# Colorful-Noise: Training-Free Low-Frequency Noise Manipulation for Color-Based Conditional Image Generation

arXiv: https://arxiv.org/abs/2605.00548 · HTML: https://mlsanity.com/paper/2605.00548/

## Key facts

- Cited by 1 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 27 of its 41 references that are in the map.
- First posted on arXiv 1 May 2026.

## 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, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [An Edit Friendly DDPM Noise Space: Inversion and Manipulations](https://mlsanity.com/paper/2304.06140/) (2023)
- [SpaText: Spatio-Textual Representation for Controllable Image Generation](https://mlsanity.com/paper/2211.14305/) (2022)
- [Negative-prompt Inversion: Fast Image Inversion for Editing with Text-guided Diffusion Models](https://mlsanity.com/paper/2305.16807/) (2023)
- [InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation](https://mlsanity.com/paper/2404.02733/) (2024)
- [Implicit Style-Content Separation using B-LoRA](https://mlsanity.com/paper/2403.14572/) (2024)
- [ReNoise: Real Image Inversion Through Iterative Noising](https://mlsanity.com/paper/2403.14602/) (2024)
- [Delta Denoising Score](https://mlsanity.com/paper/2304.07090/) (2023)
- [Real-World Image Variation by Aligning Diffusion Inversion Chain](https://mlsanity.com/paper/2305.18729/) (2023)
- [Differential Diffusion: Giving Each Pixel Its Strength](https://mlsanity.com/paper/2306.00950/) (2023)
- [Blue noise for diffusion models](https://mlsanity.com/paper/2402.04930/) (2024)
- [Score-based Denoising Diffusion with Non-Isotropic Gaussian Noise Models](https://mlsanity.com/paper/2210.12254/) (2022)
- [FreCaS: Efficient Higher-Resolution Image Generation via Frequency-aware Cascaded Sampling](https://mlsanity.com/paper/2410.18410/) (2024)
- [DyPE: Dynamic Position Extrapolation for Ultra High Resolution Diffusion](https://mlsanity.com/paper/2510.20766/) (2025)
- [FDS: Frequency-Aware Denoising Score for Text-Guided Latent Diffusion Image Editing](https://mlsanity.com/paper/2503.19191/) (2025)
- [Conditional Balance: Improving Multi-Conditioning Trade-Offs in Image Generation](https://mlsanity.com/paper/2412.19853/) (2024)
- [Balanced Image Stylization with Style Matching Score](https://mlsanity.com/paper/2503.07601/) (2025)
- [HiWave: Training-Free High-Resolution Image Generation via Wavelet-Based Diffusion Sampling](https://mlsanity.com/paper/2506.20452/) (2025)
- [One Stone with Two Birds: A Null-Text-Null Frequency-Aware Diffusion Models for Text-Guided Image Inpainting](https://mlsanity.com/paper/2510.08273/) (2025)

## Built on by (most cited first, 1 of 1)

- [Phi-Noise: Training-Free Temporal Video Conditioning via Phase-Based Noise Manipulation](https://mlsanity.com/paper/2605.24509/) (2026)

## Abstract (arXiv)

Text-to-image diffusion models generate images by gradually converting white Gaussian noise into a natural image. White Gaussian noise is well suited for producing diverse outputs from a single text prompt due to its absence of structure. However, this very property limits control over, and predictability of, specific visual attributes, as the noise is not human-interpretable. In this work, we investigate the characteristics of the input noise in diffusion models. We show that, although all frequencies in white Gaussian noise have comparable statistical energy, low-frequency components primarily determine the images global structure and color composition, while high-frequency components control finer details. Building on this observation, we demonstrate that simple manipulations of the low-frequency noise using low-frequency image priors can effectively condition the generation process to reconstruct these low-frequency visual cues. This allows us to define a simple, training-free method with minimal overhead that steers overall image structure and color, while letting high-frequency components freely emerge as fine details, enabling variability across generated outputs.