Tiled Fusion High-Resolution Photo Enhancement: Through multiple rounds of tiling and targeted-region enhancement, preserve the original photo's identity, compos…
Tiled Fusion High-Resolution Photo Enhancement · 2Tiled Fusion High-Resolution Photo Enhancement · 3
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Image · GPT Image 2

Tiled Fusion High-Resolution Photo Enhancement

Through multiple rounds of tiling and targeted-region enhancement, preserve the original photo's identity, composition, lighting, and materials as much as possible, then register and blend them into a high-resolution output.

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Prompt

---
name: flowers-upscale
description: Photo to 8K; 2/3-pass contextual AI crop enhancement and registered blending.
---
INPUT S=uploaded photo; missing→request. Unspecified→ask "2 passes (recommended) or 3? 8K default; another resolution?" Await choice. N=2|3; L=specified longest edge else8192; preserve aspect. Discover available AI image-editing+raster-processing capabilities; missing→report requirement. Use environment-native tools/paths; create helpers as needed. Preserve S; separate outputs. Execute end-to-end after choice.

E(C): inspect; AI-edit inputs=[S=context,C=target], maximum practical supported resolution; retain actual output dimensions. Prompt=Q+only relevant material constraints. Concurrent independent calls where supported; persist outputs+source-coordinate boxes+prompts immediately.

P1: S→3×3 cores; pad each side20% core dimension, clip to S→9 overlapping C→E→register/blend→M1; resize assembled M1 to L.
P2: M1→4–8 padded salient ROIs(face/hair/hands/objects/textiles as present)→E→register/blend→M2. Exclude low-information regions.
N=3: M2→4–8 tighter focal ROIs(individual eyes/lips/nose/fingers/material details as present)→E→register/blend→M3. Keep homologous features consistent. Every E receives S, never crop alone.

Q="1=original: identity/anatomy/color/light authority. 2=target: return exact crop/framing/geometry. Reconstruct plausible photographic detail; preserve expression, pose, contours, clothing, genuine imperfections, focus falloff. Optical clarity; natural edge transitions; spatially varied texture. Repair inherited artifacts. Skin: irregular pores/fine creases, quiet intervals. Hair: cohesive groups, selective fibers, natural sparse fringe. Iris: source color, irregular branching fibers, coherent pupil, restrained reflections. Textiles: original weave direction/spacing/motifs. Avoid invented structures/marks, beautification, relighting, halos, ringing, embossed/crosshatched/repeated texture, synthetic grain, blanket sharpening."

ASSEMBLE: map recorded boxes→master; global similarity registration first; bounded smooth local correction only with reliable correspondence, never distort rigid geometry/repeating patterns. Unreliable→retain prior pixels. Match broad exposure/color without transferring old fine texture. Normalized overlap/multiband blend; feather15–25% border→alpha0 at interior patch boundaries. Preserve native enhanced crops. Before next pass: inspect full image+100% focal/overlap crops; repair doubling/seams/drift/texture mismatch. ≤2 targeted retries/defect; rejected/failed→retain prior content+disclose.

EXPORT: M_N→lossless PNG at L; verify dimensions+decode; deliver file+preview+prompts/settings. Describe detail as reconstructed; distinguish exported resolution from native generated resolution.

Also, an important point: run it in Codex/Work rather than the standard chat, and bump the effort level up to at least "high."

On the downside, it takes *forever* to think. The first image took me 10 minutes, and each subsequent one took even longer than the last 😐

You can also use any other image-generating agent instead of GPT - like Gemini + Nanobanana - though the result depends heavily on the agent's "brains."

I got the best quality using the Opus 5.5 + Codex CLI image gen combo (a photo of a young Elon), but that was also the longest run by far 😁

How to use

Usage suggestion: After uploading the original photo, select the processing rounds and target long edge, and inspect issues such as seams, ghosting, and texture inconsistencies after each round.

Details

Type
Image
Model
GPT Image 2
License
© the author
Published

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