Gradient-Driven Diffusion Model-based Algorithm: Difference between revisions

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A [[Gradient-Driven Diffusion Model-based Algorithm]] is a [[generative AI algorithm]] that leverages [[gradient-based method]]s to iteratively refine and generate data by reversing a noise addition process.
A [[Gradient-Driven Diffusion Model-based Algorithm]] is a [[generative AI algorithm]] that leverages [[gradient-based method]]s to iteratively refine and generate data by reversing a [[noise addition process]].
* <B>AKA:</B> [[Diffusion Algorithm]], [[Score-Based Generative Model]], [[Denoising Diffusion Model]].
* <B>Context:</B>
* <B>Context:</B>
** [[Model Input]]: [[Noise-Corrupted Data]], [[Diffusion Timestep]], [[Training Dataset]].
** [[Model Output]]: [[Refined Data Sample]], [[Denoised Representation]], [[Generated Sample]].
** ...
** It can (typically) use [[Denoising Score Matching Technique]]s to train neural networks to predict and remove noise from data.
** It can (typically) use [[Denoising Score Matching Technique]]s to train neural networks to predict and remove noise from data.
** It can (typically) implement a [[Forward Diffusion Process]] that gradually adds [[noise]] to [[data sample]]s according to a [[predefined schedule]].
** It can (typically) train a [[Neural Network Model]] to predict the [[gradient of log-likelihood]] or [[noise component]] at each [[diffusion timestep]].
** It can (typically) perform [[Reverse Diffusion Process]] by iteratively applying [[gradient updates]] to remove [[noise]] from [[corrupted sample]]s.
** It can (typically) leverage [[Score Matching Model]] to estimate the [[gradient of data distribution]] without requiring [[explicit density computation]].
** It can (typically) utilize [[Stochastic Differential Equation Model]]s or [[Markov Chain Model]]s to model the [[diffusion process]].
** It can (typically) generate [[High-Quality Sample]]s through [[iterative denoising process]] of [[random noise]].
** ...
** It can (often) be applied in [[Image Generation]] tasks, producing realistic images from random noise.
** It can (often) be applied in [[Image Generation]] tasks, producing realistic images from random noise.
** It can (often) employ [[Langevin Dynamics Model]] to sample from the [[learned distribution]] using [[gradient information]].
** It can (often) use [[Variance-Preserving Diffusion Model]] or [[Variance-Exploding Diffusion Model]] formulations for the [[diffusion process]].
** It can (often) implement [[Classifier Guidance Model]] to steer the [[generation process]] toward [[desired attribute]]s.
** It can (often) accelerate [[Diffusion Sampling]] through [[advanced scheduler]]s and [[step-size adaptation]].
** It can (often) incorporate [[Conditional Signal]]s to control [[output characteristic]]s.
** ...
** ...
** It can range from being a [[Simple Gradient-Based Model]] to a [[Complex Multiscale Diffusion Model]], depending on the complexity and scale of the noise removal process utilized in the algorithm.
** It can range from being a [[Simple Gradient-Based Diffusion Model]] to a [[Complex Multiscale Diffusion Model]], depending on the complexity and scale of the noise removal process utilized in the algorithm.
** It can range from being a [[Discrete-Time Diffusion Model-based Algorithm]] to being a [[Continuous-Time Diffusion Model-based Algorithm]], depending on its [[mathematical formulation]].
** It can range from being a [[Unconditional Diffusion Model-based Algorithm]] to being a [[Conditional Diffusion Model-based Algorithm]], depending on its [[generation control mechanism]].
** It can range from being a [[Small-Scale Diffusion Model-based Algorithm]] to being a [[Large-Scale Diffusion Model-based Algorithm]], depending on its [[computational requirement]]s.
** It can range from being a [[Domain-Specific Diffusion Model-based Algorithm]] to being a [[General-Purpose Diffusion Model-based Algorithm]], depending on its [[application scope]].
** ...
** ...
** It can incorporate [[Latent Space Representation]]s to reduce [[computational complexity]].
** It can incorporate [[Latent Space Diffusion Model]]s to reduce [[computational complexity]].
** It can leverage [[Diffusion Model Guidance Mechanism]]s such as classifier-free guidance or conditional generation to control the attributes of the generated samples.
** It can leverage [[Diffusion Model Guidance Mechanism]]s such as classifier-free guidance or conditional generation to control the attributes of the generated samples.
** It can execute [[Gradient-Driven Diffusion]] through sequential stages:
** It can have [[Noise Scheduler Model]] for controlling the [[noise level]] at each [[diffusion step]].
*** [[Forward Process Stage]] for [[noise addition sequence]].
** It can have [[Score Network Model]] for estimating the [[gradient information]] needed for [[reverse diffusion process]].
*** [[Training Stage]] for [[denoising network optimization]].
** It can have [[Diffusion Sampling Strategy]] for determining how to traverse the [[reverse path]].
*** [[Reverse Process Stage]] for [[iterative noise removal]].
** It can execute [[Gradient-Driven Diffusion Process]] through sequential stages:
*** [[Generation Stage]] for [[final sample production]].
*** [[Forward Diffusion Process Stage]] for [[noise addition sequence]].
*** [[Diffusion Model Training Stage]] for [[denoising network optimization]].
*** [[Reverse Diffusion Process Stage]] for [[iterative noise removal]].
*** [[Diffusion Model Generation Stage]] for [[final sample production]].
** It can require specific [[Technical Component]]s such as:
** It can require specific [[Technical Component]]s such as:
*** [[High-Performance Computing Resource]]s for [[model training]].
*** [[High-Performance Computing Resource]]s for [[diffusion model training]].
*** [[Noise Schedule Optimization]]s for [[sampling efficiency]].
*** [[Noise Schedule Optimization]]s for [[diffusion sampling efficiency]].
*** [[Memory Management Strategy]]s for [[large-scale generation]].
*** [[Memory Management Strategy]]s for [[large-scale diffusion model generation]].
*** [[Gradient Computation System]]s for [[backpropagation]].
*** [[Gradient Computation System]]s for [[backpropagation]].
** It can be applied to [[Diffusion-based Image Generation Task]]s, [[Diffusion-based Audio Synthesis]], [[Diffusion-based Text Generation]], and [[Multi-Modal Diffusion Task]]s.
** It can be used by a [[Diffusion-based Generative System]] for creating [[high-quality synthetic data]].
** ...
** ...
* <B>Example(s):</B>
* <B>Examples:</B>
** [[Stable Diffusion Method]]s, that incorporate [[cross-attention layer]]s for conditioning, which align with the "Guidance Mechanism" optional input. The process of adding noise, training, and iteratively removing noise until generating a final image aligns with the "Iterative Noise Addition and Training" and "Noise Removal and Data Generation" steps in the structure.
** [[Gradient-Driven Diffusion Model Implementation]]s, such as:
** [[Guided Diffusion Method]]s, that use additional guidance, such as text prompts or class labels, aligning with the "Guidance Mechanism" optional input. The integration of guidance into the training process and data generation corresponds with the "Guidance Mechanism (Optional)" and subsequent steps in your structure.
*** [[Image Diffusion Model]]s, such as:
** [[Classifier-Free Guidance Method]]s, that adjust method predictions without a separate classifier, aligning with the "Guidance Mechanism" optional step. The process of modifying noise prediction during sampling is reflected in the guidance-related steps in your algorithm structure.
**** [[DDPM (Denoising Diffusion Probabilistic Model)]] for [[realistic image generation]] using [[Gaussian diffusion process]].
** [[CNN-based Diffusion Method]]s, that use [[Convolutional Neural Networks (CNNs)]] as the underlying architecture for the diffusion process, aligning with the "Model architecture: neural_net_model" input parameter in your structure. CNNs are particularly effective in handling image data, enhancing the diffusion method's ability to generate high-quality images.
**** [[DDIM (Denoising Diffusion Implicit Model)]] for [[accelerated sampling]] with [[deterministic diffusion generation]].
** [[Transformer-based Diffusion Method]]s, that use [[Transformer]] architectures for the diffusion process, aligning with the "Model architecture: neural_net_model" input parameter. Transformers are known for their ability to capture long-range dependencies, making these methods well-suited for tasks like text-to-image generation, where context is crucial.
**** [[Stable Diffusion Model]] for [[latent space diffusion]] with [[compression efficiency]].
** [[Base Architecture Type]]s, such as:
**** [[Guided Diffusion Model]] for [[controlled image synthesis]] using [[additional guidance]].
*** [[CNN-based Diffusion Method]]s for [[image generation task]]s.
*** [[Audio Diffusion Model]]s, such as:
*** [[Transformer-based Diffusion Method]]s for [[text-conditioned generation]].
**** [[DiffWave Model]] for [[neural waveform generation]] through [[diffusion process]]es.
** [[Guidance Implementation Type]]s, such as:
**** [[AudioLDM Model]] for [[text-conditioned audio generation]] using [[latent diffusion]].
*** [[Classifier-Free Guidance Method]]s for [[unconditioned generation]].
**** [[Grad-TTS Model]] for [[text-to-speech synthesis]] with [[flow matching]].
*** [[Guided Diffusion Method]]s for [[conditional generation]].
*** [[Text Diffusion Model]]s, such as:
** [[Efficiency-Focused Type]]s, such as:
**** [[Diffusion-LM Model]] for [[discrete text generation]] with [[continuous diffusion]].
*** [[Stable Diffusion Method]]s for [[latent space operation]]s.
**** [[LLaDA Diffusion Model]] for [[large language model diffusion]] with [[token masking]].
*** [[Accelerated Sampling Method]]s for [[fast generation]].
**** [[Mercury Diffusion Model]] for [[high-speed language generation]] through [[diffusion process]]es.
** [[Base Diffusion Model Architecture Type]]s, such as:
*** [[CNN-based Diffusion Model]]s for [[image generation task]]s.
*** [[Transformer-based Diffusion Model]]s for [[text-conditioned generation]].
** [[Diffusion Model Guidance Implementation Type]]s, such as:
*** [[Classifier-Free Guidance Diffusion Model]]s for [[unconditioned generation]].
*** [[Guided Diffusion Model]]s for [[conditional generation]].
** [[Efficiency-Focused Diffusion Model Type]]s, such as:
*** [[Stable Diffusion Model]]s for [[latent space operation]]s.
*** [[Accelerated Sampling Diffusion Model]]s for [[fast generation]].
** [[Gradient-Driven Diffusion Model Technique]]s, such as:
*** [[Diffusion Sampling Strategy]]s, such as:
**** [[DDIM Sampling Model]] for [[non-Markovian generation]] with [[fewer steps]].
**** [[DPM-Solver Model]] for [[high-order solver application]] to [[diffusion ODE]]s.
**** [[Ancestral Sampling Model]] for [[stochastic trajectory generation]] in [[reverse diffusion]].
*** [[Diffusion Model Conditioning Method]]s, such as:
**** [[Classifier Guidance Diffusion Model]] for [[attribute-directed generation]] using [[gradient penalty]].
**** [[Classifier-Free Guidance Diffusion Model]] for [[controllable synthesis]] without [[separate classifier]].
**** [[Textual Inversion Diffusion Model]] for [[concept embedding]] in [[text-to-image diffusion]].
** [[Gradient-Driven Diffusion Model Application]]s, such as:
*** [[Image Editing Diffusion Model Application]]s, such as:
**** [[InstructPix2Pix Diffusion Model]] for [[text-guided image manipulation]] through [[diffusion process]]es.
**** [[ControlNet Diffusion Model]] for [[structure-conditioned image generation]] with [[spatial control]].
**** [[DreamBooth Diffusion Model]] for [[subject-driven personalization]] of [[diffusion model]]s.
*** [[Scientific Diffusion Model Application]]s, such as:
**** [[Molecular Diffusion Model]] for [[drug discovery]] through [[chemical structure generation]].
**** [[Protein Diffusion Model]] for [[biomolecule design]] using [[gradient-based sampling]].
**** [[Climate Pattern Diffusion Model]] for [[weather simulation]] using [[diffusion process]]es.
** ...
** ...
* <B>Counter-Example(s):</B>
* <B>Counter-Examples:</B>
** [[Generative Adversarial Networks (GANs)]], which use a different approach involving two competing networks to generate data.
** [[Generative Adversarial Network Model]], which uses [[adversarial training]] rather than [[gradient-based diffusion process]].
** [[Auto-regressive Models]], which generate data one step at a time without the gradual noise addition and removal process.
** [[Autoregressive Model]], which generates data [[sequentially]] rather than through [[iterative diffusion refinement]].
** [[Variational Autoencoders (VAEs)]], which use a different mechanism for generating data by encoding and decoding latent representations.
** [[Variational Autoencoder Model]], which relies on [[explicit latent space]] rather than [[diffusion process]]es.
* <B>See:</B> [[Deep Neural Networks]], [[Generative AI]], [[Probabilistic Models]], [[Denoising Diffusion Probabilistic Models]], [[Stable Diffusion Model]], [[Guided Diffusion Model]], [[Classifier-Free Guidance]].
** [[Energy-Based Model]], which directly models [[energy function]]s rather than [[diffusion trajectory]]s.
** [[Flow-Based Model]], which uses [[invertible transformation]]s rather than [[stochastic diffusion process]].
* <B>See:</B> [[Deep Neural Network Model]], [[Generative AI Model]], [[Probabilistic Model]], [[Denoising Diffusion Probabilistic Model]], [[Stable Diffusion Model]], [[Guided Diffusion Model]], [[Classifier-Free Guidance Diffusion Model]], [[Score-Based Generative Model]], [[Stochastic Differential Equation Model]], [[Denoising Diffusion Process]], [[Diffusion-based Large Language Model]].


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[[Category:Concept]]
[[Category:Concept]]
[[Category:Machine Learning]]
[[Category:Machine Learning]]
[[Category:Artificial Intelligence]]
[[Category:Generative AI]]
[[Category:Quality Silver]]
[[Category:Quality Silver]]

Latest revision as of 02:01, 7 March 2025

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.