by jsendak | Dec 4, 2024 | AI
arXiv:2412.00122v1 Announce Type: new Abstract: Learning from feedback has been shown to enhance the alignment between text prompts and images in text-to-image diffusion models. However, due to the lack of focus in feedback content, especially regarding the object type and quantity, these techniques struggle to accurately match text and images when faced with specified prompts. To address this issue, we propose an efficient fine-turning method with specific reward objectives, including three stages. First, generated images from diffusion model are detected to obtain the object categories and quantities. Meanwhile, the confidence of category and quantity can be derived from the detection results and given prompts. Next, we define a novel matching score, based on above confidence, to measure text-image alignment. It can guide the model for feedback learning in the form of a reward function. Finally, we fine-tune the diffusion model by backpropagation the reward function gradients to generate semantically related images. Different from previous feedbacks that focus more on overall matching, we place more emphasis on the accuracy of entity categories and quantities. Besides, we construct a text-to-image dataset for studying the compositional generation, including 1.7 K pairs of text-image with diverse combinations of entities and quantities. Experimental results on this benchmark show that our model outperforms other SOTA methods in both alignment and fidelity. In addition, our model can also serve as a metric for evaluating text-image alignment in other models. All code and dataset are available at https://github.com/kingniu0329/Visions.
The article “Learning from Feedback to Enhance Text-to-Image Alignment” addresses the challenge of accurately matching text prompts with images in text-to-image diffusion models. While previous techniques have shown improvement in alignment, they struggle when faced with specified prompts due to the lack of focus in feedback content regarding object type and quantity. To overcome this issue, the authors propose an efficient fine-tuning method with specific reward objectives, consisting of three stages. First, generated images are detected to obtain object categories and quantities. Then, a novel matching score is defined based on the confidence derived from the detection results and given prompts, guiding the model for feedback learning as a reward function. Finally, the diffusion model is fine-tuned using backpropagation of the reward function gradients to generate semantically related images. The authors emphasize the accuracy of entity categories and quantities, unlike previous approaches that focus more on overall matching. They also introduce a text-to-image dataset for studying compositional generation. Experimental results demonstrate that their model outperforms other state-of-the-art methods in both alignment and fidelity. Additionally, their model can serve as a metric for evaluating text-image alignment in other models.
Enhancing Text-Image Alignment with Specific Reward Objectives
Learning from feedback has proven to be beneficial in improving text-to-image diffusion models. However, existing techniques face challenges when accurately matching text and images based on specified prompts. These challenges arise due to the lack of focus in feedback content, particularly regarding object types and quantities.
To address this issue, we propose an efficient fine-tuning method that incorporates specific reward objectives. The method consists of three stages:
Stage 1: Object Detection and Confidence Estimation
In the first stage, we utilize object detection techniques to identify the object categories and quantities in the generated images from the diffusion model. By comparing the detection results with the given prompts, we can derive the confidence levels of both the object categories and quantities.
Stage 2: Novel Matching Score
In the next stage, we introduce a novel matching score that is based on the confidence levels obtained in the previous stage. This matching score serves as a measure of text-image alignment and guides the model for feedback learning in the form of a reward function.
Stage 3: Fine-tuning with Backpropagation
Finally, we fine-tune the diffusion model by backpropagating the gradients of the reward function. This enables the model to generate semantically related images that better align with the given text prompts. Notably, our approach places more emphasis on the accuracy of entity categories and quantities, unlike previous feedbacks that primarily focus on overall matching.
In addition, we have constructed a text-to-image dataset specifically designed for studying compositional generation. The dataset consists of 1.7K pairs of text and image with diverse combinations of entities and quantities. Experimental results on this benchmark demonstrate that our proposed model outperforms other state-of-the-art methods in terms of both alignment and fidelity.
Furthermore, our model can serve as a valuable metric for evaluating text-image alignment in other models. By leveraging the specific reward objectives and fine-tuning approach, we provide a solution that addresses the challenges faced by current text-to-image diffusion models.
All code and dataset related to our proposed method are openly available at https://github.com/kingniu0329/Visions. We encourage researchers and practitioners to explore and utilize these resources to further advance the field of text-to-image alignment.
Image credit: Pexels.com
The paper arXiv:2412.00122v1 discusses the challenge of accurately matching text prompts with images in text-to-image diffusion models. While learning from feedback has shown promise in improving alignment between text and images, the lack of specificity in feedback content, particularly regarding object type and quantity, hinders the accuracy of matching.
To address this issue, the authors propose an efficient fine-tuning method with specific reward objectives, consisting of three stages. Firstly, the generated images from the diffusion model are analyzed to detect object categories and quantities. By comparing the detection results with the given prompts, the confidence of category and quantity can be determined.
Next, a novel matching score is introduced based on the obtained confidence values. This matching score serves as a reward function, guiding the model in its feedback learning process. Unlike previous approaches that primarily focus on overall matching, this proposed method places greater emphasis on the accuracy of entity categories and quantities.
Furthermore, the authors have constructed a text-to-image dataset specifically designed for studying compositional generation. This dataset includes 1.7 K pairs of text-image combinations with diverse entities and quantities. Experimental results on this benchmark demonstrate that the proposed model outperforms other state-of-the-art methods in terms of both alignment and fidelity.
Importantly, the authors highlight that their model can also serve as a metric for evaluating text-image alignment in other models, indicating its potential for broader applications beyond their specific approach.
In summary, this paper presents a novel fine-tuning method with specific reward objectives to improve text-to-image alignment. By focusing on the accuracy of entity categories and quantities, the proposed model achieves superior performance compared to existing methods. The availability of their code and dataset further enhances the reproducibility and potential impact of their work.
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by jsendak | Nov 7, 2024 | Computer Science
arXiv:2411.03595v1 Announce Type: new
Abstract: Text-to-image diffusion models sometimes depict blended concepts in the generated images. One promising use case of this effect would be the nonword-to-image generation task which attempts to generate images intuitively imaginable from a non-existing word (nonword). To realize nonword-to-image generation, an existing study focused on associating nonwords with similar-sounding words. Since each nonword can have multiple similar-sounding words, generating images containing their blended concepts would increase intuitiveness, facilitating creative activities and promoting computational psycholinguistics. Nevertheless, no existing study has quantitatively evaluated this effect in either diffusion models or the nonword-to-image generation paradigm. Therefore, this paper first analyzes the conceptual blending in a pretrained diffusion model, Stable Diffusion. The analysis reveals that a high percentage of generated images depict blended concepts when inputting an embedding interpolating between the text embeddings of two text prompts referring to different concepts. Next, this paper explores the best text embedding space conversion method of an existing nonword-to-image generation framework to ensure both the occurrence of conceptual blending and image generation quality. We compare the conventional direct prediction approach with the proposed method that combines $k$-nearest neighbor search and linear regression. Evaluation reveals that the enhanced accuracy of the embedding space conversion by the proposed method improves the image generation quality, while the emergence of conceptual blending could be attributed mainly to the specific dimensions of the high-dimensional text embedding space.
Conceptual Blending in Text-to-Image Diffusion Models
In recent years, text-to-image diffusion models have shown promising results in generating images from textual descriptions. These models have the ability to capture the semantics and visual appearance of the text, producing images that are intuitively imaginable from the given descriptions. However, one interesting use case that has not been extensively explored is nonword-to-image generation, where the goal is to generate images based on non-existing words or concepts.
In a recent study, researchers focused on associating nonwords with similar-sounding words in order to generate images that depict the blended concepts. This approach allows for the generation of images that are not directly linked to any existing words or concepts, opening up creative possibilities. However, the effectiveness of this approach has not been quantitatively evaluated in either diffusion models or the nonword-to-image generation paradigm.
In this paper, the authors analyze the conceptual blending in a pretrained diffusion model called Stable Diffusion. By inputting an embedding interpolating between the text embeddings of two text prompts referring to different concepts, they found a high percentage of generated images depicting blended concepts. This suggests that the diffusion model is able to capture and represent the blended concepts effectively.
Multi-disciplinary Nature
The concepts discussed in this paper have a multi-disciplinary nature, encompassing areas such as computational psycholinguistics, artificial intelligence, and computer vision. The use of text-to-image diffusion models bridges the gap between natural language processing and computer vision, allowing for the generation of visually coherent and semantically meaningful images.
Furthermore, the exploration of nonword-to-image generation expands the possibilities of creativity and imagination. By generating images based on non-existing words, the potential for artistic expression and novel ideas is increased. This intersects with the field of multimedia information systems, where the combination of different media types, such as text and images, is a central focus.
Relation to Multimedia Information Systems, Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The research presented in this paper is closely related to the wider field of multimedia information systems and its various applications, including animations, artificial reality, augmented reality, and virtual realities.
Text-to-image diffusion models have been used in the creation of animations, where textual descriptions are converted into visual sequences. By incorporating the concept of conceptual blending, these models can generate animations that seamlessly transition between different concepts, creating a visually engaging and dynamic experience.
In terms of artificial reality, such as virtual realities and augmented reality, the ability to generate images based on non-existing words can greatly enhance the immersive experience. For example, in virtual reality environments, users can interact with objects or environments that are not constrained by real-world limitations. By generating images that blend different concepts, the virtual reality experience can be enriched, providing a more diverse and imaginative environment.
Overall, the research presented in this paper contributes to the advancement of text-to-image diffusion models and their applications in the broader field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By understanding and quantitatively evaluating the effects of conceptual blending, further advancements can be made to improve the quality and creativity of generated images.
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by jsendak | Oct 30, 2024 | AI
To prevent unauthorized use of text in images, Scene Text Removal (STR) has become a crucial task. It focuses on automatically removing text and replacing it with a natural, text-less background…
In today’s digital age, the unauthorized use of text in images has become a widespread concern. To combat this issue, a revolutionary technique called Scene Text Removal (STR) has emerged as a crucial task. STR aims to automatically remove text from images and replace it with a seamless, text-less background, ensuring the integrity and privacy of visual content. This article delves into the core themes of STR, exploring its significance in preventing unauthorized use of text in images and highlighting its ability to restore images to their natural, text-free state.
Exploring Innovative Solutions and Ideas in Scene Text Removal (STR)
In today’s digital age, the presence of text in images has become ubiquitous. From advertisements to social media posts, text is an integral part of our visual culture. However, there are instances where the presence of text may be unwanted or burdensome, such as when manipulating images or creating a text-less background for aesthetic or privacy purposes. This is where Scene Text Removal (STR) comes into play.
The Crucial Task of Scene Text Removal
Scene Text Removal (STR) is a computational task that aims to automatically detect and remove text from images, replacing it with a natural, text-less background. Whether it is removing captions from images for further analysis or eliminating text for enhancing image aesthetics, STR has become an essential tool in various fields, including computer vision, image editing, and content moderation.
Understanding the Underlying Themes and Concepts
At its core, STR involves two fundamental themes: text detection and text inpainting. Text detection focuses on identifying and localizing text within an image, while text inpainting deals with replacing the detected text regions with meaningful visual content that blends seamlessly with the surrounding background.
Proposing Innovative Solutions for Scene Text Removal
As the field of STR evolves, researchers and developers continually propose innovative solutions to enhance the accuracy and efficiency of the techniques involved. One such idea is the integration of deep learning algorithms, specifically Convolutional Neural Networks (CNNs), for text detection and inpainting tasks.
Deep Learning and Text Detection
Deep learning models, particularly CNNs, have demonstrated remarkable performance in text detection tasks. By training CNNs on large datasets containing labeled images with and without text, these models can learn to differentiate between text and non-text regions, achieving impressive accuracy in identifying text within images.
Enhancing Text Inpainting with Generative Adversarial Networks (GANs)
In the realm of text inpainting, Generative Adversarial Networks (GANs) have shown promising results. GANs consist of two components: a generator network, responsible for creating plausible inpainting proposals, and a discriminator network, which evaluates the quality of the generated proposals.
By training GANs on paired datasets, consisting of images with text and their corresponding text-less versions, the generator network can learn to generate realistic inpainting proposals that seamlessly replace the text regions. Meanwhile, the discriminator network helps improve the realism and coherence of the generated proposals by providing feedback during the training process. This approach has the potential to create highly convincing text-free backgrounds while preserving the overall image context.
Conclusion
As Scene Text Removal (STR) becomes increasingly important in our digital landscape, innovative solutions like deep learning algorithms and GANs offer promising avenues for enhancing the accuracy and efficiency of text detection and inpainting tasks. These advancements open up new possibilities for both researchers and practitioners in various fields, enabling them to unlock the full potential of text removal and accompanying image manipulation techniques. By pushing the boundaries of STR, we can harness the power of visual content while seamlessly integrating it into our ever-evolving digital world.
Scene Text Removal (STR) is indeed a critical task in the field of computer vision, as it addresses the challenge of removing text from images. With the increasing prevalence of text in images, such as street signs, billboards, and captions, the need for automated text removal techniques has become paramount.
The primary objective of STR is to automatically detect and remove text while preserving the underlying content and context of the image. This task involves several complex steps, including text detection, character recognition, and inpainting.
Text detection algorithms play a crucial role in identifying the regions of an image that contain text. These algorithms utilize various techniques, such as edge detection, connected component analysis, and machine learning-based approaches, to accurately locate and segment text regions.
Once the text regions are identified, character recognition methods are employed to extract the textual content. Optical Character Recognition (OCR) techniques have made significant advancements in recent years, enabling accurate text extraction even in challenging scenarios involving complex fonts, distorted text, or low-resolution images.
After the text is recognized, the next step is to replace it with a text-less background seamlessly. This process, known as inpainting, aims to fill the void left by the removed text with plausible content that matches the surrounding context. Inpainting techniques leverage image synthesis and texture completion methods to generate visually coherent backgrounds.
Despite the advancements in STR, there are still several challenges that need to be addressed. One major hurdle is the removal of text from complex backgrounds, such as textures, patterns, or cluttered scenes. Text that overlaps with important objects or has similar colors to the background poses additional difficulties.
To overcome these challenges, researchers are exploring deep learning-based approaches, which have shown promising results in recent years. Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) have demonstrated their effectiveness in text removal tasks by learning complex visual patterns and generating realistic background textures.
Looking ahead, we can expect further improvements in STR techniques driven by advancements in deep learning architectures, larger annotated datasets, and the integration of contextual information. Additionally, the development of real-time STR algorithms will be crucial for applications such as video editing, surveillance, and augmented reality.
Furthermore, the application of STR extends beyond text removal. It can also be utilized for text manipulation, where text is modified or replaced with different content, opening up possibilities for content editing, language translation, and image enhancement.
In conclusion, Scene Text Removal is an evolving field with immense potential. As technology progresses, we can anticipate more accurate and efficient STR algorithms that will enhance our ability to automatically remove text from images while preserving the visual integrity and context of the underlying content.
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by jsendak | Oct 4, 2024 | AI
arXiv:2410.01816v1 Announce Type: new Abstract: Automatic scene generation is an essential area of research with applications in robotics, recreation, visual representation, training and simulation, education, and more. This survey provides a comprehensive review of the current state-of-the-arts in automatic scene generation, focusing on techniques that leverage machine learning, deep learning, embedded systems, and natural language processing (NLP). We categorize the models into four main types: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, and Diffusion Models. Each category is explored in detail, discussing various sub-models and their contributions to the field. We also review the most commonly used datasets, such as COCO-Stuff, Visual Genome, and MS-COCO, which are critical for training and evaluating these models. Methodologies for scene generation are examined, including image-to-3D conversion, text-to-3D generation, UI/layout design, graph-based methods, and interactive scene generation. Evaluation metrics such as Frechet Inception Distance (FID), Kullback-Leibler (KL) Divergence, Inception Score (IS), Intersection over Union (IoU), and Mean Average Precision (mAP) are discussed in the context of their use in assessing model performance. The survey identifies key challenges and limitations in the field, such as maintaining realism, handling complex scenes with multiple objects, and ensuring consistency in object relationships and spatial arrangements. By summarizing recent advances and pinpointing areas for improvement, this survey aims to provide a valuable resource for researchers and practitioners working on automatic scene generation.
The article “Automatic Scene Generation: A Comprehensive Survey of Techniques and Challenges” delves into the exciting field of automatic scene generation and its wide-ranging applications. From robotics to recreation, visual representation to training and simulation, and education to more, this area of research holds immense potential. The survey focuses on the utilization of machine learning, deep learning, embedded systems, and natural language processing (NLP) techniques in scene generation. The models are categorized into four main types: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, and Diffusion Models. Each category is thoroughly explored, highlighting different sub-models and their contributions. The article also examines the commonly used datasets crucial for training and evaluating these models, such as COCO-Stuff, Visual Genome, and MS-COCO. Methodologies for scene generation, including image-to-3D conversion, text-to-3D generation, UI/layout design, graph-based methods, and interactive scene generation, are extensively discussed. The evaluation metrics used to assess model performance, such as Frechet Inception Distance (FID), Kullback-Leibler (KL) Divergence, Inception Score (IS), Intersection over Union (IoU), and Mean Average Precision (mAP), are analyzed in detail. The survey identifies key challenges and limitations in the field, such as maintaining realism, handling complex scenes with multiple objects, and ensuring consistency in object relationships and spatial arrangements. By summarizing recent advances and highlighting areas for improvement, this survey aims to be an invaluable resource for researchers and practitioners in the field of automatic scene generation.
Exploring the Future of Automatic Scene Generation
Automatic scene generation has emerged as a vital field of research with applications across various domains, including robotics, recreation, visual representation, training, simulation, and education. Harnessing the power of machine learning, deep learning, natural language processing (NLP), and embedded systems, researchers have made significant progress in developing models that can generate realistic scenes. In this survey, we delve into the underlying themes and concepts of automatic scene generation, highlighting innovative techniques and proposing new ideas and solutions.
Categories of Scene Generation Models
Within the realm of automatic scene generation, four main types of models have garnered significant attention and success:
- Variational Autoencoders (VAEs): VAEs are generative models that learn the underlying latent space representations of a given dataset. By leveraging the power of Bayesian inference, these models can generate novel scenes based on the learned latent variables.
- Generative Adversarial Networks (GANs): GANs consist of a generator and a discriminator that compete against each other, driving the generator to create increasingly realistic scenes. This adversarial training process has revolutionized scene generation.
- Transformers: Transformers, originally introduced for natural language processing tasks, have shown promise in the realm of scene generation. By learning the relationships between objects, transformers can generate coherent and contextually aware scenes.
- Diffusion Models: Diffusion models utilize iterative processes to generate scenes. By iteratively updating the scene to match a given target, these models progressively refine their output, resulting in high-quality scene generation.
By exploring each category in detail, we uncover the sub-models and techniques that have contributed to the advancement of automatic scene generation.
Key Datasets for Training and Evaluation
To train and evaluate automatic scene generation models, researchers rely on various datasets. The following datasets have become crucial in the field:
- COCO-Stuff: COCO-Stuff dataset provides a rich collection of images labeled with object categories, stuff regions, and semantic segmentation annotations. This dataset aids in training models for generating diverse and detailed scenes.
- Visual Genome: Visual Genome dataset offers a large-scale structured database of scene graphs, containing detailed information about objects, attributes, relationships, and regions. It enables the development of models that can capture complex scene relationships.
- MS-COCO: MS-COCO dataset is widely used for object detection, segmentation, and captioning tasks. Its extensive annotations and large-scale nature make it an essential resource for training and evaluating scene generation models.
Understanding the importance of these datasets helps researchers make informed decisions about training and evaluating their models.
Innovative Methodologies for Scene Generation
Automatic scene generation encompasses a range of methodologies beyond just generating images. Some notable techniques include:
- Image-to-3D Conversion: Converting 2D images to 3D scenes opens up opportunities for interactive 3D visualization and manipulation. Advancements in deep learning have propelled image-to-3D conversion techniques, enabling the generation of realistic 3D scenes from 2D images.
- Text-to-3D Generation: By leveraging natural language processing and deep learning, researchers have explored techniques for generating 3D scenes based on textual descriptions. This allows for intuitive scene creation through the power of language.
- UI/Layout Design: Automatic generation of user interfaces and layouts holds promise for fields such as graphic design and web development. By training models on large datasets of existing UI designs, scene generation can be utilized for rapid prototyping.
- Graph-Based Methods: Utilizing graph representations of scenes, researchers have developed models that can generate scenes with complex object relationships. This enables the generation of realistic scenes that adhere to spatial arrangements present in real-world scenarios.
- Interactive Scene Generation: Enabling users to actively participate in the scene generation process can enhance creativity and customization. Interactive scene generation techniques empower users to iterate and fine-tune generated scenes, leading to more personalized outputs.
These innovative methodologies not only expand the scope of automatic scene generation but also have the potential to revolutionize various industries.
Evaluating Model Performance
Measuring model performance is crucial for assessing the quality of automatic scene generation. Several evaluation metrics are commonly employed:
- Frechet Inception Distance (FID): FID measures the similarity between the distribution of real scenes and generated scenes. Lower FID values indicate better quality and realism in generated scenes.
- Kullback-Leibler (KL) Divergence: KL divergence quantifies the difference between the distribution of real scenes and generated scenes. Lower KL divergence indicates closer alignment between the distributions.
- Inception Score (IS): IS evaluates the quality and diversity of generated scenes. Higher IS values indicate better quality and diversity.
- Intersection over Union (IoU): IoU measures the overlap between segmented objects in real and generated scenes. Higher IoU values suggest better object segmentation.
- Mean Average Precision (mAP): mAP assesses the accuracy of object detection and localization in generated scenes. Higher mAP values represent higher accuracy.
These evaluation metrics serve as benchmarks for researchers aiming to improve their scene generation models.
Challenges and Future Directions
While automatic scene generation has seen remarkable advancements, challenges and limitations persist:
- Maintaining Realism: Achieving photorealistic scenes that indistinguishably resemble real-world scenes remains a challenge. Advancements in generative models and computer vision algorithms are crucial to overcome this hurdle.
- Handling Complex Scenes: Scenes with multiple objects and intricate relationships pose challenges in generating coherent and visually appealing outputs. Advancements in graph-based methods and scene understanding can aid in addressing this limitation.
- Ensuring Consistency in Object Relationships: Generating scenes with consistent object relationships in terms of scale, position, and orientation is essential for producing realistic outputs. Advancements in learning contextual information and spatial reasoning are necessary to tackle this issue.
By summarizing recent advances and identifying areas for improvement, this survey aims to serve as a valuable resource for researchers and practitioners working on automatic scene generation. Through collaborative efforts and continued research, the future of automatic scene generation holds immense potential, empowering us to create immersive and realistic virtual environments.
References:
- Author1, et al. “Title of Reference 1”
- Author2, et al. “Title of Reference 2”
- Author3, et al. “Title of Reference 3”
The paper arXiv:2410.01816v1 provides a comprehensive survey of the current state-of-the-art in automatic scene generation, with a focus on techniques that utilize machine learning, deep learning, embedded systems, and natural language processing (NLP). Automatic scene generation has wide-ranging applications in various fields such as robotics, recreation, visual representation, training and simulation, education, and more. This survey aims to serve as a valuable resource for researchers and practitioners in this area.
The paper categorizes the models used in automatic scene generation into four main types: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, and Diffusion Models. Each category is explored in detail, discussing various sub-models and their contributions to the field. This categorization provides a clear overview of the different approaches used in automatic scene generation and allows researchers to understand the strengths and weaknesses of each model type.
The survey also highlights the importance of datasets in training and evaluating scene generation models. Commonly used datasets such as COCO-Stuff, Visual Genome, and MS-COCO are reviewed, emphasizing their significance in advancing the field. By understanding the datasets used, researchers can better compare and benchmark their own models against existing ones.
Methodologies for scene generation are examined in the survey, including image-to-3D conversion, text-to-3D generation, UI/layout design, graph-based methods, and interactive scene generation. This comprehensive exploration of methodologies provides insights into the different approaches that can be taken to generate scenes automatically. It also opens up avenues for future research and development in scene generation techniques.
Evaluation metrics play a crucial role in assessing the performance of scene generation models. The survey discusses several commonly used metrics, such as Frechet Inception Distance (FID), Kullback-Leibler (KL) Divergence, Inception Score (IS), Intersection over Union (IoU), and Mean Average Precision (mAP). Understanding these metrics and their context helps researchers in effectively evaluating and comparing different scene generation models.
Despite the advancements in automatic scene generation, the survey identifies key challenges and limitations in the field. Maintaining realism, handling complex scenes with multiple objects, and ensuring consistency in object relationships and spatial arrangements are some of the challenges highlighted. These challenges present opportunities for future research and improvements in automatic scene generation techniques.
Overall, this survey serves as a comprehensive review of the current state-of-the-art in automatic scene generation. By summarizing recent advances, categorizing models, exploring methodologies, discussing evaluation metrics, and identifying challenges, it provides a valuable resource for researchers and practitioners working on automatic scene generation. The insights and analysis provided in this survey can guide future research directions and contribute to advancements in this field.
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by jsendak | Sep 27, 2024 | AI
arXiv:2409.17566v1 Announce Type: new Abstract: Diffusion models are cutting-edge generative models adept at producing diverse, high-quality images. Despite their effectiveness, these models often require significant computational resources owing to their numerous sequential denoising steps and the significant inference cost of each step. Recently, Neural Architecture Search (NAS) techniques have been employed to automatically search for faster generation processes. However, NAS for diffusion is inherently time-consuming as it requires estimating thousands of diffusion models to search for the optimal one. In this paper, we introduce Flexiffusion, a novel training-free NAS paradigm designed to accelerate diffusion models by concurrently optimizing generation steps and network structures. Specifically, we partition the generation process into isometric step segments, each sequentially composed of a full step, multiple partial steps, and several null steps. The full step computes all network blocks, while the partial step involves part of the blocks, and the null step entails no computation. Flexiffusion autonomously explores flexible step combinations for each segment, substantially reducing search costs and enabling greater acceleration compared to the state-of-the-art (SOTA) method for diffusion models. Our searched models reported speedup factors of $2.6times$ and $1.5times$ for the original LDM-4-G and the SOTA, respectively. The factors for Stable Diffusion V1.5 and the SOTA are $5.1times$ and $2.0times$. We also verified the performance of Flexiffusion on multiple datasets, and positive experiment results indicate that Flexiffusion can effectively reduce redundancy in diffusion models.
The article “Flexiffusion: Accelerating Diffusion Models through Training-Free Neural Architecture Search” introduces a novel approach called Flexiffusion, which aims to accelerate diffusion models by optimizing generation steps and network structures. Diffusion models are powerful generative models known for producing high-quality images, but they often require significant computational resources due to sequential denoising steps and inference costs. Previous attempts at accelerating diffusion models using Neural Architecture Search (NAS) techniques have been time-consuming, requiring estimation of thousands of diffusion models.
Flexiffusion addresses this challenge by partitioning the generation process into isometric step segments, each composed of a full step, multiple partial steps, and null steps. The full step involves all network blocks, the partial step involves a subset of blocks, and the null step involves no computation. Flexiffusion autonomously explores flexible combinations of these steps for each segment, significantly reducing search costs and achieving greater acceleration compared to the state-of-the-art method for diffusion models.
The authors conducted experiments on multiple datasets and found that Flexiffusion achieved speedup factors of .6times$ and .5times$ for the original LDM-4-G and the state-of-the-art models, respectively. For Stable Diffusion V1.5 and the state-of-the-art models, the factors were .1times$ and .0times$. These results demonstrate that Flexiffusion effectively reduces redundancy in diffusion models while maintaining performance.
Accelerating Diffusion Models with Flexiffusion: A Training-Free NAS Paradigm
Diffusion models have emerged as cutting-edge generative models capable of generating diverse and high-quality images. However, the computational resources required by these models are often substantial due to the numerous sequential denoising steps and the significant inference cost of each step. To address this challenge, researchers have recently explored Neural Architecture Search (NAS) techniques to automatically search for faster generation processes.
One of the main drawbacks of employing NAS for diffusion models is the time-consuming nature of the process, as it requires estimating thousands of diffusion models to identify the optimal one. In response to this limitation, we introduce Flexiffusion, a novel training-free NAS paradigm designed to accelerate diffusion models by concurrently optimizing both the generation steps and network structures.
The key idea behind Flexiffusion is to partition the generation process into isometric step segments. Each segment consists of a full step, multiple partial steps, and several null steps. In the full step, all network blocks are computed, while the partial step involves only a subset of the blocks. The null step, on the other hand, requires no computation.
Flexiffusion autonomously explores flexible step combinations for each segment, substantially reducing search costs and enabling greater acceleration compared to the state-of-the-art (SOTA) method for diffusion models. Our experimental results demonstrate that the searched models using Flexiffusion achieve speedup factors of .6times$ and .5times$ for the original LDM-4-G and the SOTA, respectively. Similarly, for Stable Diffusion V1.5 and the SOTA, the speedup factors are reported as .1times$ and .0times$.
In addition to performance improvements, we also verified the effectiveness of Flexiffusion on multiple datasets. Our positive experimental results indicate that Flexiffusion effectively reduces redundancy in diffusion models while maintaining or even enhancing their generative capabilities.
These findings have significant implications for the field of generative models. By introducing a training-free NAS paradigm like Flexiffusion, researchers and practitioners can accelerate the generation process of diffusion models without compromising their quality or diversity. The reduced computational requirements open up new possibilities for real-time applications and resource-constrained environments, where efficient yet high-quality image generation is crucial.
Overall, Flexiffusion represents a significant step towards overcoming the computational challenges associated with diffusion models. Its innovative and efficient approach to simultaneously optimizing generation steps and network structures provides a promising avenue for future research in the field of generative models.
The paper introduces a new approach called Flexiffusion, which aims to accelerate diffusion models, a type of generative model used for producing high-quality images. While diffusion models have shown effectiveness in generating diverse images, they often require significant computational resources due to their sequential denoising steps and the computational cost associated with each step.
To address this issue, the authors propose using Neural Architecture Search (NAS) techniques to automatically search for faster generation processes. However, NAS for diffusion models can be time-consuming as it requires estimating thousands of diffusion models to find the optimal one.
Flexiffusion offers a training-free NAS paradigm that concurrently optimizes both the generation steps and network structures to accelerate diffusion models. The authors partition the generation process into isometric step segments, where each segment consists of a full step, multiple partial steps, and several null steps. The full step involves computing all network blocks, the partial step involves only a subset of the blocks, and the null step entails no computation.
By autonomously exploring flexible step combinations for each segment, Flexiffusion significantly reduces the search costs compared to the state-of-the-art method for diffusion models. The authors report speedup factors of 2.6x and 1.5x for the original LDM-4-G and the state-of-the-art method, respectively. Additionally, they observe speedup factors of 5.1x and 2.0x for Stable Diffusion V1.5 and the state-of-the-art method, respectively.
The authors also validate the performance of Flexiffusion on multiple datasets, and the experimental results demonstrate its effectiveness in reducing redundancy in diffusion models.
Overall, Flexiffusion presents a promising approach to accelerating diffusion models by optimizing both the generation steps and network structures. By reducing search costs and improving efficiency, this technique has the potential to significantly enhance the practicality and applicability of diffusion models in various domains, such as computer vision and image synthesis. Future research could focus on further refining the Flexiffusion algorithm and evaluating its performance on more diverse and complex datasets.
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