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Produktivität · SKILL.md

model-internals

model-internals

Explain and verify the photo2cartoon generator, discriminator, normalization layers, face-ID feature path, tensor utilities, and checkpoint structure.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill-Dokumentation

GitHub

name: model-internals description: "Explain and verify the photo2cartoon generator, discriminator, normalization layers, face-ID feature path, tensor utilities, and checkpoint structure." disable-model-invocation: true metadata: disco-role: operating license: MIT

Model Internals

Use this sub-skill for the model core only: architecture, forward signatures, normalization layers, checkpoint contents, and safe synthetic verification.

Start here

  • Architecture reference for generator/discriminator tuples, hourglass topology, Soft-AdaLIN / LIN behavior, CAM heatmaps, and verified smoke shapes.
  • Training object reference for UgatitSadalinHourglass, losses, optimizer setup, checkpoint keys, and save/load behavior.
  • Face ID loss reference for FaceFeatures, MobileFaceNet cropping, 112×112 embeddings, and cosine-distance loss.
  • Image and tensor utilities for normalization range, CAM rendering, and helper semantics.
  • Troubleshooting for shape mismatch, checkpoint key errors, deprecated F.upsample, device mismatch, and missing face-ID assets.
  • Synthetic smoke for safe forward checks and optional checkpoint/face-ID validation.

Route elsewhere

  • Inference commands, asset download steps, and end-user cartoon generation workflows -> ../portrait-inference/
  • Face detection, alignment, crop, and segmentation details -> ../preprocessing/
  • Dataset layout, preprocessing batches, CLI training, and long-running optimization -> ../data-and-training/

What this sub-skill covers

  • ResnetGenerator, Discriminator, HourGlass, HourGlassBlock, ResnetBlock, ResnetSoftAdaLINBlock, SoftAdaLIN, adaLIN, and LIN
  • Generator and discriminator return tuples: (out, cam_logit, heatmap)
  • CAM logits and heatmap interpretation
  • Tensor normalization range used by the repo
  • Face-ID embedding path through MobileFaceNet(512)
  • Training checkpoint contents and safe load checks
  • Safe synthetic verification only; no downloads, no training, no destructive writes

Practical commands

python scripts/model_forward_smoke.py --help
python scripts/model_forward_smoke.py --repo-root /path/to/photo2cartoon
python scripts/model_forward_smoke.py --repo-root /path/to/photo2cartoon --checkpoint /path/to/checkpoint.pt --face-model /path/to/model_mobilefacenet.pth
Gespeicherte Skills