"""Invisible watermark removal engine. Wraps the vendored noai-watermark code for removing invisible AI watermarks (SynthID, StableSignature, TreeRing) via diffusion-based regeneration. This module requires the 'gpu' extra dependencies: uv pip install 'remove-ai-watermarks[gpu]' """ # cv2/torch boundary: this engine wraps cv2 (resize/imwrite/cvtColor) and the # humanizer, none of which carry usable element types; relax the unknown-type # rules for this file only. # pyright: reportUnknownMemberType=false, reportUnknownArgumentType=false, reportUnknownVariableType=false, reportUnknownParameterType=false, reportMissingTypeArgument=false, reportMissingTypeStubs=false, reportMissingImports=false, reportArgumentType=false, reportAssignmentType=false, reportReturnType=false, reportCallIssue=false, reportIndexIssue=false, reportOperatorIssue=false, reportOptionalMemberAccess=false, reportOptionalCall=false, reportOptionalSubscript=false, reportOptionalOperand=false, reportAttributeAccessIssue=false, reportPrivateImportUsage=false, reportPrivateUsage=false, reportInvalidTypeForm=false, reportConstantRedefinition=false, reportUnnecessaryComparison=false from __future__ import annotations import logging import os import warnings from pathlib import Path from typing import TYPE_CHECKING, Any from .noai.watermark_profiles import ( DEFAULT_MODEL_ID as DEFAULT_SDXL_MODEL_ID, ) from .noai.watermark_profiles import ( resolve_seed, ) if TYPE_CHECKING: from collections.abc import Callable # Suppress verbose deprecation warnings from diffusers/transformers/huggingface_hub warnings.filterwarnings("ignore", category=FutureWarning) warnings.filterwarnings("ignore", category=UserWarning, module="huggingface_hub") warnings.filterwarnings("ignore", category=UserWarning, module="diffusers") warnings.filterwarnings("ignore", module="transformers") # Suppress HuggingFace internal logging os.environ["TRANSFORMERS_VERBOSITY"] = "error" os.environ["DIFFUSERS_VERBOSITY"] = "error" logger = logging.getLogger(__name__) def is_available() -> bool: """Check if invisible watermark removal dependencies are installed.""" from .optional_deps import module_available return module_available("diffusers", "torch") def _target_size(width: int, height: int, max_resolution: int, min_resolution: int = 0) -> tuple[int, int] | None: """Compute the (width, height) to process at, or None for native. Two opposite long-side adjustments, in precedence order: - ``max_resolution`` (cap): if the long side exceeds it, scale DOWN to it (integer-truncated, matching the PIL ``resize`` call site). 0/negative = no cap. Set only to bound GPU/MPS memory on very large inputs (issue #10). - ``min_resolution`` (floor): else if the long side is below it, scale UP to it (rounded) so SDXL img2img runs near its ~1024 training resolution instead of degrading on a tiny latent (a 381x512 portrait distorts badly at native). The output is restored to the original size by the caller, so the floor is a transparent quality boost. 0 = no floor. Skipped on a ``min > max`` misconfig. Returns None when neither applies (native resolution). Pure function so the resolution decision is unit-testable without loading the diffusion model. """ long_side = max(width, height) if max_resolution > 0 and long_side > max_resolution: ratio = max_resolution / long_side # Clamp the short side to >=1: extreme aspect ratios (e.g. 5000x3 capped # at 1024) would otherwise truncate it to 0 and crash image.resize(). return (max(1, int(width * ratio)), max(1, int(height * ratio))) if min_resolution > 0 and long_side < min_resolution and (max_resolution <= 0 or min_resolution <= max_resolution): ratio = min_resolution / long_side return (max(1, round(width * ratio)), max(1, round(height * ratio))) return None class InvisibleEngine: """Remove invisible AI watermarks using diffusion model regeneration. Based on noai-watermark by mertizci: https://github.com/mertizci/noai-watermark The approach encodes the image into latent space, injects controlled noise to break watermark patterns, and reconstructs via reverse diffusion. """ # SDXL base is the default since May 2026; the vendor-adaptive strength # removes the current SynthID (see watermark_profiles + docs/synthid.md). DEFAULT_MODEL_ID = DEFAULT_SDXL_MODEL_ID def __init__( self, model_id: str | None = None, device: str | None = None, pipeline: str = "controlnet", hf_token: str | None = None, progress_callback: Callable[[str], None] | None = None, controlnet_conditioning_scale: float = 1.0, cpu_offload: bool = False, ) -> None: """Initialize the invisible watermark removal engine. Args: model_id: HuggingFace model ID. None = use the SDXL base default. device: Device for inference (auto/cpu/mps/cuda/xpu). None = auto. pipeline: Pipeline profile. "controlnet" (DEFAULT; SDXL + canny ControlNet that preserves text/face structure via edge conditioning while removing SynthID), "sdxl" (plain SDXL img2img, lighter but leaves SynthID on flat-graphic content), or "qwen" (Qwen-Image 20B img2img, best text/ structure preservation but CUDA/cloud-class), or "qwen-zimage" (Qwen-Image-2512 Lightning + Canny, then SAM-masked Z-Image face repair; CUDA-only). "default" aliases "sdxl". hf_token: HuggingFace API token. progress_callback: Optional callback for progress messages. controlnet_conditioning_scale: ControlNet structure-preservation strength (controlnet pipeline only). cpu_offload: Offload model components to CPU between CUDA calls instead of keeping the whole pipeline in VRAM, at the cost of speed. For qwen-zimage, force the face stack to offload instead of using automatic residency. CUDA only. """ from remove_ai_watermarks.noai.watermark_remover import WatermarkRemover effective_model = model_id or self.DEFAULT_MODEL_ID self._remover = WatermarkRemover( model_id=effective_model, device=device, progress_callback=progress_callback, hf_token=hf_token, pipeline=pipeline, controlnet_conditioning_scale=controlnet_conditioning_scale, cpu_offload=cpu_offload, ) self._progress_callback = progress_callback def preload(self, *, global_only: bool = False) -> None: """Eagerly load the pipeline so download progress is visible. For ``qwen-zimage``, ``global_only=True`` loads the mandatory Qwen stage and leaves the optional Z-Image and SAM face stack lazy until a face is detected. Other profiles have no optional stage and ignore the flag. """ self._remover.preload(global_only=global_only) def _esrgan_upscale(self, image: Any, target: tuple[int, int]) -> Any: """Upscale a PIL image to ``target`` with Real-ESRGAN, else Lanczos. Runs Real-ESRGAN at its native factor (on the remover's device, CPU fallback), then resizes to the exact ``target`` with Lanczos. Falls back to a plain Lanczos resize when the ``esrgan`` extra is absent or the model errors. """ import cv2 import numpy as np from PIL import Image from remove_ai_watermarks import upscaler if not upscaler.is_available(): logger.debug("esrgan upscaler requested but the extra is absent; using Lanczos") return image.resize(target, Image.Resampling.LANCZOS) try: bgr = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR) big = upscaler.upscale(bgr, device=self._remover.device) if (big.shape[1], big.shape[0]) != target: big = cv2.resize(big, target, interpolation=cv2.INTER_LANCZOS4) return Image.fromarray(cv2.cvtColor(big, cv2.COLOR_BGR2RGB)) except Exception as e: # never let an optional upscaler break removal logger.warning("Real-ESRGAN upscale failed (%s); using Lanczos", e) return image.resize(target, Image.Resampling.LANCZOS) def remove_watermark( self, image_path: Path, output_path: Path | None = None, strength: float | None = None, num_inference_steps: int | None = None, guidance_scale: float | None = None, seed: int | None = None, humanize: float = 0.0, max_resolution: int = 0, min_resolution: int = 1024, vendor: str | None = None, unsharp: float = 0.0, adaptive_polish: bool = False, upscaler: str = "lanczos", tile: bool = False, tile_size: int = 1024, tile_overlap: int = 128, ) -> Path: """Remove invisible watermark from an image. Args: image_path: Path to the watermarked image. output_path: Output path (None = overwrite source). strength: Denoising strength (0.0-1.0). None -> the vendor-adaptive default. num_inference_steps: Number of denoising steps. None keeps the existing 100-step library default, except qwen-zimage uses its required four-step Lightning schedule. guidance_scale: Classifier-free guidance scale. seed: Random seed for reproducibility. None resolves to 0 for qwen-zimage and stays random for the other profiles. humanize: Intensity of Analog Humanizer film grain (0 = off). unsharp: Final unsharp-mask sharpening strength (0 = off, default). Applied last to counter the soft / over-smoothed look of the diffusion pass; ~0.5-0.8 is a safe range, higher risks edge halos. adaptive_polish: When True (the CLI default), restore the input's detail level in the softened output: a capped unsharp + edge-masked grain targeting the input's Laplacian variance. Self-limiting -- a no-op when the output already meets the input's detail level (text/flat graphics), so it only acts on over-smoothed photo/face texture. Runs LAST. max_resolution: Cap the long side (px) before diffusion. 0 (default) = no cap. Set a positive value only to bound GPU/MPS memory on very large inputs (it reintroduces a lossy downscale->upscale round-trip). min_resolution: Upscale the long side UP to this (px) before diffusion when the input is smaller, so SDXL runs near its ~1024 training resolution (small inputs degrade/distort badly at native). 1024 (default) = on; 0 = off. The output is restored to the original input size, so this is a transparent quality boost; it adds time and memory on small inputs. Ignored on a min > max misconfig. upscaler: How to upscale a small input to the ``min_resolution`` floor: ``"lanczos"`` (default, cv2, no deps) or ``"esrgan"`` (Real-ESRGAN via the ``esrgan`` extra). Only applies when UPscaling (the floor case); a ``max_resolution`` downscale always uses Lanczos. Falls back to Lanczos if the extra is absent. tile: Process the diffusion pass in overlapping tiles instead of one forward pass. This retains the input's native dimensions instead of applying ``max_resolution``, but each tile is still regenerated. Engages only when the long side exceeds ``tile_size``. tile_size: Tile dimension in px (default 1024). tile_overlap: Overlap between adjacent tiles in px (default 128). Returns: Path to the cleaned image. """ import tempfile if num_inference_steps is None: profile = getattr(self._remover, "model_profile", None) num_inference_steps = 4 if profile == "qwen-zimage" else 100 profile = getattr(self._remover, "model_profile", "controlnet") seed = resolve_seed(seed, profile) from PIL import Image, ImageOps # Resolution policy: a max_resolution cap (0 = none) bounds memory on huge # inputs, and a min_resolution floor (1024 = default) upscales tiny inputs so # SDXL img2img runs near its ~1024 training size instead of distorting on a # tiny latent (a 381x512 portrait wrecks at native -- issue #36 follow-up). # The output is restored to orig_size below, so the floor is transparent. # Register the HEIF/AVIF opener so a .heic/.avif input (now a SUPPORTED_FORMAT) # decodes here too. The --force skip path bypasses image_io.imread, which is # what would otherwise register it, so a bare Image.open would fail on HEIC. from remove_ai_watermarks import image_io image_io._register_heif() image = Image.open(image_path) image = ImageOps.exif_transpose(image) orig_size = image.size # (width, height) # Full-res original, kept for the adaptive-polish detail target (image is # reassigned to the resized copy below; PIL resize returns a new object). reference_pil = image # qwen-zimage operates at the input's native geometry in its reference graph. # Keep an explicit max cap available for callers, but do not apply the SDXL # 1024px minimum-resolution floor to this profile. effective_min_resolution = ( 0 if getattr(self._remover, "model_profile", None) == "qwen-zimage" else min_resolution ) target = _target_size( image.width, image.height, max_resolution, effective_min_resolution, ) if target is not None: upscaling = max(target) > max(image.width, image.height) if self._progress_callback: reason = ( f"min-resolution floor {min_resolution}px" if upscaling else f"max-resolution cap {max_resolution}px" ) verb = "Upscaling" if upscaling else "Downscaling" self._progress_callback(f"{verb} {image.width}x{image.height} to {target[0]}x{target[1]} ({reason})...") # Real-ESRGAN only helps when UPscaling (the floor case); a downscale cap # always uses Lanczos. _esrgan_upscale falls back to Lanczos if the extra is absent. if upscaling and upscaler == "esrgan": image = self._esrgan_upscale(image, target) else: image = image.resize(target, Image.Resampling.LANCZOS) # Always persist to a temp file, even without downscaling: WatermarkRemover # reloads by path, so the EXIF-transposed pixels must be saved or rotation # is lost. Written as PNG (lossless) regardless of the input format, so a JPEG # input does not feed a re-compressed copy into the diffusion pass. # Cleaned up in the finally block via _tmp_path. _tmp_fd, _tmp_str = tempfile.mkstemp(suffix=".png") _tmp_path = Path(_tmp_str) # Convert to RGB before the PNG temp: the diffusion pass is RGB anyway, and a # non-RGB source mode (e.g. a CMYK JPEG) cannot be written as PNG and would raise. image.convert("RGB").save(_tmp_path) os.close(_tmp_fd) image_path = _tmp_path try: out_path = self._remover.remove_watermark( image_path=image_path, output_path=output_path, strength=strength, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, seed=seed, vendor=vendor, tile=tile, tile_size=tile_size, tile_overlap=tile_overlap, ) # Post-processing chain: decode the diffusion output ONCE, apply the # optional stages in memory in order (humanize -> restore original # resolution -> unsharp -> adaptive polish), and write ONCE. Previously # each stage independently imread/imwrote the full-res output, so a run # with several stages PNG-decoded+re-encoded the same image 2-4 times. # PNG is lossless, so the single-write output is byte-identical. # Diffusers rounds native dimensions down to the latent grid (multiples # of 8), even when our own resolution policy did not resize the input. # Route those outputs through the same final resize so --no-polish does # not silently change e.g. 1448x1086 into 1448x1080. needs_restore = target is not None or any(dimension % 8 for dimension in orig_size) if humanize > 0.0 or unsharp > 0.0 or adaptive_polish or needs_restore: import cv2 from remove_ai_watermarks import image_io out_cv = image_io.imread(out_path, cv2.IMREAD_COLOR) if out_cv is None: return out_path if humanize > 0.0: if self._progress_callback: self._progress_callback(f"Applying Analog Humanizer (grain: {humanize})...") from remove_ai_watermarks.humanizer import apply_analog_humanizer out_cv = apply_analog_humanizer(out_cv, grain_intensity=humanize, chromatic_shift=1) # Restore original resolution if the input was resized for diffusion. if (out_cv.shape[1], out_cv.shape[0]) != orig_size: if self._progress_callback: self._progress_callback( f"Upscaling result back to original resolution {orig_size[0]}x{orig_size[1]}..." ) out_cv = cv2.resize(out_cv, orig_size, interpolation=cv2.INTER_LANCZOS4) if unsharp > 0.0: if self._progress_callback: self._progress_callback(f"Sharpening (unsharp mask: {unsharp})...") from remove_ai_watermarks.humanizer import unsharp_mask out_cv = unsharp_mask(out_cv, amount=unsharp) # Adaptive polish (CLI default): restore the input's detail level in the # softened output, sparing text/edges. Self-limiting where no deficit. if adaptive_polish: import numpy as np from remove_ai_watermarks import humanizer ref = cv2.cvtColor(np.array(reference_pil.convert("RGB")), cv2.COLOR_RGB2BGR) if (ref.shape[1], ref.shape[0]) != (out_cv.shape[1], out_cv.shape[0]): ref = cv2.resize(ref, (out_cv.shape[1], out_cv.shape[0]), interpolation=cv2.INTER_LANCZOS4) if self._progress_callback: self._progress_callback("Adaptive polish (sharpen + grain to the input's detail level)...") out_cv = humanizer.adaptive_polish(out_cv, ref, seed=seed) image_io.imwrite(out_path, out_cv) return out_path finally: # _tmp_path is always set above (we persist the image unconditionally). if _tmp_path.exists(): _tmp_path.unlink() def remove_watermark_batch( self, input_dir: Path, output_dir: Path, strength: float | None = None, steps: int | None = None, ) -> list[Path]: """Remove invisible watermarks from all images in a directory.""" if steps is None: profile = getattr(self._remover, "model_profile", None) steps = 4 if profile == "qwen-zimage" else 50 return self._remover.remove_watermark_batch( input_dir=input_dir, output_dir=output_dir, strength=strength, num_inference_steps=steps, )