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KSamplerSelect

“Convenient Sampler Selection — without the guesswork or typo-induced breakdowns.”

🧠 What is KSamplerSelect?​

The KSamplerSelect node is a zero-friction utility node designed for one purpose: selecting a sampler from the comfy.samplers.SAMPLER_NAMES list and outputting it as a usable SAMPLER object. Think of it as your sampler sommelier—it helps you choose the right flavor of sampling algorithm without fumbling through dropdowns on the KSampler node itself.

This node is perfect for:

  • Rapid experimentation with multiple samplers
  • Swapping sampling strategies between workflows
  • Keeping your pipeline modular and neat
  • Avoiding the wrath of typo gremlins (e.g., “ddim” vs “DDIM” vs “dpmpp_2m_sde_gpu” 🙃)

KSamplerSelect

🧩 Inputs​

🔹 sampler_name​

Type: String (dropdown from comfy.samplers.SAMPLER_NAMES)
Required: ✅ Absolutely
Default: None (and yes, that means you must set it)

The sampler_name is the only input this node needs, and it does all the heavy lifting. When you select a value, the node fetches the appropriate internal sampling object used by other diffusion nodes (primarily KSampler).

Examples of available samplers:

  • euler, euler_ancestral
  • heun, heunpp2
  • dpmpp_2m, dpmpp_2m_sde, dpmpp_2m_cfg_pp
  • ddpm, lcm, ipndm
  • ...and yes, even uni_pc, er_sde, gradient_estimation, and the rest of the alphabet soup.

🧠 Tip: All sampler names are pulled from comfy.samplers.SAMPLER_NAMES. If it’s not in that list, it’s not valid.

🔻 Outputs​

🔸 SAMPLER​

Type: SAMPLER object
Description: This is the internal sampler instance tied to your selected algorithm.
Usage: Feed this directly into the KSampler node (or any other node expecting a SAMPLER input).

This output is what makes the node functionally useful. It’s a direct reference to the logic that governs how your image is actually generated—so yes, it matters a lot.

  • 🎛 Dynamic Workflows: Build UI-style interfaces where users pick samplers without opening the backend spaghetti.
  • 🧪 Sampler A/B Testing: Wire up multiple KSamplerSelects to test how different samplers affect a prompt.
  • 📦 Reusable Templates: Create modular templates where only the sampler changes between styles or projects.
  • ☁️ Cloud Workflows: Great for ComfyUI cloud setups like ComfyUI-Manager, where toggling options remotely matters.

🧾 Example Workflow​


[Load Checkpoint] → [KSamplerSelect] → [KSampler] → [VAE Decode] → [Save Image]

Add multiple KSamplerSelect nodes to feed alternate branches:


↘ [KSamplerSelect: euler] ↘ [Load Checkpoint]              → [KSampler] → [VAE Decode]        ↗ [KSamplerSelect: dpmpp_2m_sde] ↗

📌 Prompting Tips​

  • Prompt remains constant → Sampler determines how it gets interpreted.
  • Samplers like dpmpp_2m_sde are great for long prompts, high coherence, and complex compositions.
  • euler_ancestral tends to be fast and flexible, good for rough sketches or previews.
  • If you're using lcm, remember to drop your steps or risk overbaking your image into a pile of visual mush.

❌ What-Not-To-Do-Unless-You-Want-a-Fire​

  • 🔥 Leave sampler_name blank – The node won't output anything, which breaks your pipeline.
  • 🔥 Assume sampler order matters – This is not a tier list, just a list of available options.
  • 🔥 Use incompatible samplers with schedulers – Some combos (e.g. ddim + karras) will silently fail or give junk. Check your compatibility.
  • 🔥 Forget to connect the SAMPLER output – The KSampler node won’t magically know what sampler you wanted.

⚠️ Known Issues​

IssueCauseSolution
Invalid sampler nameYou typed something not in SAMPLER_NAMES (or copy-pasted from StackOverflow again 😑)Use the dropdown. Seriously.
Missing sampler_nameYou didn’t select anything.Select a sampler before running.
“My image looks weird now”You changed the sampler and expected the same resultsThat’s not how any of this works. Different samplers = different behaviors.

📝 Final Notes​

The KSamplerSelect node is the behind-the-scenes MVP for workflow clarity, testability, and modularity. Use it to cleanly define sampling behavior without overloading your KSampler node with hardcoded settings. This node doesn’t generate images—it just makes sure the right algorithm does.

🧠 Pro move: Pair this with a Conditioning Select and Checkpoint Select for a fully modular generation system that looks like you actually know what you're doing.