Gradient-Driven Diffusion Model-based Algorithm

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A Gradient-Driven Diffusion Model-based Algorithm is a generative AI algorithm that leverages gradient-based methods to iteratively refine and generate data by reversing a noise addition process.

References

2024

  • LLM
    • Algorithm: Gradient-Driven Diffusion Model-based Algorithm
    • Input Parameters:
      • Training dataset: original_data
      • Number of iterations: n_iterations
      • Noise level schedule: noise_schedule
      • Model architecture: neural_net_model
      • Optional:
        • Latent space representation: latent_representation
        • Guidance mechanism: guidance
    • Output:
      • Generated data: generated_sample
    • Steps:
      • Initialize Neural Network Model
        • Initialize the neural_net_model using the specified architecture.
      • Latent Space Representation (Optional)
        • If used:
          • Transform original_data into latent space using an encoder.
          • Proceed with noise addition in latent space.
        • Else:
          • Proceed with noise addition directly on the original_data.
      • Iterative Noise Addition and Training
        • For each iteration (i) from 1 to n_iterations:
          • Apply Noise Schedule:
            • Add noise to the data to create noisy_data.
          • Train the Neural Network Model:
          • Guidance Mechanism (Optional):
            • If guidance is used, modify the model's prediction by incorporating the guidance (e.g., classifier-free guidance or conditional generation).
      • Noise Removal and Data Generation
        • After all iterations are completed:
          • Reverse the Noise Addition Process:
            • Iteratively remove noise from the noisy_data using the trained neural_net_model to generate a refined_sample.
          • Latent Space Decoding (If used):
            • Decode the refined_sample from latent space back to the original data space.
      • Output Final Generated Data
        • Output the final generated_sample as the generated data.

2024

  • Perplexity
    • Diffusion models, also known as score-based generative models, have gained significant attention in the field of generative AI due to their ability to produce high-quality samples across various domains. Here are some well-known gradient-driven diffusion model-based algorithms:
      • Denoising Diffusion Probabilistic Models (DDPM): Proposed by Ho et al. (2020), DDPM is a pioneering work that introduced the concept of denoising diffusion probabilistic models for image generation.[1] It formulates the generation process as a sequence of denoising steps, where a neural network is trained to predict the noise that was added to the data at each step. By iteratively removing the predicted noise, the model can generate high-fidelity samples from pure noise.
      • Latent Diffusion Models (LDMs): Introduced by Rombach et al. (2022), LDMs aim to address the computational challenges of DDPM by operating in a compressed latent space instead of the high-dimensional pixel space.[2] An autoencoder is first trained to map images to a lower-dimensional latent representation, and then a diffusion model is trained on this latent space. This approach significantly reduces the computational cost while maintaining high-quality image generation.
      • Stable Diffusion Algorithm: Stable Diffusion, introduced by Rombach et al. (2022), is a popular implementation of LDMs that incorporates cross-attention layers, enabling the model to condition on various inputs, such as text prompts or bounding boxes.[2] This versatility has made Stable Diffusion a widely adopted tool for text-to-image synthesis, image editing, and other creative applications.
      • Guided Diffusion Model: Guided diffusion models, proposed by Dhariwal and Nichol (2021), introduce a way to control the generation process by conditioning the diffusion model on additional guidance.[3] This guidance can take various forms, such as class labels, text prompts, or reference images, allowing for more targeted and controllable generation.
      • Classifier-Free Guidance :Classifier-Free Guidance, introduced by Ho and Salimans (2022), is a technique that enables better control over the generation process without relying on a separate classifier.[4] It works by adjusting the cross-attention maps of the diffusion model during sampling, effectively steering the generation towards desired characteristics without the need for an additional classifier network.
    • Citations:
[1] https://arxiv.org/html/2404.07771v1
[2] https://neurips.cc/virtual/2023/workshop/66539
[3] https://deepgram.com/ai-glossary/diffusion-model
[4] https://encord.com/blog/diffusion-models/
[5] https://developer.nvidia.com/blog/generative-ai-research-spotlight-demystifying-diffusion-based-models/

2023

  • (Croitoru et al., 2023) ⇒ Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. (2023). "Diffusion Models in Vision: A Survey.” In: IEEE Transactions on Pattern Analysis and Machine Intelligence.
    • QUOTE: "In this survey, we provide a comprehensive review of articles on denoising diffusion models ... diffusion modeling frameworks, which are based on denoising diffusion probabilistic models, ..."
    • NOTE: It reviews various articles on denoising diffusion models and their applications in vision tasks.

2021

  • (Austin et al., 2021) ⇒ Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. (2021). "Structured Denoising Diffusion Models in Discrete State-Spaces.” In: Advances in Neural Information Processing Systems, 34, pp. 17981-17993.
    • QUOTE: "Diffusion models for quantized images, taking inspiration from the locality exploited by continuous diffusion models. This ... Beyond designing several new structured diffusion models, we ..."
    • NOTE: It focuses on structured diffusion models for quantized images and their local properties.

2021

  • (Lam et al., 2021) ⇒ Max W.Y. Lam, Jun Wang, Rongjie Huang, Dan Su, and Dong Yu. (2021). "Bilateral Denoising Diffusion Models.” In: arXiv preprint arXiv:2108.11514.
    • QUOTE: "The denoising diffusion implicit models (DDIMs) [33] considered non-Markovian diffusion processes and used a subsequence of the noise schedule to accelerate the denoising process."
    • NOTE: It discusses non-Markovian diffusion processes and the use of noise scheduling in DDIMs to accelerate denoising.

2020

  • (Ho et al., 2020) ⇒ Jonathan Ho, Ajay Jain, and Pieter Abbeel. (2020). "Denoising Diffusion Probabilistic Models.” In: Advances in Neural Information Processing Systems, 33, pp. 6840-6851.
    • QUOTE: "In addition, we show that a certain parameterization of diffusion models reveals an equivalence with denoising score matching over multiple noise levels during training and with ..."
    • NOTE: It explains the equivalence between certain parameterizations of diffusion models and denoising score matching.
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