Singleis in the Cryptocurrencies Industry

Single

A Single Point of Access or Chaos — Unlocking Non-Blocking API Gateways

How to Troubleshoot 'hostpolicy.dll' and 'singlefilehost.exe' Errors in .NET 8.0.11

Kunjamas ‘hostpolicy.dll’ ukat ‘singlefilehost.exe’ pantjasiwinak askichañax .NET 8.0.11

Com solucionar els errors "hostpolicy.dll" i "singlefilehost.exe" a .NET 8.0.11

Jak odstraňovat chyby 'hostpolicy.dll' a 'singlefilehost.exe' v .NET 8.0.11

Τρόπος αντιμετώπισης σφαλμάτων "hostpolicy.dll" και "singlefilehost.exe" στο .NET 8.0.11

Cómo solucionar los errores 'hostpolicy.dll' y 'singlefilehost.exe' en .NET 8.0.11

نحوه عیب‌یابی خطاهای «hostpolicy.dll» و «singlefilehost.exe» در NET 8.0.11

Kako otkloniti pogreške 'hostpolicy.dll' i 'singlefilehost.exe' u .NET 8.0.11

.NET 8.0.11 での「hostpolicy.dll」および「singlefilehost.exe」エラーのトラブルシューティング方法

.NET 8.0.11 හි 'hostpolicy.dll' සහ 'singlefilehost.exe' දෝෂ නිරාකරණය කරන්නේ කෙසේද?

Maitiro eku Troubleshoot 'hostpolicy.dll' uye 'singlefilehost.exe' Zvakanganisa mu.NET 8.0.11

Чӣ гуна хатогиҳои "hostpolicy.dll" ва "singlefilehost.exe" -ро дар .NET 8.0.11 бартараф кардан мумкин аст

วิธีการแก้ไขปัญหาข้อผิดพลาด 'hostpolicy.dll' และ 'singlefilehost.exe' ใน .NET 8.0.11

Table of Links Abstract and 1 Introduction 2. Related Works 2.1. 2D Diffusion Models for 3D Generation 2.2. 3D Generative Models and 2.3. Multi-view Diffusion Models 3. Problem Formulation 3.1. Diffusion Models 3.2. The Distribution of 3D Assets 4. Method and 4.1. Consistent Multi-view Generation 4.2. Cross-Domain Diffusion 4.3. Textured Mesh Extraction 5. Experiments 5.1. Implementation Details 5.2. Baselines 5.3. Evaluation Protocol 5.4. Single View Reconstruction 5.5. Novel View Synthesis and 5.6. Discussions 6. Conclusions and Future Works, Acknowledgements and References Abstract In this work, we introduce Wonder3D, a novel method for efficiently generating high-fidelity textured meshes from single-view images. Recent methods based on Score Distillation Sampling (SDS) have shown the potential to recover 3D geometry from 2D diffusion priors, but they typically suffer from time-consuming per-shape optimization and inconsistent geometry. In contrast, certain works directly produce 3D information via fast network inferences, but their results are often of low quality and lack geometric details. To holistically improve the quality, consistency, and efficiency of single-view reconstruction tasks, we propose a cross-domain diffusion model that generates multiview normal maps and the corresponding color images. To ensure the consistency of generation, we employ a multiview cross-domain attention mechanism that facilitates information exchange across views and modalities. Lastly, we introduce a geometry-aware normal fusion algorithm that extracts high-quality surfaces from the multi-view 2D representations. Our extensive evaluations demonstrate that our method achieves high-quality reconstruction results, robust generalization, and good efficiency compared to prior works Introduction Reconstructing 3D geometry from a single image stands as a fundamental task in computer graphics and 3D computer vision [12, 25, 31, 33, 35, 38, 41, 44], offering a wide range of versatile applications such as virtual reality, video games, 3D content creation, and the precision of robotics grasping. However, this task is notably challenging since it is ill-posed and demands the ability to discern the 3D geometry of both visible and invisible parts. This ability requires extensive knowledge of the 3D world. knowledge of the 3D world. Recently, the field of 3D generation has experienced rapid and flourishing development with the introduction of diffusion models. A growing body of research [5, 29, 43, 59, 63], such as DreamField [24], DreamFusion [43], and Magic3D [29], resort to distilling prior knowledge of 2D image diffusion models or vision language models to create 3D models from text or images via Score Distillation Sampling (SDS) [43]. Despite their compelling results, these methods suffer from two main limitations: efficiency and consistency. The per-shape optimization process typically entails tens of thousands of iterations, involving full-image volume rendering and inferences of the diffusion models. Consequently, it often consumes tens of minutes or even hours on per-shape optimization. Moreover, the 2D prior model operates by considering only a single view at each iteration and strives to make every view resemble the input image. This often results in the generation of 3D shapes exhibiting inconsistencies, thus, often leading to the generation of 3D shapes with inconsistencies such as multiple faces (i.e., the Janus problem [43]). There exists another group of works that endeavor to directly produce 3D geometries like point clouds [37, 41, 71, 75], meshes [16, 34], neural fields [1, 4, 7, 14, 17, 21, 25– 27, 40, 42, 61, 72] via network inference to avoid timeconsuming per-shape optimization. Most of them attempt to train 3D generative diffusion models from scratch on 3D assets. However, due to the limited size of publicly available 3D datasets, these methods demonstrate poor generalizability, most of which can only generate shapes on specific categories. More recently, several methods have emerged that directly generate multi-view 2D images, with representative works including SyncDreamer [33] and MVDream [51]. By enhancing the multi-view consistency of image generation, these methods can recover 3D shapes from the generated multi-view images. Following these works, our method also adopts a multi-view generation scheme to favor the flexibility and efficiency of 2D representations. However, due to only relying on color images, the fidelity of the generated shapes is not well-maintained, and they struggle to recover geometric details or come with enormous computational costs. To better address the issues of fidelity, consistency, generalizability and efficiency in the aforementioned works, in this paper, we introduce a new approach to the task of single-view 3D reconstruction by generating multi-view consistent normal maps and their corresponding color images with a cross-domain diffusion model. The key idea is to extend the stable diffusion framework to model the joint distribution of two different domains, i.e., normals and colors. We demonstrate that this can be achieved by introducing a domain switcher and a cross-domain attention scheme. In particular, the domain switcher allows the diffusion model to generate either normal maps or color images, while the cross-domain attention mechanisms assist in the information exchange between the two domains, ultimately improving consistency and quality. Finally, in order to stably extract surfaces from the generated views, we propose a geometry-aware normal fusion algorithm that is robust to the inaccuracies and capable of reconstructing clean and high-quality geometries (see Figure 1). We conduct extensive experiments on the Google Scanned Object dataset [13] and various 2D images with different styles. The experiments validate that Wonder3D is capable of producing high-quality geometry with high efficiency in comparison with baseline methods. Wonder3D possesses several distinctive properties and accordingly has the following contributions: • Wonder3D holistically considers the issues of generation quality, efficiency, generalizability, and consistency for 2 single-view 3D reconstruction. It has achieved a leading level of geometric details with reasonably good efficiency among current zero-shot single-view reconstruction methods. • We propose a new multi-view cross-domain 2D diffusion model to predict normal maps and color images. This representation not only adapts to the original data distribution of Stable Diffusion model but also effectively captures the rich surface details of the target shape. • We propose a cross-domain attention mechanism to produce multi-view normal maps and color images that are consistently aligned. This mechanism facilitates information perception across different domains, enabling our method to recover high-fidelity geometry. • We introduce a novel geometry-aware normal fusion algorithm that can robustly extract surfaces from the generated normal maps and color images. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors:
(1) Xiaoxiao Long, The University of Hong Kong, VAST, MPI Informatik and Equal Contributions;
(2) Yuan-Chen Guo, Tsinghua University, VAST and Equal Contributions.
(3) Cheng Lin, The University of Hong Kong with Corresponding authors;
(4) Yuan Liu, The University of Hong Kong;
(5) Zhiyang Dou, The University of Hong Kong;
(6) Lingjie Liu, University of Pennsylvania;
(7) Yuexin Ma, Shanghai Tech University;
(8) Song-Hai Zhang, The University of Hong Kong;
(9) Marc Habermann, MPI Informatik;
(10) Christian Theobalt, MPI Informatik;
(11) Wenping Wang, Texas A&M University with Corresponding authors. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Works 2.1. 2D Diffusion Models for 3D Generation 2.1. 2D Diffusion Models for 3D Generation 2.2. 3D Generative Models and 2.3. Multi-view Diffusion Models 2.2. 3D Generative Models and 2.3. Multi-view Diffusion Models 3. Problem Formulation 3.1. Diffusion Models 3.1. Diffusion Models 3.2. The Distribution of 3D Assets 3.2. The Distribution of 3D Assets 4. Method and 4.1. Consistent Multi-view Generation 4. Method and 4.1. Consistent Multi-view Generation 4.2. Cross-Domain Diffusion 4.2. Cross-Domain Diffusion 4.3. Textured Mesh Extraction 4.3. Textured Mesh Extraction 5. Experiments 5.1. Implementation Details 5.1. Implementation Details 5.2. Baselines 5.2. Baselines 5.3. Evaluation Protocol 5.3. Evaluation Protocol 5.4. Single View Reconstruction 5.4. Single View Reconstruction 5.5. Novel View Synthesis and 5.6. Discussions 5.5. Novel View Synthesis and 5.6. Discussions 6. Conclusions and Future Works, Acknowledgements and References 6. Conclusions and Future Works, Acknowledgements and References Abstract In this work, we introduce Wonder3D , a novel method for efficiently generating high-fidelity textured meshes from single-view images. Recent methods based on Score Distillation Sampling (SDS) have shown the potential to recover 3D geometry from 2D diffusion priors, but they typically suffer from time-consuming per-shape optimization and inconsistent geometry. In contrast, certain works directly produce 3D information via fast network inferences, but their results are often of low quality and lack geometric details. Wonder3D To holistically improve the quality, consistency, and efficiency of single-view reconstruction tasks, we propose a cross-domain diffusion model that generates multiview normal maps and the corresponding color images. To ensure the consistency of generation, we employ a multiview cross-domain attention mechanism that facilitates information exchange across views and modalities. Lastly, we introduce a geometry-aware normal fusion algorithm that extracts high-quality surfaces from the multi-view 2D representations. Our extensive evaluations demonstrate that our method achieves high-quality reconstruction results, robust generalization, and good efficiency compared to prior works Introduction Reconstructing 3D geometry from a single image stands as a fundamental task in computer graphics and 3D computer vision [12, 25, 31, 33, 35, 38, 41, 44], offering a wide range of versatile applications such as virtual reality, video games, 3D content creation, and the precision of robotics grasping. However, this task is notably challenging since it is ill-posed and demands the ability to discern the 3D geometry of both visible and invisible parts. This ability requires extensive knowledge of the 3D world. knowledge of the 3D world. Recently, the field of 3D generation has experienced rapid and flourishing development with the introduction of diffusion models. A growing body of research [5, 29, 43, 59, 63], such as DreamField [24], DreamFusion [43], and Magic3D [29], resort to distilling prior knowledge of 2D image diffusion models or vision language models to create 3D models from text or images via Score Distillation Sampling (SDS) [43]. Despite their compelling results, these methods suffer from two main limitations: efficiency and consistency. The per-shape optimization process typically entails tens of thousands of iterations, involving full-image volume rendering and inferences of the diffusion models. Consequently, it often consumes tens of minutes or even hours on per-shape optimization. Moreover, the 2D prior model operates by considering only a single view at each iteration and strives to make every view resemble the input image. This often results in the generation of 3D shapes exhibiting inconsistencies, thus, often leading to the generation of 3D shapes with inconsistencies such as multiple faces (i.e., the Janus problem [43]). There exists another group of works that endeavor to directly produce 3D geometries like point clouds [37, 41, 71, 75], meshes [16, 34], neural fields [1, 4, 7, 14, 17, 21, 25– 27, 40, 42, 61, 72] via network inference to avoid timeconsuming per-shape optimization. Most of them attempt to train 3D generative diffusion models from scratch on 3D assets. However, due to the limited size of publicly available 3D datasets, these methods demonstrate poor generalizability, most of which can only generate shapes on specific categories. More recently, several methods have emerged that directly generate multi-view 2D images, with representative works including SyncDreamer [33] and MVDream [51]. By enhancing the multi-view consistency of image generation, these methods can recover 3D shapes from the generated multi-view images. Following these works, our method also adopts a multi-view generation scheme to favor the flexibility and efficiency of 2D representations. However, due to only relying on color images, the fidelity of the generated shapes is not well-maintained, and they struggle to recover geometric details or come with enormous computational costs. To better address the issues of fidelity, consistency, generalizability and efficiency in the aforementioned works, in this paper, we introduce a new approach to the task of single-view 3D reconstruction by generating multi-view consistent normal maps and their corresponding color images with a cross-domain diffusion model. The key idea is to extend the stable diffusion framework to model the joint distribution of two different domains, i.e., normals and colors. We demonstrate that this can be achieved by introducing a domain switcher and a cross-domain attention scheme. In particular, the domain switcher allows the diffusion model to generate either normal maps or color images, while the cross-domain attention mechanisms assist in the information exchange between the two domains, ultimately improving consistency and quality. Finally, in order to stably extract surfaces from the generated views, we propose a geometry-aware normal fusion algorithm that is robust to the inaccuracies and capable of reconstructing clean and high-quality geometries (see Figure 1). We conduct extensive experiments on the Google Scanned Object dataset [13] and various 2D images with different styles. The experiments validate that Wonder3D is capable of producing high-quality geometry with high efficiency in comparison with baseline methods. Wonder3D possesses several distinctive properties and accordingly has the following contributions: • Wonder3D holistically considers the issues of generation quality, efficiency, generalizability, and consistency for 2 single-view 3D reconstruction. It has achieved a leading level of geometric details with reasonably good efficiency among current zero-shot single-view reconstruction methods. • We propose a new multi-view cross-domain 2D diffusion model to predict normal maps and color images. This representation not only adapts to the original data distribution of Stable Diffusion model but also effectively captures the rich surface details of the target shape. • We propose a cross-domain attention mechanism to produce multi-view normal maps and color images that are consistently aligned. This mechanism facilitates information perception across different domains, enabling our method to recover high-fidelity geometry. • We introduce a novel geometry-aware normal fusion algorithm that can robustly extract surfaces from the generated normal maps and color images. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors: (1) Xiaoxiao Long, The University of Hong Kong, VAST, MPI Informatik and Equal Contributions; (2) Yuan-Chen Guo, Tsinghua University, VAST and Equal Contributions. (3) Cheng Lin, The University of Hong Kong with Corresponding authors; (4) Yuan Liu, The University of Hong Kong; (5) Zhiyang Dou, The University of Hong Kong; (6) Lingjie Liu, University of Pennsylvania; (7) Yuexin Ma, Shanghai Tech University; (8) Song-Hai Zhang, The University of Hong Kong; (9) Marc Habermann, MPI Informatik; (10) Christian Theobalt, MPI Informatik; (11) Wenping Wang, Texas A&M University with Corresponding authors. Authors: Authors: (1) Xiaoxiao Long, The University of Hong Kong, VAST, MPI Informatik and Equal Contributions; (2) Yuan-Chen Guo, Tsinghua University, VAST and Equal Contributions. (3) Cheng Lin, The University of Hong Kong with Corresponding authors; (4) Yuan Liu, The University of Hong Kong; (5) Zhiyang Dou, The University of Hong Kong; (6) Lingjie Liu, University of Pennsylvania; (7) Yuexin Ma, Shanghai Tech University; (8) Song-Hai Zhang, The University of Hong Kong; (9) Marc Habermann, MPI Informatik; (10) Christian Theobalt, MPI Informatik; (11) Wenping Wang, Texas A&M University with Corresponding authors.

What Is Wonder3D? A Method for Generating High-Fidelity Textured Meshes From Single-View Images

Authors:
(1) Hanoona Rasheed, Mohamed bin Zayed University of AI and equally contributing first authors;
(2) Muhammad Maaz, Mohamed bin Zayed University of AI and equally contributing first authors;
(3) Sahal Shaji, Mohamed bin Zayed University of AI;
(4) Abdelrahman Shaker, Mohamed bin Zayed University of AI;
(5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University;
(6) Hisham Cholakkal, Mohamed bin Zayed University of AI;
(7) Rao M. Anwer, Mohamed bin Zayed University of AI and Aalto University;
(8) Eric Xing, Mohamed bin Zayed University of AI and Carnegie Mellon University;
(9) Ming-Hsuan Yang, University of California - Merced and Google Research;
(10) Fahad S. Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 9 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Table of Links Abstract and 1 Introduction
2. Related Work
3. Method
4. Data Annotation Pipeline
5. Experiments
6. Conclusion and References Supplementary Material (Part 1) A. Additional Implementation Details
B. Additional Downstream Tasks
C. Additional Qualitative Results Supplementary Material (Part 2) D. Dataset Visualization
E. Limitations and Future Work
F. Ethics and Societal Impact C. Additional Qualitative Results In this section, we provide more qualitative examples to better understand the capacity of GLaMM. C.1. Grounded Conversation Generation (GCG) Fig. 7 shows qualitative results of GLaMM finetuned on GranDf dataset. The model could produce dense captions and provide dense pixel-level groundings of the caption. C.2. Referring Segmentation Fig. 8 shows the effectiveness of GLaMM in understanding the natural language query and segmenting the corresponding objects. Note that GLaMM can also segment multiple objects via multi-round conversations. C.3. Region-level Captioning Fig. 9 shows the qualitative results of GLaMM for regionlevel understanding. Our model can generate detailed descriptions about the user-specified regions in an image. C.4. Image-level Captioning Fig. 10 shows GLaMM’s qualitative results on captioning tasks. Our model can generate dense captions for images. C.5. Conditional Image Generation Fig. 12 shows GLaMM’s seamless integration for generative tasks. We use the Stable Diffusion inpainting model stable-diffusion-xl-1.0-inpainting [41] for this task. We first generate a segmentation mask using our GlaMM model based on the user query. This segmentation mask along with the user prompt is given as the input to the Stable Diffusion inpainting model, which generates the final output. Figure 14. Dataset samples from GranDf. The figure shows the GPT4 [34] prompts used and the created dataset samples from Grandf dataset. This repurposed human-annotated dataset provides rich semantics to GLaMM for GCG task. C.6. Conversations Fig. 13 illustrates the unique functionality of GLaMM to engage in multi-purpose task conversations. GLaMM is a generic conversational model that can accept prompts in the form of text and/or region and can answer in the form of text and/or segmentation masks. Note that our model is not explicitly trained to handle such scenarios, and this behavior emerges mainly due to our pretraining on GranD dataset, where an image is presented to LMM in different contexts. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Hanoona Rasheed, Mohamed bin Zayed University of AI and equally contributing first authors; (2) Muhammad Maaz, Mohamed bin Zayed University of AI and equally contributing first authors; (3) Sahal Shaji, Mohamed bin Zayed University of AI; (4) Abdelrahman Shaker, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Hisham Cholakkal, Mohamed bin Zayed University of AI; (7) Rao M. Anwer, Mohamed bin Zayed University of AI and Aalto University; (8) Eric Xing, Mohamed bin Zayed University of AI and Carnegie Mellon University; (9) Ming-Hsuan Yang, University of California - Merced and Google Research; (10) Fahad S. Khan, Mohamed bin Zayed University of AI and Linköping University. Authors: Authors: (1) Hanoona Rasheed, Mohamed bin Zayed University of AI and equally contributing first authors; (2) Muhammad Maaz, Mohamed bin Zayed University of AI and equally contributing first authors; (3) Sahal Shaji, Mohamed bin Zayed University of AI; (4) Abdelrahman Shaker, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Hisham Cholakkal, Mohamed bin Zayed University of AI; (7) Rao M. Anwer, Mohamed bin Zayed University of AI and Aalto University; (8) Eric Xing, Mohamed bin Zayed University of AI and Carnegie Mellon University; (9) Ming-Hsuan Yang, University of California - Merced and Google Research; (10) Fahad S. Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 9 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Editor's Note: This is Part 9 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Editor's Note: This is Part 9 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Editor's Note: This is Part 9 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Table of Links Abstract and 1 Introduction 2. Related Work 3. Method 4. Data Annotation Pipeline 5. Experiments 6. Conclusion and References Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Work 2. Related Work 3. Method 3. Method 4. Data Annotation Pipeline 4. Data Annotation Pipeline 5. Experiments 5. Experiments 6. Conclusion and References 6. Conclusion and References Supplementary Material (Part 1) Supplementary Material (Part 1) A. Additional Implementation Details B. Additional Downstream Tasks C. Additional Qualitative Results A. Additional Implementation Details A. Additional Implementation Details B. Additional Downstream Tasks B. Additional Downstream Tasks C. Additional Qualitative Results C. Additional Qualitative Results Supplementary Material (Part 2) Supplementary Material (Part 2) D. Dataset Visualization E. Limitations and Future Work F. Ethics and Societal Impact D. Dataset Visualization D. Dataset Visualization E. Limitations and Future Work E. Limitations and Future Work F. Ethics and Societal Impact F. Ethics and Societal Impact C. Additional Qualitative Results In this section, we provide more qualitative examples to better understand the capacity of GLaMM. C.1. Grounded Conversation Generation (GCG) Fig. 7 shows qualitative results of GLaMM finetuned on GranD f dataset. The model could produce dense captions and provide dense pixel-level groundings of the caption. f C.2. Referring Segmentation Fig. 8 shows the effectiveness of GLaMM in understanding the natural language query and segmenting the corresponding objects. Note that GLaMM can also segment multiple objects via multi-round conversations. C.3. Region-level Captioning Fig. 9 shows the qualitative results of GLaMM for regionlevel understanding. Our model can generate detailed descriptions about the user-specified regions in an image. C.4. Image-level Captioning Fig. 10 shows GLaMM’s qualitative results on captioning tasks. Our model can generate dense captions for images. C.5. Conditional Image Generation Fig. 12 shows GLaMM’s seamless integration for generative tasks. We use the Stable Diffusion inpainting model stable-diffusion-xl-1.0-inpainting [41] for this task. We first generate a segmentation mask using our GlaMM model based on the user query. This segmentation mask along with the user prompt is given as the input to the Stable Diffusion inpainting model, which generates the final output. Figure 14. Dataset samples from GranDf. The figure shows the GPT4 [34] prompts used and the created dataset samples from Grandf dataset. This repurposed human-annotated dataset provides rich semantics to GLaMM for GCG task. Dataset samples from GranD f . f C.6. Conversations Fig. 13 illustrates the unique functionality of GLaMM to engage in multi-purpose task conversations. GLaMM is a generic conversational model that can accept prompts in the form of text and/or region and can answer in the form of text and/or segmentation masks. Note that our model is not explicitly trained to handle such scenarios, and this behavior emerges mainly due to our pretraining on GranD dataset, where an image is presented to LMM in different contexts. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

This New AI Can See, Talk, and Even Edit Images in a Single Conversation

This article outlines how to transition from singletons to dependency injection in a SwiftUI app with minimal effort. If you're here, you likely already know that you should avoid singletons with various of reasons like: Hidden Dependencies: Classes rely on singletons without explicit indicators, making it harder to track app dependencies and leading to unintended coupling.
Circular Dependencies: Singletons depending on each other can create cycles, leading to initialization deadlocks as instances wait for each other.
Testing Challenges: Using singletons complicates testing and SwiftUI previews since singletons can't easily be swapped for mocks. Lack of Flexibility: Singletons make it difficult to provide different implementations for different environments (e.g., live vs. mock versions for testing). And many others. Dependency Injection To overcome most issues we can use Dependency injection (DI). Using it, swapping in a new implementation becomes much easier, as components don’t rely on a single shared instance but on protocols that can be implemented in different ways. This promotes flexibility, easier testing, and adaptability as app requirements evolve. Constructor Injection Constructor injection is a straightforward technique for injecting dependencies by passing them through initializers. While effective, it often increases the size and complexity of initializers. A common challenge with constructor injection is passing dependencies through a deep UI hierarchy. If a dependency is only needed on a specific screen but must be passed down through multiple intermediary screens, this can quickly become cumbersome. One way to handle this is by using the Coordinator pattern, which centralizes navigation and manages dependencies more efficiently. Check out this article to learn how to make it. Why Not Use @EnvironmentObject? While @EnvironmentObject simplifies dependency sharing in SwiftUI, it has limitations: It only works with ObservableObject
It doesn’t allow using protocols for implementation switching.
It only works in SwiftUI views hierarchy
It only checks for service existence when accessed, increasing the risk of unintentional omissions in views. Dependency injection with property wrappers This is similar to the @EnvironmentObject solution where the dependency management happens inside of property wrappers, but without drawbacks. DependencyContainer This small library contains only a couple files, can be copied directly into your project or integrated via SPM. Please, make sure you put a star on it if you like it. Here is the link on GitHub. Let’s create an example of the service we need to pass as a dependency. As we are assuming that we are going to make a mock for testing, we need to implement our service as a protocol. Also we make it as ObservableObject, to review additional features of the framework. import Combine

protocol DataRepository: Actor, ObservableObject {
    
    func fetchData() async throws
}

actor DataRepositoryImp: DataRepository {
    
    func fetchData() async throws {
        // do real stuff
    }
}

actor DataRepositoryMock: DataRepository {
    
    func fetchData() async throws {
        // do mocked stuff
    }
} Defining a DI container Once integrated, you’ll set up a DI container to manage your services. Each service requires a unique key. You have no limitations in creating keys with the same service type. import DependencyContainer

extension DI {
    static let data = Key<any DataRepository>()
} Next let’s create a function for registering service in DI Container. extension DI.Container {
    
    static func setup() {
        register(DI.data, DataRepositoryImp())
    }
} Then, call this function at app startup. @main
struct ExampleApp: App {
    
    @MainActor final class AppState: ObservableObject {
        
        init() {
            DI.Container.setup()
        }
    }
    @StateObject var state = AppState()
    
    var body: some Scene {
        WindowGroup {
            MainView()
        }
    }
} Injecting Dependencies The actual injection is done with several property wrappers. The most common one is @DI.Static. You just need to pass a key we defined earlier. struct MainView: View {
    
    @DI.Static(DI.data) var data
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} The points we need to make attention to: No Need to Specify Type. As you may have noticed we don’t need to write a type here, because Swift gets the type from the key.
It’s very convenient in case you start with a concrete type for your service and later decide to use a protocol to enable mocking. You only need to update the type in the key definition. This change propagates automatically without requiring updates to any code referencing that service. Dependency Existence Checked on Initialization. The property wrapper checks that the service exists when initialized. This helps prevent accidental circular dependencies, where services might try to reference each other. The framework enforces a clear initialization order, encouraging thoughtful dependency management and avoiding deadlocks. Additional property wrappers: @DI.Observed Use this in SwiftUI views when your service is ObservableObject. Views will be updated when the service is changed. struct MainView: View {
    
    //updates the view when data is posting objectWillChange
    @DI.Observed(DI.data) var data
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} @DI.RePublished Should be used in ObservableObject. If your service is ObservableObject, the update will be republished further by the owner of the property wrapper. struct MainView: View {
    
    final class State: ObservableObject {
        
        // Republishes the data.objectWillChange to the State.objectWillChange
        // The view will be updated
        @DI.RePublished(DI.data) var data
    }
    
    @StateObject var state = State()
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} In addition, if you have nested services you can use a key path in property wrappers. In this case the observing logic uses the service referenced by a keypath. @DI.Static(DI.data, \.innerService) var innerService

@DI.Observed(DI.data, \.innerService) var innerService

@DI.RePublished(DI.data, \.innerService) var innerService Mocking To swap out implementations for testing or previews, you have two options: Initialize with mocked dependency
MainView(data: .init(value: DataRepositoryMock())) Replace service with mock in DI Container
DI.Container.register(DI.data, DataRepositoryMock())
    
MainView() Conclusion This guide provides a straightforward approach to adopting DI using a DependencyContainer framework and moving away from singletons, enhancing flexibility, modularity, and testability in your iOS apps. This article outlines how to transition from singletons to dependency injection in a SwiftUI app with minimal effort. singletons dependency injection minimal If you're here, you likely already know that you should avoid singletons with various of reasons like: reasons Hidden Dependencies: Classes rely on singletons without explicit indicators, making it harder to track app dependencies and leading to unintended coupling. Circular Dependencies: Singletons depending on each other can create cycles, leading to initialization deadlocks as instances wait for each other. Testing Challenges: Using singletons complicates testing and SwiftUI previews since singletons can't easily be swapped for mocks. Hidden Dependencies: Classes rely on singletons without explicit indicators, making it harder to track app dependencies and leading to unintended coupling. Hidden Dependencies: Circular Dependencies: Singletons depending on each other can create cycles, leading to initialization deadlocks as instances wait for each other. Circular Dependencies: Testing Challenges : Using singletons complicates testing and SwiftUI previews since singletons can't easily be swapped for mocks. Testing Challenges Lack of Flexibility : Singletons make it difficult to provide different implementations for different environments (e.g., live vs. mock versions for testing). Lack of Flexibility And many others. Dependency Injection Dependency Injection To overcome most issues we can use Dependency injection (DI). Using it, swapping in a new implementation becomes much easier, as components don’t rely on a single shared instance but on protocols that can be implemented in different ways. This promotes flexibility, easier testing, and adaptability as app requirements evolve. Dependency injection protocols Constructor Injection Constructor Injection Constructor injection is a straightforward technique for injecting dependencies by passing them through initializers . While effective, it often increases the size and complexity of initializers. initializers A common challenge with constructor injection is passing dependencies through a deep UI hierarchy . If a dependency is only needed on a specific screen but must be passed down through multiple intermediary screens, this can quickly become cumbersome. deep UI hierarchy One way to handle this is by using the Coordinator pattern, which centralizes navigation and manages dependencies more efficiently. Check out this article to learn how to make it. Coordinator this article this article this article Why Not Use @EnvironmentObject ? Why Not Use @EnvironmentObject ? While @EnvironmentObject simplifies dependency sharing in SwiftUI, it has limitations : @EnvironmentObject limitations It only works with ObservableObject It doesn’t allow using protocols for implementation switching. It only works in SwiftUI views hierarchy It only checks for service existence when accessed, increasing the risk of unintentional omissions in views. It only works with ObservableObject ObservableObject It doesn’t allow using protocols for implementation switching. protocols It only works in SwiftUI views hierarchy views It only checks for service existence when accessed, increasing the risk of unintentional omissions in views. existence Dependency injection with property wrappers Dependency injection with property wrappers This is similar to the @EnvironmentObject solution where the dependency management happens inside of property wrappers , but without drawbacks. @EnvironmentObject property wrappers DependencyContainer DependencyContainer This small library contains only a couple files , can be copied directly into your project or integrated via SPM . couple files SPM Please, make sure you put a star on it if you like it. star Here is the link on GitHub . link on GitHub link on GitHub link on GitHub link on GitHub Let’s create an example of the service we need to pass as a dependency. As we are assuming that we are going to make a mock for testing, we need to implement our service as a protocol. Also we make it as ObservableObject , to review additional features of the framework. ObservableObject import Combine

protocol DataRepository: Actor, ObservableObject {
    
    func fetchData() async throws
}

actor DataRepositoryImp: DataRepository {
    
    func fetchData() async throws {
        // do real stuff
    }
}

actor DataRepositoryMock: DataRepository {
    
    func fetchData() async throws {
        // do mocked stuff
    }
} import Combine

protocol DataRepository: Actor, ObservableObject {
    
    func fetchData() async throws
}

actor DataRepositoryImp: DataRepository {
    
    func fetchData() async throws {
        // do real stuff
    }
}

actor DataRepositoryMock: DataRepository {
    
    func fetchData() async throws {
        // do mocked stuff
    }
} Defining a DI container Defining a DI container Once integrated, you’ll set up a DI container to manage your services. Each service requires a unique key. You have no limitations in creating keys with the same service type . DI container unique key. service type import DependencyContainer

extension DI {
    static let data = Key<any DataRepository>()
} import DependencyContainer

extension DI {
    static let data = Key<any DataRepository>()
} Next let’s create a function for registering service in DI Container. function extension DI.Container {
    
    static func setup() {
        register(DI.data, DataRepositoryImp())
    }
} extension DI.Container {
    
    static func setup() {
        register(DI.data, DataRepositoryImp())
    }
} Then, call this function at app startup . app startup @main
struct ExampleApp: App {
    
    @MainActor final class AppState: ObservableObject {
        
        init() {
            DI.Container.setup()
        }
    }
    @StateObject var state = AppState()
    
    var body: some Scene {
        WindowGroup {
            MainView()
        }
    }
} @main
struct ExampleApp: App {
    
    @MainActor final class AppState: ObservableObject {
        
        init() {
            DI.Container.setup()
        }
    }
    @StateObject var state = AppState()
    
    var body: some Scene {
        WindowGroup {
            MainView()
        }
    }
} Injecting Dependencies Injecting Dependencies The actual injection is done with several property wrappers. The most common one is @DI.Static . You just need to pass a key we defined earlier. injection common @DI.Static key struct MainView: View {
    
    @DI.Static(DI.data) var data
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} struct MainView: View {
    
    @DI.Static(DI.data) var data
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} The points we need to make attention to: attention No Need to Specify Type. As you may have noticed we don’t need to write a type here, because Swift gets the type from the key.
It’s very convenient in case you start with a concrete type for your service and later decide to use a protocol to enable mocking. You only need to update the type in the key definition. This change propagates automatically without requiring updates to any code referencing that service. No Need to Specify Type. As you may have noticed we don’t need to write a type here, because Swift gets the type from the key. It’s very convenient in case you start with a concrete type for your service and later decide to use a protocol to enable mocking. You only need to update the type in the key definition. This change propagates automatically without requiring updates to any code referencing that service. No Need to Specify Type. As you may have noticed we don’t need to write a type here, because Swift gets the type from the key. No Need to Specify Type. type It’s very convenient in case you start with a concrete type for your service and later decide to use a protocol to enable mocking. You only need to update the type in the key definition. This change propagates automatically without requiring updates to any code referencing that service. concrete type protocol Dependency Existence Checked on Initialization. The property wrapper checks that the service exists when initialized. This helps prevent accidental circular dependencies, where services might try to reference each other. The framework enforces a clear initialization order, encouraging thoughtful dependency management and avoiding deadlocks. Dependency Existence Checked on Initialization. The property wrapper checks that the service exists when initialized . This helps prevent accidental circular dependencies, where services might try to reference each other. The framework enforces a clear initialization order, encouraging thoughtful dependency management and avoiding deadlocks . Dependency Existence Checked on Initialization. initialized deadlocks Additional property wrappers: @DI.Observed Use this in SwiftUI views when your service is ObservableObject . Views will be updated when the service is changed. @DI.Observed ObservableObject struct MainView: View {
    
    //updates the view when data is posting objectWillChange
    @DI.Observed(DI.data) var data
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} struct MainView: View {
    
    //updates the view when data is posting objectWillChange
    @DI.Observed(DI.data) var data
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} @DI.RePublished Should be used in ObservableObject . If your service is ObservableObject , the update will be republished further by the owner of the property wrapper. @DI.RePublished ObservableObject ObservableObject struct MainView: View {
    
    final class State: ObservableObject {
        
        // Republishes the data.objectWillChange to the State.objectWillChange
        // The view will be updated
        @DI.RePublished(DI.data) var data
    }
    
    @StateObject var state = State()
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} struct MainView: View {
    
    final class State: ObservableObject {
        
        // Republishes the data.objectWillChange to the State.objectWillChange
        // The view will be updated
        @DI.RePublished(DI.data) var data
    }
    
    @StateObject var state = State()
    
    var body: some View {
        Color.clear.task {
            try? await data.fetchData()
        }
    }
} In addition, if you have nested services you can use a key path in property wrappers. In this case the observing logic uses the service referenced by a keypath. key path observing logic @DI.Static(DI.data, \.innerService) var innerService

@DI.Observed(DI.data, \.innerService) var innerService

@DI.RePublished(DI.data, \.innerService) var innerService @DI.Static(DI.data, \.innerService) var innerService

@DI.Observed(DI.data, \.innerService) var innerService

@DI.RePublished(DI.data, \.innerService) var innerService Mocking Mocking To swap out implementations for testing or previews, you have two options: Initialize with mocked dependency
MainView(data: .init(value: DataRepositoryMock())) Initialize with mocked dependency MainView(data: .init(value: DataRepositoryMock())) Initialize with mocked dependency MainView(data: .init(value: DataRepositoryMock())) MainView(data: .init(value: DataRepositoryMock())) Replace service with mock in DI Container
DI.Container.register(DI.data, DataRepositoryMock())
    
MainView() Replace service with mock in DI Container DI.Container.register(DI.data, DataRepositoryMock())
    
MainView() Replace service with mock in DI Container DI.Container.register(DI.data, DataRepositoryMock())
    
MainView() DI.Container.register(DI.data, DataRepositoryMock())
    
MainView() Conclusion Conclusion This guide provides a straightforward approach to adopting DI using a DependencyContainer framework and moving away from singletons, enhancing flexibility, modularity, and testability in your iOS apps. DependencyContainer DependencyContainer DependencyContainer DependencyContainer

How to Stop Making Singletons in Swift: A Dependency Injection Guide

Breaking Free from the Evil Singleton TL;DR: Refactor singletons to reduce coupling Problems Addressed High coupling
Difficult testability
Multi-threading issues Related Code Smells https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-vii-8dk31x0?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-v-evj3zs9?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-v-evj3zs9?embedable=true Steps Identify the singleton
Locate all references to its getInstance() method
Refactor the singleton to a standard class
Inject it as a dependency Sample Code Before public class DatabaseConnection {
    private static DatabaseConnection instance;

    private DatabaseConnection() {}

    public static DatabaseConnection getInstance() {
        if (instance == null) {
            instance = new DatabaseConnection();
        }
        return instance;
    }

    public void connect() { 
    }
}

public class Service {
    public void performTask() {
        DatabaseConnection connection = DatabaseConnection.getInstance();
        connection.connect(); 
    }
} After public class DatabaseConnection {  
    // 1. Identify the singleton 
    public void connect() { 
    }
}

public class Service {
    // 2. Locate all references to its getInstance() method.
    private DatabaseConnection connection;

    // 3. Refactor the singleton to a standard class. 
    public Service(DatabaseConnection connection) {
        // 4. Inject it as a dependency.
        this.connection = connection;
    }

    public void performTask() {
        connection.connect(); 
    }
}

DatabaseConnection connection = new DatabaseConnection();
// You can also mock the connection in your tests

Service service = new Service(connection);
service.performTask(); Type [x]Semi-Automatic Safety This refactoring is safe when you update all references to the singleton and handle its dependencies correctly. Testing each step ensures that no references to the singleton are missed. Why Is the Code Better? Refactoring away from a singleton makes the code more modular, testable, and less prone to issues caused by the global state. Injecting dependencies allows you to easily replace DatabaseConnection with a mock or different implementation in testing and other contexts. Tags Coupling Related Refactorings https://maximilianocontieri.com/refactoring-007-extract-class?embedable=true See also https://hackernoon.com/singleton-pattern-the-root-of-all-evil-e4r3up7?embedable=true https://hackernoon.com/coupling-the-one-and-only-software-designing-problem-9z5a321h?embedable=true Credits Image by PublicDomainPictures from Pixabay This article is part of the Refactoring Series. How to Improve Your Code With Easy Refactorings Breaking Free from the Evil Singleton Breaking Free from the Evil Singleton TL;DR: Refactor singletons to reduce coupling TL;DR: Refactor singletons to reduce coupling Problems Addressed High coupling Difficult testability Multi-threading issues High coupling coupling Difficult testability testability Multi-threading issues Related Code Smells https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-vii-8dk31x0?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-vii-8dk31x0?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-v-evj3zs9?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-v-evj3zs9?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-v-evj3zs9?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-v-evj3zs9?embedable=true Steps Identify the singleton Locate all references to its getInstance() method Refactor the singleton to a standard class Inject it as a dependency Identify the singleton Locate all references to its getInstance() method getInstance() Refactor the singleton to a standard class Inject it as a dependency Sample Code Before public class DatabaseConnection {
    private static DatabaseConnection instance;

    private DatabaseConnection() {}

    public static DatabaseConnection getInstance() {
        if (instance == null) {
            instance = new DatabaseConnection();
        }
        return instance;
    }

    public void connect() { 
    }
}

public class Service {
    public void performTask() {
        DatabaseConnection connection = DatabaseConnection.getInstance();
        connection.connect(); 
    }
} public class DatabaseConnection {
    private static DatabaseConnection instance;

    private DatabaseConnection() {}

    public static DatabaseConnection getInstance() {
        if (instance == null) {
            instance = new DatabaseConnection();
        }
        return instance;
    }

    public void connect() { 
    }
}

public class Service {
    public void performTask() {
        DatabaseConnection connection = DatabaseConnection.getInstance();
        connection.connect(); 
    }
} After public class DatabaseConnection {  
    // 1. Identify the singleton 
    public void connect() { 
    }
}

public class Service {
    // 2. Locate all references to its getInstance() method.
    private DatabaseConnection connection;

    // 3. Refactor the singleton to a standard class. 
    public Service(DatabaseConnection connection) {
        // 4. Inject it as a dependency.
        this.connection = connection;
    }

    public void performTask() {
        connection.connect(); 
    }
}

DatabaseConnection connection = new DatabaseConnection();
// You can also mock the connection in your tests

Service service = new Service(connection);
service.performTask(); public class DatabaseConnection {  
    // 1. Identify the singleton 
    public void connect() { 
    }
}

public class Service {
    // 2. Locate all references to its getInstance() method.
    private DatabaseConnection connection;

    // 3. Refactor the singleton to a standard class. 
    public Service(DatabaseConnection connection) {
        // 4. Inject it as a dependency.
        this.connection = connection;
    }

    public void performTask() {
        connection.connect(); 
    }
}

DatabaseConnection connection = new DatabaseConnection();
// You can also mock the connection in your tests

Service service = new Service(connection);
service.performTask(); Type [x]Semi-Automatic [x] Semi-Automatic [x] Safety This refactoring is safe when you update all references to the singleton and handle its dependencies correctly. Testing each step ensures that no references to the singleton are missed. Why Is the Code Better? Refactoring away from a singleton makes the code more modular, testable, and less prone to issues caused by the global state. Injecting dependencies allows you to easily replace DatabaseConnection with a mock or different implementation in testing and other contexts. Tags Coupling Coupling Related Refactorings https://maximilianocontieri.com/refactoring-007-extract-class?embedable=true https://maximilianocontieri.com/refactoring-007-extract-class?embedable=true See also https://hackernoon.com/singleton-pattern-the-root-of-all-evil-e4r3up7?embedable=true https://hackernoon.com/singleton-pattern-the-root-of-all-evil-e4r3up7?embedable=true https://hackernoon.com/coupling-the-one-and-only-software-designing-problem-9z5a321h?embedable=true https://hackernoon.com/coupling-the-one-and-only-software-designing-problem-9z5a321h?embedable=true Credits Image by PublicDomainPictures from Pixabay PublicDomainPictures Pixabay This article is part of the Refactoring Series. How to Improve Your Code With Easy Refactorings How to Improve Your Code With Easy Refactorings

Refactoring 018 - How to Replace a Singleton

Authors:
(1) Dustin Podell, Stability AI, Applied Research;
(2) Zion English, Stability AI, Applied Research;
(3) Kyle Lacey, Stability AI, Applied Research;
(4) Andreas Blattmann, Stability AI, Applied Research;
(5) Tim Dockhorn, Stability AI, Applied Research;
(6) Jonas Müller, Stability AI, Applied Research;
(7) Joe Penna, Stability AI, Applied Research;
(8) Robin Rombach, Stability AI, Applied Research. Table of Links Abstract and 1 Introduction 2 Improving Stable Diffusion 2.1 Architecture & Scale 2.2 Micro-Conditioning 2.3 Multi-Aspect Training 2.4 Improved Autoencoder and 2.5 Putting Everything Together 3 Future Work Appendix A Acknowledgements B Limitations C Diffusion Models D Comparison to the State of the Art E Comparison to Midjourney v5.1 F On FID Assessment of Generative Text-Image Foundation Models G Additional Comparison between Single- and Two-Stage SDXL pipeline References G Additional Comparison between Single- and Two-Stage SDXL pipeline H Comparison between SD 1.5 vs. SD 2.1 vs. SDXL I Multi-Aspect Training Hyperparameters We use the following image resolutions for mixed-aspect ratio finetuning as described in Sec. 2.3. J Pseudo-code for Conditioning Concatenation along the Channel Axis This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Dustin Podell, Stability AI, Applied Research; (2) Zion English, Stability AI, Applied Research; (3) Kyle Lacey, Stability AI, Applied Research; (4) Andreas Blattmann, Stability AI, Applied Research; (5) Tim Dockhorn, Stability AI, Applied Research; (6) Jonas Müller, Stability AI, Applied Research; (7) Joe Penna, Stability AI, Applied Research; (8) Robin Rombach, Stability AI, Applied Research. Authors: Authors: (1) Dustin Podell, Stability AI, Applied Research; (2) Zion English, Stability AI, Applied Research; (3) Kyle Lacey, Stability AI, Applied Research; (4) Andreas Blattmann, Stability AI, Applied Research; (5) Tim Dockhorn, Stability AI, Applied Research; (6) Jonas Müller, Stability AI, Applied Research; (7) Joe Penna, Stability AI, Applied Research; (8) Robin Rombach, Stability AI, Applied Research. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Improving Stable Diffusion 2 Improving Stable Diffusion 2.1 Architecture & Scale 2.1 Architecture & Scale 2.2 Micro-Conditioning 2.2 Micro-Conditioning 2.3 Multi-Aspect Training 2.3 Multi-Aspect Training 2.4 Improved Autoencoder and 2.5 Putting Everything Together 2.4 Improved Autoencoder and 2.5 Putting Everything Together 3 Future Work 3 Future Work Appendix Appendix A Acknowledgements A Acknowledgements B Limitations B Limitations C Diffusion Models C Diffusion Models D Comparison to the State of the Art D Comparison to the State of the Art E Comparison to Midjourney v5.1 E Comparison to Midjourney v5.1 F On FID Assessment of Generative Text-Image Foundation Models F On FID Assessment of Generative Text-Image Foundation Models G Additional Comparison between Single- and Two-Stage SDXL pipeline G Additional Comparison between Single- and Two-Stage SDXL pipeline References References G Additional Comparison between Single- and Two-Stage SDXL pipeline H Comparison between SD 1.5 vs. SD 2.1 vs. SDXL I Multi-Aspect Training Hyperparameters We use the following image resolutions for mixed-aspect ratio finetuning as described in Sec. 2.3. J Pseudo-code for Conditioning Concatenation along the Channel Axis This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Additional Comparison between Single- and Two-Stage SDXL pipeline

Authors:
(1) Seokil Ham, KAIST;
(2) Jungwuk Park, KAIST;
(3) Dong-Jun Han, Purdue University;
(4) Jaekyun Moon, KAIST. Table of Links Abstract and 1. Introduction 2. Related Works 3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks 3.2 Algorithm Description 4. Experiments and 4.1 Experimental Setup 4.2. Main Experimental Results 4.3. Ablation Studies and Discussions 5. Conclusion, Acknowledgement and References A. Experiment Details B. Clean Test Accuracy and C. Adversarial Training via Average Attack D. Hyperparameter Tuning E. Discussions on Performance Degradation at Later Exits F. Comparison with Recent Defense Methods for Single-Exit Networks G. Comparison with SKD and ARD and H. Implementations of Stronger Attacker Algorithms F Comparison with Recent Defense Methods for Single-Exit Networks The baselines in the main paper were generally the adversarial defense methods designed for multi-exit networks. In this section, we conduct additional experiments with a recent defense method, TEAT [7], and compare with our method. Since TEAT was originally designed for the single-exit network, we first adapt TEAT to the multi-exit network setting. Instead of the original TEAT that generates the adversarial examples considering the final output of the network, we modify TEAT to generate adversarial examples that maximizes the average loss of all exits in the multi-exit network. Table A3 below shows the results using max-average attack on CIFAR-10/100. It can be seen that our NEO-KD, which is designed for multi-exit networks, achieves higher adversarial test accuracy compared to the TEAT methods (PGD-TE and TRADES-TE) designed for single-exit networks. The results highlight the necessity of developing adversarial defense techniques geared to multi-exit networks rather than adapting general defense methods used for single-exit networks. This paper is available on arxiv under CC 4.0 license. Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, KAIST. Authors: Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, KAIST. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Works 2. Related Works 3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks 3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks 3.2 Algorithm Description 3.2 Algorithm Description 4. Experiments and 4.1 Experimental Setup 4. Experiments and 4.1 Experimental Setup 4.2. Main Experimental Results 4.2. Main Experimental Results 4.3. Ablation Studies and Discussions 4.3. Ablation Studies and Discussions 5. Conclusion, Acknowledgement and References 5. Conclusion, Acknowledgement and References A. Experiment Details A. Experiment Details B. Clean Test Accuracy and C. Adversarial Training via Average Attack B. Clean Test Accuracy and C. Adversarial Training via Average Attack D. Hyperparameter Tuning D. Hyperparameter Tuning E. Discussions on Performance Degradation at Later Exits E. Discussions on Performance Degradation at Later Exits F. Comparison with Recent Defense Methods for Single-Exit Networks F. Comparison with Recent Defense Methods for Single-Exit Networks G. Comparison with SKD and ARD and H. Implementations of Stronger Attacker Algorithms G. Comparison with SKD and ARD and H. Implementations of Stronger Attacker Algorithms F Comparison with Recent Defense Methods for Single-Exit Networks The baselines in the main paper were generally the adversarial defense methods designed for multi-exit networks. In this section, we conduct additional experiments with a recent defense method, TEAT [7], and compare with our method. Since TEAT was originally designed for the single-exit network, we first adapt TEAT to the multi-exit network setting. Instead of the original TEAT that generates the adversarial examples considering the final output of the network, we modify TEAT to generate adversarial examples that maximizes the average loss of all exits in the multi-exit network. Table A3 below shows the results using max-average attack on CIFAR-10/100. It can be seen that our NEO-KD, which is designed for multi-exit networks, achieves higher adversarial test accuracy compared to the TEAT methods (PGD-TE and TRADES-TE) designed for single-exit networks. The results highlight the necessity of developing adversarial defense techniques geared to multi-exit networks rather than adapting general defense methods used for single-exit networks. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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