Gradient-Driven Diffusion Model-based Algorithm

A Gradient-Driven Diffusion Model-based Algorithm is a generative AI algorithm that produces data by gradually adding noise to a dataset and then learning to reverse this process to generate new, high-quality samples. This technique is especially useful for creating images and other forms of media.

Context:
- It can (typically) involve the use of deep neural networks to predict and remove noise from data, ensuring high-quality generation of images or other media.
- It can (often) be based on probabilistic models, leveraging the principles of probability to ensure varied and realistic outputs.
- It can range from being a simple algorithm for specific tasks to being a complex model integrated into larger systems for diverse applications.
- It can use random noise as a starting point, progressively refining this noise to produce coherent outputs.
- It can be particularly effective for generating photo-realistic images, with applications in art, media, and design.
- It can optimize the number of steps required for generation, balancing quality and computational efficiency.
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Example(s):
- A Denoising Diffusion Probabilistic Model (DDPM) that demonstrates high-quality image generation by iteratively removing noise from an initially random image.
- A Stable Diffusion Model used in applications such as AI-based image editors and content creation tools.
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Counter-Example(s):
- Generative Adversarial Networks (GANs), which use a different approach involving two competing networks to generate data.
- Auto-regressive Models, which generate data one step at a time without the gradual noise addition and removal process.
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See: Deep Neural Networks, Generative AI, Probabilistic Models, Denoising Diffusion Probabilistic Models, Stable Diffusion Model

References

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.