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name: advanced-controls description: "Tune neural-style-tf multi-style, masks, original-color, layer/loss, initialization, pooling, and optimizer controls for image workflows." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

advanced-controls

Use this sub-skill when a neural_style.py image run needs expert controls beyond a basic content/style command: multiple styles, style interpolation weights, segmentation masks, original-color transfer, layer/loss weighting, initialization, pooling, or optimizer tuning.

Do not use this sub-skill for:

• building the base single-image command, choosing content/style directories, or locating image outputs; use image-stylization.
• video frame loops, optical-flow files, temporal consistency, or video assembly; use video-stylization.
• install/import/runtime setup, VGG weights, TensorFlow 1.x compatibility, or cross-cutting failures; use the root runtime notes and root troubleshooting.

Operating route

1. Start from a valid base command from image-stylization.
2. Read references/advanced-parameters.md to choose the right flag family and confirm source-verified defaults and quirks.
3. Use scripts/plan_advanced_args.py before editing a long command. It validates count-sensitive options such as style weights, masks, and layer weights, prints normalized weights, and emits a shell-safe flag fragment.
4. If a run fails or the intended effect does not appear in output, read references/troubleshooting.md before changing multiple weights at once.

High-value controls

• Multiple styles and interpolation: pass several style image filenames with --style_imgs and matching raw weights through --style_imgs_weights; the source normalizes weights internally.
• Masked/segmented transfer: add --style_mask and provide one mask filename per style with --style_mask_imgs; masks are loaded from the content image directory.
• Original colors: add --original_colors and choose --color_convert_type yuv|ycrcb|luv|lab when preserving content-image chroma is more important than copying style colors.
• Layer/loss tuning: change --content_layers, --style_layers, corresponding weight lists, --content_loss_function, and --pooling_type only after recording a baseline command.
• Optimizer and memory: use --optimizer adam and lower --max_size when L-BFGS or large images exhaust memory; tune --learning_rate only for Adam.

Source-verified caveats

• neural_style.py normalizes style-image, content-layer, and style-layer weights after parsing; raw ratios such as 2 8 and 0.2 0.8 are equivalent.
• The source does not validate list lengths. Python zip(...) can silently ignore extra style weights or layer weights, so validate counts before running.
• --style_mask expects --style_mask_imgs; missing masks can produce runtime errors before useful output.
• --color_convert_time is parsed with choices after|before, but the source conversion path runs after stylization. Treat before as unsupported unless the target checkout changed.