Cold MacerationAI ExperienceDrawn Sept. 2026

Background replacement with a LoRA, keeping the face and the logo, wired in knob and tubeA finder and segmenter cut masks: keep the face and the logo, regenerate everything else. The operator, with a key, selects one of three preset background pictures; it and a blank card go through an image-prompt encoder. There is no text prompt. The input picture is encoded to a latent. In a loop of thirty steps, starting from pure noise, a frozen U-Net with small low-rank adapters on its attention layers predicts noise, guided by comparing the runs with the preset and with the blank; the scheduler takes one step; and the keep regions are re-injected from the original latent, re-noised to the new step. After the last step the decoder makes pixels, the original face and logo pixels are pasted back, feathered outward, and a check confirms they are identical to the input. An inset shows the adapter trained beforehand on photos of the three backgrounds, in its own loop, the gradient updating only the adapter.DENOISING LOOP · 30 STEPSLoRA TRAINING · BEFOREHANDINPUTOUTPUTFACE & LOGO FINDER · SEGMENTERKEEPREGENERATE123BACKGROUND · ONE OF THREE PRESETSBLANKOPERATOR’S KEYPRESET ENCODERIP-ADAPTERNOISE · SEED 42IMAGE ENCODERVAELATENT z₀DOWNDOWNDOWNMIDDLEUPUPUPPRESET → CROSS-ATTENTION · TEXT PROMPT LEFT EMPTYU-NET · FROZEN W · LoRA A·B ON ITS ATTENTION LAYERSGUIDANCE × 7WITH PRESET / BLANKSCHEDULER · tRE-NOISE z₀TO STEP t−1STEPz(t) → z(t−1)RE-INJECTKEEP REGIONS↓8× 30FIRST PASS: PURE NOISEIMAGE DECODERVAEPASTE BACKORIGINAL FACE & LOGOFEATHERED OUTWARDPIXEL CHECK · IDENTICAL TO INPUTPHOTOS OF THE 3 BACKGROUNDS+ NOISEFROZEN W + A·BLOSSNOISE ERRORGRADIENT → A·B ONLY× 2,000 STEPSA·B
coldmaceration