Krea2 Turbo Distill 4 step LoRA (NOT a New Checkpoint... YET to share / still mid training) ... but fun 2-step extreme experiment with surprising results... (OUT OF TRAINING SPEC, which is 4 steps!)

I am sure for those of you who have been following my 4 step Krea 2 Turbo LoRA, you would know from my previous posts the work of progress I have been sharing with you ( if not see here - https://www.reddit.com/r/StableDiffusion/comments/1vxtizs/krea2_turbo_distill_4_step_lora_new_checkpoint/ ) This post is not an announcement of a new release/checkpoint (26K is still the latest released checkpoint). I'm midway through training with a tweaked recipe and a new element in the flow: the Progressive Distillation I've used since the beginning is now paired with a GAN critic in the latent space — LADD-style (Latent Adversarial Diffusion Distillation). A key twist: the critic isn't judging against teacher outputs alone — it's partly fed real photographs as its "real" reference, which is exactly where the surprising robustness you see in these 2-step images comes from. It makes training 2–3× slower, but it has paid off well so far, and I'm not done with it yet. The critic exists only at training time — the released LoRA stays a plain drop-in file. I was impressed with the results (in progress) so much that I decided to see what would happen if I push the LoRA to an extreme challenge - run it on Krea 2 Turbo at only 2 steps, with applied strength of 2 (way outside its spec - the trained 4 steps) - and I had tried this with earlier checkpoints in the past and the results were not as good... but now with the latest in progress LoRA (which I will share once its training is fully complete), I think it showcases how far this LoRA has progressed. For those wondering how real photos (from public datasets) can supervise arbitrary prompts: they don't match the prompts at all — the critic is unconditional and never sees the text. GANs match distributions, not pairs: the critic just learns what real-image texture statistics look like and pushes the model's outputs toward that signature, while the Progressive Distillation side remains responsible for content and composition. And the progress so far shows much improved textures and details (at 4 steps even with normal strength 1), so much to look forward to when I make the next checkpoint public after training completes. I would still discourage you from using the 2 step as any form of production, but I'll let the comparison images speak for themselves... I did a side by side comparison with the native Krea 2 Turbo and my latest in progress LoRA both at 2 steps - and the results are here for you to check: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA/tree/main/checkpoint_resolution_sweeps/2step-strength2-extreme-test-native-vs-lora-experiment I will post as comments some of the side by side images (2 step). What this means is ... while this is an extreme, out-of-spec experiment — not recommended for production use - it is however useful, as a fast preview*: at 2 steps with strength ~1.5–2.0 the LoRA gives a reliable read on composition and the general look of an image at a quarter of the 8 steps. It works best on closer subjects (seems best on portraits and close ups), and it gets worse on further away subjects (see market example).* It also means I am seriously considering a later training project - properly trained 2-step LoRA based on these results. NOTE: Like I said a few times, this is an extreme experiment and not for real use/production yet may work on some prompts and be used for fast previews before you decide to run 4 or 8 steps Turbo or 14+/28 steps with RAW in full production mode. So no complaining :) this is all but a fun experiment and showing how far the LoRA has come: at 2 steps the native model produces ghosted, smeared, half-formed images, while the LoRA side delivers coherent, sharp compositions — the difference is striking on every one of the 15 test prompts. submitted by /u/TimeTruth2490 [link] [comments]

Source: r/StableDiffusion | 2026-08-27