Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Implicit Differentiation: Problem Solving01:29

Implicit Differentiation: Problem Solving

Curves defined implicitly, where variables cannot be separated algebraically, require specialized techniques for analysis. The conchoid of Nicomedes exemplifies such a case. Its equation links x and y in a way that prevents isolation of one variable, making implicit differentiation essential to determine the slope and behavior at any point on the curve.The implicit form of the conchoid can be expressed as:To differentiate this equation, y is treated as a function of x, and the chain rule is...
Differential Equations: Problem Solving01:21

Differential Equations: Problem Solving

When analyzing the motion of falling objects, it is essential to consider not only the force of gravity but also the opposing force of air resistance. A practical example involves releasing a heavy test weight during a safety check on a ship. As the weight falls from rest, gravity accelerates it downward while air resistance exerts an upward force that increases with velocity. This dynamic interplay of forces is well described by differential equations, which provide a mathematical framework...
State Function, Exact and Inexact Differentials01:27

State Function, Exact and Inexact Differentials

A state function is a thermodynamic property that depends solely on the current state of a system, irrespective of its history or how it arrived at that state. These functions are represented by capital letters, such as U, H, and S, which stand for internal energy, enthalpy, and entropy, respectively.For instance, the value of internal energy depends on the system's state variables and remains unaffected by the process path. This means that whether the system underwent a linear process or a...
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
Separable Differential Equations01:20

Separable Differential Equations

A separable differential equation is a type of first-order differential equation where the derivative dy/dx can be expressed as a product of two functions: one that depends only on x and another that depends only on y. This allows for the rearrangement of the equation so that all terms involving y are on one side, and all terms involving x are on the other. This process, known as the separation of variables, simplifies the process of solving the equation by enabling the integration of both...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Generalized Kullback-Leibler Divergence Loss.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Detail++: Training-Free Detail Enhancer for T2I Diffusion Models.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Hybrid Granularity Distribution Estimation for Few-Shot Learning: Statistics Transfer From Categories and Instances.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Biomaterials for neuroengineering: applications and challenges.

Regenerative biomaterials·2025
Same author

Structure-Coherent Deep Feature Learning for Robust Face Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2021
Same author

Influence of casting solvent on phenyl ordering at the surface of spin cast polymer thin films.

Journal of colloid and interface science·2014
Same journal

Raising the Bar in Graph OOD Generalization: Invariant Learning beyond Explicit Environment Modeling.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

LoRASculpt: Harmonious Low-Rank Adaptation for Multimodal Large Language Models.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Linearly Solving Robust Rotation Estimation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Adapting Dense Vision-Language Relationships for Multi-label Classification with Partial Label.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Forensics Adapter: Unleashing CLIP for Generalizable Face Forgery Detection.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

MoE-Enhanced Explainable Deep Manifold Transformation for Complex Data Embedding and Visualization.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles
  1. Home
  3. Parallel Diffusion Solver Via Residual Dirichlet Policy Optimization.
  1. Home
  3. Parallel Diffusion Solver Via Residual Dirichlet Policy Optimization.

Related Experiment Video

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
10:20

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

Parallel Diffusion Solver via Residual Dirichlet Policy Optimization.

Ruoyu Wang, Ziyu Li, Beier Zhu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 12, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces the Ensemble Parallel Direction solver (EPD-Solver) to accelerate diffusion models by reducing sampling latency without sacrificing image quality. EPD-Solver achieves state-of-the-art results on image generation and text-to-image tasks with fewer steps.

    Related Experiment Videos

    Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
    10:20

    Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

    Published on: September 5, 2019

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Vision

    Background:

    • Diffusion models (DMs) excel at generative tasks but are limited by slow sampling speeds due to sequential denoising.
    • Existing acceleration methods often degrade image quality, especially under low-latency constraints, due to accumulated errors in capturing complex data distributions.

    Purpose of the Study:

    • To develop a novel Ordinary Differential Equation (ODE) solver, the Ensemble Parallel Direction solver (EPD-Solver), to accelerate diffusion model sampling.
    • To mitigate image quality degradation in accelerated diffusion models by addressing truncation errors in high-curvature trajectory segments.
    • To enhance performance in text-to-image generation tasks through parameter-efficient fine-tuning using Reinforcement Learning (RL).

    Main Methods:

  • Proposed EPD-Solver, an ODE solver that uses multiple parallel gradient evaluations per step to reduce truncation errors.
  • Leveraged the Mean Value Theorem for vector-valued functions for more accurate integral approximation of sampling trajectories.
  • Introduced a two-stage optimization: initial distillation-based parameter learning followed by parameter-efficient RL fine-tuning for solver optimization.

    • Main Results:

    • EPD-Solver achieved state-of-the-art FID scores at 5 NFE (Number of Function Evaluations): 4.47 (CIFAR-10), 7.97 (FFHQ), 8.17 (ImageNet), and 8.26 (LSUN Bedroom).
    • The RL-tuned EPD-Solver significantly improved human preference scores on Stable Diffusion v1.5 and SD3-Medium text-to-image generation.
    • Outperformed the official 28-step SD3-Medium baseline with only 20 steps, demonstrating effective inference efficiency and high-fidelity generation.

    Conclusions:

    • EPD-Solver effectively accelerates diffusion model sampling while maintaining or improving image quality, addressing limitations of previous methods.
    • The parameter-efficient RL fine-tuning scheme enhances performance on complex text-to-image tasks without extensive computational overhead.
    • EPD-Solver offers a flexible, plugin-based solution (EPD-Solverplugin) to improve existing ODE samplers for diffusion models.