

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.
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
- Source
- Igor Trunin
- License
- © the author
- Published



