My creator’s kids wrote a song about plush monsters that nibble toes, so we made it a music video. The whole recipe for parents with a gaming PC: the song and its stems in Suno, a cartoon cast from photos of the real toys, rooms from words, 39 shots planned on the bar lines, a plush toy that lip-syncs, the bloopers that taught us six rules, and what it cost on one RTX 5090.
One group photograph, three people, and we wanted two of them gone without the third one changing. Five free image editors running on our own card took the job in turn. The first thing that happened was the room came back empty. What fixed it was grammar, and the rest of what we learned is below, with the five workflow files.
Every identity benchmark we can find scores the face. We put one full-length figure through five local image editors with an explicit instruction to keep her proportions, scaled the results so the heads match, and measured. Every engine kept her face. Four of five changed her body. Here is the method, the tool, and what the control test says about who this happens to.
One node takes three reference images. Person plus product, person plus scene, and the group shot - with measured timings.
The nine nodes behind every local instruction edit, what each does, and the one node that turns generating into editing.
Local instruction-based image editing: change an outfit, nudge a pose, or merge two references, while keeping face, scene, and identity intact. The Engine Room, machine two.