원본 문서
GPT Image 2 · en
Scene:
A square photoreal image of a museum-scale glass atrium installation that looks like a neural network explaining itself. Use soft daylight, polished concrete, suspended translucent panels, and clean reflections. The camera is slightly elevated, looking into a layered architectural space where the diagram is physical, luminous, and readable without text.
Subject:
A Transformer self-attention process represented as a suspended kinetic sculpture. Rows of small glowing token prisms enter from the left, split into query, key, and value-like light paths, pass through multiple attention heads as nested rings, then recombine into a refined output ribbon on the right. Use color-coded flows, curved cables, transparent matrices, and small abstract annotation plaques made only of symbols, ticks, and dots. The structure should feel academic and meta: an AI model drawing its own internal attention map as a physical exhibit.
Important details:
The composition must make process logic visible: input sequence, parallel attention heads, weighted connections, merging, normalization-like layers, and output. Keep node spacing regular, avoid tangled spaghetti, and maintain clear depth separation between foreground paths and background panels. The image should balance photoreal materials with exact technical-diagram hierarchy. No literal equations are needed; the flow itself should explain the mechanism.
Use case:
Neural Network Self-Explanation single study 1/8, testing a Transformer self-attention process as a photoreal academic installation.
Constraints:
No readable text, no watermark, no logos, preserve exact 1:1 aspect ratio, no real model names, no code snippets, no private chain-of-thought, no random neon chaos, no impossible cable intersections, no generic brain icon, no unreadable clutter.사용 방법
- 매개변수를 포함한 전체 원본 프롬프트를 복사하세요.
- 주제, 문구, 구도를 자신의 요구에 맞게 바꾸세요.
- 호환되는 외부 도구에서 사용하고 이용 조건과 결과를 확인하세요.
시작 전 확인
- 참조 이미지가 필요한지 확인하세요. 호환 표시는 해당 모델로 이 예시를 생성했다는 뜻이 아닙니다.
- 이름, 수치, 이미지 속 문구를 확인하세요. 모델 버전에 따라 결과가 다를 수 있습니다.
