ArtiFade: Learning to Generate High-quality Subject from Blemished Images (2409.03745v1)

Abstract: Subject-driven text-to-image generation has witnessed remarkable advancements in its ability to learn and capture characteristics of a subject using only a limited number of images. However, existing methods commonly rely on high-quality images for training and may struggle to generate reasonable images when the input images are blemished by artifacts. This is primarily attributed to the inadequate capability of current techniques in distinguishing subject-related features from disruptive artifacts. In this paper, we introduce ArtiFade to tackle this issue and successfully generate high-quality artifact-free images from blemished datasets. Specifically, ArtiFade exploits fine-tuning of a pre-trained text-to-image model, aiming to remove artifacts. The elimination of artifacts is achieved by utilizing a specialized dataset that encompasses both unblemished images and their corresponding blemished counterparts during fine-tuning. ArtiFade also ensures the preservation of the original generative capabilities inherent within the diffusion model, thereby enhancing the overall performance of subject-driven methods in generating high-quality and artifact-free images. We further devise evaluation benchmarks tailored for this task. Through extensive qualitative and quantitative experiments, we demonstrate the generalizability of ArtiFade in effective artifact removal under both in-distribution and out-of-distribution scenarios.

• The paper introduces ArtiFade, a framework that fine-tunes diffusion models to generate artifact-free images from blemished inputs.
• It leverages paired training data and optimized cross-attention layers with an artifact-free embedding to improve reconstruction fidelity.
• Evaluations using I-DINO, I-CLIP, and T-CLIP metrics show that ArtiFade outperforms methods like Textual Inversion and DreamBooth in both ID and OOD scenarios.

An Analytical Overview of "ArtiFade: Learning to Generate High-quality Subject from Blemished Images"

The paper "ArtiFade: Learning to Generate High-quality Subject from Blemished Images" addresses the challenge of generating high-quality artifact-free images from datasets marred by various visual artifacts. This task is especially pertinent in scenarios where input images are sourced from the internet and may be blemished by watermarks, stickers, or even invisible artifacts such as adversarial noise.

The core contribution of this paper is ArtiFade, an innovative framework designed to adapt current subject-driven text-to-image generation techniques for blemished datasets. ArtiFade builds upon the foundational Latent Diffusion Model (LDM) with significant enhancements, including the partial fine-tuning of the model to specifically address artifact-related disruptions.

Technical Approach

ArtiFade employs a multifaceted strategy to solve the challenge of artifact removal:

1. Dataset Construction: The authors construct a specialized training set that includes pairs of unblemished and corresponding blemished images. These pairs serve as the basis for learning the implicit relationship and alignment between natural images and their artifact-ridden counterparts.
2. Model Fine-Tuning: The ArtiFade framework fine-tunes a pre-trained text-to-image model, specifically by optimizing the key and value weights within the cross-attention layers of the diffusion model. Additionally, an artifact-free embedding is trained to further enhance the robustness of the model against artifacts.
3. Artifact Rectification Training: The training process involves optimizing a reconstruction loss between the latent representations of unblemished images and their blemished counterparts. The artifact-free embedding complements this by retaining textual information and improving prompt fidelity.

Evaluation and Results

The authors introduce a comprehensive evaluation benchmark that comprises multiple test sets with different types of artifacts and tailored metrics to assess the performance of artifact removal methods. This benchmark includes in-distribution (ID) and out-of-distribution (OOD) scenarios to test the generalizability of ArtiFade.

Quantitative Metrics:

• I\textsuperscript{DINO} and I\textsuperscript{CLIP}: These metrics measure the similarity between generated images and unblemished input images, assessing subject reconstruction fidelity.
• T\textsuperscript{CLIP}: This evaluates the fidelity of text conditioning by comparing the similarity between generated images and text prompts.
• R\textsuperscript{DINO} and R\textsuperscript{CLIP}: These relative ratios assess the degree of artifact removal by comparing the similarity scores between generated images and both unblemished and blemished images.

The results demonstrate the efficacy of ArtiFade across various metrics. Notably, ArtiFade significantly outperforms Textual Inversion and DreamBooth in both ID and OOD scenarios by consistently achieving higher subject fidelity and better artifact removal.

Practical and Theoretical Implications

Practical Implications:

ArtiFade's ability to generate artifact-free images from blemished datasets has several practical applications. It can be utilized in scenarios where high-quality input images are rare or costly to acquire, thereby streamlining workflows in domains such as content creation, digital restoration, and augmented reality.

Theoretical Implications:

The paper's contribution extends the theoretical understanding of how generative models can be adapted to tolerate and rectify input artifacts. By fine-tuning cross-attention parameters and incorporating artifact-free embeddings, the research provides insights into improving the robustness and generalizability of diffusion models.

Future Directions in AI

Speculating on future developments, there are a few promising avenues:

• Enhanced Artifact Recognition: Future work could involve integrating more sophisticated methods for recognizing and categorizing different types of artifacts.
• Scalability and Efficiency: Improving the scalability of the model to handle larger datasets and more complex artifacts without compromising performance.
• Real-time Applications: Adapting ArtiFade for real-time or near-real-time applications could open new possibilities in live content generation and manipulation.

In conclusion, the "ArtiFade" paper makes substantial contributions to the field of subject-driven text-to-image generation by addressing the novel challenge of blemished image datasets. Through extensive evaluation and robust methodological innovations, it provides a framework that significantly enhances the capability of generative models to produce high-quality, artifact-free images, setting a new benchmark for future research and applications in this domain.