Schedulers
Introduction
In the context of diffusion models, a scheduler (also known as a sampler) is the algorithm responsible for managing the iterative process of turning pure noise into a clean, coherent image.
The Core Concept
While the model provides the "intelligence" to recognize patterns in noise, the scheduler provides the "roadmap" for how to navigate from chaos to clarity.
Diffusion models are trained by gradually adding noise to an image until it becomes unrecognizable. To generate a new image, the model performs this process in reverse. However, the neural network doesn't "jump" to the final image in one step. Instead, it predicts the noise present in its current input, and the scheduler calculates how to subtract that noise to move one step closer to the final result.
Speed vs Quality Trade-offs
The choice of scheduler significantly impacts the final result:
- Efficiency: Schedulers like UniPC, LCM, or TCD are optimized for speed, producing high-quality images in just 4–8 steps
- Precision: Schedulers like DPM++ 2M or Heun provide high mathematical accuracy and smoother gradients, typically requiring 20–30 steps for optimal results
- Consistency: Deterministic schedulers (ODE-based) will always produce the same image for the same seed, while stochastic schedulers (SDE-based) may vary slightly even with the same seed
Model Compatibility
For a scheduler to work correctly, it must match the prediction type the model was trained with:
- Epsilon: The standard for most models (e.g., SD 1.5, SDXL). The model predicts the noise added to the image
- V-Prediction: Used in models like SD 2.1-v and certain SDXL refinements. The model predicts a velocity vector that balances noise and signal, which helps avoid color shifts at very high/low noise levels
- Flow Matching: The modern standard for models like Flux and SD3. The model predicts a constant-speed linear path ("flow") between pure noise and pure data. This typically results in much faster convergence and higher detail retention
Important
Using mismatched scheduler/precition-type will result in pure noise or artifacts
List
List of schedulers available in SD.Next is broken down into 3 groups:
- Schedulers present in core Diffusers Library
- Additional schedulers ported by SDNext Extensions
- Collection of RES4LYF Custom Suite
Diffusers
1. Foundational Gaussian Schedulers
The original samplers that defined the diffusion era, focusing on iterative denoising through Markovian and non-Markovian processes. - Foundational Math: Based on the original Ho et al. and Song et al. formulations. - Versatility: Support for inversion and parallel sampling.
| Scheduler | Description | Variants |
|---|---|---|
| DDPMScheduler | Denoising Diffusion Probabilistic Models; the standard for training and original inference. | 2 (PT, Parallel) |
| DDIMScheduler | Denoising Diffusion Implicit Models; enables faster, non-Markovian sampling and inversion. | 4 (PT, Parallel, CogVideoX, Inverse) |
| PNDMScheduler | Pseudo Numerical Methods for Diffusion Models; uses multi-step Runge-Kutta logic. | 1 |
2. DPM-Solver Family
A suite of high-order solvers specifically designed to solve the Probability Flow ODE of diffusion models with fewer steps. - ODE Efficiency: Purpose-built for the semi-linear structure of diffusion ODEs. - Karras Support: Deep integration with Karras-style noise schedules.
| Scheduler | Description | Variants |
|---|---|---|
| DPMSolverMultistepScheduler | Efficient high-order multistep solver; the standard recommendation for Stable Diffusion. | 4 (PT, Inverse, CogVideoX, Cosine) |
| DPMSolverSinglestepScheduler | Higher-order singlestep solver for precise but slightly slower sampling. | 1 |
| DPMSolverSDEScheduler | Stochastic variant using SDE integration for improved texture and variety. | 1 |
| EDMDPMSolverMultistepScheduler | DPM-Solver implementation optimized for the EDM (Elucidating Design Space) framework. | 1 |
3. Euler & Heun (Karras-style) Schedulers
Classical numerical methods adapted for discrete-time diffusion, often preferred for their predictable and clean convergence.
- Prediction Modes: Heavy support for epsilon, v_prediction, and sample targets.
- Ancestral Sampling: Includes ancestral variants that add noise at each step.
| Scheduler | Description | Variants |
|---|---|---|
| EulerDiscreteScheduler | Simple and effective first-order ODE solver; excellent for most SD models. | 2 (PT, Flax) |
| EulerAncestralDiscreteScheduler | Euler method with added stochastic noise; known for creative diversity and stability. | 2 (PT, LTX) |
| HeunDiscreteScheduler | Second-order method providing better accuracy than Euler at the cost of double model calls. | 1 |
| LMSDiscreteScheduler | Linear Multi-step solver using a history of gradients to improve convergence. | 2 (PT, Flax) |
4. Modern Distillation & High-Speed Solvers
State-of-the-art solvers designed for 1-4 step inference through consistency or distillation techniques. - Extreme Speed: Enables near real-time generation. - Consistency Models: Based on the Consistency Models (CM) and Latent Consistency (LCM) research.
| Scheduler | Description | Variants |
|---|---|---|
| LCMScheduler | Latent Consistency Models; the industry standard for 4-step real-time generation. | 2 (PT, FlowMatch) |
| TCDScheduler | Trajectory Consistency Distillation; improves on LCM for better 1-step quality. | 1 |
| SCMScheduler | Simple Consistency Models; streamlined distillation for high-quality fast sampling. | 1 |
| ConsistencyDecoderScheduler | Specialized scheduler for the DALL-E 3 consistency decoder models. | 1 |
5. Flow Matching & Rectified Flow
Schedulers for the latest generation of "Flow" models (like Flux, SD3, and AuraFlow) which use linear velocity targets. - Velocity Prediction: Designed specifically for models trained on flow-matching objectives. - Linear Trajectories: Optimal for models that denoise along a straight line.
| Scheduler | Description | Variants |
|---|---|---|
| FlowMatchEulerDiscreteScheduler | The standard Euler solver for Flow Matching models (SD3, Flux). | 1 |
| FlowMatchHeunDiscreteScheduler | Higher-order Heun solver for Flow Matching trajectories. | 1 |
SDNext
1. High-Precision & Advanced ODE Solvers
Solvers designed for superior convergence and precision, often using predictor-corrector or boundary-diffusion frameworks.
| Scheduler | Description | Variants |
|---|---|---|
| BDIA_DDIMScheduler | Boundary-Diffusion Implicit Algorithm; high-precision extension of DDIM with non-Markovian guidance and gamma-weighted trajectory correction. | 2 |
| DCSolverMultistepScheduler | Dynamic Compensation solver; multi-step UniPC framework with dynamic extrapolation and ratio optimization to minimize approximation error. | 2 |
| TDDScheduler | Time-Dependent Diffusion; experimental sampler that extends DPMSolverSinglestep with special jump logic and TDD-specific training step support. | 3 |
2. Flow Matching Optimized Solvers
Specialized solvers designed for the latest generation of Flow-based models (e.g., Flux, SD3, AuraFlow), supporting resolution-aware trajectory shifting.
| Scheduler | Description | Variants |
|---|---|---|
| FlowMatchDPMSolverMultistepScheduler | Dedicated high-order DPM solver for Flow Matching with integrated Brownian Tree smoke/noise stability and multiple SDE/ODE modes. | 7 |
| FlowUniPCMultistepScheduler | Multi-step UniPC framework adapted for flow-prediction models, supporting dynamic shifting and high-order B(h) updates. | 1 |
| FlashFlowMatchEulerDiscreteScheduler | Optimized Euler-based scheduler for FlashFlow models, featuring resolution-aware dynamic shifting (mu/base/max shift). | 1 |
3. Fast-Step & Distillation Solvers
Production-grade solvers optimized for extremely low step counts (1-4 steps) while maintaining visual fidelity.
| Scheduler | Description | Variants |
|---|---|---|
| UFOGenScheduler | Diffusion GAN-based sampler implementing both one-step and multi-step sampling trajectories with thresholding support. | 1 |
4. Continuous & Variational Frameworks
Schedulers based on specific mathematical foundations for variational objectives and continuous time formulations.
| Scheduler | Description | Variants |
|---|---|---|
| VDMScheduler | Variational Diffusion Models; supports both discrete and continuous formulations of VDM objectives (linear, cosine, or sigmoid schedules). | 1 |
Unique Features
- Dynamic Compensation (DC): Implements dynamic extrapolation to correct for trajectory drift during the denoising process.
- Brownian Tree Noise Sampler: Provides significantly more stable convergence in Flow Matching compared to standard random noise.
- Resolution-Aware Shifting: Automatically adjusts noise schedules based on the image sequence length (total pixels) to optimize quality across resolutions.
- BDIA Guidance: Uses boundary-diffusion implicit algorithm logic to guide DDIM samples with a configurable gamma-weighted correction.
RES4LYF
1. RES Family (Refined Exponential Solvers)
The core of the suite, implementing state-of-the-art exponential integration with high-order accuracy and perfect variance tracking. - High-Order Convergence: Maintains structural integrity at low step counts. - Variance Preservation: Eliminates brightness drift and color shift during generation. - Unified Interface: Seamless switching between multistep and multistage modes.
| Scheduler | Description | Variants |
|---|---|---|
| RESUnifiedScheduler | Unified interface for switching between various RES and DEIS integration schemes. | 9 |
| RESMultistepScheduler | High-order multistep solver using historical gradients for efficient sampling. | 4 |
| RESMultistepSDEScheduler | Stochastic multistep solver that maintains variance while improving sample diversity. | 2 |
| RESSinglestepScheduler | Multi-stage singlestep solver offering high accuracy within a single timestep interval. | 4 |
| RESSinglestepSDEScheduler | Stochastic variant of the singlestep solver for high-quality, diverse image generation. | 4 |
2. Exponential Time Differencing (ETD) & Lawson
Advanced integrators that solve the linear part of the Probability Flow ODE exactly, providing superior stability for high-order updates. - Exact ODE Handling: Solves the deterministic part of the diffusion process without approximation. - Superior Stability: Prevents numerical explosions in high-order (3rd and 4th) sampling steps.
| Scheduler | Description | Variants |
|---|---|---|
| ETDRKScheduler | Implements Exponential Time Differencing Runge-Kutta methods for exact linear ODE handling. | 5 |
| LawsonScheduler | Uses Lawson's transformation to simplify and stabilize exponential integration steps. | 3 |
| ABNorsettScheduler | Implements Adams-Bashforth Norsett methods for stable multistep diffusion sampling. | 3 |
| DEISMultistepScheduler | Diffusion Exponential Integrator Sampler utilizing multistep polynomial extrapolation. | 3 |
3. Classical Numerical Integrators
Standard mathematical integrators optimized and refactored for the specific dynamics of the diffusion reverse process. - Familiar Tableaus: Uses proven RK, Radau, and Lobatto logic. - Modern Refactor: Fully updated to operate in normalized signal space for VP/VE compatibility.
| Scheduler | Description | Variants |
|---|---|---|
| LinearRKScheduler | Flexible implementation of standard Runge-Kutta methods from Euler to RK4. | 7 |
| RungeKuttaScheduler | Classic nth-order, m-stage Runge-Kutta integrator with optimal balance. | 3 |
| SpecializedRKScheduler | Collection of advanced solvers including SSPRK (Strong Stability Preserving) and TSI schemes. | 5 |
| LobattoScheduler | High-order solvers based on Lobatto IIIA schemes, known for their strong stability properties. | 3 |
| RadauIIAScheduler | Specialized solvers based on Radau IIA quadrature for robust stiff-ODE integration. | 2 |
| GaussLegendreScheduler | High-precision symmetric solvers based on Gauss-Legendre quadrature. | 3 |
4. Flow Matching & Physics-Inspired Samplers
Solvers designed for Flow Matching/Rectified Flow models and physics-based sampling dynamics. - Non-Euclidean Flows: Supports Hyperbolic, Spherical, and Lorentzian geometries. - Stochastic Refinement: Uses Langevin and tangent-based methods for unique textures.
| Scheduler | Description | Variants |
|---|---|---|
| RiemannianFlowScheduler | Solves flow matching problems on Euclidean, Spherical, Hyperbolic, and Lorentzian manifolds. | 4 |
| FlowEuclideanScheduler | standard Euclidean metric flow matching variant. | 1 |
| FlowHyperbolicScheduler | Flow matching on hyperbolic (Poincaré) manifolds. | 1 |
| FlowSphericalScheduler | Flow matching on spherical manifolds. | 1 |
| FlowLorentzianScheduler | Flow matching on Lorentzian manifolds. | 1 |
| PECScheduler | Predictor-Corrector framework for refined, multi-pass sampling steps. | 2 |
| BongTangentScheduler | Implements the Bong Tangent method for geometrically-guided diffusion sampling. | 1 |
| LangevinDynamicsScheduler | Uses Langevin-style gradient steps for stochastic refinement of samples. | 1 |
| SimpleExponentialScheduler | Lightweight solver using simple exponential decay for fast, low-step sampling. | 1 |
5. Utilities & Sigma Generators
Infrastructure for controlling noise profiles and driving the integration process.
| Scheduler | Description | Variants |
|---|---|---|
| CommonSigmaScheduler | Centralized schedule generator for Arcsine, Easing, Sigmoid, and Sine noise profiles. | 5 |
| SigmaSigmoidScheduler | S-shaped sigma profile for balanced noise distribution. | 1 |
| SigmaSineScheduler | Sine-based profile for periodic noise modulation. | 1 |
| SigmaEasingScheduler | Natural easing curves (In/Out/In-Out) for sigma progression. | 1 |
| SigmaArcsineScheduler | Arcsine-based profile for concentrated sampling near boundaries. | 1 |
| SigmaSmoothScheduler | Smoothstep-based profile for consistent noise transitions. | 1 |
Credits
Based on the RES4LYF implementation for ComfyUI by @ClownsharkBatwing