---
title: "Krea 2 Best Samplers & Schedulers (Tested)"
description: "Which sampler and scheduler give the best Krea 2 Turbo results? A tested comparison — res_multistep, res_2s + bong_tangent, euler — plus the torch.compile gotcha."
slug: krea-2-best-samplers
published: 2026-08-24
updated: 2026-08-24
tags: ["Krea 2", "samplers", "ComfyUI", "how-to"]
tldr:
  - "For the best quality and identity fidelity on Krea 2 Turbo: res_2s with the bong_tangent scheduler (about 2× slower)."
  - "For the best speed/quality balance: res_multistep with simple — nearly as good and faster than euler."
  - "Avoid er_sde on Turbo — identity tends to drift; euler + simple is a soft baseline."
  - "torch.compile is incompatible with Krea 2 today, so there's no free speed-up from compiling."
reading_time: 5
---

Sampler choice changes Krea 2 output more than most people expect — not just speed, but how faithfully it holds a subject's identity across seeds. We compared samplers on **Krea 2 Turbo** at matched seeds. Here's what actually held up.

## The short answer

- **Best quality (and identity fidelity):** `res_2s` + `bong_tangent` scheduler. It's the most faithful, at roughly **2× the generation time**.
- **Best balance (recommended default):** `res_multistep` + `simple`. It lands *very* close to `res_2s` in quality and is actually **faster than euler** — a rare case of better *and* quicker.
- **Soft baseline:** `euler` + `simple`. Fine, a touch softer, nothing special.
- **Avoid on Turbo:** `er_sde`. Identity tends to drift between seeds on the distilled Turbo model (it behaves better on un-distilled setups at very low steps, but that's a different regime).

If you only remember one thing: **`res_multistep` + `simple` is the sensible default** for Krea 2 Turbo, and you reach for `res_2s` + `bong_tangent` when a specific image needs the extra fidelity.

## Where these samplers come from

`res_2s`, `res_multistep`, `res_3s` and the `bong_tangent` scheduler are part of the **RES4LYF** sampler family. In ComfyUI they can be injected into the standard KSampler, so you don't need a special sampler node to use them — just select them where you'd normally pick euler/dpmpp.

## Two Krea-2 gotchas worth knowing

1. **torch.compile doesn't work with Krea 2.** Compiling the model currently throws a dtype error (bf16 vs float) in the inductor backend, so there's no output-identical speed-up from `torch.compile` right now. Worth re-testing after ComfyUI updates, but don't count on it today.
2. **Negative prompts barely apply on Turbo.** Because Turbo runs at CFG 1, sampler choice won't rescue a prompt that relies on negatives — you have to phrase things positively. (See [how to prompt Krea 2](/blog/how-to-prompt-krea-2).)

## Skip the tuning — run a tuned pipeline online

Dialing in samplers, schedulers, and step counts is exactly the kind of fiddly work a hosted pipeline handles for you. On **AIFLUX**, Krea 2 Turbo runs on a tuned graph, so you just prompt and generate.

👉 **[Generate with Krea 2 online](/create)**

## Frequently asked questions

**What is the best sampler for Krea 2 Turbo?**
`res_2s` + `bong_tangent` for maximum quality; `res_multistep` + `simple` for the best speed/quality balance (and it's faster than euler).

**Which sampler should I avoid on Krea 2?**
`er_sde` on Turbo — identity tends to drift across seeds.

**Does torch.compile speed up Krea 2?**
Not currently — it fails with a dtype error on this architecture. There's no output-identical accelerator available today.

**Do I need a special node for res_2s / bong_tangent?**
No — the RES4LYF samplers and schedulers can be injected into the standard ComfyUI KSampler.

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*Prefer results over tuning?* **[Run Krea 2 on AIFLUX →](/create)**