wmr model hub

This repository hosts the models wmr downloads for its SynthID regeneration (--synthid-attack regen). wmr is a command-line tool that removes visible watermarks from Gemini / Veo / NotebookLM generated media and scrubs the invisible SynthID watermark via lossy SDXL img2img regeneration (the only SynthID attack the published literature reports as validated, confirmed here against Google's official SynthID verifier).

wmr auto-downloads the files it needs on first use and caches them under ~/.cache/wmr/; you do not need to download anything from here manually. Pass --regen-no-download to refuse the network. The SHA256 of every file is pinned in wmr's source and verified after download. On upgrade, wmr re-verifies each cached file against its pin and re-downloads only what changed, removing the old copy first (the cache does not grow).

There are two regeneration backends, each with its own model set:

1. CoreML SDXL (macOS Apple Silicon)

Used by --synthid-attack regen on macOS Apple Silicon (the default there). Native CoreML .mlpackage directories converted from apple/ml-stable-diffusion tag 1.1.1, tarred for hosting.

File	Size	SHA256
coreml-sdxl-unet.mlpackage.tar.gz	~4.7 GB	9625f95c9da0fe7a46e8ac0d5cc2b112be42c7c7ed32487966be5a5838fb430c
coreml-sdxl-vae-encoder.mlpackage.tar.gz	~60 MB	370232dd23330abe34c17b19d8b7c08f55c5938887ad1ef608b2bc0b4e000036
coreml-sdxl-vae-decoder.mlpackage.tar.gz	~88 MB	f477d3ca98a19143d81c199c1d02d3ed1737d4638487042c71775016fe399424
empty_prompt_embeds.bin	~0.6 MB	e27ab49bda70deda842a83afa00e67488533f881f5a359bf31b514c00a8038fe

The UNet + VAE are converted from Stability AI's SDXL base 1.0 + madebyollin/sdxl-vae-fp16-fix. The empty-prompt embeddings are pre-baked CLIP outputs (the text encoders are not shipped).

Attention implementation: ORIGINAL

The UNet is converted with --attention-implementation ORIGINAL. apple/ml-stable-diffusion offers two attention implementations for the converted UNet:

• ORIGINAL (used here): the native scaled-dot-product attention. CoreML places it on the GPU under the default compute units.
• SPLIT_EINSUM: an Apple-specific recast that splits attention into multiple einsum operations, designed for the Neural Engine (ANE). For SDXL it does not actually run on the ANE (the large attention matmuls are ANE-ineligible), so it runs on the GPU too, slightly slower per step than ORIGINAL.

ORIGINAL is the faster of the two on the GPU (the ANE is unused by both for SDXL). wmr's $WMR_COREML_SD_COMPUTE_UNITS (all / cpu_gpu / cpu_ane / cpu) overrides the compute unit.

2. CPU sdcpp SDXL (Linux, Windows, macOS Intel)

Used by --synthid-attack regen on Linux, Windows, and macOS Intel, and by --regen-backend cpu everywhere. Run natively via leejet/stable-diffusion.cpp (ggml), CPU-only in the release binaries.

File	Size	SHA256
sd_xl_base_1.0.safetensors	~6.5 GB	31e35c80fc4829d14f90153f4c74cd59c90b779f6afe05a74cd6120b893f7e5b
sdxl_vae.safetensors	~335 MB	235745af8d86bf4a4c1b5b4f529868b37019a10f7c0b2e79ad0abca3a22bc6e1

sd_xl_base_1.0.safetensors is the official Stability AI SDXL base 1.0 checkpoint (unchanged bytes; its SHA is the HuggingFace LFS content oid, so it verifies identically to the upstream file). sdxl_vae.safetensors is the fp16-stable VAE from madebyollin/sdxl-vae-fp16-fix.

Licenses

• SDXL base 1.0: CreativeML Open RAIL++-M (stabilityai/stable-diffusion-xl-base-1.0)
• VAE fp16-fix: MIT (madebyollin/sdxl-vae-fp16-fix)
• Conversion tool: MIT (apple/ml-stable-diffusion)