Krea 2 Turbo — 2-Step Distillation LoRA

🧪 A fast-preview adapter, from a project still in training. The published checkpoint files give usable two-step renders and are measured honestly below 4-step or 8-step renders; they are not the 4-step LoRA's quality, and that adapter remains the recommendation for quality renders. Training continues one recipe change at a time, and a later checkpoint replaces this one only when the sweeps and I visually agree it is better. Known issues — see Known issues.

A LoRA for Krea 2 Turbo that takes the model from its usual 8 steps down to 2 — Turbo's own weights and its own two sigmas, guidance 0.0, a quarter of the denoising passes — aiming at the best quality two steps can give. Two steps give up more than four: this adapter is for fast previews and drafts at half the 4-step adapter's cost and a quarter of the teacher's, and the 4-step LoRA remains the recommendation for quality renders.

• 🎯 The aim — the best two-step quality this base can give, at every one of the same 12 resolutions, measured against the 8-step teacher and against the 4-step LoRA as the reference. Not a claim to reach either.

• ⚡ A quarter of the steps — 8 → 2, on Turbo's own deployment sigmas.

• ⏱️ 3.8× faster denoising — the model runs twice instead of eight times, and denoising is the part this adapter changes: 79.9 s → 20.9 s measured at 1024×1024 on the same prompts, the adapter itself costing about 3.5% per call. What a whole render costs on top of that is unchanged by the LoRA and depends on your pipeline; see Performance.

• 📊 Distribution matching, not imitation — the training objective that got the renders improving again after the 4-step project's recipe had stopped helping at two steps (see Method).

• 🗣️ Prompt-conditioned throughout — both scores in the distribution match, the teacher's and the fake adapter's, are evaluated on each prompt's own conditioning, so the student is matched to what the teacher makes for that prompt, not to a prompt-free look. There is no separate adherence term: instead a vision-language judge checks every checkpoint — each render scored alone against the prompt's objects, counts, attributes and relations, with the teacher scored the same way — and a term would only be added if that meter showed adherence slipping.

• 📐 12 trained resolutions — multi-aspect from 512×512 up to 1440×1440, each with its sweep.

• 🔌 Drop-in, no exceptions — a plain LoRA sampled by stock Euler at sigmas [1.0, 0.5128] in diffusers, ComfyUI or MLX. No custom sampler, no policy head, no per-step tricks. If the quality needs a special sampler it is not this project.

• 🧬 Same shape as the 4-step adapter — rank 64 on the same 228 modules; a second adapter exists during training only and never ships.

• 🎲 The same 13,750 recorded teacher trajectories the 4-step adapter trained on, reused without a single teacher re-run.

• 🔢 13,663 training samples in the 2-step stages, on top of the 4-step LoRA's 78,000 — all of them drawn from the same recorded material: no new prompts, no new text embeddings and not one new teacher run. A training sample is one pass over a prompt that was already encoded and already traced by the teacher for the 4-step project, read again at the two sigmas this schedule uses.

• 📅 5 days from the first 2-step training launch to this checkpoint, on a single RTX 3090 — and the project continues.

• 🔁 15 recipe adjustments across two methods so far — seven of trajectory distillation before the switch, eight of distribution matching since.

• 🖥️ One RTX 3090, and a recipe shaped by its 24 GB.

All of the above were created with this LoRA at 2 steps: the 15 test prompts, Krea 2 Turbo + the LoRA, seed 4242, each at one of its trained resolutions. Click for full size. The side-by-side comparisons with the 8-step teacher are in Examples.

Files

file what it is krea2_turbo_2step_rank_64_lora.safetensors the LoRA in diffusers key format — see Inference with diffusers; also for MLX or anything that reads safetensors krea2_turbo_2step_rank_64_lora_comfyui.safetensors the same weights under ComfyUI's key names — see ComfyUI krea2_turbo_2step_lora_t2i.json a ready ComfyUI workflow, stock nodes only krea2_turbo_2step_rank_64_lora_checkpoint_ the quick place to check which checkpoint the two weight files are based on. The pair above keeps its names and is updated in place as better checkpoints ship; this file always says what they are today. Every published checkpoint also sits in _archive/checkpoints/ under its number LICENSE.pdf the Krea 2 Community License Agreement, which covers this adapter — see License NOTICE.txt the attribution notice the license requires of a derivative

The two weight files are one adapter — only the key names differ. Both carry the training details in their safetensors metadata: base model, lineage, method, the checkpoint and the inference settings. Their file names never change; when a better checkpoint ships they are replaced in place, and krea2_turbo_2step_rank_64_lora_checkpoint_ is the quick place to check which checkpoint the current files are based on.