CalvinBackup/remove-ai-watermarks
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fix(invisible): process at native resolution by default

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The invisible pipeline force-downscaled inputs >1024px to 1024 before
diffusion, then upscaled the result back -- a lossy round-trip that was
the main cause of the quality loss reported in #10. The hosted raiw.cc
backend (fal fast-sdxl) does no pre-downscale, and at strength ~0.05
SDXL img2img doesn't need it.

Default is now native resolution (max_resolution=0). New --max-resolution
flag (invisible / all / batch) re-introduces an opt-in long-side cap only
to bound GPU/MPS memory on very large inputs.

Addresses #10. End-to-end quality/removal not re-verified locally (no GPU
here); matches raiw-app's proven production config.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
test-user
2026-05-25 09:50:06 -07:00
parent 93c664f7fb
commit 18740969ae
3 changed files with 42 additions and 7 deletions
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src/remove_ai_watermarks/invisible_engine.py
+15 -6
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@@ -107,6 +107,7 @@ class InvisibleEngine:
seed: int | None = None,
humanize: float = 0.0,
protect_faces: bool = True,
max_resolution: int = 0,
) -> Path:
"""Remove invisible watermark from an image.
@@ -119,6 +120,11 @@ class InvisibleEngine:
seed: Random seed for reproducibility.
humanize: Intensity of Analog Humanizer film grain (0 = off).
protect_faces: Boolean to extract and restore faces intact.
max_resolution: Cap the long side (px) before diffusion. 0 (default)
= native resolution, no pre-downscale -- matches the hosted
raiw.cc backend. Set a positive value only to bound GPU/MPS
memory on very large inputs (it reintroduces a lossy
downscale->upscale round-trip).
Returns:
Path to the cleaned image.
@@ -127,22 +133,25 @@ class InvisibleEngine:
from PIL import Image, ImageOps
# SDXL is trained at 1024px and degrades both quality and watermark-removal
# efficacy below that.
max_dimension = 1024
# Process at native resolution by default (max_resolution=0). The hosted
# raiw.cc backend (fal fast-sdxl) does NO pre-downscale either, and at
# strength ~0.05 SDXL img2img does not need the input shrunk to ~1024 --
# the old forced downscale->upscale round-trip was the main quality loss
# (see issue #10). A positive max_resolution caps the long side only to
# bound GPU/MPS memory on very large inputs.
image = Image.open(image_path)
image = ImageOps.exif_transpose(image)
orig_size = image.size # (width, height)
_tmp_path = None
if max(image.width, image.height) > max_dimension:
ratio = max_dimension / max(image.width, image.height)
if max_resolution > 0 and max(image.width, image.height) > max_resolution:
ratio = max_resolution / max(image.width, image.height)
new_size = (int(image.width * ratio), int(image.height * ratio))
if self._progress_callback:
self._progress_callback(
f"Downscaling {image.width}x{image.height} "
f"to {new_size[0]}x{new_size[1]} "
f"(model trained at {max_dimension}px)..."
f"(max-resolution cap {max_resolution}px)..."
)
image = image.resize(new_size, Image.Resampling.LANCZOS)