Which ComfyUI Sampler Should I Select? Complete Guide to Samplers 2025

Open ComfyUI's sampler dropdown and you're confronted with two dozen cryptic options - Euler, Euler A, DPM++ 2M, DPM++ 2M SDE, DPM++ SDE GPU, and the list goes on. Which one produces the best images? Which one runs fastest? Does it even matter?

Samplers define how your model removes noise during the diffusion process, fundamentally affecting image quality, generation speed, and creative variation. The right sampler choice transforms mediocre outputs into stunning results, while the wrong choice wastes time and GPU resources.

Understanding sampler selection eliminates guesswork and gives you precise control over the quality-speed trade-off in your ComfyUI workflows.

What Samplers Actually Do - Understanding the Diffusion Process

Samplers are algorithms that progressively remove noise from pure static to create coherent images. Different sampling algorithms follow different mathematical approaches to this denoising process, producing varying quality and speed characteristics.

The Denoising Challenge: Starting from random noise, the model must predict and remove noise at each step. Naive approaches (simply applying the model once) produce poor results. Sophisticated sampling algorithms apply the model multiple times with decreasing noise levels, refining the image progressively.

How Sampling Works:

Deterministic vs Stochastic: Deterministic samplers produce identical results given the same seed and parameters. Stochastic samplers introduce controlled randomness, generating variations even with identical seeds.

Deterministic samplers allow perfect reproduction. Stochastic samplers enable creative exploration.

Convergent vs Non-Convergent: Convergent samplers stabilize on a final result - adding more steps beyond a certain point produces no changes. Non-convergent (ancestral) samplers continue evolving with additional steps, never fully converging.

This distinction matters for iteration efficiency and creative workflows.

Why Multiple Samplers Exist: No single sampling algorithm perfectly balances speed, quality, and versatility. Different samplers optimize for different priorities - some prioritize speed, others maximize quality, and some enable creative variation.

Understanding these trade-offs helps you choose the right tool for each workflow.

For users who want excellent results without technical complexity, platforms like Apatero.com automatically select optimal samplers based on your creative goals and selected models.

The Top 5 Samplers You Should Actually Use

ComfyUI includes dozens of samplers, but most users only need to understand a handful of high-performers. Here are the five samplers that cover 95% of use cases.

1. DPM++ 2M Karras (Most Popular - Quality Focus):

DPM++ 2M Karras combines the DPM++ 2M sampling algorithm with Karras noise scheduling (see our Karras scheduler guide for details). It produces exceptional quality with moderate step counts (20-30 steps) and works reliably across all model types.

2. Euler (Fastest - Good Quality):

Euler sampler provides excellent quality-to-speed ratio. It's the go-to choice for experimentation, testing, and workflows where generation time matters.

3. Euler A (Creative Variation):

Euler A (Ancestral) introduces controlled randomness to the Euler algorithm. It produces creative variations even with identical seeds, perfect for exploring different interpretations of prompts.

4. DPM++ SDE Karras (Maximum Quality):

DPM++ SDE Karras provides the absolute best quality output at the cost of generation time. Use this for final renders, client work, and situations where maximum quality justifies the time investment.

5. Heun (Low Distortion):

Heun excels at image-to-image workflows where preserving source image characteristics matters. It introduces minimal distortion while applying stylistic changes.

Quick Selection Guide:

Understanding Ancestral Samplers - When Randomness Helps

Samplers with "A" or "ancestral" in their names behave fundamentally differently from deterministic samplers. Understanding this distinction helps you leverage them effectively.

What Makes Samplers Ancestral: Ancestral samplers introduce controlled noise at each step, creating a stochastic (random) element in the denoising process. This prevents convergence - the sampler continues evolving the image with additional steps rather than stabilizing.

Ancestral Sampler Characteristics:

When to Use Ancestral Samplers: Use ancestral samplers when exploring creative variations of a prompt, when you want multiple different interpretations rather than refinement of one, for artistic work where controlled unpredictability adds value, or when you're stuck and need fresh variations.

When to Avoid Ancestral Samplers: Avoid ancestral samplers for workflows requiring exact reproduction, when you need batch consistency, for client work with specific requirements, or when you've found a perfect result and want to refine it further.

Popular Ancestral Samplers:

Controlling Randomness: Changing the seed with ancestral samplers produces dramatically different results compared to deterministic samplers. Small seed changes create substantial variation.

This characteristic makes ancestral samplers excellent for generating diverse outputs from a single prompt.

Step Count Behavior: With deterministic samplers, 30 steps produces a more refined version of the 20-step result. With ancestral samplers, 30 steps may produce a completely different composition than 20 steps.

This non-convergent behavior requires different workflow approaches for optimization.

GPU-Optimized Samplers - Do They Actually Help?

ComfyUI includes GPU-specific sampler variants like DPM++ SDE GPU and DPM++ 2M SDE GPU. Understanding when these variants provide real benefits helps you optimize workflows effectively.

What GPU Optimization Means: GPU-optimized samplers restructure sampling calculations to leverage GPU parallel processing capabilities more efficiently. They shift computational patterns to maximize GPU utilization.

Performance Benefits:

When GPU Variants Help: GPU-optimized samplers provide meaningful benefits on high-end GPUs (RTX 3080+) with abundant VRAM, when generating at high resolutions (1024px+), and for workflows using complex multi-model compositions.

When GPU Variants Don't Matter: On budget GPUs (GTX 1660, RTX 3060), performance differences are minimal. At low resolutions (512x512), overhead eliminates benefits. When VRAM is limited, standard variants may actually perform better by using slightly less memory.

VRAM Considerations: GPU-optimized samplers trade slightly higher VRAM usage for speed gains. On systems near VRAM limits, this trade-off may cause out-of-memory errors.

Test both standard and GPU variants on your specific hardware to determine which performs better.

Practical Recommendations:

Alternative Speed Optimization: If generation speed concerns you, switching from DPM++ SDE to Euler provides far larger speedups (2-3x) than GPU-optimized variants (15-25%) at modest quality cost.

Focus on sampler algorithm selection before worrying about GPU variants.

Sampler Settings for Different Workflows

Different creative workflows benefit from specific sampler choices. Here's practical guidance for common use cases.

Text-to-Image Generation:

Image-to-Image Workflows:

Inpainting and Outpainting: DPM++ 2M Karras provides excellent results for inpainting work - balancing quality with reasonable generation time. Use 25-30 steps for seamless blending.

Euler A works well for creative outpainting where you want varied extensions rather than predictable continuation.

ControlNet Workflows: ControlNet conditioning reduces sampler impact on composition. Euler at 15-20 steps often produces results equivalent to DPM++ 2M at 25-30 steps when strong ControlNet conditioning is present.

Prioritize speed with ControlNet workflows since composition control comes from conditioning rather than sampling quality.

Batch Generation:

Video Frame Generation: Video workflows require consistency across frames. Use deterministic samplers (Euler, DPM++ 2M) exclusively - ancestral samplers create frame-to-frame inconsistency.

Euler at 20 steps provides good speed-quality balance for multi-frame generation.

Low-VRAM Optimization: On limited VRAM systems, Euler uses significantly less memory than DPM++ variants. It's the go-to choice for 4-6GB GPUs.

See our complete low-VRAM survival guide for comprehensive optimization techniques.

Common Sampler Mistakes and How to Fix Them

Even experienced users make sampler configuration errors. Here are the most common mistakes and their solutions.

Mistake 1 - Using Too Many Steps:

Running Euler at 50 steps doesn't improve quality over 20 steps - it just wastes time. Match step counts to sampler convergence characteristics.

Mistake 2 - Wrong Sampler for Workflow Type: Using Euler A (non-convergent) for workflows requiring exact reproduction creates frustration. Deterministic samplers are essential for reproducible results.

Conversely, using DPM++ 2M for creative exploration misses opportunities that ancestral samplers provide.

Mistake 3 - Ignoring Speed-Quality Trade-offs: Blindly using DPM++ SDE Karras for all workflows wastes time. Rapid iteration workflows benefit enormously from switching to Euler.

Reserve premium samplers for final renders where quality justifies time investment.

Mistake 4 - Not Testing on Your Hardware:

Mistake 5 - Copying Settings Blindly: Workflow shared online may use exotic samplers unnecessary for the actual results. Core samplers (Euler, DPM++ 2M) often produce identical results with better performance.

Test simplified sampler choices before assuming complex configurations are required.

Troubleshooting Checklist: If results look wrong, try switching to DPM++ 2M Karras at 25 steps - this known-good configuration helps isolate whether sampler choice is causing issues. For more troubleshooting help, see our ComfyUI red box troubleshooting guide.

Compare outputs side-by-side rather than trusting memory about quality differences.

Advanced Sampler Techniques and Optimization

Beyond basic selection, advanced techniques leverage samplers for specific optimization goals.

Two-Stage Sampler Workflows: Use fast Euler sampler for initial generation at low resolution, then refine with DPM++ SDE Karras during upscaling. This balances speed for iteration with quality for final output.

Sampler Switching Mid-Generation: Advanced workflows can switch samplers partway through generation - fast sampler for initial composition (first 10 steps), quality sampler for refinement (final 15 steps).

This technique requires custom ComfyUI nodes but can optimize quality-time trade-offs.

Denoising Strength Interaction:

Resolution-Specific Optimization:

Model-Specific Considerations: Some fine-tuned models have preferred samplers mentioned in their documentation. SDXL models generally work well with all standard samplers. Anime models sometimes show sampler preferences - test both Euler and DPM++ variants.

Batch Comparison Workflows: Create workflows generating identical prompts with multiple samplers simultaneously. This empirical testing reveals which samplers work best for your specific prompts and models. Learn more about ComfyUI batch comparison techniques.

Stop guessing and start measuring actual quality differences.

Conclusion - Sampler Selection Simplified

Samplers seem overwhelming initially, but practical guidance is straightforward. Use DPM++ 2M Karras for quality work, Euler for speed and iteration, Euler A for creative exploration, DPM++ SDE Karras when quality trumps everything, and Heun for image-to-image workflows.

The 80/20 Rule: DPM++ 2M Karras handles 80% of use cases excellently. Learn this one sampler thoroughly before worrying about exotic alternatives.

What Actually Matters More: Prompt quality and composition affect output far more than sampler choice. Spending time refining prompts produces bigger improvements than testing every sampler variant.

When to Experiment: If you're hitting quality limits with DPM++ 2M Karras at 30 steps, trying DPM++ SDE Karras provides a meaningful variable to adjust. Beyond that, you're likely hitting model capability limits rather than sampler constraints.

Platform Alternatives: For users who want excellent results without technical optimization, platforms like Apatero.com automatically select optimal samplers based on workflow requirements.

Final Recommendations: Set DPM++ 2M Karras at 25 steps as your default. Use Euler at 15-20 steps for rapid testing. Switch to DPM++ SDE Karras for final quality renders. Everything else is optional experimentation.

Your creative vision matters infinitely more than which sampler you select. Master the basics, use proven defaults, and focus your energy on what makes your work unique.

The perfect sampler won't save a poorly composed image, but a strong composition shines regardless of sampling method.