Paint-Anything: Unified Any-Color Control for Image Generation and Editing
Paint-Anything achieves any-color control for image generation/editing via shared hex prompts, improving ACBench-T2I by 85.3%.
Key Findings
Methodology
Paint-Anything learns a shared hex-prompt interface through object-level color supervision. A data pipeline converts real images into the Paint-500K dataset, complemented by pure-color anchors at high-noise timesteps. Introduces Any Color Benchmark (ACBench) to evaluate color fidelity.
Key Results
- On FLUX.2-4B, Paint-Anything improves ACBench-T2I and ACBench-Edit scores by 85.3% and 28.3%, respectively.
- Achieves the highest average CompColor score among compared methods.
- Ablations show pure-color anchors are crucial for reliable pixel-space color control.
Significance
This research provides a unified interface for color control in image generation and editing, addressing issues of dedicated color representations and complex inference procedures in previous methods. It has broad applications in professional design requiring precise color control.
Technical Contribution
Paint-Anything unifies generation and editing tasks through a shared hex-prompt interface and object-level color supervision, significantly improving color fidelity without relying on dedicated color representations or inference procedures.
Novelty
First to achieve object-level color control via 24-bit hex values, overcoming the separation of color generation, editing, and colorization in previous methods.
Limitations
- The model's color fidelity may decrease under specific lighting conditions due to shadows affecting color label accuracy.
- Current training data lacks palette-specific supervision.
Future Work
Future work could extend to more color control tasks and incorporate palette-specific supervision to enhance applicability across different scenarios.
AI Executive Summary
In professional design, color control is a critical requirement, yet existing methods often rely on dedicated color representations or complex inference procedures. Paint-Anything addresses this by achieving any-color control for image generation and editing through a shared hex-prompt interface.
The method learns through object-level color supervision, converting real images into the Paint-500K dataset, and uses pure-color anchors at high-noise timesteps. The introduced Any Color Benchmark (ACBench) evaluates color fidelity, showing that on FLUX.2-4B, Paint-Anything improves ACBench-T2I and ACBench-Edit scores by 85.3% and 28.3%, respectively.
While the model's color fidelity may decrease under specific lighting conditions, its unified color control interface offers new possibilities for professional design. Future work could extend to more color control tasks and incorporate palette-specific supervision.
Deep Analysis
Background
In computer vision, color control has been a significant issue in image generation and editing. Traditional methods often rely on dedicated color representations or complex inference procedures, limiting their application across different tasks. Advances in large models, particularly language models, offer new possibilities.
Core Problem
Existing methods face issues of strong specificity and difficulty in expansion for color control. Professional design requires the ability to precisely specify object colors, not just simple color descriptions.
Innovation
Paint-Anything achieves any-color control for image generation and editing through a shared hex-prompt interface. It learns through object-level color supervision and uses pure-color anchors at high-noise timesteps, significantly improving color fidelity.
Methodology
- �� Uses object-level color supervision to learn a shared hex-prompt interface.
- �� Data pipeline converts real images into the Paint-500K dataset.
- �� Uses pure-color anchors at high-noise timesteps for training.
- �� Introduces Any Color Benchmark (ACBench) to evaluate color fidelity.
Experiments
Experiments conducted on the FLUX.2-4B model evaluated color fidelity on ACBench-T2I and ACBench-Edit. Ablation studies confirmed the importance of pure-color anchors for reliable pixel-space color control.
Results
On FLUX.2-4B, Paint-Anything improves ACBench-T2I and ACBench-Edit scores by 85.3% and 28.3%, respectively. Achieves the highest average CompColor score among compared methods.
Applications
This method can be widely applied in professional design fields requiring precise color control, such as brand design and product image generation.
Limitations & Outlook
The model's color fidelity may decrease under specific lighting conditions due to shadows affecting color label accuracy. Future work could extend to more color control tasks and incorporate palette-specific supervision.
Plain Language Accessible to non-experts
Imagine you're painting a picture and want each object to have a specific color. Traditional methods are like needing different paint sets to mix colors, while Paint-Anything is like a universal palette where you just enter a color code, and it automatically mixes the color for you. This way, you can focus more on creating without worrying about getting the color wrong.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a game where you can change your character's color with a code. Paint-Anything is like a super tool that lets you change your character's color just by entering a color code! Isn't that cool? Now you can design your game character any way you want!
Glossary
Hex Value
A method of representing colors using six-digit hexadecimal numbers, commonly used in web design.
Used in the paper to specify target colors.
Object-Level Color Supervision
A method of training models by labeling object colors.
Used to learn the shared hex-prompt interface.
Pure-Color Anchor
Solid-color image samples used at high-noise timesteps to provide a clear color reference.
Used to improve color fidelity.
ACBench (Any Color Benchmark)
A benchmark used to evaluate color fidelity in generation and editing tasks.
Used to validate model performance.
FLUX.2-4B
A deep learning model used for image generation and editing.
Used as the base model for experiments.
Open Questions Unanswered questions from this research
- 1 How to maintain color fidelity under different lighting conditions? Current methods perform poorly under shadows, requiring more robust color labeling methods.
- 2 How to extend to more color control tasks without increasing computational cost?
Applications
Immediate Applications
Brand Design
Designers can use Paint-Anything to create brand logos and images with specific colors, ensuring color consistency.
Long-term Vision
Automated Design Tools
In the future, Paint-Anything could be integrated into automated design tools to help users quickly generate designs that meet specific color requirements.
Abstract
Professional design requires any-color control: the ability to specify an object's target color with any 24-bit hex value for image generation and editing. Prior work has explored color generation, editing, and colorization, but often relies on dedicated color representations or specialized inference procedures. Advances in large language models offer a simpler starting point: even compact models can associate hex values with color semantics. We present Paint-Anything, which learns a shared hex-prompt interface for generation and editing through object-level color supervision. We develop a data pipeline that constructs Paint-500K from real images through object grounding, perceptual color labeling, and editing-pair synthesis. Since shadows make real-image labels only approximate colors, we complement this supervision with pure-color anchors whose pixels exactly match their paired hex values. These anchors are used only at high-noise timesteps, leaving low-noise training to natural images. We further introduce Any Color Benchmark (ACBench), comprising ACBench-T2I and ACBench-Edit, to measure object-level hex color fidelity across both tasks. On FLUX.2-4B, Paint-Anything improves ACBench-T2I and ACBench-Edit scores by 85.3% and 28.3%, respectively, relative to the base model, with ablations supporting the training recipe. It also achieves the highest average CompColor score among the compared methods.