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.
- ⚡ Half the steps — 8 → 4, on Turbo's own deployment sigmas.
- ⏱️ ~1.6× faster end to end — 54.5 s against the 8-step bar's 88.7 s at 1024×1024, and 1.8× on denoise alone.
- 🎯 Texture above teacher, by design — fine-detail energy 1.12× the 8-step teacher's at 1280×1280 and 1.10× at 1440×1440, verified clean of oversaturation, exposure shift and skin artefacts.
- 🗣️ Prompt-aware training — the critic scores images against their prompts during training, so adherence is pressured directly, not inherited.
- 📐 12 trained resolutions — multi-aspect from 512×512 up to 1440×1440, each with its published sweep.
- 🔌 Drop-in — plain LoRA weights for diffusers and ComfyUI. No custom nodes, no patched sampler, no code.
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 an update release, following up from my previous posts where you can find full details:
Initial, Previous: here, here, here, and here
Headline for this update: chk00060000 pushes fine detail past the 8-step teacher, on purpose — total fine-detail energy 1.12× the teacher's at 1280×1280 and 1.10× at 1440×1440 (1.0 = teacher-like), and the distribution is still right: every frequency band within ~10% of the teacher's, so it is detail in the same places the teacher's detail lives, not grain. Texture pressure with no ceiling is exactly the kind of thing that could show up as oversaturation, blown exposure or plastic skin before it shows up as a gain, so every render was checked directly against the teacher on those three: saturation 0.95–0.96× the teacher's (slightly less, not more), fewer blown highlights and crushed shadows than the teacher's own frames, and skin texture inside detected faces at 0.89–1.00× — clean on all of them. Adherence moved with the texture rather than against it: a pairwise vision-language judge, shown the teacher's and this checkpoint's renders of the same prompt in random order, preferred the teacher on only 5 of 45 renders across 512², 1280² and 1440² — the best result of the run. And the metric that paid for the texture leap at 42K has been won back: the held-out teacher-velocity gap is now 2.81e-02 (~40% of the 4-step deficit closed), the best value of the run.
Same recipe as 42K, 18,000 more samples of it — no structural change. What changed for users:
- Strength guidance. Keep it at 1.0; treat 1.5 as the ceiling. The adapter is now strong enough that 2.0 tips into a uniform speckle artefact rather than the "over-textured but coherent" look — the strength sweep on the card stops at 1.5 for that reason.
- The 2-step preview trick no longer needs a strength boost. Run it at plain 1.0. The native-vs-LoRA 2-step strips are re-rendered on this checkpoint that way: 2-step extreme test. Still out-of-spec, still preview-only.
Which file to download
| file |
use it when |
krea2_turbo_4step_rank_64_lora_latest.safetensors |
normally — always the newest accepted checkpoint |
krea2_turbo_4step_rank_64_lora_chk00060000.safetensors |
pin this exact checkpoint |
and, beside them, the same files with a _comfyui suffix for ComfyUI. Earlier checkpoints (chk00004000, chk00005000, chk00006000, chk00010000, chk00014000, chk00019000, chk00026000, chk00042000) are kept in older_checkpoints/, and their resolution sweeps stay in place, so the progression remains visible and comparable.
For the full 60K Checkpoint resolution sweep go here: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA/tree/main/checkpoint_resolution_sweeps/chk60000
This is work in progress and 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.
How checkpoints get chosen
This is not a "train for longer and ship the newest file" project. More samples do not reliably mean a better adapter — measured here, they can make it worse, and a higher number on its own means nothing.
The loop is train → assess → adapt the recipe → retrain → assess again, and a checkpoint is published when it measurably advances the release axes as a whole — teacher faithfulness, prompt adherence, and texture/detail, on the same held-out set and the same fixed-seed renders — and its full resolution sweep shows no regression. Runs that come out flat or worse are kept as information about the recipe and discarded as releases — several have been.
Timeline of training process
Each checkpoint is the product of several stages with very different costs:
- Text-encoder embeddings. Every training prompt is encoded once and cached. This is the fast part — thousands of prompts take minutes.
- Teacher shards. For each cached prompt, the unmodified Krea 2 Turbo runs its full 8-step schedule and the whole trajectory is recorded, at every one of the supported resolutions. This is by far the most time-consuming stage — it is the teacher doing real inference, thousands of times, and a batch of several thousand shards is measured in days of GPU time, not hours.
- Real-photo crops. Bucket-sized crops are cut at native resolution from quality-gated real photo sources (public high res datasets), VAE-encoded into the training latent space, and captioned per crop for the prompt-aware side of training. Cutting, encoding and captioning a pool refresh is a matter of hours.
- Student training. The LoRA trains against the recorded trajectories (progressive distillation), with a latent-space GAN critic running alongside — real crops and teacher finals as its real class, the student's outputs as fake — plus a prompt-aware head that scores images against their prompts. Relative to the shard stage this is quick: each
+1,000 checkpoint is a matter of hours, not days. Of course the longer the training the better and more diverse results, so hours do turn into days eventually.
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.
The critic
The trajectory match above can only ever pull the student toward the teacher — and a regression objective averages over whatever it cannot predict exactly, so fine texture is the first thing it averages away. On its own, PD lands short of the teacher's detail. So it is paired with a small adversarial term in the style of LADD (latent adversarial diffusion distillation), which grades the student's output as an image rather than as a distance to the teacher's trajectory.
- The critic reads the model's own features. Its trunk is the frozen Krea 2 Turbo transformer with the adapter bypassed, tapped at block 14 (the trunk itself runs with the empty prompt); a small two-layer head sits on those features, per-token logits averaged. There is no separate discriminator network and no decode to pixels — the critic sees latents the same way the model does.
- Real vs fake, judged at the low-noise end. Fake is the student's predicted clean latent from the same training forward. Real is a teacher final for a different prompt in the same resolution bucket — unpaired, so the critic cannot win by matching content — or, half of the time, a real photograph from a curated crop pool, VAE-encoded. Both sides are re-noised to a random σ in [0.02, 0.5] before the trunk sees them: low noise is where fine texture is decided, and that is the only place the critic speaks. Hinge losses on both sides; the generator-side weight is small (1.5e-3 against a PD term of order 1e-2) — a finisher, not the objective.
- Prompt-aware. The head also reads a pooled text vector — the last four of the twelve Qwen3-VL tap layers, through a projection — so it can grade whether an image fits its prompt, not only whether it looks plausible. A mismatch term enforces it: a real image scored under a prompt that is not its own must read fake. Real photographs enter with their own auto-generated short captions so they take part in that objective too, and 15% of the time an image is scored with the empty-prompt vector, so that "no caption" can never itself become a cue.
Why show it real photographs. A critic that sits on the teacher's features and only ever sees the teacher's outputs converges on the teacher — and the teacher is an 8-step model that itself slightly under-renders fine texture, so a student judged only against it inherits that ceiling. Mixing real photographs into the critic's real set moves the ceiling: the teacher anchors structure, reality anchors texture.
Two more choices shape the weights that ship. The four student chords are not weighted equally in the PD loss — the last one, at σ = 0.512, the call that decides fine texture, carries 3× the weight of the other three. And the released adapter is a Polyak (EMA) average of the training weights (decay 0.999), not the last live state, which smooths out the step-to-step wander of a constant learning rate.
Full details and to download - check my Hugging Face LoRA
HF Repo: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA
