Ship the border filler trained against MI-GAN's own discriminator: texture in the deep bands, level with stock on LPIPS
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@@ -538,9 +538,24 @@ each loss weighting did, and the two runs abandoned (blur under L1 in
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the hole; a brick pattern under a strong adversarial term against a
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discriminator that had not learned) — is `runs/` in `darkroom-infill`.
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**What is still wrong.** The ground fill is softer than its context —
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texture, not structure, is what a night on a laptop GPU could not finish.
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The levers, in order: a discriminator that learns (a pretrained one —
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MI-GAN's own from the unfused checkpoint — instead of a PatchGAN from
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scratch), feature matching, and more steps at 512. FR-MRG-4's
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*experimental* stays.
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**Second model, the same day.** The morning's fill was soft in the deep
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ground bands. The afternoon's run trained the generator against MI-GAN's
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own pretrained discriminator (non-saturating loss, lazy R1, feature
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matching), with fresh noise inputs while training and flip/translation
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augmentation of the discriminator's input — both needed, or the generator
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settles into a periodic texture the discriminator cannot see. The shipped
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weights (step 4 750 of `runs/border-v6`) are level with the stock model on
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LPIPS (edge 0.125 / corner 0.187 against 0.121 / 0.183) while keeping the
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PSNR gain (edge 18.0 / corner 15.7 against 16.9 / 14.6). On the fixture
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the ground bands now carry texture at the right tone; at 1:1 a faint
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regular hatch is visible in the deepest part.
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**What is still wrong.** The hatch, and any deep textured void the
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generator must invent. The better answer for those is not generative:
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seed the void with the picture's own texture in hexagonal cells, let the
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discriminator rank the candidates, and let the generator heal only the
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gaps between cells — built and measured in `darkroom-infill`
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(`infill/hexfill.py`), the most convincing scree corner produced so far,
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and the next thing to port into `dr_pano::fill` (it needs the
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discriminator as a second model, ~80 MB fp16). FR-MRG-4's *experimental*
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stays.
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