Gradient-Driven Diffusion Model-based Algorithm: Difference between revisions
Jump to navigation
Jump to search
No edit summary |
No edit summary |
||
Line 1: | Line 1: | ||
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 | ** 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> | * <B>Examples:</B> | ||
** [[ | ** [[Gradient-Driven Diffusion Model Implementation]]s, such as: | ||
** [[ | *** [[Image Diffusion Model]]s, such as: | ||
** [[ | **** [[DDPM (Denoising Diffusion Probabilistic Model)]] for [[realistic image generation]] using [[Gaussian diffusion process]]. | ||
** [[ | **** [[DDIM (Denoising Diffusion Implicit Model)]] for [[accelerated sampling]] with [[deterministic diffusion generation]]. | ||
** [[ | **** [[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 | *** [[Audio Diffusion Model]]s, such as: | ||
*** [[Transformer-based Diffusion | **** [[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 | **** [[Grad-TTS Model]] for [[text-to-speech synthesis]] with [[flow matching]]. | ||
*** [[Guided Diffusion | *** [[Text Diffusion Model]]s, such as: | ||
** [[Efficiency-Focused Type]]s, such as: | **** [[Diffusion-LM Model]] for [[discrete text generation]] with [[continuous diffusion]]. | ||
*** [[Stable Diffusion | **** [[LLaDA Diffusion Model]] for [[large language model diffusion]] with [[token masking]]. | ||
*** [[Accelerated Sampling Method]]s for [[ | **** [[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- | * <B>Counter-Examples:</B> | ||
** [[Generative Adversarial | ** [[Generative Adversarial Network Model]], which uses [[adversarial training]] rather than [[gradient-based diffusion process]]. | ||
** [[ | ** [[Autoregressive Model]], which generates data [[sequentially]] rather than through [[iterative diffusion refinement]]. | ||
** [[Variational | ** [[Variational Autoencoder Model]], which relies on [[explicit latent space]] rather than [[diffusion process]]es. | ||
* <B>See:</B> [[Deep Neural | ** [[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]]. | |||
---- | ---- | ||
Line 125: | Line 179: | ||
[[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.
- AKA: Diffusion Algorithm, Score-Based Generative Model, Denoising Diffusion Model.
- Context:
- 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 Techniques 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 samples 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 samples.
- 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 Models or Markov Chain Models to model the diffusion process.
- It can (typically) generate High-Quality Samples 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) 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 attributes.
- It can (often) accelerate Diffusion Sampling through advanced schedulers and step-size adaptation.
- It can (often) incorporate Conditional Signals to control output characteristics.
- ...
- 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 requirements.
- 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 Diffusion Models to reduce computational complexity.
- It can leverage Diffusion Model Guidance Mechanisms such as classifier-free guidance or conditional generation to control the attributes of the generated samples.
- It can have Noise Scheduler Model for controlling the noise level at each diffusion step.
- It can have Score Network Model for estimating the gradient information needed for reverse diffusion process.
- It can have Diffusion Sampling Strategy for determining how to traverse the reverse path.
- It can execute Gradient-Driven Diffusion Process through sequential stages:
- It can require specific Technical Components such as:
- It can be applied to Diffusion-based Image Generation Tasks, Diffusion-based Audio Synthesis, Diffusion-based Text Generation, and Multi-Modal Diffusion Tasks.
- It can be used by a Diffusion-based Generative System for creating high-quality synthetic data.
- ...
- Examples:
- Gradient-Driven Diffusion Model Implementations, such as:
- Image Diffusion Models, such as:
- DDPM (Denoising Diffusion Probabilistic Model) for realistic image generation using Gaussian diffusion process.
- DDIM (Denoising Diffusion Implicit Model) for accelerated sampling with deterministic diffusion generation.
- Stable Diffusion Model for latent space diffusion with compression efficiency.
- Guided Diffusion Model for controlled image synthesis using additional guidance.
- Audio Diffusion Models, such as:
- Text Diffusion Models, such as:
- Image Diffusion Models, such as:
- Base Diffusion Model Architecture Types, such as:
- Diffusion Model Guidance Implementation Types, such as:
- Efficiency-Focused Diffusion Model Types, such as:
- Gradient-Driven Diffusion Model Techniques, such as:
- Diffusion Sampling Strategys, such as:
- Diffusion Model Conditioning Methods, such as:
- Gradient-Driven Diffusion Model Applications, such as:
- Image Editing Diffusion Model Applications, such as:
- Scientific Diffusion Model Applications, such as:
- ...
- Gradient-Driven Diffusion Model Implementations, such as:
- Counter-Examples:
- Generative Adversarial Network Model, which uses adversarial training rather than gradient-based diffusion process.
- Autoregressive Model, which generates data sequentially rather than through iterative diffusion refinement.
- Variational Autoencoder Model, which relies on explicit latent space rather than diffusion processes.
- Energy-Based Model, which directly models energy functions rather than diffusion trajectorys.
- Flow-Based Model, which uses invertible transformations rather than stochastic diffusion process.
- See: 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.
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.
- If used:
- 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:
- Use Denoising Score Matching Technique to train the model to predict and remove the added noise.
- Optimize the model using gradient-based methods.
- Guidance Mechanism (Optional):
- If guidance is used, modify the model's prediction by incorporating the guidance (e.g., classifier-free guidance or conditional generation).
- Apply Noise Schedule:
- For each iteration (i) from 1 to n_iterations:
- 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.
- Reverse the Noise Addition Process:
- After all iterations are completed:
- Output Final Generated Data
- Output the final generated_sample as the generated data.
- Initialize Neural Network Model
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:
- 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:
[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.