피파 한 줄 정리: '완벽한 한 prompt' 대신 iteration loop. Generate-first (탐색) vs edit-first (실행) — 실제 프로젝트는 둘 다 써.
Mental model: A photographer doesn't take one photo and submit it. She shoots 200 frames, selects the best 10, crops and adjusts them, retouches a couple, and delivers 3 finals. A sculptor doesn't carve a masterpiece in one cut — they rough out the form, refine, detail, polish. Creative work is inherently iterative. AI image generation is no different, but most beginners don't realize this.
The One-Prompt Myth
There's a widespread misconception that skilled AI artists write one brilliant prompt and get perfect results. The reality? They generate dozens or hundreds of variations, curate ruthlessly, then edit the best ones. The "skill" isn't in writing one magic prompt — it's in the iteration loop:
The Professional Iteration Loop: ┌──────────────────────────────────────────────────────────┐ │ │ │ Generate ──→ Curate ──→ Refine ──→ Edit ──→ Finalize │ │ │ │ │ │ │ │ │ │ │ │ │ │ ▼ ▼ ▼ ▼ │ │ Broad batch Pick best Adjust Inpaint, │ │ (many seeds) candidates prompt outpaint, │ │ & params composite │ │ │ │ ←─── Loop back as needed ──────────────────────────── │ └──────────────────────────────────────────────────────────┘
Generation-First vs. Edit-First Workflows
Two legitimate approaches, each with strengths:
Generation-First: Start with broad text-to-image exploration. Generate many variations. Pick the best. Refine with image-to-image. Fix details with inpainting. This works well when you don't have a specific vision — you're discovering what works.
Edit-First: Start with an existing image (photo, sketch, previous generation). Use image-to-image to transform it. Use inpainting for targeted changes. This works well when you have a clear vision — you're executing a specific idea.
Why Iteration Wins
Several factors make iteration superior to single-shot generation:
- Reduce risk: One generation might fail. Ten generations almost certainly include something usable.
- Discover unexpected directions: The model sometimes produces surprising results that are better than what you imagined. You can't discover these without generating broadly.
- Incremental control: Each editing pass adds precision. You can fix one thing at a time instead of trying to control everything simultaneously.
- Manage complexity: A prompt with 15 requirements will likely fail several of them. But generating with 3 core requirements, then adding the rest through editing, manages complexity effectively.
"A photorealistic portrait of a woman in her 30s with specific facial features, wearing a blue silk dress, holding a glass of red wine, in a dimly lit Italian restaurant with brick walls and candles, shot on a 85mm lens, f/1.4, warm color grade, cinematic composition, no text, no artifacts, perfect hands" → Trying to nail everything in one prompt
Step 1: "Woman in Italian restaurant, warm candlelight, cinematic" (broad) → Step 2: Pick best composition, image-to-image with more detail → Step 3: Inpaint to fix hands → Step 4: Adjust color grade in editor → Done
- Professional AI art is iterative: generate broadly, curate, refine, edit, finalize.
- The "one perfect prompt" is a myth — skill is in the loop, not the single shot.
- Generation-first explores; edit-first executes. Use both depending on where you are in the project.
- Complex requirements are better handled incrementally through editing passes than simultaneously through one prompt.