Both call sites convert outputs to numpy immediately, so the stricter
inference-mode tensors are safe; saves autograd view/version tracking
overhead. Verified end-to-end on CUDA (alpha finite, no NaN).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Strategic pivot: the dropout-fill rescue, the anime-seg fill backend, the
BiRefNet+anime-seg intersection, and the recolor pass all existed to repair
source images whose hair was already green-contaminated or broken at generation
time. Fixing the source instead (clean pastel-background generation) makes the
matte high-contrast, so that whole compensation layer is unnecessary. Remove it.
- Two pipelines via segmentation.enabled:
false -> chroma-only (Chroma + ViTMatte)
true -> single segmenter (default anime-seg, switchable birefnet) -> trimap
then ViTMatte refines, pymatting estimates foreground, despill cleans spill.
- Generalise the green-hardcoded colour logic to a screen_color prior (hex, e.g.
"#CFEFFF"; default null = green auto-detect, byte-identical). chroma keys off
Lab/RGB distance to the colour; despill removes chroma along its Lab direction.
Added --screen-color CLI flag.
- Removed dead code: fill_seg_dropouts + seg_fill*/intersection params,
SegmentationSettings.fill_backend/fill_model_name/sharpen, unsharp_mask,
recolor.py + RecolorSettings.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add SegmentationSettings.fill_backend / fill_model_name so the dropout-fill can
use a different (higher-recall) segmenter than the trimap. Default is now the
combination the investigation converged on: BiRefNet builds the trimap (reliable
topology -- finger gap removed, strands kept as sure_fg), anime-seg drives the
fill (more, more-coherent anime wisps). Because the fill only adds (max), a
higher-recall-but-less-reliable mask there is harmless.
On TestImage2 this eliminates the strand breakage a full anime-seg pipeline
caused (lost 699 -> 0) while keeping ~91% of its extra wisp recall (gained
1133 -> 1031). Strands stay continuous; the recovered wisps are anime-seg's
coherent curves rather than BiRefNet's grainy fill.
Also adds an optional unsharp-mask knob (segmentation.sharpen, default off).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add AnimeSegSegmenter (skytnt/anime-seg ISNet ONNX via onnxruntime, no remote
code) and a make_segmenter factory selected by SegmentationSettings.backend
("birefnet" | "anime-seg"). The ONNX output is already 0..1, so it slots into the
same soft-mask interface BiRefNetSegmenter uses.
On the anime samples anime-seg recovers more and more-coherent hair wisps than
BiRefNet (TestImage2 shoulder rescue: added px 1886 -> 3278, largest connected
component 147 -> 454), as expected from an anime-trained model. Default backend
stays birefnet.
Adds onnxruntime to requirements.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds bgfilter/segmentation.py: BiRefNetSegmenter produces a soft subject mask
that defines topology -- it includes colour-contaminated foreground (green-tinted
hair) and excludes see-through holes (finger gaps), the distinction chroma/colour
cues cannot make. Validated on both samples: green hair -> foreground, finger gap
-> background, both correct.
Foundation of the semantic-guided redesign; the mask will drive trimap
construction next, with ViTMatte still refining soft edges.
- SegmentationSettings (ZhengPeng7/BiRefNet, 1024 input, fp32).
- Deps: torchvision, timm, einops, kornia. Weights load from HuggingFace via
trust_remote_code; behind a firewall set HF_ENDPOINT=https://hf-mirror.com and
bypass the proxy.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
lhk229