Neural Residual Diffusion Models for Deep Scalable Vision Generation

Zhiyuan Ma, Liangliang Zhao, Biqing Qi, Bowen Zhou·June 19, 2024

Summary

This paper investigates the limitations of deep stacked networks in neural diffusion models for vision generation, particularly in terms of numerical errors and reduced noisy prediction. The authors propose Neural Residual Diffusion Models (Neural-RDM), a unified and scalable framework that incorporates learnable gated residual parameters. Neural-RDM connects residual-style networks to their implicit ODEs, improving denoising and generation efficiency. The model addresses stability, scalability, and error sensitivity through a gating-residual mechanism, unifying U-Net and Transformer-like architectures. Experiments demonstrate state-of-the-art performance on image and video generation tasks, with improved sample quality and stability. The paper also highlights the potential of Neural-RDM for supporting emergent abilities similar to large language models. Key findings include the model's ability to enhance deep generative training, address frame quality in video generation, and outperform various baselines in benchmark tests, showcasing its adaptability and effectiveness in the field of generative modeling.

Key findings

6

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the scalability dilemma faced by current diffusion models in deep generative training on large-scale vision data . This issue is crucial in determining the models' ability to support scalable training and develop emergent capabilities similar to large language models (LLMs) . The proposed Neural Residual Diffusion Models framework (Neural-RDM) introduces a simple yet significant change to the architecture of deep generative networks by incorporating learnable gated residual parameters to enhance generative abilities and support large-scale training . While the problem of scalability in deep generative training is not new, the paper introduces a novel approach through Neural-RDM to improve the fidelity and consistency of generated content, demonstrating advancements in state-of-the-art generative benchmarks .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the utilization of Neural Residual Diffusion Models (Neural-RDM) for deep scalable vision generation. The research focuses on exploring the dynamics of diffusion models and their application in generating high-resolution images and videos based on text prompts . The study delves into the properties of diffusion models, such as reverse ODEs, denoising dynamics parameterization, and latent space projection, to enhance the fidelity and consistency of generated visual content . The paper also investigates the control of residual sensitivity to address numerical errors in back-propagation and ensure stable and scalable training of the models .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Neural Residual Diffusion Models for Deep Scalable Vision Generation" introduces several innovative ideas, methods, and models in the field of deep generative training for vision data . One key contribution is the proposal of latent diffusion models for high-resolution image synthesis, which aim to improve the quality of generated images . These models incorporate conditional control to enhance text-to-image synthesis, allowing for more precise image generation based on textual input . Additionally, the paper presents text-to-video diffusion models for generating high-fidelity videos with arbitrary lengths, expanding the application of diffusion models to video generation tasks .

Moreover, the paper introduces latent video diffusion models for video generation, enabling the creation of high-quality videos with arbitrary lengths . It also proposes multi-view synthesis and 3D generation from a single image using latent video diffusion, showcasing advancements in multi-view image synthesis and 3D content creation . Another novel contribution is the space-time diffusion model for video generation, which focuses on generating videos with spatial and temporal consistency .

Furthermore, the paper discusses the scalability dilemma faced by current diffusion models and emphasizes the importance of scalable deep generative training on large-scale vision data . It mentions the emergence of Sora, a system that pushes the boundaries of intelligent emergence capabilities in generative models by treating video models as world simulators . This highlights the potential for diffusion models to exhibit emergent abilities similar to large language models (LLMs) .

In summary, the paper presents a range of innovative ideas and models, including latent diffusion models, text-to-image/video diffusion models, multi-view synthesis, 3D generation, and space-time diffusion models, aiming to advance the field of deep generative training for vision data and address scalability challenges in current models . The "Neural Residual Diffusion Models for Deep Scalable Vision Generation" paper introduces several key characteristics and advantages compared to previous methods in deep generative networks .

  1. Gating-Residual Mechanism: The paper emphasizes the significance of the residual unit for effective denoising and generation from a new dynamics perspective, introducing a simple gating-residual mechanism . This mechanism enables stable training of extremely deep networks by parameterizing a learnable mean-variance scheduler, avoiding manual design and supporting infinitely deep scalable training .

  2. Dynamic Consistency: The proposed models exhibit dynamic consistency, ensuring that different dynamic systems describe the same motion path or change rate of data distribution for any time-dependent signal .

  3. Latent Space Projection: The paper introduces latent space projection to compress input images into a high-dimensional space, leveraging a pretrained VQ-VAE model for effective denoising and generation .

  4. Scalability: The Neural-RDM framework offers excellent scalability, enabling massively scalable generative training on large-scale vision data . The models achieve dynamic consistency to denoising probability flow ODE, supporting stable training of deep networks .

  5. Adaptive Stability Maintenance: The introduction of sensitivity-related ODE ensures stable denoising and effective sensitivity control, addressing numerical errors in back-propagation and supporting stable and scalable training .

  6. Qualitative and Quantitative Results: Experimental results consistently demonstrate the effectiveness of the proposed models in improving fidelity, consistency of generated content, and supporting large-scale scalable training .

In summary, the Neural Residual Diffusion Models offer advancements in stability, scalability, denoising effectiveness, dynamic consistency, and latent space projection compared to previous methods, showcasing their potential for enhancing deep generative networks for vision data .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field of deep scalable vision generation, several related research papers and notable researchers have contributed to advancements in this area. Some noteworthy researchers and their works include:

  • Haoxin Chen, Menghan Xia, Yingqing He, Yong Zhang, Xiaodong Cun, Shaoshu Yang, Jinbo Xing, Yaofang Liu, Qifeng Chen, Xintao Wang, and others who worked on Videocrafter1 and Videocrafter2 for high-quality video generation .
  • Wenhao Chai, Xun Guo, Gaoang Wang, Yan Lu, who developed Stablevideo for text-driven consistency-aware diffusion video editing .
  • Omer Bar-Tal, Hila Chefer, Charles Herrmann, Roni Paiss, and others who introduced Lumiere, a space-time diffusion model for video generation .
  • Ben Poole, Ajay Jain, Jonathan T Barron, who presented Dreamfusion for text-to-3D using 2D diffusion .
  • Yichun Shi, Peng Wang, Jianglong Ye, and others who worked on Mvdream for multi-view diffusion for 3D generation .

The key to the solution mentioned in the paper is the development and application of diffusion models for various tasks such as video generation, text-to-image synthesis, text-to-3D conversion, and high-fidelity video generation. These models leverage techniques like latent diffusion, stable video diffusion, and text-driven diffusion to achieve impressive results in generating visual content based on different input modalities .


How were the experiments in the paper designed?

The experiments in the paper were designed by maintaining approximately the same model size for class-conditional and text-conditional image generation experiments, as shown in Table 1. The experiments involved comparing the Neural-RDM with state-of-the-art conditional/unconditional diffusion models for image synthesis and video generation . Additionally, the experiments evaluated the generative performance of Neural-RDM, visualized and analyzed the effects of the proposed gated residuals, and illustrated their advantages in enabling deep scalable training . The paper also conducted comparison experiments on different residual variants and explored the effects of various residual settings in deep training, showcasing the performance of each variant for video generation .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the SkyTimelapse, Taichi-HD, and UCF-101 datasets . The code for the research is open source, as the authors aim to encourage generative emergence capabilities in the open-source community .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper explores various residual settings in deep training, comparing different residual variants and their impact on model performance for video generation . Through these experiments, it was observed that the proposed approach, Variant-0, achieved the best FVD scores, indicating its effectiveness in maintaining dynamic consistency with the reverse denoising process . Additionally, the paper delves into deep scalability experiments, demonstrating that increasing the depth of residual units further improves model performance, highlighting the positive correlation between model performance and the depth of residual units .

Furthermore, the paper introduces innovative concepts such as the reverse ODE (PF-ODE) and the Residual-Sensitivity ODE to guide the dynamics of reverse denoising and control numerical errors in back-propagation for scalable training . These novel approaches contribute to the advancement of diffusion models for deep scalable vision generation by addressing key challenges in training dynamics and numerical stability.

Overall, the experiments and results presented in the paper not only validate the scientific hypotheses put forth but also provide valuable insights into the effectiveness of different residual settings, the importance of deep scalability, and the novel methods introduced to enhance training dynamics and stability in diffusion models for vision generation .


What are the contributions of this paper?

The paper makes several contributions in the field of deep scalable vision generation:

  • It introduces Neural Residual Diffusion Models for generating high-resolution images and videos .
  • The paper explores text-to-3D content creation with models like Magic3D and Hifa .
  • It delves into multi-view synthesis and 3D generation from a single image using latent video diffusion with Sv3d .
  • The research focuses on fast sampling of diffusion models through operator learning and progressive distillation .
  • It discusses consistency models for synthesizing high-resolution images and videos efficiently .

What work can be continued in depth?

To further advance the work on deep scalable vision generation models, one area that can be continued in depth is the exploration of adaptive stability maintenance and error sensitivity control in neural residual diffusion models. This involves addressing the challenge of reducing numerical errors caused by network propagation and ensuring stability in the denoising process, especially when infinitely stacking residual units to express the dynamics of the overall network . By introducing sensitivity-related ODEs and demonstrating the theoretical advantages of gated residual networks in enabling stable denoising and effective sensitivity control, researchers can enhance the robustness and reliability of deep generative networks .

Additionally, researchers can delve deeper into theoretical proofs and experimental validation of the proposed neural residual diffusion models to showcase their advantages in improving the fidelity and consistency of generated content, especially in large-scale scalable training scenarios . By conducting rigorous theoretical analyses and extensive experiments, the effectiveness of the simple gated residual mechanism consistent with dynamic modeling can be further substantiated, leading to advancements in the field of vision generation models .

Furthermore, exploring the scalability of the proposed Neural-RDM framework can be a promising direction for future research. By investigating the framework's ability to support training in new scalable deep generative architectures beyond traditional models like U-Net and Transformers, researchers can unlock the potential for emergent capabilities in vision generation tasks . Understanding the scalability limits, performance enhancements, and adaptability of Neural-RDM in various generative tasks can pave the way for more efficient and effective deep learning models for vision generation .

Tables

2

Introduction
Background
Deep stacked networks in neural diffusion models
Current limitations: numerical errors and reduced noisy prediction
Objective
To propose Neural-RDM: a novel framework for addressing these issues
Aim to enhance stability, scalability, and error sensitivity
Method
Data Collection
Comparison with existing deep generative models
Benchmark datasets for image and video generation
Data Preprocessing
Handling numerical errors in deep stacked networks
Noise injection for training Neural-RDM
Neural Residual Diffusion Models (Neural-RDM)
Unified Framework
Integration of residual-style networks and ODEs
Gated residual parameters for learnability
Gating-Residual Mechanism
Enhancing stability and scalability
U-Net and Transformer-like architecture unification
Training and Optimization
Techniques for efficient denoising and generation
Addressing error sensitivity during training
Experiments and Results
Performance Evaluation
State-of-the-art results on image and video generation tasks
Improved sample quality and stability
Case Studies
Video frame quality enhancement
Comparison with baseline models
Emergent Abilities
Potential for supporting large language model-like capabilities
Key Findings
Advantages of Neural-RDM in deep generative training
Adaptability and effectiveness in generative modeling
Conclusion
Summary of contributions and implications for the field
Future directions for research and applications
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
What is the proposed solution introduced by the authors, Neural Residual Diffusion Models (Neural-RDM)?
How does Neural-RDM improve over traditional models in terms of denoising and generation efficiency?
What problem does the paper address in the context of deep stacked networks for neural diffusion models?
What are the key findings of the experiments conducted with Neural-RDM in image and video generation tasks?

Neural Residual Diffusion Models for Deep Scalable Vision Generation

Zhiyuan Ma, Liangliang Zhao, Biqing Qi, Bowen Zhou·June 19, 2024

Summary

This paper investigates the limitations of deep stacked networks in neural diffusion models for vision generation, particularly in terms of numerical errors and reduced noisy prediction. The authors propose Neural Residual Diffusion Models (Neural-RDM), a unified and scalable framework that incorporates learnable gated residual parameters. Neural-RDM connects residual-style networks to their implicit ODEs, improving denoising and generation efficiency. The model addresses stability, scalability, and error sensitivity through a gating-residual mechanism, unifying U-Net and Transformer-like architectures. Experiments demonstrate state-of-the-art performance on image and video generation tasks, with improved sample quality and stability. The paper also highlights the potential of Neural-RDM for supporting emergent abilities similar to large language models. Key findings include the model's ability to enhance deep generative training, address frame quality in video generation, and outperform various baselines in benchmark tests, showcasing its adaptability and effectiveness in the field of generative modeling.
Mind map
Addressing error sensitivity during training
Techniques for efficient denoising and generation
U-Net and Transformer-like architecture unification
Enhancing stability and scalability
Gated residual parameters for learnability
Integration of residual-style networks and ODEs
Potential for supporting large language model-like capabilities
Comparison with baseline models
Video frame quality enhancement
Improved sample quality and stability
State-of-the-art results on image and video generation tasks
Training and Optimization
Gating-Residual Mechanism
Unified Framework
Noise injection for training Neural-RDM
Handling numerical errors in deep stacked networks
Benchmark datasets for image and video generation
Comparison with existing deep generative models
Aim to enhance stability, scalability, and error sensitivity
To propose Neural-RDM: a novel framework for addressing these issues
Current limitations: numerical errors and reduced noisy prediction
Deep stacked networks in neural diffusion models
Future directions for research and applications
Summary of contributions and implications for the field
Adaptability and effectiveness in generative modeling
Advantages of Neural-RDM in deep generative training
Emergent Abilities
Case Studies
Performance Evaluation
Neural Residual Diffusion Models (Neural-RDM)
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Key Findings
Experiments and Results
Method
Introduction
Outline
Introduction
Background
Deep stacked networks in neural diffusion models
Current limitations: numerical errors and reduced noisy prediction
Objective
To propose Neural-RDM: a novel framework for addressing these issues
Aim to enhance stability, scalability, and error sensitivity
Method
Data Collection
Comparison with existing deep generative models
Benchmark datasets for image and video generation
Data Preprocessing
Handling numerical errors in deep stacked networks
Noise injection for training Neural-RDM
Neural Residual Diffusion Models (Neural-RDM)
Unified Framework
Integration of residual-style networks and ODEs
Gated residual parameters for learnability
Gating-Residual Mechanism
Enhancing stability and scalability
U-Net and Transformer-like architecture unification
Training and Optimization
Techniques for efficient denoising and generation
Addressing error sensitivity during training
Experiments and Results
Performance Evaluation
State-of-the-art results on image and video generation tasks
Improved sample quality and stability
Case Studies
Video frame quality enhancement
Comparison with baseline models
Emergent Abilities
Potential for supporting large language model-like capabilities
Key Findings
Advantages of Neural-RDM in deep generative training
Adaptability and effectiveness in generative modeling
Conclusion
Summary of contributions and implications for the field
Future directions for research and applications
Key findings
6

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the scalability dilemma faced by current diffusion models in deep generative training on large-scale vision data . This issue is crucial in determining the models' ability to support scalable training and develop emergent capabilities similar to large language models (LLMs) . The proposed Neural Residual Diffusion Models framework (Neural-RDM) introduces a simple yet significant change to the architecture of deep generative networks by incorporating learnable gated residual parameters to enhance generative abilities and support large-scale training . While the problem of scalability in deep generative training is not new, the paper introduces a novel approach through Neural-RDM to improve the fidelity and consistency of generated content, demonstrating advancements in state-of-the-art generative benchmarks .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the utilization of Neural Residual Diffusion Models (Neural-RDM) for deep scalable vision generation. The research focuses on exploring the dynamics of diffusion models and their application in generating high-resolution images and videos based on text prompts . The study delves into the properties of diffusion models, such as reverse ODEs, denoising dynamics parameterization, and latent space projection, to enhance the fidelity and consistency of generated visual content . The paper also investigates the control of residual sensitivity to address numerical errors in back-propagation and ensure stable and scalable training of the models .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Neural Residual Diffusion Models for Deep Scalable Vision Generation" introduces several innovative ideas, methods, and models in the field of deep generative training for vision data . One key contribution is the proposal of latent diffusion models for high-resolution image synthesis, which aim to improve the quality of generated images . These models incorporate conditional control to enhance text-to-image synthesis, allowing for more precise image generation based on textual input . Additionally, the paper presents text-to-video diffusion models for generating high-fidelity videos with arbitrary lengths, expanding the application of diffusion models to video generation tasks .

Moreover, the paper introduces latent video diffusion models for video generation, enabling the creation of high-quality videos with arbitrary lengths . It also proposes multi-view synthesis and 3D generation from a single image using latent video diffusion, showcasing advancements in multi-view image synthesis and 3D content creation . Another novel contribution is the space-time diffusion model for video generation, which focuses on generating videos with spatial and temporal consistency .

Furthermore, the paper discusses the scalability dilemma faced by current diffusion models and emphasizes the importance of scalable deep generative training on large-scale vision data . It mentions the emergence of Sora, a system that pushes the boundaries of intelligent emergence capabilities in generative models by treating video models as world simulators . This highlights the potential for diffusion models to exhibit emergent abilities similar to large language models (LLMs) .

In summary, the paper presents a range of innovative ideas and models, including latent diffusion models, text-to-image/video diffusion models, multi-view synthesis, 3D generation, and space-time diffusion models, aiming to advance the field of deep generative training for vision data and address scalability challenges in current models . The "Neural Residual Diffusion Models for Deep Scalable Vision Generation" paper introduces several key characteristics and advantages compared to previous methods in deep generative networks .

  1. Gating-Residual Mechanism: The paper emphasizes the significance of the residual unit for effective denoising and generation from a new dynamics perspective, introducing a simple gating-residual mechanism . This mechanism enables stable training of extremely deep networks by parameterizing a learnable mean-variance scheduler, avoiding manual design and supporting infinitely deep scalable training .

  2. Dynamic Consistency: The proposed models exhibit dynamic consistency, ensuring that different dynamic systems describe the same motion path or change rate of data distribution for any time-dependent signal .

  3. Latent Space Projection: The paper introduces latent space projection to compress input images into a high-dimensional space, leveraging a pretrained VQ-VAE model for effective denoising and generation .

  4. Scalability: The Neural-RDM framework offers excellent scalability, enabling massively scalable generative training on large-scale vision data . The models achieve dynamic consistency to denoising probability flow ODE, supporting stable training of deep networks .

  5. Adaptive Stability Maintenance: The introduction of sensitivity-related ODE ensures stable denoising and effective sensitivity control, addressing numerical errors in back-propagation and supporting stable and scalable training .

  6. Qualitative and Quantitative Results: Experimental results consistently demonstrate the effectiveness of the proposed models in improving fidelity, consistency of generated content, and supporting large-scale scalable training .

In summary, the Neural Residual Diffusion Models offer advancements in stability, scalability, denoising effectiveness, dynamic consistency, and latent space projection compared to previous methods, showcasing their potential for enhancing deep generative networks for vision data .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field of deep scalable vision generation, several related research papers and notable researchers have contributed to advancements in this area. Some noteworthy researchers and their works include:

  • Haoxin Chen, Menghan Xia, Yingqing He, Yong Zhang, Xiaodong Cun, Shaoshu Yang, Jinbo Xing, Yaofang Liu, Qifeng Chen, Xintao Wang, and others who worked on Videocrafter1 and Videocrafter2 for high-quality video generation .
  • Wenhao Chai, Xun Guo, Gaoang Wang, Yan Lu, who developed Stablevideo for text-driven consistency-aware diffusion video editing .
  • Omer Bar-Tal, Hila Chefer, Charles Herrmann, Roni Paiss, and others who introduced Lumiere, a space-time diffusion model for video generation .
  • Ben Poole, Ajay Jain, Jonathan T Barron, who presented Dreamfusion for text-to-3D using 2D diffusion .
  • Yichun Shi, Peng Wang, Jianglong Ye, and others who worked on Mvdream for multi-view diffusion for 3D generation .

The key to the solution mentioned in the paper is the development and application of diffusion models for various tasks such as video generation, text-to-image synthesis, text-to-3D conversion, and high-fidelity video generation. These models leverage techniques like latent diffusion, stable video diffusion, and text-driven diffusion to achieve impressive results in generating visual content based on different input modalities .


How were the experiments in the paper designed?

The experiments in the paper were designed by maintaining approximately the same model size for class-conditional and text-conditional image generation experiments, as shown in Table 1. The experiments involved comparing the Neural-RDM with state-of-the-art conditional/unconditional diffusion models for image synthesis and video generation . Additionally, the experiments evaluated the generative performance of Neural-RDM, visualized and analyzed the effects of the proposed gated residuals, and illustrated their advantages in enabling deep scalable training . The paper also conducted comparison experiments on different residual variants and explored the effects of various residual settings in deep training, showcasing the performance of each variant for video generation .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the SkyTimelapse, Taichi-HD, and UCF-101 datasets . The code for the research is open source, as the authors aim to encourage generative emergence capabilities in the open-source community .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper explores various residual settings in deep training, comparing different residual variants and their impact on model performance for video generation . Through these experiments, it was observed that the proposed approach, Variant-0, achieved the best FVD scores, indicating its effectiveness in maintaining dynamic consistency with the reverse denoising process . Additionally, the paper delves into deep scalability experiments, demonstrating that increasing the depth of residual units further improves model performance, highlighting the positive correlation between model performance and the depth of residual units .

Furthermore, the paper introduces innovative concepts such as the reverse ODE (PF-ODE) and the Residual-Sensitivity ODE to guide the dynamics of reverse denoising and control numerical errors in back-propagation for scalable training . These novel approaches contribute to the advancement of diffusion models for deep scalable vision generation by addressing key challenges in training dynamics and numerical stability.

Overall, the experiments and results presented in the paper not only validate the scientific hypotheses put forth but also provide valuable insights into the effectiveness of different residual settings, the importance of deep scalability, and the novel methods introduced to enhance training dynamics and stability in diffusion models for vision generation .


What are the contributions of this paper?

The paper makes several contributions in the field of deep scalable vision generation:

  • It introduces Neural Residual Diffusion Models for generating high-resolution images and videos .
  • The paper explores text-to-3D content creation with models like Magic3D and Hifa .
  • It delves into multi-view synthesis and 3D generation from a single image using latent video diffusion with Sv3d .
  • The research focuses on fast sampling of diffusion models through operator learning and progressive distillation .
  • It discusses consistency models for synthesizing high-resolution images and videos efficiently .

What work can be continued in depth?

To further advance the work on deep scalable vision generation models, one area that can be continued in depth is the exploration of adaptive stability maintenance and error sensitivity control in neural residual diffusion models. This involves addressing the challenge of reducing numerical errors caused by network propagation and ensuring stability in the denoising process, especially when infinitely stacking residual units to express the dynamics of the overall network . By introducing sensitivity-related ODEs and demonstrating the theoretical advantages of gated residual networks in enabling stable denoising and effective sensitivity control, researchers can enhance the robustness and reliability of deep generative networks .

Additionally, researchers can delve deeper into theoretical proofs and experimental validation of the proposed neural residual diffusion models to showcase their advantages in improving the fidelity and consistency of generated content, especially in large-scale scalable training scenarios . By conducting rigorous theoretical analyses and extensive experiments, the effectiveness of the simple gated residual mechanism consistent with dynamic modeling can be further substantiated, leading to advancements in the field of vision generation models .

Furthermore, exploring the scalability of the proposed Neural-RDM framework can be a promising direction for future research. By investigating the framework's ability to support training in new scalable deep generative architectures beyond traditional models like U-Net and Transformers, researchers can unlock the potential for emergent capabilities in vision generation tasks . Understanding the scalability limits, performance enhancements, and adaptability of Neural-RDM in various generative tasks can pave the way for more efficient and effective deep learning models for vision generation .

Tables
2
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