Demo for F16/krea2-turbo-sda, a rank-32 LoRA for
Krea 2 Turbo trained with SDA (Semantic Directional
Alignment) to restore the sampling diversity that the Turbo distillation removed — without
losing image quality or prompt adherence.
Because the LoRA's effect only appears across seeds, the demo samples one prompt with several seeds twice: plain Krea 2 Turbo on the left, Turbo + SDA LoRA on the right, using the same seeds on both sides.
Gating. The LoRA is trained at a single high-noise sigma node (σ = 0.9567, the second step of
the 8-step Turbo schedule) and must only be active for the first 2 of the 8 denoise steps — the
demo does this with a callback_on_step_end that calls pipe.disable_lora() after the gate step.
The gate is exposed in Advanced settings so you can see the documented quality collapse at gate = 8.
Runs on ZeroGPU with 8-step Turbo sampling, guidance_scale=0.0.
Usage of Krea 2 and its derivatives is subject to the Krea 2 Community License.
7 commits
Demo for F16/krea2-turbo-sda, a rank-32 LoRA for
Krea 2 Turbo trained with SDA (Semantic Directional
Alignment) to restore the sampling diversity that the Turbo distillation removed — without
losing image quality or prompt adherence.
Because the LoRA's effect only appears across seeds, the demo samples one prompt with several seeds twice: plain Krea 2 Turbo on the left, Turbo + SDA LoRA on the right, using the same seeds on both sides.
Gating. The LoRA is trained at a single high-noise sigma node (σ = 0.9567, the second step of
the 8-step Turbo schedule) and must only be active for the first 2 of the 8 denoise steps — the
demo does this with a callback_on_step_end that calls pipe.disable_lora() after the gate step.
The gate is exposed in Advanced settings so you can see the documented quality collapse at gate = 8.
Runs on ZeroGPU with 8-step Turbo sampling, guidance_scale=0.0.
Usage of Krea 2 and its derivatives is subject to the Krea 2 Community License.
7 commits