2025) - [Towards Scalable Human-aligned Benchmark for Text-guided Image Editing](https://mlsanity.com/paper/2505.00502/) (2025) - [ASTRA: Let Arbitrary Subjects Transform in Video Editing](https://mlsanity.com/paper/2510.01186/) (2025) - [FreqEdit: Preserving High-Frequency Features for Robust Multi-Turn Image Editing](https://mlsanity.com/paper/2512.01755/) (2025) - [NeuralRemaster: Phase-Preserving Diffusion for Structure-Aligned Generation](https://mlsanity.com/paper/2512.05106/) (2025) - [HOFAR: High-Order Augmentation of Flow Autoregressive Transformers](https://mlsanity.com/paper/2503.08032/) (2025) - [Trade-offs in Image Generation: How Do Different Dimensions Interact?](https://mlsanity.com/paper/2507.22100/) (2025) - [Gait Recognition via Collaborating Discriminative and Generative Diffusion Models](https://mlsanity.com/paper/2511.06245/) (2025) - [Characterizing Motion Encoding in Video Diffusion Timesteps](https://mlsanity.com/paper/2512.22175/) (2025) - [BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing](https://mlsanity.com/paper/2606.18906/) (2026) - [Wavelet-Guided Semantic Signal Compensation for Inversion-Free Image Editing](https://mlsanity.com/paper/2607.02421/) (2026) - [MDN-Control: Mask-Depth-Noise Guided Region Control for Multi-Subject Video Editing](https://mlsanity.com/paper/2609.16475/) (2026) ## Abstract (arXiv) Precise image editing with text-to-image models has attracted increasing interest due to their remarkable generative capabilities and user-friendly nature. However, such attempts face the pivotal challenge of misalignment between the intended precise editing target regions and the broader area impacted by the guidance in practice. Despite excellent methods leveraging attention mechanisms that have been developed to refine the editing guidance, these approaches necessitate modifications through complex network architecture and are limited to specific editing tasks. In this work, we re-examine the diffusion process and misalignment problem from a frequency perspective, revealing that, due to the power law of natural images and the decaying noise schedule, the denoising network primarily recovers low-frequency image components during the earlier timesteps and thus brings excessive low-frequency signals for editing. Leveraging this insight, we introduce a novel fine-tuning free approach that employs progressive Frequency truncation to refine the guidance of Diffusion models for universal editing tasks (FreeDiff). Our method achieves comparable results with state-of-the-art methods across a variety of editing tasks and on a diverse set of images, highlighting its potential as a versatile tool in image editing applications.