UniColor: A Unified Framework for Multi-Modal Colorization with Transformer (2209.11223v1)

Abstract: We propose the first unified framework UniColor to support colorization in multiple modalities, including both unconditional and conditional ones, such as stroke, exemplar, text, and even a mix of them. Rather than learning a separate model for each type of condition, we introduce a two-stage colorization framework for incorporating various conditions into a single model. In the first stage, multi-modal conditions are converted into a common representation of hint points. Particularly, we propose a novel CLIP-based method to convert the text to hint points. In the second stage, we propose a Transformer-based network composed of Chroma-VQGAN and Hybrid-Transformer to generate diverse and high-quality colorization results conditioned on hint points. Both qualitative and quantitative comparisons demonstrate that our method outperforms state-of-the-art methods in every control modality and further enables multi-modal colorization that was not feasible before. Moreover, we design an interactive interface showing the effectiveness of our unified framework in practical usage, including automatic colorization, hybrid-control colorization, local recolorization, and iterative color editing. Our code and models are available at https://luckyhzt.github.io/unicolor.

• The paper introduces a two-stage process that unifies diverse modalities through a Transformer-driven framework for image colorization.
• It leverages Chroma-VQGAN for effective chroma disentangling and a Hybrid-Transformer to generate high-quality color outputs from varied hints.
• Results outperform existing models based on metrics like FID, LPIPS, and colorfulness, demonstrating versatility across digital art and restoration applications.

A Unified Framework for Multi-Modal Colorization with Transformers: An Overview

The paper "UniColor: A Unified Framework for Multi-Modal Colorization with Transformer" introduces a novel approach for colorizing grayscale images by integrating various modalities into a common framework. The proposed approach, UniColor, leverages a Transformer-based model to offer diverse colorization options, including both unconditional and conditional modalities such as stroke, exemplar, and text-based inputs. This exploration aims to provide a detailed understanding of the methodology and its positioning within the existing literature on image colorization.

Key Contributions

UniColor's primary innovation lies in unifying multi-modal inputs into a single framework. Previous approaches typically require separate models for each modality, limiting their flexibility and usability. UniColor, in contrast, offers a two-stage process: first converting diverse conditions into hint points, and then utilizing a Transformer framework for colorization. The design includes a novel CLIP-based mechanism that translates text into hint points, enabling text-conditioned colorization.

The core components of the framework consist of Chroma-VQGAN and a Hybrid-Transformer network:

• Chroma-VQGAN: This component is responsible for disentangling chroma representations from grayscale features while retaining essential image details.
• Hybrid-Transformer: This network predicts diverse and high-quality colorizations conditioned on the grayscale input and hint points.

The paper demonstrates UniColor's superiority over state-of-the-art models in terms of both performance and versatility across various modalities. The paper substantiates these claims through quantitative metrics such as FID, colorfulness, and LPIPS, showcasing improved results over existing methods.

Implications and Future Directions

UniColor represents a significant step forward in the domain of image colorization by allowing users to blend multiple input modalities seamlessly. This has profound implications for practical applications such as film restoration, digital art, and interactive media, where user-guided colorization is essential. By supporting hybrid modalities, UniColor expands user flexibility, catering to complex artistic needs.

From a theoretical standpoint, the paper underscores the potential of Transformers in handling multi-modal inputs, highlighting the feasibility of utilizing grid-based hint point conversion for enhancing Transformer architectures. Future research may build upon this work to explore further optimization of hint point generation or simplifying the Transformer model's complexity to improve computational efficiency.

The application of CLIP embeddings in translating textual descriptions into image conditions opens up avenues for exploring richer text-to-image transformations. This could potentially be expanded into broader image synthesis tasks beyond colorization, offering intriguing research directions for the AI community.

Conclusion

The UniColor framework represents a pivotal advancement in image colorization technologies, offering a unified solution capable of handling multiple input modalities with high fidelity and diverse results. By integrating a robust hint points conversion strategy and a sophisticated Transformer-based colorization network, the paper not only addresses existing limitations in the domain but also sets the stage for further innovations in AI-driven image synthesis.