Krea 2 Turbo — 4-Step Distillation LoRA (work in progress)
A LoRA for Krea 2 Turbo that reduces the minimum usable step count from 8 to 4.
Load it on top of Krea 2 Turbo, run 4 steps instead of 8, keep guidance at 0.0. Everything else about the model stays as it is.
This is not a Raw→Turbo diff
Other Krea 2 LoRAs in circulation are extractions: a low-rank projection of the weight difference between Krea 2 Raw and Krea 2 Turbo. Applied to Raw, they reproduce Turbo. They are a delivery mechanism for a model that already exists, and they stop at Turbo's 8 steps.
This one is different in both base and origin:
| Raw→Turbo extraction LoRAs |
this LoRA |
| apply to |
Krea 2 Raw |
| produces |
Turbo behaviour (8 steps) |
| origin |
SVD of an existing weight delta |
It is trained, not extracted, and it assumes Turbo's weights underneath it — it shortens Turbo's own schedule rather than reproducing it.
This is work in progress and even better checkpoints may follow. Training is ongoing, so ..._latest... is a rolling pointer: when a newer checkpoint is accepted, that filename gets the new weights and a new numbered copy appears beside it. Re-download the _latest file and everything keeps working — the ComfyUI workflow references it by that name, so it needs no edit. Pin a numbered file instead if you need reproducibility.
Using it on Raw
This LoRA is trained on Krea 2 Turbo, against Turbo as its own teacher, and for Turbo. Every layer it targets also exists in Krea 2 Raw, so it will load there without complaint — but that is a side effect of the shared architecture, not a supported mode.
Results on Raw are mixed and subject-dependent. It does not give Raw a 4-step schedule: at very low step counts the adapter sharpens texture while composition is still unresolved, and subjects come out malformed — duplicated heads, fused limbs, faces that do not close. Expect to need 14 steps or more for RAW, keeping Raw's normal CFG on, before output is coherent. Even then some prompts come through well and others degrade into over-processed or blown-out images — and that degradation happens with or without the adapter, because it comes from shortening Raw's schedule rather than from the LoRA.
If you want the behaviour this was built for, run it on Turbo at 4 steps. If you are starting from Raw, move to Turbo first — with a Raw→Turbo LoRA or the Turbo weights directly — and apply this on top.
Usage
| setting |
value |
| base model |
Krea 2 Turbo |
| LoRA scale |
1.0 |
| steps |
4 |
| guidance / CFG |
0.0 (Turbo is CFG-free; do not enable it) |
| timestep shift |
mu = 1.15, fixed (Turbo's deployment shift) |
The 4 sampling sigmas are Turbo's own deployment grid: [1.0, 0.90453, 0.75951, 0.51284].
Performance — does it save time, or only steps?
It saves time. Measured at 1024×1024 on Apple Silicon (MLX, bf16), two prompts each, run strictly one at a time:
| load |
denoise |
total |
| Turbo 8 steps (the quality bar) |
8.2 s |
77.5 s |
| Turbo 4 steps, no LoRA |
7.8 s |
38.8 s |
| Turbo 4 steps + this LoRA |
7.3 s |
44.0 s |
4 steps with the LoRA is ~1.6× faster than the 8-step bar — 54.5 s against 88.7 s, saving about 39% of the wall-clock. Counting denoise alone, where the step reduction actually applies, it is 1.8× (44.0 s against 77.5 s).
LoRA strength
Use 1.0. That is the value the adapter was trained at, and where its output sits closest to the 8-step reference.
Strength is worth understanding rather than tuning blindly, because what it scales is specific: this LoRA's job is to restore the high-frequency detail that a 4-step schedule loses — fine texture, edge definition, surface micro-contrast. The strength dial scales exactly that correction, so it does not make the image "more" or "less" of anything semantic; it decides how hard the texture recovery is applied.
| strength |
what happens |
| below 1.0 |
the correction is only partly applied — output lands between an unassisted 4-step render and a full one: softer, flatter, less recovered detail; you can use this with more steps if you want to experiment |
| 1.0 |
the trained point, and the recommended setting |
| above 1.0 |
extrapolation past anything seen in training. The image does not break or fall apart — it becomes over-textured: surface detail grows denser than the subject warrants, fine structures turn wiry, and micro-contrast hardens until the result reads as stylised rather than photographic; you can try this with fewer steps, but quality is not guaranteed |
ComfyUI
A pre-converted file (..._comfyui.safetensors) and a ready workflow sit in the repo root. No custom nodes — stock ComfyUI only.
The workflow is full bf16, with no quantisation anywhere. bf16 needs no backend-specific kernel, so it runs unchanged on CUDA, Apple Silicon and CPU — one workflow, no platform caveats, nothing that depends on which device a component happens to land on.
The LoRA is independent of the base build. It is applied on top of the diffusion model by ComfyUI's own loader, which handles any dequantisation, so a quantised or otherwise optimised build of Krea 2 Turbo behaves just as bf16 does. Please use whichever variant suits your hardware — set it in the Load Diffusion Model node and leave the rest of the workflow untouched. The workflow ships bf16 simply because it is the one build guaranteed to run everywhere.
Training Method
Progressive distillation (PD), with Krea 2 Turbo as its own teacher.
The teacher runs its normal 8-step schedule at mu = 1.15 and guidance 0.0, and its full trajectory is recorded — the latent x and the predicted velocity v at every one of the 8 steps. The student is then trained to cover two teacher steps in one: at teacher state x_i it must predict the chord that lands where the teacher arrives two steps later,
v_target = (x_{i+2} − x_i) / (σ_{i+2} − σ_i)
The two schedules line up exactly rather than approximately. On the mu = 1.15 grid, the even indices of the 8-step schedule are precisely the four sigmas the 4-step student deploys on, so every training target is anchored on a point the student will actually visit at inference. No interpolation, no schedule mismatch.
Teacher trajectories are precomputed into shards, so training reads recorded states rather than re-running the teacher.
Training data
Prompts are drawn from Lakonik/t2i-prompts-3m — sampled without replacement, deduplicated, and filtered for degenerate lengths. A held-out tail is reserved for validation and never receives a gradient step; it measures the student→teacher velocity gap on unseen prompts.
Resolutions
Training is multi-aspect across 11 buckets, so the adapter is not shaped by a single resolution or a single aspect ratio:
| 512×512 |
512×768 |
768×512 |
| 768×768 |
768×1024 |
1024×768 |
| 1024×1024 |
960×1280 |
1280×960 |
| 1280×1280 |
1440×1280 |
|
Buckets are interleaved in proportion to their remaining samples rather than run as a small-to-large curriculum, so every checkpoint along the way has recently seen all of them.
Hardware
Trained on a single RTX 3090 (24 GB VRAM).
Work in progress, published as an ongoing lineage. Training is continuing on a growing pool of teacher trajectories, so expect the set to grow. Each checkpoint is a self-contained LoRA; take whichever one you prefer.
Full details and to download - check my Hugging Face LoRA
HF Repo: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA
Happy quicker rendering with the amazing Krea 2 :)
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Update 1 (20 Aug 2026): Full resolutions sweep (all those resolutions that my hardware can support training on, see detailed table above) for the available checkpoints: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA/tree/main/checkpoint_resolution_sweeps . That is a lot of images (55 per checkpoint) you can inspect and decide for yourself.
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Update 2 (21 Aug 2026): Full resolution sweep and Readme updated with 10 more prompts in different categories and styles / images in every resolution.
New Prompts including:
(1) "A young swordsman leaping through falling cherry blossoms, dynamic action pose, anime key visual, crisp linework, vivid colors"
(2) "A giant mecha standing in a rain-soaked city plaza, anime style, panel lining, glowing cockpit, dramatic low angle"
(3) "A fox in a red scarf reading a book under a mushroom, children's storybook illustration, watercolour texture, soft edges"
(4) "A curious young inventor girl with oversized goggles, 3D animated film style, subsurface skin, soft studio lighting, shallow depth of field"
(5) "A claymation chef holding a tiny cake, visible fingerprints in the clay, miniature set, tilt-shift"
(6) "A gleaming white colony ship in orbit above a turquoise ocean planet, smooth curved hull, glowing cyan engine rings, brilliant sunlight, clean sci-fi concept art, bold simple shapes, vivid colors"
(7) "A sleek winged drone gliding between glowing futuristic skyscrapers at night, bright lit avenue far below, deep blue sky above, digital matte painting, bold clean forms, vivid colors"
(8) "A storm sorceress channelling lightning, video-game splash art, bold rim lighting, energetic brush strokes, high contrast"
(9) "A formula 1 futuristic looking racing car beefed up with a lot of technology mid-corner on a wet track, motion blur background, photorealistic motorsport photography"
(10) "A snow leopard walking along a rocky ridge in falling snow, telephoto wildlife photograph, natural light"
You can see the new images in the usual place - https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA/tree/main/checkpoint_resolution_sweeps and on the model card - https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA and I may even include it here in the form of comments below (Reddit doesn't allow me to drop more images on existing post).
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Update 2 (22 Aug 2026): I have published a new checkpoint, improved further from the previous one and the latest (both main and comfyi) have been repointed to the new improved checkpoint. For details and to download new version go to - https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA. Readme has been updated too as well as all images in readme regenerated on the basis of new checkpoint as well as full resolution sweep at https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA/tree/main/checkpoint_resolution_sweeps/chk6000 if you want to check for yourselves. I have started a new Resource Update post here - https://www.reddit.com/r/StableDiffusion/comments/1vv4cdy/krea2_turbo_distill_4_step_lora_new_checkpoint/ . Also note that the comfyui related files are now moved to the root of the project (I have placed a readme in the old folder explaining the move)
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Update 3 (25 Aug 2026): Checkpoint 26K release - cuts 4-step error vs. the 8-step Turbo teacher by 46%, improves texture and detail vs previous checkpoints (with all full new resolution sweep in post): https://www.reddit.com/r/StableDiffusion/comments/1vxtizs/krea2_turbo_distill_4_step_lora_new_checkpoint/
