Release 0.36.0

README.md

@@ -245,7 +245,8 @@ ControlNet, followed by SAM-masked Z-Image repair of any detected face. The
 alternative, `sdxl-zimage`, swaps the global stage for SDXL and keeps the same face
 stage. A third profile, `chroma-zimage`, uses the Apache-2.0 Chroma1 global pass
 with its own flat vendor floors; see `docs/chroma1-engine-research.md` for the
-calibration. All are CUDA only.
+calibration. `--pipeline auto` picks chroma-zimage for OpenAI and Microsoft
+provenance and qwen-zimage otherwise. All are CUDA only.
 
 ```bash
 uv tool install --force "remove-ai-watermarks[qwen-zimage]"

docs/cli.md

@@ -410,8 +410,10 @@ The same option exists on `all` and `batch`, and as
 | --- | --- |
 | `qwen-zimage` | Default. Qwen-Image-2512 global pass plus a SAM-masked Z-Image face stage |
 | `sdxl-zimage` | The same recipe and face stage on an SDXL global pass, at a higher denoise |
+| `chroma-zimage` | The same face stage on an Apache-2.0 Chroma1 global pass, with lower OpenAI and Microsoft floors and higher Google and Meta floors |
+| `auto` | Pick the engine from provenance: chroma-zimage for OpenAI and Microsoft, qwen-zimage for Google, Meta, and unknown |
 
-**Both are CUDA-only.** There is no CPU or MPS profile for invisible-watermark
+**All four are CUDA-only.** There is no CPU or MPS profile for invisible-watermark
 removal. The former `controlnet`, `sdxl`, `qwen` and `default` profiles were removed
 rather than kept as a CPU path: none of them matched this recipe's face preservation,
 so offering them implied a quality the library no longer delivers. Passing a retired

docs/installation.md

@@ -115,7 +115,7 @@ application actually uses:
 | `diffusion` | Torch and Diffusers runtime; video SynthID regeneration | `pixels`, Torch, Diffusers | Yes |
 | `migan` | MI-GAN ONNX fill backend | `visible`, ONNX Runtime | Model download, no Torch |
 | `lama` | big-LaMa ONNX fill backend | `visible`, ONNX Runtime | Model download, no Torch |
-| `qwen-zimage` | Invisible image-watermark removal, both CUDA-only profiles | `diffusion`, DiffSynth | Yes |
+| `qwen-zimage` | Invisible image-watermark removal, all CUDA-only profiles | `diffusion`, DiffSynth | Yes |
 | `text-restoration` | Opt-in verified Qwen-VAE glyph restoration | `qwen-zimage`, `lama` | Yes |
 | `text-draft` | Draft OCR proposals for operator verification | PaddleOCR, PaddlePaddle | Model download, no Torch |
 | `all` | Every production feature available on the active Python | All compatible rows above | Yes |

docs/known-limitations.md

@@ -73,10 +73,12 @@ as SynthID lives in the pixels everywhere, including inside the glyphs, so
 frozen text regions would keep the watermark
 ([text protection research](text-protection-research.md)).
 
-`qwen-zimage` is the default profile and `sdxl-zimage` the only alternative.
-Both are CUDA only and differ only in the global regeneration model: each
-conditions that stage on a canny edge map, which preserves structure but not
-identity or exact texture, and each then runs the same face stage.
+`qwen-zimage` is the default profile. `sdxl-zimage` and `chroma-zimage` keep
+the same face stage and swap the global model. `auto` picks chroma-zimage for
+OpenAI and Microsoft provenance and qwen-zimage otherwise. All are CUDA only.
+Qwen and SDXL condition the global stage on a canny edge map, which preserves
+structure but not identity or exact texture. Chroma1 is a plain strength pass
+without Canny.
 Existing face evaluations favor `qwen-zimage`, but there is no blanket fidelity
 ordering across content types. A fixed-seed, three-scene text comparison at the
 profile defaults found no stable winner: SDXL won one poster, Qwen won one, and
@@ -235,6 +237,8 @@ are certified at a fixed seed. The live resolver is
 | --- | --- |
 | `qwen-zimage` | CUDA only, large model stack, and limited broad certification across seeds and content. |
 | `sdxl-zimage` | CUDA only. Its strength ladder is flat per vendor, not a resolution curve, because flat values are what was measured. |
+| `chroma-zimage` | CUDA only. Higher Google and Meta floors than qwen; Google face content uses 0.125 instead of 0.40. |
+| `auto` | CUDA only. Routes OpenAI and Microsoft to chroma-zimage and everything else to qwen-zimage. |
 
 Only manually verified `vae-glyphs` is an optional production stage, and it is
 experimental rather than a default.

docs/module-internals.md

@@ -942,10 +942,12 @@ is the source of truth for:
 - the SDXL global-stage checkpoint id (`SDXL_MODEL_ID`) and the Canny ControlNet id;
 - strength resolution for every profile.
 
-The current profiles are `qwen-zimage` (the default), `sdxl-zimage`, and
-`chroma-zimage`, and all three are CUDA-only. `controlnet`, `sdxl`, `qwen` and
-`default` were removed rather than kept as a CPU path, and are rejected rather
-than aliased onward. There is no content-dependent automatic router.
+The current profiles are `qwen-zimage` (the default), `sdxl-zimage`,
+`chroma-zimage`, and `auto`, and all four are CUDA-only. `controlnet`, `sdxl`,
+`qwen` and `default` were removed rather than kept as a CPU path, and are
+rejected rather than aliased onward. `auto` is a per-cohort engine router:
+chroma-zimage for OpenAI and Microsoft, qwen-zimage for Google, Meta, and
+unknown. It is not a content-class split inside one engine.
 
 `qwen-zimage` normally resolves global denoise from image area for unknown content.
 Measured provider cohorts bypass that curve with flat operating points. The values,

docs/python-api.md

@@ -596,7 +596,7 @@ from pathlib import Path
 from remove_ai_watermarks.invisible_engine import InvisibleEngine
 
 engine = InvisibleEngine(
-    pipeline="qwen-zimage",  # the default; also "sdxl-zimage" or "chroma-zimage"
+    pipeline="qwen-zimage",  # the default; also "sdxl-zimage", "chroma-zimage", or "auto"
     device=None,
     cpu_offload=False,
 )
@@ -628,8 +628,10 @@ Qwen:
 engine = InvisibleEngine(pipeline="sdxl-zimage")
 ```
 
-The `qwen-zimage` extra is required for all three profiles: each runs the same
-DiffSynth Z-Image face stage.
+The `qwen-zimage` extra is required for every profile, including `auto`: each
+concrete engine runs the same DiffSynth Z-Image face stage. `pipeline="auto"`
+selects chroma-zimage for OpenAI and Microsoft provenance and qwen-zimage
+otherwise, after the vendor is known and before strength resolution.
 
 The opt-in verified-text stage uses the same `text_manifest` argument as the CLI:
 

docs/supported-signals.md

@@ -131,10 +131,12 @@ The `invisible` command uses diffusion regeneration. It targets watermark
 patterns by changing the image rather than decoding and deleting a known
 payload.
 
-Current pipeline values, both CUDA-only:
+Current pipeline values, all CUDA-only:
 
 - `qwen-zimage`, the default;
-- `sdxl-zimage`, the same recipe and the same face stage on an SDXL global pass.
+- `sdxl-zimage`, the same recipe and the same face stage on an SDXL global pass;
+- `chroma-zimage`, the same face stage on a Chroma1 global pass;
+- `auto`, chroma-zimage for OpenAI and Microsoft provenance, otherwise qwen-zimage.
 
 The `controlnet`, `sdxl`, `qwen` and `default` values were removed. A retired name
 is rejected at parse time rather than remapped onto a surviving profile.

docs/synthid.md

@@ -461,9 +461,9 @@ conditioning, never by copying original pixels.**
 
 > **Superseded in 0.24.0.** The `controlnet`, `sdxl`, `qwen` and `default` profiles
 > were removed, and a retired name is now rejected at parse time rather than routed
-> onward. `--pipeline` accepts only `qwen-zimage` (the default) and `sdxl-zimage`;
-> both are CUDA-only, both condition their global stage on a canny edge map, and both
-> run the same Z-Image face stage. The bullets below are a record of what was measured
+> onward. Current `--pipeline` values are `qwen-zimage` (the default),
+> `sdxl-zimage`, `chroma-zimage`, and `auto`; all are CUDA-only and share the
+> Z-Image face stage. The bullets below are a record of what was measured
 > on the removed profiles, kept because the oracle verdicts are still the evidence the
 > current design rests on.
 

pyproject.toml

@@ -4,7 +4,7 @@
 # on it, including the ComfyUI node package. The console script below carries
 # the same weight, since users have it on PATH.
 name = "remove-ai-watermarks"
-version = "0.35.1"
+version = "0.36.0"
 description = "AI watermark remover for visible, invisible, and provenance marks in images and video"
 readme = "README.md"
 requires-python = ">=3.11,<3.15"

src/remove_ai_watermarks/__init__.py

@@ -32,7 +32,7 @@ _os.environ.setdefault("TRANSFORMERS_VERBOSITY", "error")
 _warnings.filterwarnings("ignore", message=r".*ImageProcessorFast.*")
 
 
-__version__ = "0.35.1"
+__version__ = "0.36.0"
 
 __all__ = [
     "BatchSummary",

uv.lock

@@ -3169,7 +3169,7 @@ wheels = [
 
 [[package]]
 name = "remove-ai-watermarks"
-version = "0.35.1"
+version = "0.36.0"
 source = { editable = "." }
 dependencies = [
     { name = "c2pa-python" },