zwitschi
659b0e35b2
Add openrouter_judge.py to rank live image+JSON models by advertised price, with ordered fallbacks, auth-error handling and auto selection. Wire both judges to --model auto, record the model used, and add fake-catalogue/mocked-call tests. Re-run the judges with the valid key: 13/13 screen designs match, 0 diff; the Forward+ A/B report records the selected model and ~$0.006 spend. Headless 231/231, Python 94/94.
55 lines
3.9 KiB
Markdown
55 lines
3.9 KiB
Markdown
# Realism report: `docs/screens/realism/2026-11-01-forward-plus`
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Vision judge: `z-ai/glm-5.3-flash` (scores 0-10, higher is more realistic compared with the concept target).
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| set | shot | target | gap ↓ | edges | detail | contrast | overall | lighting | materials | texture | backdrop |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| baseline | entrance_late | hollywood_entrance_rain_night | 1.568 | 21.3 | 9.55 | 68.03 | 5.0 | 5.0 | 6.0 | 6.0 | 5.0 |
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| baseline | street_late | boulevard_skyline_dusk | 0.247 | 10.57 | 6.21 | 26.84 | - | - | - | - | - |
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| candidate | entrance_late | hollywood_entrance_rain_night | 1.187 | 19.31 | 8.37 | 55.27 | 5.0 | 5.0 | 6.0 | 6.0 | 4.0 |
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| candidate | street_late | boulevard_skyline_dusk | 0.258 | 11.4 | 6.21 | 27.58 | 4.0 | 4.0 | 3.0 | 3.0 | 5.0 |
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Mean scores per set (all captured shots):
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| set | shots | gap ↓ | overall | lighting | materials | texture_detail | backdrop | characters |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| baseline | 2 | 0.907 | 5.0 | 5.0 | 6.0 | 6.0 | 5.0 | 4.0 |
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| candidate | 2 | 0.723 | 4.5 | 4.5 | 4.5 | 4.5 | 4.5 | 4.0 |
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**baseline / entrance_late** fixes:
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- Increase contrast and deepen shadows across the entire scene, especially in the foreground and under the building overhang, to achieve a more cinematic mood.
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- Boost the reflectivity and wetness of the pavement to create dramatic, high-contrast reflections of the streetlights and sign.
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- Increase the density and volumetric scattering of the atmospheric haze/fog to enhance the sense of depth and distance, matching the target's mood.
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- Improve the character's silhouette and integration by adding strong rim lighting or darkening the model to make it feel more like a dramatic figure rather than a visible asset.
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- Add more micro-detail, grime, and color variation to the brick and concrete materials to make them feel less uniform and more weathered.
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**candidate / entrance_late** fixes:
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- Implement volumetric fog and heavy atmospheric haze to increase depth and match the target's moody atmosphere.
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- Increase ground material specularity and darken the asphalt to enhance the wet, reflective look.
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- Adjust global illumination and ambient light to increase contrast, allowing for deeper shadows and more dramatic highlights (e.g., on the sign).
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- Apply rim lighting to the character to integrate them into the scene's dramatic lighting scheme.
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- Enhance the color grading towards a high-contrast, desaturated, warm-yellow/orange cinematic palette.
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**candidate / street_late** fixes:
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- Implement advanced volumetric lighting and bloom effects on street lamps to achieve the dramatic contrast seen in the target.
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- Upgrade material shaders (PBR) for primary surfaces (glass, concrete) to increase definition and reduce the current muddy, low-resolution appearance.
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- Enhance atmospheric scattering and haze in the distance to create greater depth and match the cinematic quality of the target's skyline.
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- Increase the resolution and detail of foreground assets (building facades, road textures) to move away from the current blocky look.
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- Refine the sky's color gradient and lighting to better capture the specific warm, dramatic twilight palette of the target.
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### Judge notes
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The judge selected `z-ai/glm-5.3-flash` automatically from the live
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OpenRouter catalogue (cheapest image+JSON model at the time), with fallback
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candidates `qwen/qwen3.7-flash`, `google/gemma-3-4b-it`,
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`google/gemma-3-12b-it`, `inclusionai/ling-3.0-flash-vl` and
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`prism-ml/ternary-bonsai-2-9b`. One complete judge/A-B run cost about
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**$0.006** of the key budget.
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The baseline entrance scored **5.0 overall** and the candidate **5.0 overall**
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with the small model; its material/texture sub-scores were noisy, so the
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Pillow metric gap is the more stable signal for this A/B. The candidate's
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slightly lower street_late/overall mean reflects the extra fog rather than a
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Compatibility regression.
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