How Control Information Influences Multilingual Text Image Generation and Editing?

Summary

This article investigates an intriguing issue: how controlling information affects performance in the process of text image editing and generation. Through a series of observations and experiments, the authors extract valuable insights. Building upon these findings, they craft a novel framework that demonstrates impressive performance in both the generation and editing of text images.

Strengths

1. The experimental design, performance analysis, and underlying motivations are presented in a systematic manner with high-quality writing. Some findings offer valuable guidance for the community. 2. The performance gain in recognition accuracy is substantial. 3.The dataset introduced holds significant value.

Weaknesses

1. There is an absence of detailed quantitative analysis for the internal workings of the designed modules. 2. Although some perspectives are effectively illustrated through visualizations, they lack the backing of quantitative analysis. 3. Typos in references, such as Diffute: Universal text editing diffusion model. Trivial writing of the experimental section.

Questions

1. Within an equitable comparison framework, the performance improvement in TextGen on the FID metric is less pronounced compared to its advances in recognition accuracy. Are there any further analyses on this issue? 2. The author notes, "Although TG2M... it is highly effective for training and achieves superior performance." Is there any quantitative evidence available to support this claim?

Rating

7

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

The authors list the potential limitations and analyze the reasons briefly.

Summary

This paper introduces a novel framework to enhance the quality of multilingual text image generation and editing by optimizing control information. The authors investigate the impact of control information from three perspectives: input encoding, the role at different stages, and output features. They find some insight conclusion and propose a framework (TextGen) employs Fourier analysis to emphasize relevant information and reduce noise, uses a two-stage generation framework to align the different roles of control information at various stages. Furthermore, they introduce an effective and lightweight dataset for training. The method achieves state-of-the-art performance in both Chinese and English visual text generation.

Strengths

1. The paper investigate the influence of control information, which can greatly inspire future works of the community. In summary, this paper is well organized, with reasonable motivation and insights. 2. The use of Fourier analysis to enhance input and output features, along with the two-stage generation framework, offers innovative methodological approaches in the field of text image generation. 3. The creation of a new lightweight yet effective dataset, TG2M, provides a valuable resource for training in visual text generation and editing.

Weaknesses

1. To my best knowledge, there exist other benchmarks such as the benchmark in TextDiffuser[1]. Although these benchmarks may not be of good quality, it would be better to compare TextGen with other works on these benchmarks or construct a high-quality benchmark based on TG-2M. 2. The data building process is not described in detail. Even though there are some differences (the recaption using Qwen-VL, the data selection), it looks a bit similar to AnyWord-3M and the author needs to describe the differences in paper or Appendix. [1] Chen, Jingye, et al. "TextDiffuser: Diffusion Models as Text Painters" NeurlPS 2023 [2] Tuo, Yuxiang, et al. "AnyText: Multilingual Visual Text Generation And Editing" ICLR 2024

Questions

1. Could TextGen generate other languages, such as Korean? 2. How about the diversity of textgen's generation of the same prompt? The authors can put some visualization examples.

Rating

7

Confidence

4

Soundness

3

Presentation

4

Contribution

4

Limitations

refer to weaknesses.

Summary

This study explores the advancement of visual text generation using diffusion models within a ControlNet-based framework, focusing on the impact of control information. It examines input encoding, the role of control information during different stages of the denoising process, and the resulting output features. The authors propose TextGen, a method that aims to enhance generation quality by optimizing control information with Fourier analysis and a two-stage generation process.

Strengths

1. Analyzed the impact of three control factors on text image generation. 2. Achieved comparable results in text image generation and editing.

Weaknesses

1. The method lacks novelty, the writing of the paper is unclear, and the experimental results are insufficient. The experimental analysis does not provide new insight into the field. 2. The paper's analysis of control factors for text image generation is limited to three control conditions, which may not be the most effective. For example, character embeddings could be used as input, sentence prompts through an LLM could be used, or text coordinate encodings could be used as control conditions. 3. The necessity of the FEC module is not very clear, as it does not specifically target text image scenarios. Additionally, the computational efficiency of FFT and IFFT needs to be considered. 4. Why are global control and detail control split into two stages? Would it not be possible to consider both global and detail control simultaneously during the whole diffusion process? 5. The improvement of this method over existing methods is not obvious, especially in terms of visual and perceptual quality. Training on a small dataset also affects the method's generalizability. For example, can it still correctly generate text that appears infrequently or not at all in the proposed dataset? 6. The results lack demonstrations of “scene” text image generation; most images resemble text printing and have less realism compared to Anytext. This may be due to dataset bias, as shown in Figure 11. Thus, a small dataset is not a contribution of this paper and could be considered a drawback for large models. 7. The ablation experiments are insufficient, and the effectiveness of the modules is not convincingly demonstrated.

Questions

1. Why only consider these three control methods. 2. What is the reason and necessity for introducing Fourier transform. 3. Why can't both global and detail control be considered simultaneously during the diffusion process? Wouldn't it be possible to use a learnable module to control the weights of these two controls. The two seperated control stages proposed in this paper is clearly not optimal. 4. It is recommended to add more ablation experiments, such as without the Fourier module, and considering both global and detail control simultaneously. More visualization results should be provided, such as visualizations of the ablation experiments. 5. How does the method perform on characters that appear infrequently in the dataset. 6. It is recommended to include failure cases and analyze the reasons. Additional results for text image editing should also be provided. 7. Please adequately address the concerns raised in the weaknesses section.

Rating

5

Confidence

5

Soundness

2

Presentation

2

Contribution

1

Limitations

Yes

Summary

This paper analyzes the issue of control in image generation models. Specifically, the article addresses three aspects: input control information, the impact of control information at different stages, and output control information. The model was optimized for two tasks: text-to-image and image editing. Using a method similar to FreeU, the paper conducts Fourier frequency domain analysis on the input and output features and proposes a two-stage generation model based on previous findings.

Strengths

1.The writing of the article is excellent, with clear and concise sentences and well-organized structure. 2.The proposed final model FEC+GP+TS+IFE shows significant improvement. 3.The method is quite innovative, with a novel approach.

Weaknesses

1.Some models lack detailed theoretical analysis, which makes the purpose of the model unclear. 2. It is not clear what the task of this paper issue. Please point it out in a prominent position in the first chapter. 3. The layout is a bit messy. For example, should Table 2 be placed above Table 1?

Questions

1.Will the dataset be released? If so, please address the privacy, copyright, and consent of the data. 2.The convolution theorem states that the Fourier transform of a function's convolution is the product of the Fourier transforms of the functions. In other words, convolution in one domain corresponds to multiplication in another domain, such as time-domain convolution corresponding to frequency-domain multiplication. Can the FEC network in the article be considered as performing a simple point-wise multiplication operation? 3. I have questions about the effectiveness of FEC. The article only shows that using FEC is better through ablation experiments, but I have two concerns: 1) The improvement for English is not significant, while it is more noticeable for Chinese. Is the main reason for this that the model was fine-tuned on Chinese? 2) When using methods like depth control, the output might not match the control, but the output could still be reasonable. Using glyph images for control requires a strong match between the output and the input text. From a human observation perspective, glyph control might seem more precise, but for model training with MSE loss, both approaches could be similar. 4. Section 4.3 does not have accompanying text? 5. Why does convolution perform poorly in understanding text? Shouldn't there be some small experiments to demonstrate this? Alternatively, could it be explained through some visualization analysis or derivation process? 6. Figure 4, which serves as the overall pipeline diagram, does not seem to reflect the inference structure discussed in Chapter 3.5.

Rating

3

Confidence

4

Soundness

2

Presentation

3

Contribution

2

Limitations

The article does not specifically discuss the potential negative societal impacts. For example, generating high-quality images guided by text might facilitate the creation and spread of fake news. Additionally, using more complex models could lead to increased resource consumption, among other issues.