DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention

Lianghui Zhu, Zilong Huang, Bencheng Liao, Jun Hao Liew, Hanshu Yan, Jiashi Feng, Xinggang Wang·May 28, 2024

Summary

The paper "DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention" presents a novel approach to diffusion models that enhances their scalability and efficiency by incorporating gated linear attention (GLA). The authors propose the DiG model, which outperforms existing methods like DiT and DiS in terms of training speed and GPU memory usage, particularly at high resolutions. DiG addresses the quadratic scaling issue in ViT-based architectures and introduces the Spatial Reorient & Enhancement Module (SREM) for better handling of large-scale visual data and local awareness. The model is designed with different variants (DiG-S, DiG-B, DiG-L, DiG-XL) to assess performance and efficiency, with DiG-XL demonstrating competitive results with fewer computational resources. The study also highlights the potential of DiG for long-sequence generation tasks and suggests future research in applying it to other domains like video and audio modeling. Overall, the paper contributes to the advancement of efficient and scalable generative models, particularly in image synthesis.

Key findings

5

Paper digest

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

The paper aims to address the issue of poor performance in visual generation when using a linear attention Transformer due to unidirectional modeling. To tackle this problem, the paper proposes a lightweight spatial reorient & enhancement module to handle both global context modeling in crisscross directions and local information . This problem of poor performance in visual generation due to unidirectional modeling is not a new problem, but the paper introduces a novel solution to enhance the modeling process and improve performance in diffusion generation models .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the hypothesis that the proposed DiG diffusion model, compared to the baseline method DiT, demonstrates superior performance across four model scales with 400K training iterations. Additionally, the DiG-XL/2 model with classifier-free guidance also shows competitive results compared to previous state-of-the-art methods .


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

The paper "DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention" proposes several innovative ideas, methods, and models in the field of diffusion models and attention mechanisms . Here are some key contributions outlined in the paper:

  1. Gated Linear Attention Transformers: The paper introduces Gated Linear Attention Transformers, which aim to address the limitations of using a linear attention Transformer for visual generation. This new approach enhances modeling by incorporating a lightweight spatial reorient & enhancement module to capture global context in crisscross directions and local information effectively .

  2. Diffusion Probabilistic Models: The paper discusses diffusion probabilistic models, such as the ILVR method for denoising diffusion probabilistic models and improved denoising diffusion probabilistic models, which contribute to advancements in image synthesis and generation .

  3. Attention Mechanisms: The study explores novel attention mechanisms like Flashattention-2, which focuses on faster attention with better parallelism and work partitioning, and Performers, which offer a new perspective on rethinking attention mechanisms .

  4. Transformer Models: The paper delves into the application of Transformers for image recognition at scale, as well as the utilization of transformers for text-to-image generation, demonstrating the versatility and effectiveness of these models in various tasks .

  5. State-of-the-Art Image Synthesis: The research presents state-of-the-art techniques for high-resolution image synthesis, including rectified flow transformers, vector quantized diffusion models, and hierarchical text-conditional image generation, showcasing advancements in generating high-fidelity images .

  6. Text-to-Image Generation: The paper discusses innovative approaches for text-to-image synthesis, such as Pixart-sigma and Pixart-alpha, which focus on training diffusion transformers for photorealistic text-to-image synthesis, highlighting progress in this domain .

Overall, the paper introduces a range of cutting-edge ideas, methods, and models that contribute to the advancement of diffusion models, attention mechanisms, and image synthesis techniques, showcasing the ongoing innovation in the field of deep learning and artificial intelligence. The paper "DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention" introduces several key characteristics and advantages compared to previous methods, as detailed in the document :

  1. Efficiency and Scalability: DiG presents superior performance in dealing with long-sequence generation tasks compared to the baseline method DiT. The proposed DiG outperforms DiT across different model scales with 400K training iterations, showcasing its efficiency and scalability in handling complex generation tasks .

  2. Competitive Results: The DiG-XL/2 model, with classifier-free guidance, demonstrates competitive results when compared with previous state-of-the-art methods. This highlights the effectiveness of DiG in achieving high-quality outputs and advancing the field of diffusion models .

  3. Improved Training Process: The paper simplifies the training process of Diffusion Denoising Probabilistic Models (DDPM) by reparameterizing the noise prediction network and minimizing the mean squared error loss between the predicted noise and true Gaussian noise. This approach enhances the training efficiency and effectiveness of diffusion models .

  4. Faithfulness to Standard GLA Architecture: DiG maintains faithfulness to the standard Gated Linear Attention (GLA) architecture, ensuring scalability and high-efficiency properties. By following best practices from previous vision transformer architectures, DiG effectively processes DDPM training for images, enhancing the overall performance and reliability of the model .

  5. Superior Performance: Extensive experiments conducted on the ImageNet dataset demonstrate that DiG exhibits scalable ability and achieves superior performance compared to DiT. This superiority positions DiG as a promising next-generation backbone for diffusion models, particularly in the context of large-scale long-sequence generation tasks .

Overall, the characteristics and advantages of DiG, such as efficiency, scalability, competitive results, improved training processes, and faithfulness to the GLA architecture, underscore its significance in advancing diffusion models and enhancing the generation of high-quality images.


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?

Several related research papers and notable researchers exist in the field of diffusion models with gated linear attention:

  • Noteworthy researchers in this field include:
    • Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, Jun Zhu
    • Andrew Brock, Jeff Donahue, Karen Simonyan
    • Tim Brooks, Bill Peebles, Connor Holmes, and others
    • Hanqun Cao, Cheng Tan, Zhangyang Gao, and others
    • Junsong Chen, Chongjian Ge, Enze Xie, and others
    • Jonathan Ho, William Chan, Chitwan Saharia, and others
    • Vincent Tao Hu, Stefan Andreas Baumann, Ming Gui, and others

The key to the solution mentioned in the paper is the utilization of a linear attention Transformer, which is a linear RNN with matrix-valued-format hidden states. This model introduces a similarity kernel and an associated feature map to calculate the output, allowing for efficient processing of sequences and achieving superior performance in large-scale long-sequence generation tasks .


How were the experiments in the paper designed?

The experiments in the paper were designed with several key considerations and variations to evaluate the proposed method:

  • The baseline method chosen for comparison was DiT-S/2 .
  • Different configurations were tested, including a naive version of DiG with only causal modeling, bidirectional scanning added to DiG, and experiments with DWConv2d with and without identity initialization .
  • The impact of these variations on performance metrics such as FID (Fréchet Inception Distance) was assessed to understand the importance of global context, identity initialization, and local awareness in the diffusion models .
  • The experiments aimed to demonstrate the significance of incorporating global context, identity initialization, and local awareness in the diffusion models to achieve optimal performance .

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

The dataset used for quantitative evaluation in the context of diffusion models with Gated Linear Attention (DiG) is not explicitly mentioned in the provided excerpts. However, the research papers focus on diffusion models, image generation, and various architectures for visual data processing . The code availability or open-source status is not specified in the context provided. If you are looking for specific information regarding the dataset used for quantitative evaluation or the open-source status of the code related to diffusion models, further details or additional sources may be required to provide a more precise answer.


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 to be verified. The paper introduces Diffusion GLA (DiG), a new architecture for diffusion generation, aiming to maintain the scaling ability and efficiency of the standard GLA architecture . The proposed DiG outperforms the baseline method, DiT, across different model scales with 400K training iterations, demonstrating its superiority . Additionally, the DiG-XL/2 model with classifier-free guidance shows competitive results compared to previous state-of-the-art methods, further validating the effectiveness of the proposed approach .

Moreover, the paper includes a case study showcasing samples from DiG-XL/2 trained on the ImageNet dataset at a resolution of 256 × 256. The results exhibit correct semantic understanding and accurate spatial relationships, indicating the model's capability in generating high-quality images . This empirical evidence from the case study reinforces the validity of the scientific hypotheses tested in the paper.

Overall, the experiments conducted and the results obtained in the paper provide substantial evidence supporting the scientific hypotheses put forth by demonstrating the superior performance of DiG over the baseline method, as well as showcasing the model's ability to generate high-fidelity images with correct semantic interpretation and spatial relationships .


What are the contributions of this paper?

The contributions of the paper include proposing a lightweight spatial reorient & enhancement module to address the limitations of using a linear attention Transformer for visual generation. This module aims to improve performance by modeling global context in crisscross directions and incorporating local information .


What work can be continued in depth?

To delve deeper into the research on diffusion models with Gated Linear Attention (GLA) for continued work in depth, one can focus on exploring the efficiency and scalability aspects of these models. Specifically, further investigation can be conducted on enhancing the performance of diffusion models by addressing the limitations of Vision Transformer (ViT)-based backbones, such as their quadratic complexity, which hinders their practicality in tasks like high-resolution image synthesis and video generation .

Moreover, a promising avenue for future research could involve refining the spatial reorient & enhancement module proposed to handle both global context modeling in crisscross directions and local information in diffusion models using linear attention Transformers. This module aims to improve the performance of visual generation tasks by overcoming the unidirectional modeling issues associated with linear attention Transformers .

Additionally, researchers can explore the potential of diffusion models with GLA backbones in handling long-sequence generation tasks more efficiently compared to other baseline methods like DiT. Further studies could focus on optimizing the model scales, training iterations, and exploring classifier-free guidance to achieve competitive results and potentially surpass previous state-of-the-art methods in image synthesis tasks .

Overall, future research directions in the field of diffusion models with Gated Linear Attention could involve refining the efficiency, scalability, and performance of these models, exploring innovative modules for improved global and local context modeling, and optimizing model configurations and training strategies for enhanced results in visual data generation tasks.

Tables

2
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
What are the different DiG variants, and how do they contribute to the model's performance and efficiency?
What is the primary innovation in the "DiG" paper?
What module does DiG introduce to handle large-scale visual data effectively?
How does the DiG model address the scalability issue in ViT-based architectures?

DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention

Lianghui Zhu, Zilong Huang, Bencheng Liao, Jun Hao Liew, Hanshu Yan, Jiashi Feng, Xinggang Wang·May 28, 2024

Summary

The paper "DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention" presents a novel approach to diffusion models that enhances their scalability and efficiency by incorporating gated linear attention (GLA). The authors propose the DiG model, which outperforms existing methods like DiT and DiS in terms of training speed and GPU memory usage, particularly at high resolutions. DiG addresses the quadratic scaling issue in ViT-based architectures and introduces the Spatial Reorient & Enhancement Module (SREM) for better handling of large-scale visual data and local awareness. The model is designed with different variants (DiG-S, DiG-B, DiG-L, DiG-XL) to assess performance and efficiency, with DiG-XL demonstrating competitive results with fewer computational resources. The study also highlights the potential of DiG for long-sequence generation tasks and suggests future research in applying it to other domains like video and audio modeling. Overall, the paper contributes to the advancement of efficient and scalable generative models, particularly in image synthesis.
Mind map
SREM Components
Results and Discussion
Experiments
Model Variants
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Future Research
Method
Introduction
Key findings
5

Paper digest

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

The paper aims to address the issue of poor performance in visual generation when using a linear attention Transformer due to unidirectional modeling. To tackle this problem, the paper proposes a lightweight spatial reorient & enhancement module to handle both global context modeling in crisscross directions and local information . This problem of poor performance in visual generation due to unidirectional modeling is not a new problem, but the paper introduces a novel solution to enhance the modeling process and improve performance in diffusion generation models .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the hypothesis that the proposed DiG diffusion model, compared to the baseline method DiT, demonstrates superior performance across four model scales with 400K training iterations. Additionally, the DiG-XL/2 model with classifier-free guidance also shows competitive results compared to previous state-of-the-art methods .


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

The paper "DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention" proposes several innovative ideas, methods, and models in the field of diffusion models and attention mechanisms . Here are some key contributions outlined in the paper:

  1. Gated Linear Attention Transformers: The paper introduces Gated Linear Attention Transformers, which aim to address the limitations of using a linear attention Transformer for visual generation. This new approach enhances modeling by incorporating a lightweight spatial reorient & enhancement module to capture global context in crisscross directions and local information effectively .

  2. Diffusion Probabilistic Models: The paper discusses diffusion probabilistic models, such as the ILVR method for denoising diffusion probabilistic models and improved denoising diffusion probabilistic models, which contribute to advancements in image synthesis and generation .

  3. Attention Mechanisms: The study explores novel attention mechanisms like Flashattention-2, which focuses on faster attention with better parallelism and work partitioning, and Performers, which offer a new perspective on rethinking attention mechanisms .

  4. Transformer Models: The paper delves into the application of Transformers for image recognition at scale, as well as the utilization of transformers for text-to-image generation, demonstrating the versatility and effectiveness of these models in various tasks .

  5. State-of-the-Art Image Synthesis: The research presents state-of-the-art techniques for high-resolution image synthesis, including rectified flow transformers, vector quantized diffusion models, and hierarchical text-conditional image generation, showcasing advancements in generating high-fidelity images .

  6. Text-to-Image Generation: The paper discusses innovative approaches for text-to-image synthesis, such as Pixart-sigma and Pixart-alpha, which focus on training diffusion transformers for photorealistic text-to-image synthesis, highlighting progress in this domain .

Overall, the paper introduces a range of cutting-edge ideas, methods, and models that contribute to the advancement of diffusion models, attention mechanisms, and image synthesis techniques, showcasing the ongoing innovation in the field of deep learning and artificial intelligence. The paper "DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention" introduces several key characteristics and advantages compared to previous methods, as detailed in the document :

  1. Efficiency and Scalability: DiG presents superior performance in dealing with long-sequence generation tasks compared to the baseline method DiT. The proposed DiG outperforms DiT across different model scales with 400K training iterations, showcasing its efficiency and scalability in handling complex generation tasks .

  2. Competitive Results: The DiG-XL/2 model, with classifier-free guidance, demonstrates competitive results when compared with previous state-of-the-art methods. This highlights the effectiveness of DiG in achieving high-quality outputs and advancing the field of diffusion models .

  3. Improved Training Process: The paper simplifies the training process of Diffusion Denoising Probabilistic Models (DDPM) by reparameterizing the noise prediction network and minimizing the mean squared error loss between the predicted noise and true Gaussian noise. This approach enhances the training efficiency and effectiveness of diffusion models .

  4. Faithfulness to Standard GLA Architecture: DiG maintains faithfulness to the standard Gated Linear Attention (GLA) architecture, ensuring scalability and high-efficiency properties. By following best practices from previous vision transformer architectures, DiG effectively processes DDPM training for images, enhancing the overall performance and reliability of the model .

  5. Superior Performance: Extensive experiments conducted on the ImageNet dataset demonstrate that DiG exhibits scalable ability and achieves superior performance compared to DiT. This superiority positions DiG as a promising next-generation backbone for diffusion models, particularly in the context of large-scale long-sequence generation tasks .

Overall, the characteristics and advantages of DiG, such as efficiency, scalability, competitive results, improved training processes, and faithfulness to the GLA architecture, underscore its significance in advancing diffusion models and enhancing the generation of high-quality images.


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?

Several related research papers and notable researchers exist in the field of diffusion models with gated linear attention:

  • Noteworthy researchers in this field include:
    • Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, Jun Zhu
    • Andrew Brock, Jeff Donahue, Karen Simonyan
    • Tim Brooks, Bill Peebles, Connor Holmes, and others
    • Hanqun Cao, Cheng Tan, Zhangyang Gao, and others
    • Junsong Chen, Chongjian Ge, Enze Xie, and others
    • Jonathan Ho, William Chan, Chitwan Saharia, and others
    • Vincent Tao Hu, Stefan Andreas Baumann, Ming Gui, and others

The key to the solution mentioned in the paper is the utilization of a linear attention Transformer, which is a linear RNN with matrix-valued-format hidden states. This model introduces a similarity kernel and an associated feature map to calculate the output, allowing for efficient processing of sequences and achieving superior performance in large-scale long-sequence generation tasks .


How were the experiments in the paper designed?

The experiments in the paper were designed with several key considerations and variations to evaluate the proposed method:

  • The baseline method chosen for comparison was DiT-S/2 .
  • Different configurations were tested, including a naive version of DiG with only causal modeling, bidirectional scanning added to DiG, and experiments with DWConv2d with and without identity initialization .
  • The impact of these variations on performance metrics such as FID (Fréchet Inception Distance) was assessed to understand the importance of global context, identity initialization, and local awareness in the diffusion models .
  • The experiments aimed to demonstrate the significance of incorporating global context, identity initialization, and local awareness in the diffusion models to achieve optimal performance .

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

The dataset used for quantitative evaluation in the context of diffusion models with Gated Linear Attention (DiG) is not explicitly mentioned in the provided excerpts. However, the research papers focus on diffusion models, image generation, and various architectures for visual data processing . The code availability or open-source status is not specified in the context provided. If you are looking for specific information regarding the dataset used for quantitative evaluation or the open-source status of the code related to diffusion models, further details or additional sources may be required to provide a more precise answer.


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 to be verified. The paper introduces Diffusion GLA (DiG), a new architecture for diffusion generation, aiming to maintain the scaling ability and efficiency of the standard GLA architecture . The proposed DiG outperforms the baseline method, DiT, across different model scales with 400K training iterations, demonstrating its superiority . Additionally, the DiG-XL/2 model with classifier-free guidance shows competitive results compared to previous state-of-the-art methods, further validating the effectiveness of the proposed approach .

Moreover, the paper includes a case study showcasing samples from DiG-XL/2 trained on the ImageNet dataset at a resolution of 256 × 256. The results exhibit correct semantic understanding and accurate spatial relationships, indicating the model's capability in generating high-quality images . This empirical evidence from the case study reinforces the validity of the scientific hypotheses tested in the paper.

Overall, the experiments conducted and the results obtained in the paper provide substantial evidence supporting the scientific hypotheses put forth by demonstrating the superior performance of DiG over the baseline method, as well as showcasing the model's ability to generate high-fidelity images with correct semantic interpretation and spatial relationships .


What are the contributions of this paper?

The contributions of the paper include proposing a lightweight spatial reorient & enhancement module to address the limitations of using a linear attention Transformer for visual generation. This module aims to improve performance by modeling global context in crisscross directions and incorporating local information .


What work can be continued in depth?

To delve deeper into the research on diffusion models with Gated Linear Attention (GLA) for continued work in depth, one can focus on exploring the efficiency and scalability aspects of these models. Specifically, further investigation can be conducted on enhancing the performance of diffusion models by addressing the limitations of Vision Transformer (ViT)-based backbones, such as their quadratic complexity, which hinders their practicality in tasks like high-resolution image synthesis and video generation .

Moreover, a promising avenue for future research could involve refining the spatial reorient & enhancement module proposed to handle both global context modeling in crisscross directions and local information in diffusion models using linear attention Transformers. This module aims to improve the performance of visual generation tasks by overcoming the unidirectional modeling issues associated with linear attention Transformers .

Additionally, researchers can explore the potential of diffusion models with GLA backbones in handling long-sequence generation tasks more efficiently compared to other baseline methods like DiT. Further studies could focus on optimizing the model scales, training iterations, and exploring classifier-free guidance to achieve competitive results and potentially surpass previous state-of-the-art methods in image synthesis tasks .

Overall, future research directions in the field of diffusion models with Gated Linear Attention could involve refining the efficiency, scalability, and performance of these models, exploring innovative modules for improved global and local context modeling, and optimizing model configurations and training strategies for enhanced results in visual data generation tasks.

Tables
2
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