Type-a-prompt generation makes new pictures. Instruction editing changes the picture you already have: “change her jacket to leather, keep everything else the same.” Locally, free, no masks. This is the machine we reach for more than any other.

The tool: an instruction-edit model

Modern edit models (we run Qwen Image Edit locally in ComfyUI) take an input image + a plain-English instruction, and re-render the image with the change applied. No brush, no inpaint mask, the model figures out what “the jacket” is.

The graph reads like the basic seven boxes, with two differences: the canvas comes from your loaded image (encoded, not empty), and the prompt node also sees the image.

Two speeds, two personalities

  • Lightning mode (a 4-step accelerator LoRA): seconds-fast, and conservative, great for small, surgical edits.
  • Full mode (20+ steps, higher CFG): slower, braver, bigger transformations, but more drift risk.

Our working doctrine:

  1. Small pose/prop nudges → Lightning, with an instruction that ends: “keep her face, hair, outfit, and the background exactly the same.” That trailing clause is load-bearing.
  2. Big changes → full mode, in small steps. One mega-instruction (“new outfit + new pose + new lighting”) tangles; three focused edits in a row win.
  3. Re-pick between steps. Generate a small batch per edit, pick the best, feed it forward.

What instruction editing won’t do

Fine asymmetric expression control (a wink, a raised single brow) resists editing, the model repaints the state it believes faces have. For those, go back to generation with weighted prompt craft (singular feature + repetition, see From Zero), or composite manually.

Combining two images

Two-image combine workflows take a subject reference and a target (a pose, an outfit, a scene) and merge them. In ComfyUI these are just graphs with two Load Image nodes feeding a combine-aware prompt encoder. Uses we ship weekly:

  • putting a locked character into a new scene,
  • borrowing a pose from a reference photo,
  • outfit transfer between renders.

Expect drift on identity-critical combines, run your character LoRA in the same graph to hold the face (the character-LoRA guide explains why that works).

The honest failure list

  • Edits inherit your input’s flaws: garbage in, confidently-polished garbage out.
  • “Keep everything the same” is a request, not a contract: audit hands, logos, and background text after every edit.
  • Waist-crop inputs make the model invent lower bodies. Edit full-framed sources when the output needs the full frame.

Engine Room series: From Zero · Video · Upscale · 3D · Character pipeline

Go deeper: one guide per technique

This page is the overview. Each technique now has its own full walkthrough with real ComfyUI screenshots, the exact node fields, and measured timings from our own run: