Scheduler Options
The scheduler parameter in the KSampler node determines how noise is added and removed across the diffusion process, i.e., how your model hallucination spirals into a majestic image instead of becoming pixel soup.
Different schedulers shape the trajectory of denoising across timesteps—some ramp up slowly and gently (like a spa day for your U-Net), while others hit it hard and fast (like Monday morning meetings).
⚠️ TL;DR: Choosing the wrong scheduler can make even the best checkpoint and sampler combo underperform. Choose wisely, and don't just throw
karrasat everything like it's seasoning.
🧭 Available Schedulers
Each of these is a timestep weighting function—a fancy way to say it controls how noise levels change during sampling.
| Scheduler | Description | Strengths | Weaknesses | Best Use Case |
|---|---|---|---|---|
normal | Linear timesteps. Simple, straightforward. | Predictable, works well with basic samplers. | Not optimized for high-frequency detail. | Baseline testing, quick iterations. |
karras | Sigmoid-inspired distribution from Karras et al. (yes, that Karras). Emphasizes low-noise steps. | More steps in low-noise region = sharper results. | Needs compatible samplers like dpmpp_*. | High-quality render goals, realism checkpoints. |
exponential | Applies an exponential weighting curve. | Adds flexibility in contrast and detail gradation. | May over-emphasize early noise steps. | Stylized or abstract renders. |
sgm_uniform | Uniform SDE sampler from Score-Based Generative Modeling. | Balanced treatment across steps. | Doesn’t favor fine detail as much. | Experimental workflows or SGM-style outputs. |
simple | Even simpler than normal—fixed intervals. | Good for debugging or ultra-fast tests. | Crashes and burns with complex prompts. | Internal testing, stress scenarios. |
ddim_uniform | Uniform steps used in DDIM (Deterministic Denoising Implicit Models). | Consistent, reliable for DDIM. | May lack sharpness in fewer steps. | DDIM workflows, predictable outputs. |
beta | Uses a beta schedule curve, often for noise variance control. | Smooth interpolation, soft gradients. | Requires tuning to really shine. | Portraits, fantasy art. |
linear_quadratic | Timesteps follow a linear-to-quadratic curve. | Gradual build-up; favors softer transitions. | Can appear too “blurred” at low steps. | Landscapes, inpainting workflows. |
kl_optimal | KL divergence optimized scheduler (yes, it’s as nerdy as it sounds). | Produces highly optimized, compressed noise profiles. | Extremely picky with samplers. | Research-level workflows, when min-maxing every pixel. |
🛠️ How Each Scheduler Works (In Painstaking Detail)
🔹 normal
- Curve: Linear (e.g., timestep 1, 2, 3, ..., N).
- What It Does: Assigns equal weight across timesteps.
- Why It Matters: Offers the most “average” trajectory. Works fine with samplers like
euler,heun, ordpm_fast. - Good For: Basic tests, learning workflows, and as a fallback when the rest don’t work.
🔹 karras
- Curve: Sigmoid-ish. Condenses most denoising toward the lower-noise region (the end).
- What It Does: Saves more time for fine detail at the end, delaying major denoising until later.
- Why It Matters: It’s the darling of high-fidelity image samplers (
dpmpp_2m,dpmpp_sde, etc.). - Good For: Realism, detail-heavy workflows, portraits, concept art.
- Caution: Pair only with samplers designed to play nicely—otherwise you’ll wonder why your image looks like it went through a microwave.
🔹 exponential
- Curve: Rapid early steps, diminishing returns.
- What It Does: Gets most of the denoising done early.
- Why It Matters: Good for stylized workflows or high-noise, fast-degeneration scenarios.
- Good For: Abstract renders, anime styles, wild style LoRAs.
- Watch Out: Might blow past fine details.
🔹 sgm_uniform
- Curve: Uniform spread, SDE-compliant.
- What It Does: Applies Score-Based Generative Modeling’s noise treatment uniformly.
- Why It Matters: Helpful if you’re using an SGM-based model or exploring consistency in variance across timesteps.
- Good For: Science! Experiments! 🤓
🔹 simple
- Curve: Dumb as bricks—just flat.
- What It Does: Applies no intelligence to the steps. A scheduler only in name.
- Why It Matters: It doesn’t, unless you’re benchmarking something.
- Good For: Testing custom scheduler integrations.
🔹 ddim_uniform
- Curve: Uniform timestep schedule tailored to DDIM.
- What It Does: Equal distribution for deterministic sampling.
- Why It Matters: Stable results if you're using
ddpmorddimsamplers. - Good For: Low-step fast generations, consistency over multiple runs.
🔹 beta
- Curve: Beta-distribution (as in statistics, not “early release”).
- What It Does: Tightly controls noise variance.
- Why It Matters: Helpful in smoothing harsh noise transitions.
- Good For: Soft transitions, background-heavy compositions, vintage-looking styles.
🔹 linear_quadratic
- Curve: Starts linear, ends quadratic.
- What It Does: Gracefully builds up noise decay, almost like a cinematic pan.
- Why It Matters: Creates more natural gradients and transitions.
- Good For: Scenic illustrations, dreamy atmospheres.
🔹 kl_optimal
- Curve: Custom-fitted for minimizing Kullback-Leibler divergence.
- What It Does: Matches denoising to information-theoretic optimal paths.
- Why It Matters: This is for math nerds and research papers. Optimized for minimal image loss.
- Good For: Quantitative benchmark work, precision-tuned generation.
- ⚠️ Danger: May explode (metaphorically) if used with incompatible samplers.
🧪 Compatible Sampler Pairings (Summary Table)
| Scheduler | Best Samplers |
|---|---|
normal | euler, heun, dpm_fast, ddpm |
karras | dpmpp_2m, dpmpp_sde, dpmpp_2s_ancestral |
exponential | euler_ancestral, heunpp2, dpm_adaptive |
sgm_uniform | dpmpp_sde, heun, ddpm |
simple | dpm_fast, ddpm, euler |
ddim_uniform | ddpm, dpm_adaptive, simple |
beta | dpmpp_2m_cfg_pp, heun, dpmpp_sde_gpu |
linear_quadratic | euler_cfg, heunpp2, dpmpp_2s_ancestral_cfg_pp |
kl_optimal | dpmpp_sde, dpmpp_sde_gpu, dpmpp_2m |
Want to learn more about samplers? Check out even more documentation here!
🧠 Pro Tips
- Consistency across renders: If your results suddenly start melting like wax fruit, check if the
schedulerchanged on accident. - Experimentation: Each scheduler interacts differently with steps and denoise strength. Keep those constant if you want valid A/B tests.
- Match your scheduler to your sampler: Treat them like best friends. Or frenemies—either way, they’ve got chemistry, so don’t mismatch.
🔥 What-Not-To-Do-Unless-You-Want-a-Fire
Because sometimes, you just want to watch your GPU cry.
❌ Don’t Mix karras With Non-Compatible Samplers
If you're out here trying to use karras with euler or heun, just... no. They weren't designed to play nice. You’re essentially asking a potato to run a sprint. You will get mud (i.e., blurry results and artifacts).
🔥 Symptom: Images that are either grotesquely smooth or inexplicably noisy.
👎 Why:karrasexpects samplers likedpmpp_2m,dpmpp_sde, etc., that understand its fancy "low-noise priority" scheme.
❌ Using kl_optimal Without a Degree in Statistical Thermodynamics
Yes, it's called “optimal,” but if you're not pairing it with the correct KL-aware samplers or you're running 4 denoise steps hoping for a miracle, you're not optimizing anything—you’re inviting entropy.
🔥 Symptom: Either nothing renders, or you get something that looks like modern art... but not in a good way.
👎 Why: KL-optimal schedules assume high sampler precision and lots of steps.
❌ Cranking Scheduler + Sampler Settings Without Adjusting Denoise
Let’s say you picked karras and dpmpp_2m, great. But then you leave your denoise at 0.9 and wonder why everything looks like it was pulled from a VCR in 1993.
🔥 Symptom: Overbaked, grainy images with lost details and lighting gone rogue.
👎 Why: These schedulers require thoughtful denoise balancing—especially around 0.4–0.7.
❌ Assuming All Schedulers Are Created Equal
They’re not. You wouldn't put diesel in a Tesla, right? (Okay, maybe you would. That's why this section exists.)
🔥 Symptom: Dull, inconsistent results even with high-quality checkpoints.
👎 Why: The scheduler directly influences how well the sampler interprets latent space. Compatibility is key.
❌ Running High-Step linear_quadratic Without Patience or a Coffee IV
Yes, it can be buttery smooth—but it’s slow. Like slow-cooker slow. If you’re in a 30-step render using that with dpmpp_2s_ancestral_cfg_pp, congrats—you’ve now entered ComfyUI retirement mode.
🔥 Symptom: Performance bottlenecks and render times that rival cooking a turkey.
👎 Why: Heavy step weighting + high step count = latency hell.
❌ Blindly Copy-Pasting Settings From Some Random Reddit Workflow
Just because "some guy" posted a great result with sgm_uniform and heunpp2 doesn’t mean your project—or checkpoint—is compatible.
🔥 Symptom: Mild panic as every output is just slightly wrong.
👎 Why: Context matters. That workflow might use a LoRA, specific VAE, or niche base model.
🧯 Golden Rule: The Scheduler Is Not a Skin Cream
Don't just apply it and hope for the best. It’s not surface-level—it controls the soul of the denoising arc. Treat it with respect. Or better yet, RTFM (which you're doing right now, so gold star for you).