ComfyUI custom nodes for remove-ai-watermarks: identify provenance, remove visible and invisible AI watermarks, strip AI metadata, run the complete image pipeline, and erase arbitrary regions inside a ComfyUI graph.

Nodes

| Node | What it does | | --- | --- | | Remove Visible Watermark (RAIW) | Removes every detected registered AI-provenance mark when mark = auto, or forces one selected mark. Choose the fill backend and detection sensitivity. No GPU is required for detection or the default cv2 fill. | | Detect Visible Watermark (RAIW) | Reports per-mark detection confidence on the input image. Outputs a text report, a detected boolean, the best confidence, and the detected mark key. | | Erase Region (RAIW) | Inpaints whatever a MASK covers. Choose cv2 for the dependency-free path or LaMa for a heavier learned fill. | | Remove Invisible Watermark / SynthID (RAIW) | Diffusion regeneration through the CUDA-only qwen-zimage, sdxl-zimage, chroma-zimage, or auto profile. | | Identify Provenance (RAIW) | Reads an original file and returns the versioned provenance report, verdict, platform, and confidence. | | Strip AI Metadata (RAIW) | Losslessly strips and verifies AI metadata from an original file, then returns the cleaned image and path. | | Remove All Watermarks (RAIW) | Runs visible removal, conditional invisible removal, and verified metadata stripping against one original file. |

The four pixel nodes operate in-memory on ComfyUI image batches. The three provenance-aware nodes accept a file path because ComfyUI IMAGE tensors do not carry the original C2PA, EXIF, XMP, IPTC, or container data. Their source and output paths must be accessible to the ComfyUI server. A blank output path writes <source>_clean.<ext> beside the source without overwriting it.

Install

Via ComfyUI Manager

Search for "Remove AI Watermarks" in ComfyUI Manager and install.

Manual

cd ComfyUI/custom_nodes
git clone https://github.com/wiltodelta/ComfyUI-remove-ai-watermarks
pip install -r ComfyUI-remove-ai-watermarks/requirements.txt

The package installs remove-ai-watermarks[qwen-zimage,heif], including the pixel runtime, HEIC/HEIF/AVIF decoding, GPU diffusion stack, and DiffSynth runtime. Model weights are not bundled and download on first use.

Optional backends: pip install "remove-ai-watermarks[migan]" for the memory-conscious MI-GAN fill and pip install "remove-ai-watermarks[lama]" for the heavier LaMa fill.

Notes

ComfyUI image tensors carry no file metadata. The in-memory invisible node therefore uses resolution-adaptive strength for qwen-zimage and the unknown-vendor strength for sdxl-zimage. Remove All Watermarks reads the original file and can use its vendor provenance. Set strength above 0 to override either default.
All four profiles are CUDA-only: qwen-zimage (the default), sdxl-zimage, chroma-zimage, and auto (chroma-zimage for OpenAI and Microsoft provenance, qwen-zimage otherwise). There is no CPU or MPS path, so the node has no device widget.
The invisible node exposes no steps, guidance_scale, model or device input. Each profile pins its model stack, its per-stage distilled schedule and CFG 1.0, so a widget for any of them could only produce an error inside the run. Seed 0 is the certified default.
adaptive_polish is three-way. profile default lets the library decide (off for qwen-zimage, whose output already matches the input's detail level; on for sdxl-zimage); on/off override it. A workflow saved when this input was a checkbox still loads, and its stored true/false counts as an override.
Use tile for large inputs and cpu_offload to stream both stacks instead of pinning them in VRAM.
Proprietary invisible-watermark verification is vendor-specific. Use the matching provider oracle. Higher strength removes more but drifts further from the original.
The selected extras install opencv-python-headless. If your ComfyUI install already ships opencv-python, both provide cv2 and coexist; if you hit a cv2 conflict, keep a single OpenCV distribution in the environment.

Release synchronization

The registry package has its own version. Each remove-ai-watermarks release dispatches the synchronization workflow with its exact version. The workflow updates the dependency floor, runs compatibility tests, bumps the node patch version, and publishes only after those tests pass. A daily scheduled run is the recovery path for an interrupted release dispatch. A failed compatibility test blocks publication instead of exposing an incompatible node update.

The package name is the registry id

name in pyproject.toml is the ComfyUI Registry node id, not a local label. Publishing under a different one creates a second listing and orphans every existing install, so it is not something to change casually.

It deliberately matches the PyPI library this node depends on. uv reads that as a self-dependency and refuses to resolve the project, which looks like a naming mistake and is not one: this repository is installed with pip, through requirements.txt and scripts/test.sh in CI, and pip resolves the dependency from PyPI without complaint. There is no uv.lock here and uv run is not part of the workflow.

Editing pyproject.toml on main triggers the publish workflow, so treat any change to this file as a release action rather than an edit.

License

Apache-2.0, matching the upstream library.