From ded5bb62c49be75f93399d855f144ef1425617e5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=CB=B6=F0=9D=9E=A2=E2=A4=AC=E2=AB=92=E2=B5=96s=E1=90=BC?=
 =?UTF-8?q?=CB=B6?= <91800957+exdysa@users.noreply.github.com>
Date: Mon, 2 Feb 2026 17:58:12 -0500
Subject: [PATCH 1/4] ~Switch models with evail, fix imports type checking,
 patch VAEModel path, cleanup

---
 negate/__init__.py |  4 +--
 negate/__main__.py | 68 +++++++++++++++++++++++++++++++++-------------
 negate/datasets.py | 21 ++++++--------
 negate/extract.py  |  5 ++--
 negate/save.py     | 18 +++++-------
 negate/track.py    | 16 ++++++-----
 negate/train.py    |  6 ++--
 pyproject.toml     | 31 ++++-----------------
 8 files changed, 87 insertions(+), 82 deletions(-)

diff --git a/negate/__init__.py b/negate/__init__.py
index 45e22b7..e81f415 100644
--- a/negate/__init__.py
+++ b/negate/__init__.py
@@ -1,9 +1,9 @@
 # SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
 # <!-- // /*  d a r k s h a p e s */ -->
-
+# ruff: noqa
 from negate.datasets import build_datasets, dataset_to_nparray, generate_dataset
 from negate.extract import FeatureExtractor, DeviceName, features, VAEModel
-from negate.train import TrainResult, grade, get_time, model_path
+from negate.train import TrainResult, grade, generate_datestamp_path, datestamped_folder, get_time, model_path
 from negate.track import in_console, on_graph
 from negate.save import save_model, save_to_onnx
 from negate.residuals import Residual
diff --git a/negate/__main__.py b/negate/__main__.py
index 1e6c61d..5ce92df 100644
--- a/negate/__main__.py
+++ b/negate/__main__.py
@@ -4,7 +4,7 @@
 from pathlib import Path
 import numpy as np
 
-from negate import TrainResult, build_datasets, features, generate_dataset, grade, in_console, save_to_onnx, on_graph, VAEModel
+from negate import TrainResult, build_datasets, features, generate_dataset, grade, in_console, save_to_onnx, on_graph, VAEModel, datestamped_folder, model_path
 
 
 def evaluate(prediction: np.ndarray, ground_truth: np.ndarray) -> None:
@@ -25,7 +25,7 @@ def evaluate(prediction: np.ndarray, ground_truth: np.ndarray) -> None:
     print(f"Genuine: {genu_cnt}  Synthetic: {synth_cnt}")
 
 
-def predict(image_path: Path, vae_type: VAEModel = VAEModel.MITSUA_FP16, true_label: int | None = None) -> np.ndarray:
+def predict(image_path: Path, vae_type: VAEModel, true_label: int | None = None) -> np.ndarray:
     """Predict synthetic or original for given image. (0 = genuine, 1 = synthetic)\n
     :param image_path: Path to image file or folder.
     :param vae_type: VAE model to use for feature extraction.
@@ -34,24 +34,41 @@ def predict(image_path: Path, vae_type: VAEModel = VAEModel.MITSUA_FP16, true_la
     from datasets import Dataset
     import onnxruntime as ort
     from onnxruntime import SparseTensor
+    from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument, Fail as ONNXRuntimeError
 
-    print(f"{'Evaluation' if true_label is not None else 'Detection'} selected.")
-
-    models_location = Path(__file__).parent.parent / "models"
-    model_file = models_location / "negate.onnx"
-
-    if not model_file.exists():
-        raise FileNotFoundError(f"Model file not found: {model_file}. Please run 'train' first to create the model.")
+    model_file_path_named = model_path / "negate.onnx"
+    if not model_file_path_named.exists():
+        raise FileNotFoundError(f"Model file not found: {str(model_file_path_named)}. Please run 'train' first to create the model.")
+    else:
+        model_file_path_named = str(model_file_path_named)
+    print(f"""{"Evaluation" if true_label is not None else "Detection"} selected.
+Checking path '{image_path}' with {vae_type.value}""")
 
     dataset: Dataset = generate_dataset(image_path)
     features_dataset: Dataset = features(dataset, vae_type)
-
     features_array = np.array(features_dataset["features"]).astype(np.float32)  # type: ignore[arg-type]
 
-    session = ort.InferenceSession(str(model_file))
+    session = ort.InferenceSession(model_file_path_named)
+    print(f"Model '{model_file_path_named}' loaded.")
     input_name = session.get_inputs()[0].name
-    result: SparseTensor = session.run(None, {input_name: features_array})[0]  # type: ignore
-    print(result)
+    try:
+        result: SparseTensor = session.run(None, {input_name: features_array.astype(np.float32)})[0]  # type: ignore
+        print(result)
+    except (InvalidArgument, ONNXRuntimeError) as error_log:
+        match error_log:
+            case _ if "Expected: 16384" in str(error_log):
+                print(
+                    """Training feature extractor does not match current feature extractor "
+                {vae_type.value}" : {err_log}""",
+                )
+                predict(image_path, VAEModel.MITSUA_FP16, true_label)
+            case _ if "Expected: 65536" in str(error_log):
+                print(f"Training feature extractor does not match current feature extractor {vae_type.value} : {error_log}")
+                predict(image_path, VAEModel.FLUX2_FP32, true_label)
+            case _ if "Expected: 131072" in str(error_log):
+                print(f"Training feature extractor does not match current feature extractor {vae_type.value} : {error_log}")
+                predict(image_path, VAEModel.GLM_BF16, true_label)
+
     match true_label:
         case None:
             for prediction in result:  # type: ignore
@@ -66,8 +83,10 @@ def predict(image_path: Path, vae_type: VAEModel = VAEModel.MITSUA_FP16, true_la
 
 
 def training_run(vae_type: VAEModel, file_or_folder_path: Path | None = None) -> None:
-    """Train model using dataset at path.\n
-    :param path: Dataset root."""
+    """Train \n
+    # XGB00st\n
+    model using dataset at path.\n
+    :param path: Dataset root folder."""
     from datasets import Dataset
 
     print("Training selected.")
@@ -90,12 +109,12 @@ def main() -> None:
     subparsers = parser.add_subparsers(dest="cmd", required=True)
 
     train_parser = subparsers.add_parser("train", help="Train model on the dataset in the provided path or `assets/`. The resulting model will be saved to disk.")
-    train_parser.add_argument("path", help="Dataset path", nargs="?", default=None)
+    train_parser.add_argument("path", help="Genunie/Human-original dataset path", nargs="?", default=None)
     train_parser.add_argument(
         "-m",
         "--model",
         choices=[m.value for m in VAEModel],
-        default=VAEModel.MITSUA_FP16,
+        default=VAEModel.GLM_BF16.value,
         help="Change the VAE model to use for training to a supported HuggingFace repo. Accuracy and memory use decrease from left to right",
     )
     check_parser = subparsers.add_parser(
@@ -115,12 +134,23 @@ def main() -> None:
                 dataset_location: Path | None = Path(args.path)
             else:
                 dataset_location: Path | None = None
+            datestamped_folder.mkdir(parents=True, exist_ok=True)
+
             vae_type = VAEModel(args.model)
-            training_run(file_or_folder_path=dataset_location, vae_type=vae_type)
+            training_run(
+                vae_type=vae_type,
+                file_or_folder_path=dataset_location,
+            )
         case "check":
             if args.path is None:
                 raise ValueError("Check requires an image path.")
-            predict(Path(args.path), true_label=args.label)
+            import json
+
+            results_file_path = model_path / "results.json"
+            with open(results_file_path) as result_metadata:
+                train_metadata = json.load(result_metadata)
+            vae_type = VAEModel(train_metadata["vae_type"])
+            predict(Path(args.path), vae_type=vae_type, true_label=args.label)
         case _:
             raise NotImplementedError
 
diff --git a/negate/datasets.py b/negate/datasets.py
index 5de86b8..86a3be8 100644
--- a/negate/datasets.py
+++ b/negate/datasets.py
@@ -22,10 +22,11 @@ def detect_nans(dataset: Dataset) -> Dataset:
     return dataset
 
 
-def load_remote_dataset(repo: str, folder_path: Path, label: int) -> Dataset:
+def load_remote_dataset(repo: str, folder_path: Path, label: int = 1) -> Dataset:
     """Load a remote dataset and attach a default label.\n
     :param repo: Repository ID of the dataset.
     :param folder_path: Local path to cache the dataset.
+    :param label: The default label to assign to all images in the dataset
     :return: Dataset with a ``label`` column added and NaNs removed."""
 
     remote_dataset = load_dataset(repo, cache_dir=str(folder_path), split="train").cast_column("image", Image(decode=True))
@@ -41,6 +42,7 @@ def generate_dataset(input_path: Path) -> Dataset:
 
     from PIL import Image as PillowImage
 
+    print(f"Using local images from {input_path}")
     validated_paths = []
     valid_extensions = {".jpg", ".webp", ".jpeg", ".png", ".tif", ".tiff"}
     if input_path.is_dir():
@@ -55,6 +57,8 @@ def generate_dataset(input_path: Path) -> Dataset:
             validated_paths.append({"image": str(img_path)})
     elif input_path.is_file() and input_path.suffix.lower() in valid_extensions:
         validated_paths.append({"image": str(input_path)})
+    else:
+        raise ValueError(f"Invalid path {input_path}")
 
     dataset = Dataset.from_list(validated_paths)  # NaN Prevention: decode separately
 
@@ -80,19 +84,10 @@ def build_datasets(input_folder: Path | None = None) -> Dataset:
         original_input_folder = Path(__file__).parent.parent / "assets"
         original_input_folder.mkdir(parents=True, exist_ok=True)
 
-    slice_dataset = load_remote_dataset("exdysa/nano-banana-pro-generated-1k-clone", synthetic_input_folder, 1)
-    rnd_synthetic_dataset = load_remote_dataset("exdysa/rnd_synthetic_img", synthetic_input_folder, 1)
-
-    # Check if assets folder has images, otherwise use placeholder dataset
-    valid_extensions = {".jpg", ".webp", ".jpeg", ".png", ".tif", ".tiff"}
-    has_images = original_input_folder.exists() and any(f.is_file() and f.suffix.lower() in valid_extensions for f in original_input_folder.iterdir())
+    slice_dataset = load_remote_dataset("exdysa/nano-banana-pro-generated-1k-clone", synthetic_input_folder)
+    rnd_synthetic_dataset = load_remote_dataset("exdysa/rnd_synthetic_img", synthetic_input_folder)
 
-    if has_images:
-        print(f"Using local images from {original_input_folder}")
-        original_dataset = generate_dataset(original_input_folder)
-    else:
-        print(f"No images found in {original_input_folder}, using placeholder dataset 'darkshapes/a_slice'")
-        original_dataset = load_remote_dataset("darkshapes/a_slice", original_input_folder, 0)
+    original_dataset = generate_dataset(original_input_folder)
 
     dataset = concatenate_datasets([slice_dataset, rnd_synthetic_dataset, original_dataset])
     return dataset
diff --git a/negate/extract.py b/negate/extract.py
index bfdd76d..2f58bd6 100644
--- a/negate/extract.py
+++ b/negate/extract.py
@@ -31,7 +31,8 @@ class VAEInfo:
     VAEModel.FLUX1_FP32: VAEInfo(VAEModel.FLUX1_FP32, "autoencoders.autoencoder_kl.AutoencoderKL"),
     VAEModel.FLUX1_FP16: VAEInfo(VAEModel.FLUX1_FP16, "autoencoders.autoencoder_kl.AutoencoderKL"),
     VAEModel.FLUX2_FP32: VAEInfo(VAEModel.FLUX2_FP32, "autoencoders.autoencoder_kl_flux2.AutoencoderKLFlux2"),
-    VAEModel.FLUX2_FP16: VAEInfo(VAEModel.FLUX1_FP16, "autoencoders.autoencoder_kl_flux2.AutoencoderKLFlux2"),
+    VAEModel.FLUX2_FP16: VAEInfo(VAEModel.FLUX2_FP16, "autoencoders.autoencoder_kl_flux2.AutoencoderKLFlux2"),
+    VAEModel.SANA_FP16: VAEInfo(VAEModel.SANA_FP16, "autoencoders.autoencoder_dc.AutoencoderDC"),
     VAEModel.SANA_FP32: VAEInfo(VAEModel.SANA_FP32, "autoencoders.autoencoder_dc.AutoencoderDC"),
 }
 
@@ -130,7 +131,7 @@ def batch_extract(self, dataset: Dataset):
 
             batch_tensor = torch.stack([color_tensor, *patch_stack]).to(self.device, dtype=self.dtype)
             with torch.no_grad():
-                if self.model.enum != VAEModel.SANA_FP32:  # type: ignore can't access encode
+                if self.model.enum != VAEModel.SANA_FP32 and self.model.enum != VAEModel.SANA_FP16:  # type: ignore can't access encode
                     latents_2_dim_h_w = self.vae.encode(batch_tensor).latent_dist.sample()  # type: ignore can't access encode
                     mean_latent = latents_2_dim_h_w.mean(dim=0).cpu().float().numpy()
                 else:
diff --git a/negate/save.py b/negate/save.py
index 63d59aa..8c6349a 100644
--- a/negate/save.py
+++ b/negate/save.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
 # <!-- // /*  d a r k s h a p e s */ -->
 
-from negate import TrainResult, model_path
+from negate import TrainResult, generate_datestamp_path, model_path
 
 
 def save_metadata(train_result: TrainResult, file_name: str = "negate") -> str:
@@ -15,7 +15,7 @@ def save_metadata(train_result: TrainResult, file_name: str = "negate") -> str:
     scale_pos_weight: float = train_result.scale_pos_weight
     seed: int = train_result.seed
 
-    metadata_file_name = model_path(f"{file_name}_metadata.npz")
+    metadata_file_name = generate_datestamp_path(f"{file_name}_metadata.npz")
     np.savez(metadata_file_name, seed=seed, scale_pos_weight=scale_pos_weight)
     return metadata_file_name
 
@@ -33,11 +33,11 @@ def save_model(train_result: TrainResult, file_name: str = "negate") -> None:
     model: Booster = train_result.model
     pca: PCA = train_result.pca
 
-    pca_file_name = model_path(f"{file_name}_pca.pkl")
+    pca_file_name = generate_datestamp_path(f"{file_name}_pca.pkl")
     with open(pca_file_name, "wb") as f:
         pickle.dump(pca, f)
 
-    negate_xgb_file_name = model_path(f"{file_name}.json")
+    negate_xgb_file_name = generate_datestamp_path(f"{file_name}.json")
     model.save_model(negate_xgb_file_name)
 
     metadata_file_name = save_metadata(train_result)
@@ -68,22 +68,18 @@ def save_to_onnx(train_result: TrainResult, file_name: str = "negate"):
         name="input",
         format=ModelInputFormat.FORMAT_NONE,  # Used for TensorRT
     )
-    negate_onnx_file_name = model_path(f"{file_name}.onnx")
+    negate_onnx_file_name = generate_datestamp_path(f"{file_name}.onnx")
     onnx_model = ONNXConverter.from_xgboost(model, inputs=[input_shape], opset=12)
     onnx.save(onnx_model, negate_onnx_file_name)
 
     initial_pca_types = [("input", FloatTensorType([None, num_features]))]
     negate_pca_onnx_raw = convert_sklearn(pca, initial_types=initial_pca_types)
     negate_pca_onnx = ONNXConverter.optim_onnx(negate_pca_onnx_raw)  # type: ignore[arg-type]
-    pca_file_name = model_path(f"{file_name}_pca.onnx")
+    pca_file_name = generate_datestamp_path(f"{file_name}_pca.onnx")
     onnx.save(negate_pca_onnx, pca_file_name)
 
     metadata_file_name = save_metadata(train_result)
 
-    models_dir = Path(__file__).parent.parent / "models"
-    models_dir.mkdir(parents=True, exist_ok=True)
-
-    for src in (negate_onnx_file_name, pca_file_name, metadata_file_name):
-        shutil.copy(src, models_dir / Path(src).name)  # type: ignore no overloads
+    shutil.copy(negate_onnx_file_name, model_path / Path(negate_onnx_file_name).name)  # type: ignore no overloads
 
     print(f"Models saved to disk. {pca_file_name} {negate_onnx_file_name} {metadata_file_name}")
diff --git a/negate/track.py b/negate/track.py
index 2f71807..0439a87 100644
--- a/negate/track.py
+++ b/negate/track.py
@@ -1,12 +1,14 @@
 # SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
 # <!-- // /*  d a r k s h a p e s */ -->
 
-from negate import TrainResult, VAEModel, get_time, model_path
+from negate import TrainResult, VAEModel, get_time, generate_datestamp_path, model_path
 
 
 def in_console(train_result: TrainResult, vae_type: VAEModel) -> None:
     """Print diagnostics and plots for a trained model.\n
     :param train_result: Result object from training."""
+    from pathlib import Path
+    import shutil
     import json
     from pprint import pprint
 
@@ -61,11 +63,11 @@ def in_console(train_result: TrainResult, vae_type: VAEModel) -> None:
     }
 
     pprint(results)
-    results_file = model_path("results.json")
+    results_file = generate_datestamp_path("results.json")
     result_format = {k: str(v) for k, v in results.items()}
     with open(results_file, "tw", encoding="utf-8") as out_file:
         json.dump(result_format, out_file, ensure_ascii=False, indent=4, sort_keys=True)
-
+    shutil.copy(results_file, model_path / Path(results_file).name)  # type: ignore no overloads
     separator = lambda: print("=" * 60)
     separator()
     print("CLASSIFICATION RESULTS")
@@ -104,7 +106,7 @@ def on_graph(train_result: TrainResult) -> None:
     plt.ylabel("Explained Variance Ratio")
     plt.title("First 20 Components")
     plt.tight_layout()
-    plt.savefig(model_path("score_explained_variance.png"))
+    plt.savefig(generate_datestamp_path("score_explained_variance.png"))
     plt.show()
 
     cm = confusion_matrix(train_result.y_test, y_pred)
@@ -125,7 +127,7 @@ def on_graph(train_result: TrainResult) -> None:
     ax.set_ylabel("Actual")
     ax.set_title("Confusion Matrix")
     fig.colorbar(cax)
-    plt.savefig(model_path("score_confusion_matrix.png"))
+    plt.savefig(generate_datestamp_path("score_confusion_matrix.png"))
     plt.show()
 
     plt.figure(figsize=(10, 5))
@@ -143,7 +145,7 @@ def on_graph(train_result: TrainResult) -> None:
     plt.title("PCA Transformed Data")
     plt.colorbar(label="Prediction")
     plt.tight_layout()
-    plt.savefig(model_path("pca_transform_map.png"))
+    plt.savefig(generate_datestamp_path("pca_transform_map.png"))
     plt.show()
 
     import seaborn as sns
@@ -171,5 +173,5 @@ def on_graph(train_result: TrainResult) -> None:
     )
     ax.set_title(f"Feature Correlation Heatmap (PCA Components)\nRange: [{vmin:.3e}, {vmax:.3e}]")
     plt.tight_layout()
-    figure.savefig(model_path("correlation_heatmap.png"))
+    figure.savefig(generate_datestamp_path("correlation_heatmap.png"))
     plt.show()
diff --git a/negate/train.py b/negate/train.py
index 870fb71..2c4fed1 100644
--- a/negate/train.py
+++ b/negate/train.py
@@ -12,9 +12,9 @@
 from xgboost import Booster
 
 get_time = lambda: datetime.now().strftime("%Y%m%d_%H%M%S")
-folder = Path("models", get_time())
-folder.mkdir(parents=True, exist_ok=True)
-model_path = lambda file_name: str(folder / file_name)
+datestamped_folder = Path("models", get_time())
+generate_datestamp_path = lambda file_name: str(datestamped_folder / file_name)
+model_path = Path(__file__).parent.parent / "models"
 
 
 @dataclass
diff --git a/pyproject.toml b/pyproject.toml
index b2bb723..b618b7f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,9 +6,7 @@ requires = ["setuptools", "setuptools_scm"]
 build-backend = "setuptools.build_meta"
 
 [project]
-authors = [
-    { name = "darkshapes", email = "91800957+exdysa@users.noreply.github.com" },
-]
+authors = [{ name = "darkshapes", email = "91800957+exdysa@users.noreply.github.com" }]
 name = "negate"
 description = "Synthetic image detector"
 readme = "README.md"
@@ -54,12 +52,7 @@ dependencies = [
     "xgboost>=3.1.3",
 ]
 [dependency-groups]
-dev = [
-    "pyright>=1.1.408",
-    "pytest>=9.0.2",
-    "pytest-asyncio>=1.3.0",
-    "ruff>=0.14.13",
-]
+dev = ["pyright>=1.1.408", "pytest>=9.0.2", "pytest-asyncio>=1.3.0", "ruff>=0.14.13"]
 [project.optional-dependencies]
 # -- frameworks --
 
@@ -80,15 +73,7 @@ torch-backend = "auto"
 prerelease = "allow"
 preview = true
 
-
-conflicts = [
-    [
-        { extra = "cpu" },
-        { extra = "mps" },
-        { extra = "rocm" },
-        { extra = "cuda" },
-    ],
-]
+conflicts = [[{ extra = "cpu" }, { extra = "mps" }, { extra = "rocm" }, { extra = "cuda" }]]
 
 [[tool.uv.index]]
 name = "pytorch-cpu"
@@ -107,13 +92,9 @@ explicit = true
 
 [tool.uv.sources]
 
-torchvision = [
-    { index = "pytorch-cpu", extra = "mps", marker = "sys_platform == 'darwin'" },
-]
+torchvision = [{ index = "pytorch-cpu", extra = "mps", marker = "sys_platform == 'darwin'" }]
 
-torch = [
-    { index = "pytorch-cpu", extra = "mps", marker = "sys_platform == 'darwin'" },
-]
+torch = [{ index = "pytorch-cpu", extra = "mps", marker = "sys_platform == 'darwin'" }]
 
 [tool.setuptools_scm]
 write_to = "_version.py"
@@ -141,7 +122,7 @@ ignore-paths = ["^tests/.*$", "test_.*$"]
 disable = ["C0415"]
 
 [tool.pyright]
-include = ["nnll"]
+include = ["negate"]
 exclude = [
     "^tests/.*$",
     "**/__pycache__", # cache directories

From 3ffe39f5845fb10d6020bf97b70c81f4a379d048 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=CB=B6=F0=9D=9E=A2=E2=A4=AC=E2=AB=92=E2=B5=96s=E1=90=BC?=
 =?UTF-8?q?=CB=B6?= <91800957+exdysa@users.noreply.github.com>
Date: Tue, 3 Feb 2026 00:24:39 -0500
Subject: [PATCH 2/4] +auraequivae, break things

---
 _version.py        |   6 +-
 negate/__main__.py |  39 ++++---
 negate/ae.py       | 285 +++++++++++++++++++++++++++++++++++++++++++++
 negate/extract.py  | 130 ++++++++++++++-------
 negate/save.py     |   3 +-
 pyproject.toml     |   1 +
 uv.lock            |  11 ++
 7 files changed, 410 insertions(+), 65 deletions(-)
 create mode 100644 negate/ae.py

diff --git a/_version.py b/_version.py
index aac473e..bf13d14 100644
--- a/_version.py
+++ b/_version.py
@@ -28,7 +28,7 @@
 commit_id: COMMIT_ID
 __commit_id__: COMMIT_ID
 
-__version__ = version = '0.1.dev41+g786446a62.d20260131'
-__version_tuple__ = version_tuple = (0, 1, 'dev41', 'g786446a62.d20260131')
+__version__ = version = '0.1.dev57+gded5bb62c.d20260203'
+__version_tuple__ = version_tuple = (0, 1, 'dev57', 'gded5bb62c.d20260203')
 
-__commit_id__ = commit_id = 'g786446a62'
+__commit_id__ = commit_id = 'gded5bb62c'
diff --git a/negate/__main__.py b/negate/__main__.py
index 5ce92df..feaf455 100644
--- a/negate/__main__.py
+++ b/negate/__main__.py
@@ -2,9 +2,11 @@
 # <!-- // /*  d a r k s h a p e s */ -->
 
 from pathlib import Path
+from typing import Any
+
 import numpy as np
 
-from negate import TrainResult, build_datasets, features, generate_dataset, grade, in_console, save_to_onnx, on_graph, VAEModel, datestamped_folder, model_path
+from negate import TrainResult, VAEModel, build_datasets, datestamped_folder, features, generate_dataset, grade, in_console, model_path, on_graph, save_to_onnx
 
 
 def evaluate(prediction: np.ndarray, ground_truth: np.ndarray) -> None:
@@ -18,24 +20,22 @@ def evaluate(prediction: np.ndarray, ground_truth: np.ndarray) -> None:
 
     acc = float(np.mean(prediction == ground_truth))
 
-    genu_cnt = int(np.sum(ground_truth == 0))
-    synth_cnt = int(np.sum(ground_truth == 1))
-
     print(f"Accuracy: {acc:.2%}")
-    print(f"Genuine: {genu_cnt}  Synthetic: {synth_cnt}")
 
 
-def predict(image_path: Path, vae_type: VAEModel, true_label: int | None = None) -> np.ndarray:
+def predict(image_path: Path, vae_type: VAEModel, metadata: dict[str, Any], true_label: int | None = None) -> np.ndarray:
     """Predict synthetic or original for given image. (0 = genuine, 1 = synthetic)\n
     :param image_path: Path to image file or folder.
     :param vae_type: VAE model to use for feature extraction.
-    :return: Prediction array.
-    """
-    from datasets import Dataset
+    :return: Prediction array."""
+
     import onnxruntime as ort
+    from datasets import Dataset
     from onnxruntime import SparseTensor
-    from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument, Fail as ONNXRuntimeError
+    from onnxruntime.capi.onnxruntime_pybind11_state import Fail as ONNXRuntimeError
+    from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument
 
+    pca_file_path_named = model_path / "negate_pca.onnx"
     model_file_path_named = model_path / "negate.onnx"
     if not model_file_path_named.exists():
         raise FileNotFoundError(f"Model file not found: {str(model_file_path_named)}. Please run 'train' first to create the model.")
@@ -48,19 +48,26 @@ def predict(image_path: Path, vae_type: VAEModel, true_label: int | None = None)
     features_dataset: Dataset = features(dataset, vae_type)
     features_array = np.array(features_dataset["features"]).astype(np.float32)  # type: ignore[arg-type]
 
+    session_pca = ort.InferenceSession(pca_file_path_named)
+    input_name_pca = session_pca.get_inputs()[0].name
+    features_pca = session_pca.run(None, {input_name_pca: features_array})[0]
+
+    scale_pos_weight = metadata["scale_pos_weight"]
+    print(metadata["scale_pos_weight"])
+    scale_pos_weight = float(scale_pos_weight)
+    input_name = ort.get_available_providers()[0]
+    features_model = features_pca.astype(np.float32)
+
     session = ort.InferenceSession(model_file_path_named)
     print(f"Model '{model_file_path_named}' loaded.")
     input_name = session.get_inputs()[0].name
     try:
-        result: SparseTensor = session.run(None, {input_name: features_array.astype(np.float32)})[0]  # type: ignore
+        result: SparseTensor = session.run(None, {input_name: features_model})[0]  # type: ignore
         print(result)
     except (InvalidArgument, ONNXRuntimeError) as error_log:
         match error_log:
             case _ if "Expected: 16384" in str(error_log):
-                print(
-                    """Training feature extractor does not match current feature extractor "
-                {vae_type.value}" : {err_log}""",
-                )
+                print(f"Training feature extractor does not match current feature extractor{vae_type.value} : {error_log}")
                 predict(image_path, VAEModel.MITSUA_FP16, true_label)
             case _ if "Expected: 65536" in str(error_log):
                 print(f"Training feature extractor does not match current feature extractor {vae_type.value} : {error_log}")
@@ -150,7 +157,7 @@ def main() -> None:
             with open(results_file_path) as result_metadata:
                 train_metadata = json.load(result_metadata)
             vae_type = VAEModel(train_metadata["vae_type"])
-            predict(Path(args.path), vae_type=vae_type, true_label=args.label)
+            predict(Path(args.path), vae_type=vae_type, metadata=train_metadata, true_label=args.label)
         case _:
             raise NotImplementedError
 
diff --git a/negate/ae.py b/negate/ae.py
new file mode 100644
index 0000000..a05cc43
--- /dev/null
+++ b/negate/ae.py
@@ -0,0 +1,285 @@
+# Take from FLUX
+
+# changes
+# + head dim, num_heads
+# ~ 64 ch
+# ~ forward std dev fixed 0.1
+# ~ b (h d) x y -> b h (x y) d
+# - middle block processing hs and append
+# - upscale
+# - scale.shift
+
+import math
+
+import torch
+import torch.nn.functional as F
+
+from einops import rearrange
+from torch import Tensor, nn
+from safetensors.torch import load_file
+
+
+def swish(x: Tensor) -> Tensor:
+    return x * torch.sigmoid(x)
+
+
+class AttnBlock(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.head_dim = 64
+        self.num_heads = in_channels // self.head_dim
+        self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+        self.qkv = nn.Conv2d(in_channels, in_channels * 3, kernel_size=1, bias=False)
+        self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1, bias=False)
+        nn.init.normal_(self.proj_out.weight, std=0.2 / math.sqrt(in_channels))
+
+    def attention(self, h_: Tensor) -> Tensor:
+        h_ = self.norm(h_)
+        qkv = self.qkv(h_)
+        q, k, v = qkv.chunk(3, dim=1)
+        b, c, h, w = q.shape
+        q = rearrange(q, "b (h d) x y -> b h (x y) d", h=self.num_heads, d=self.head_dim)
+        k = rearrange(k, "b (h d) x y -> b h (x y) d", h=self.num_heads, d=self.head_dim)
+        v = rearrange(v, "b (h d) x y -> b h (x y) d", h=self.num_heads, d=self.head_dim)
+        h_ = F.scaled_dot_product_attention(q, k, v)
+        h_ = rearrange(h_, "b h (x y) d -> b (h d) x y", x=h, y=w)
+        return h_
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x + self.proj_out(self.attention(x))
+
+
+class ResnetBlock(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+        self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
+        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        if self.in_channels != self.out_channels:
+            self.nin_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, x):
+        h = x
+        h = self.norm1(h)
+        h = swish(h)
+        h = self.conv1(h)
+        h = self.norm2(h)
+        h = swish(h)
+        h = self.conv2(h)
+        if self.in_channels != self.out_channels:
+            x = self.nin_shortcut(x)
+        return x + h
+
+
+class Downsample(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
+
+    def forward(self, x: Tensor):
+        pad = (0, 1, 0, 1)
+        x = nn.functional.pad(x, pad, mode="constant", value=0)
+        x = self.conv(x)
+        return x
+
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x: Tensor):
+        x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+        x = self.conv(x)
+        return x
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        resolution: int,
+        in_channels: int,
+        ch: int,
+        ch_mult: list[int],
+        num_res_blocks: int,
+        z_channels: int,
+    ):
+        super().__init__()
+        self.ch = ch
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.conv_in = nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1)
+        curr_res = resolution
+        in_ch_mult = (1,) + tuple(ch_mult)
+        self.in_ch_mult = in_ch_mult
+        self.down = nn.ModuleList()
+        block_in = self.ch
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch * in_ch_mult[i_level]
+            block_out = ch * ch_mult[i_level]
+            for _ in range(self.num_res_blocks):
+                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
+                block_in = block_out
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions - 1:
+                down.downsample = Downsample(block_in)
+                curr_res = curr_res // 2
+            self.down.append(down)
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
+        self.mid.attn_1 = AttnBlock(block_in)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
+        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
+        self.conv_out = nn.Conv2d(block_in, z_channels, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x: Tensor) -> Tensor:
+        h = self.conv_in(x)
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](h)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+            if i_level != self.num_resolutions - 1:
+                h = self.down[i_level].downsample(h)
+        h = self.mid.block_1(h)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h)
+        h = self.norm_out(h)
+        h = swish(h)
+        h = self.conv_out(h)
+        return h
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        ch: int,
+        out_ch: int,
+        ch_mult: list[int],
+        num_res_blocks: int,
+        in_channels: int,
+        resolution: int,
+        z_channels: int,
+    ):
+        super().__init__()
+        self.ch = ch
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.ffactor = 2 ** (self.num_resolutions - 1)
+        block_in = ch * ch_mult[self.num_resolutions - 1]
+        curr_res = resolution // 2 ** (self.num_resolutions - 1)
+        self.z_shape = (1, z_channels, curr_res, curr_res)
+        self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1)
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
+        self.mid.attn_1 = AttnBlock(block_in)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch * ch_mult[i_level]
+            for _ in range(self.num_res_blocks + 1):
+                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
+                block_in = block_out
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in)
+                curr_res = curr_res * 2
+            self.up.insert(0, up)
+        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
+        self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, z: Tensor) -> Tensor:
+        h = self.conv_in(z)
+        h = self.mid.block_1(h)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h)
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = self.up[i_level].block[i_block](h)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+        h = self.norm_out(h)
+        h = swish(h)
+        h = self.conv_out(h)
+        return h
+
+
+class DiagonalGaussian(nn.Module):
+    def __init__(self, sample: bool = True, chunk_dim: int = 1):
+        super().__init__()
+        self.sample = sample
+        self.chunk_dim = chunk_dim
+
+    def forward(self, z: Tensor) -> Tensor:
+        mean = z
+        if self.sample:
+            std = 0.1
+            return mean * (1 + std * torch.randn_like(mean))
+        else:
+            return mean
+
+
+class VAE(nn.Module):
+    def __init__(self, resolution, in_channels, ch, out_ch, ch_mult, num_res_blocks, z_channels):
+        super().__init__()
+        self.encoder = Encoder(
+            resolution=resolution,
+            in_channels=in_channels,
+            ch=ch,
+            ch_mult=ch_mult,
+            num_res_blocks=num_res_blocks,
+            z_channels=z_channels,
+        )
+        self.decoder = Decoder(
+            resolution=resolution,
+            in_channels=in_channels,
+            ch=ch,
+            out_ch=out_ch,
+            ch_mult=ch_mult,
+            num_res_blocks=num_res_blocks,
+            z_channels=z_channels,
+        )
+        self.reg = DiagonalGaussian()
+
+    def forward(self, x: Tensor) -> Tensor:
+        z = self.encoder(x)
+        z_s = self.reg(z)
+        decz = self.decoder(z_s)
+        return decz, z
+
+
+if __name__ == "__main__":
+    vae = VAE(
+        resolution=256,
+        in_channels=3,
+        ch=64,
+        out_ch=3,
+        ch_mult=[1, 2, 4, 4],
+        num_res_blocks=2,
+        z_channels=16,
+    )
+    device = "cuda" if torch.cuda.is_available() else "mps" if torch.mps.is_available() else "cpu"
+    vae.eval().to(device)
+    x = torch.randn(1, 3, 256, 256).to(device)
+    decz, z = vae(x)
+    print(decz.shape, z.shape)
diff --git a/negate/extract.py b/negate/extract.py
index 2f58bd6..7b5c72f 100644
--- a/negate/extract.py
+++ b/negate/extract.py
@@ -4,6 +4,7 @@
 from enum import Enum
 from dataclasses import dataclass
 from datasets import Dataset
+from PIL.Image import Image
 
 
 class VAEModel(str, Enum):
@@ -11,6 +12,7 @@ class VAEModel(str, Enum):
 
     SANA_FP32 = "exdysa/dc-ae-f32c32-sana-1.1-diffusers"
     SANA_FP16 = "exdysa/dc-ae-f32c32-sana-1.1-diffusers"
+    AURAEQUI_BF16 = "exdysa/AuraEquiVAE-SAFETENSORS"
     GLM_BF16 = "zai-org/GLM-Image"
     FLUX2_FP32 = "black-forest-labs/FLUX.2-dev"
     FLUX2_FP16 = "black-forest-labs/FLUX.2-klein-4B"
@@ -27,6 +29,7 @@ class VAEInfo:
 
 MODEL_MAP = {
     VAEModel.MITSUA_FP16: VAEInfo(VAEModel.MITSUA_FP16, "autoencoders.autoencoder_kl.AutoencoderKL"),
+    VAEModel.AURAEQUI_BF16: VAEInfo(VAEModel.AURAEQUI_BF16, "ae.VAE"),
     VAEModel.GLM_BF16: VAEInfo(VAEModel.GLM_BF16, "autoencoders.autoencoder_kl.AutoencoderKL"),
     VAEModel.FLUX1_FP32: VAEInfo(VAEModel.FLUX1_FP32, "autoencoders.autoencoder_kl.AutoencoderKL"),
     VAEModel.FLUX1_FP16: VAEInfo(VAEModel.FLUX1_FP16, "autoencoders.autoencoder_kl.AutoencoderKL"),
@@ -59,21 +62,34 @@ class FeatureExtractor:
 
     def __init__(self, vae_type: VAEModel, device: DeviceName, dtype: torch.dtype) -> None:
         """Set up the extractor with a VAE model.\n
-        :param model: Repository ID of the VAE.
+        :param vae_type: VAEModel ID of the VAE.
         :param device: Target device.
         :param dtype: Data type for tensors."""
 
         from diffusers.models.autoencoders.vae import AutoencoderMixin
-        from negate import Residual  # `B̴̨̒e̷w̷͇̃ȁ̵͈r̸͔͛ę̵͂ ̷̫̚t̵̻̐h̶̜͒ȩ̸̋ ̵̪̄ő̷̦ù̵̥r̷͇̂o̷̫͑b̷̲͒ò̷̫r̴̢͒ô̵͍s̵̩̈́` #type: ignore
+        from negate import Residual, ae  # `B̴̨̒e̷w̷͇̃ȁ̵͈r̸͔͛ę̵͂ ̷̫̚t̵̻̐h̶̜͒ȩ̸̋ ̵̪̄ő̷̦ù̵̥r̷͇̂o̷̫͑b̷̲͒ò̷̫r̴̢͒ô̵͍s̵̩̈́` #type: ignore
 
-        self.device = device
+        self.device = device.value
         self.dtype = dtype
         self.model: VAEInfo = MODEL_MAP[vae_type]
-        self.vae: AutoencoderMixin | None = None
+        self.vae: AutoencoderMixin | ae.VAE | None = None
         self.residual_transform = Residual()
         if self.vae is None:
             self.create_vae()
 
+    def aura_equi_vae(self, vae_path: str):
+        """Processing specifically for AuraEquiVAE
+        :param vae_path: The path to the VAE model directory"""
+        import os
+        from negate.ae import VAE
+        from safetensors.torch import load_file
+
+        vae = VAE(resolution=256, in_channels=3, ch=256, out_ch=3, ch_mult=[1, 2, 4, 4], num_res_blocks=2, z_channels=16).to(self.device).bfloat16()
+        vae_file = os.path.join(vae_path, "vae_epoch_3_step_49501_bf16.safetensors")
+        state_dict = load_file(vae_file)
+        vae.load_state_dict(state_dict)
+        return vae
+
     def create_vae(self):
         """Download and load the VAE from the model repo."""
 
@@ -83,31 +99,52 @@ def create_vae(self):
         from huggingface_hub.errors import LocalEntryNotFoundError
         from huggingface_hub import snapshot_download
 
-        autoencoder_cls = getattr(autoencoders, self.model.module.split(".")[-1])
+        autoencoder_cls = getattr(autoencoders, self.model.module.split(".")[-1], None)
         try:
-            vae_model = autoencoder_cls.from_pretrained(self.model.enum.value, torch_dtype=self.dtype, local_files_only=True).to(self.device.value)
-        except (LocalEntryNotFoundError, OSError):
+            vae_model = autoencoder_cls.from_pretrained(self.model.enum.value, torch_dtype=self.dtype, local_files_only=True).to(self.device)
+        except (LocalEntryNotFoundError, OSError, AttributeError):
             print("Downloading model...")
-            vae_path: str = snapshot_download(self.model.enum.value, allow_patterns=["vae/*"])  # type: ignore
-            vae_path = os.path.join(vae_path, "vae")
-            vae_model = autoencoder_cls.from_pretrained(vae_path, torch_dtype=self.dtype, local_files_only=True).to(self.device.value)
+        vae_path: str = snapshot_download(self.model.enum.value, allow_patterns=["vae/*"])  # type: ignore
+        vae_path = os.path.join(vae_path, "vae")
+        if self.model.enum == VAEModel.AURAEQUI_BF16:
+            vae_model = self.aura_equi_vae(vae_path)
+        else:
+            vae_model = autoencoder_cls.from_pretrained(vae_path, torch_dtype=self.dtype, local_files_only=True).to(self.device)
 
         vae_model.eval()
         self.vae = vae_model
 
-    def cleanup(self) -> None:
-        """Free the VAE and GPU memory."""
+    def _extract_generic(self, batch: "torch.Tensor"):
+        """Encode with standard Diffusers VAE and return mean latent.\n
+        :param batch: Tensor of image + patches.
+        :return: NumPy mean latent."""
 
-        import gc
+        latent = self.vae.encode(batch).latent_dist.sample()  # type: ignore
+        return latent.mean(dim=0).cpu().float().numpy()
 
-        import torch
+    def _extract_special(self, batch: "torch.Tensor", image: Image):
+        """Handle SANA and AuraEqui models.\n
+        :param batch: Tensor of image + patches.
+        :param img: Original PIL image.
+        :return: NumPy mean latent."""
 
-        device = self.device
-        if device != "cpu":
-            gpu = getattr(torch, device)
-            gpu.empty_cache()
-        del self.vae
-        gc.collect()
+        from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
+        import torch
+        from torch import Tensor
+
+        if self.model.enum == VAEModel.AURAEQUI_BF16:
+            latent: Tensor = self.vae.encoder(batch)
+            latent = latent.clamp(-8.0, 8.0)
+            mean = torch.mean(latent, dim=0).cpu().float()
+        else:
+            latent: Tensor = self.vae.encode(batch)  # type: ignore
+            mean = torch.mean(latent.latent, dim=0).cpu().float()  # type: ignore
+
+        logvar = torch.zeros_like(mean).cpu().float()
+        params = torch.cat([mean, logvar], dim=1)
+        dist = DiagonalGaussianDistribution(params)
+        sample = dist.sample()
+        return sample.mean(dim=0).cpu().float().numpy()
 
     def batch_extract(self, dataset: Dataset):
         """Extract VAE features from a batch of images.
@@ -121,35 +158,38 @@ def batch_extract(self, dataset: Dataset):
         patch_stack = []
 
         for image in dataset["image"]:
-            color_image = image.convert("RGB")
-            color_tensor = self.transform(color_image)
-            patches = self.residual_transform.crop_select(image, size=768, top_k=1)
-            for patch in patches:
-                patch_image = patch.convert("RGB")
-                patch_tensor = self.transform(patch_image)
-                patch_stack.append(patch_tensor)
-
-            batch_tensor = torch.stack([color_tensor, *patch_stack]).to(self.device, dtype=self.dtype)
+            rgb = image.convert("RGB")
+            col = self.transform(rgb)
+            for patches in self.residual_transform.crop_select(image, size=768, top_k=1):
+                patch_stack.append(self.transform(patches.convert("RGB")))
+
+            batch = torch.stack([col, *patch_stack]).to(self.device, self.dtype)
             with torch.no_grad():
-                if self.model.enum != VAEModel.SANA_FP32 and self.model.enum != VAEModel.SANA_FP16:  # type: ignore can't access encode
-                    latents_2_dim_h_w = self.vae.encode(batch_tensor).latent_dist.sample()  # type: ignore can't access encode
-                    mean_latent = latents_2_dim_h_w.mean(dim=0).cpu().float().numpy()
-                else:
-                    from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
-
-                    latent = self.vae.encode(batch_tensor)  # # type: ignore can't access encode
-                    mean_latent = torch.mean(latent.latent, dim=0).cpu().float()  # distribution with mean
-                    logvar_latent = torch.zeros_like(mean_latent).cpu().float()  # & logvar
-                    params = torch.cat([mean_latent, logvar_latent], dim=1)
-                    distribution = DiagonalGaussianDistribution(params)
-                    sample = distribution.sample()
-                    mean_latent = sample.mean(dim=0).cpu().float().numpy()
-                feature_vec = mean_latent.flatten()
-
-            features_list.append(feature_vec)
+                match self.model.enum:
+                    case VAEModel.SANA_FP32 | VAEModel.SANA_FP16 | VAEModel.AURAEQUI_BF16:
+                        mean_latent = self._extract_special(batch, image)
+                    case _:
+                        mean_latent = self._extract_generic(batch)
+
+            features_list.append(mean_latent.flatten())
 
         return {"features": features_list}
 
+    def cleanup(self) -> None:
+        """Free the VAE and GPU memory."""
+
+        import gc
+
+        import torch
+
+        if self.device != "cpu":
+            gpu = getattr(torch, self.device)
+            gpu.empty_cache()
+            del gpu
+        del self.vae
+        del self.device
+        gc.collect()
+
     def __enter__(self) -> "FeatureExtractor":
         return self
 
diff --git a/negate/save.py b/negate/save.py
index 8c6349a..2a2c54a 100644
--- a/negate/save.py
+++ b/negate/save.py
@@ -80,6 +80,7 @@ def save_to_onnx(train_result: TrainResult, file_name: str = "negate"):
 
     metadata_file_name = save_metadata(train_result)
 
-    shutil.copy(negate_onnx_file_name, model_path / Path(negate_onnx_file_name).name)  # type: ignore no overloads
+    for src in (negate_onnx_file_name, pca_file_name):
+        shutil.copy(src, model_path / Path(src).name)  # type: ignore no overloads
 
     print(f"Models saved to disk. {pca_file_name} {negate_onnx_file_name} {metadata_file_name}")
diff --git a/pyproject.toml b/pyproject.toml
index b618b7f..be0be7f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,6 +31,7 @@ dependencies = [
     "accelerate>=1.12.0",
     "datasets>=4.5.0",
     "diffusers>=0.36.0",
+    "einops>=0.8.2",
     "huggingface-hub>=1.3.2",
     "matplotlib>=3.10.8",
     "mongoengine>=0.29.1",
diff --git a/uv.lock b/uv.lock
index 907c388..52ee3b2 100644
--- a/uv.lock
+++ b/uv.lock
@@ -414,6 +414,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ba/5a/18ad964b0086c6e62e2e7500f7edc89e3faa45033c71c1893d34eed2b2de/dnspython-2.8.0-py3-none-any.whl", hash = "sha256:01d9bbc4a2d76bf0db7c1f729812ded6d912bd318d3b1cf81d30c0f845dbf3af", size = 331094, upload-time = "2025-09-07T18:57:58.071Z" },
 ]
 
+[[package]]
+name = "einops"
+version = "0.8.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2c/77/850bef8d72ffb9219f0b1aac23fbc1bf7d038ee6ea666f331fa273031aa2/einops-0.8.2.tar.gz", hash = "sha256:609da665570e5e265e27283aab09e7f279ade90c4f01bcfca111f3d3e13f2827", size = 56261, upload-time = "2026-01-26T04:13:17.638Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2a/09/f8d8f8f31e4483c10a906437b4ce31bdf3d6d417b73fe33f1a8b59e34228/einops-0.8.2-py3-none-any.whl", hash = "sha256:54058201ac7087911181bfec4af6091bb59380360f069276601256a76af08193", size = 65638, upload-time = "2026-01-26T04:13:18.546Z" },
+]
+
 [[package]]
 name = "filelock"
 version = "3.20.3"
@@ -1041,6 +1050,7 @@ dependencies = [
     { name = "accelerate" },
     { name = "datasets" },
     { name = "diffusers" },
+    { name = "einops" },
     { name = "huggingface-hub" },
     { name = "matplotlib" },
     { name = "mongoengine" },
@@ -1097,6 +1107,7 @@ requires-dist = [
     { name = "accelerate", specifier = ">=1.12.0" },
     { name = "datasets", specifier = ">=4.5.0" },
     { name = "diffusers", specifier = ">=0.36.0" },
+    { name = "einops", specifier = ">=0.8.2" },
     { name = "huggingface-hub", specifier = ">=1.3.2" },
     { name = "matplotlib", specifier = ">=3.10.8" },
     { name = "mongoengine", specifier = ">=0.29.1" },

From 2534ffd375d7de4c82ed3988c41dd2c690ea7a5d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=CB=B6=F0=9D=9E=A2=E2=A4=AC=E2=AB=92=E2=B5=96s=E1=90=BC?=
 =?UTF-8?q?=CB=B6?= <91800957+exdysa@users.noreply.github.com>
Date: Tue, 3 Feb 2026 21:48:40 -0500
Subject: [PATCH 3/4] +config, -auraequivae,

---
 .gitignore                           |   3 +-
 README.md                            |  21 +-
 config/config.yaml                   |  23 +
 negate/__init__.py                   |   6 +-
 negate/__main__.py                   | 146 ++++--
 negate/ae.py                         | 285 -----------
 negate/config.py                     |  52 ++
 negate/datasets.py                   |  17 -
 negate/extract.py                    | 110 +++--
 negate/residuals.py                  |  32 +-
 negate/save.py                       |  27 +-
 negate/track.py                      | 125 +++--
 negate/train.py                      |  46 +-
 results/combined_plots.png           | Bin 0 -> 304966 bytes
 results/pca_transform_map.png        | Bin 143174 -> 0 bytes
 results/results.json                 |  20 -
 results/score_confusion_matrix.png   | Bin 15311 -> 0 bytes
 results/score_explained_variance.png | Bin 35240 -> 0 bytes
 results/vae_comparison.txt           | 706 +++++++++++++++++++++++++++
 tests/test_import.py                 |  14 +-
 tests/test_vae_map.py                |   1 -
 21 files changed, 1081 insertions(+), 553 deletions(-)
 create mode 100644 config/config.yaml
 delete mode 100644 negate/ae.py
 create mode 100644 negate/config.py
 create mode 100644 results/combined_plots.png
 delete mode 100644 results/pca_transform_map.png
 delete mode 100644 results/results.json
 delete mode 100644 results/score_confusion_matrix.png
 delete mode 100644 results/score_explained_variance.png
 create mode 100644 results/vae_comparison.txt

diff --git a/.gitignore b/.gitignore
index a7c63c9..7a51b52 100644
--- a/.gitignore
+++ b/.gitignore
@@ -18,4 +18,5 @@ models/*
 
 results/features_residual/
 results/residual_analysis/
-results/features_updated/
\ No newline at end of file
+results/features_updated/
+.demo.ipynb
diff --git a/README.md b/README.md
index 219372a..d9e23d0 100644
--- a/README.md
+++ b/README.md
@@ -26,9 +26,7 @@ This repo provides a simple command‑line interface to invoke the tool and exam
 
 Future work includes the development of an automated testing framework and evaluation suite, expanding the scope of research to include wider diversity of synthetic and original human-generated datasets, benchmarking against comparable methods, and exploring additional model architectures.
 
-![Bar and grid graph comparing variance of the synthetic and real images](results/score_explained_variance.png)
-![Graph comparing before and after pca transform operation of dataset](results/pca_transform_map.png)
-![Graph comparing confusion matrix of the synthetic and real images](results/score_confusion_matrix.png)
+![Bar and grid graph comparing variance of the synthetic and real images](results/combined_plots.png)
 
 ## Requirements
 
@@ -69,32 +67,33 @@ Set-ExecutionPolicy Bypass -Scope Process -Force; .venv\Scripts\Activate.ps1
 Basic Syntax:
 
 ```sh
-usage: negate [-h] {train,check} ...
+usage: negate [-h] {train,check,compare} ...
 
 Negate CLI
 
 positional arguments:
-  {train,check}
-    train        Train model on the dataset in the provided path or `assets/`. The resulting model will be saved to disk.
-    check        Check whether an image at the provided path is synthetic or original.
+  {train,check,compare}
+    train               Train model on the dataset in the provided path or `assets/`. The resulting model will be saved to disk.
+    check               Check whether an image at the provided path is synthetic or original.
+    compare             Run extraction and training using all possible VAE.
 
 options:
-  -h, --help     show this help message and exit
+  -h, --help            show this help message and exit
 ```
 
 Training syntax:
 
 ```sh
 usage: negate train [-h]
-                    [-m {exdysa/dc-ae-f32c32-sana-1.1-diffusers,zai-org/GLM-Image,black-forest-labs/FLUX.2-dev,black-forest-labs/FLUX.2-klein-4B,Tongyi-MAI/Z-Image,Freepik/F-Lite-Texture,exdysa/mitsua-vae-SAFETENSORS}]
+                    [-m {exdysa/dc-ae-f32c32-sana-1.1-diffusers,black-forest-labs/FLUX.2-dev,black-forest-labs/FLUX.2-klein-4B,Tongyi-MAI/Z-Image,Freepik/F-Lite-Texture,exdysa/mitsua-vae-SAFETENSORS}]
                     [path]
 
 positional arguments:
-  path                  Dataset path
+  path                  Genunie/Human-original dataset path
 
 options:
   -h, --help            show this help message and exit
-  -m, --model {exdysa/dc-ae-f32c32-sana-1.1-diffusers,zai-org/GLM-Image,black-forest-labs/FLUX.2-dev,black-forest-labs/FLUX.2-klein-4B,Tongyi-MAI/Z-Image,Freepik/F-Lite-Texture,exdysa/mitsua-vae-SAFETENSORS}
+  -m, --model {exdysa/dc-ae-f32c32-sana-1.1-diffusers,black-forest-labs/FLUX.2-dev,black-forest-labs/FLUX.2-klein-4B,Tongyi-MAI/Z-Image,Freepik/F-Lite-Texture,exdysa/mitsua-vae-SAFETENSORS}
                         Change the VAE model to use for training to a supported HuggingFace repo. Accuracy and memory use decrease from left to right
 ```
 
diff --git a/config/config.yaml b/config/config.yaml
new file mode 100644
index 0000000..8ec8f3c
--- /dev/null
+++ b/config/config.yaml
@@ -0,0 +1,23 @@
+# Advanced Configuration for Negate CLI
+batch_size: 4                           # Feature extraction batch size, zero to disable batching
+cache_features: true                    # enable <chud> mode
+vae_tiling: false                       # Enable VAE tiling
+vae_slicing: false                      # Enable VAE scaling
+patch_size: 768                         # Patch resolution
+top_k: 1                                # Number of top patches to keep
+use_onnx: false                          # True → ONNX, False → native XGBoost
+dtype: bfloat16                         # vae dtype
+default_vae: "Freepik/F-Lite-Texture"   # Model path
+
+train:
+  n_components: 0.95                      # Training PCA num components
+  num_boost_round: 200                    # Boosted training rounds
+  early_stopping_rounds: 10               # Early stop training
+  colsample_bytree: 0.8
+  eval_metric: ["logloss", "aucpr"]
+  learning_rate: 0.1
+  max_depth: 4
+  objective:  "binary:logistic"
+  subsample: 0.8
+  scale_pos_weight: null
+  seed: 0
diff --git a/negate/__init__.py b/negate/__init__.py
index e81f415..47695e3 100644
--- a/negate/__init__.py
+++ b/negate/__init__.py
@@ -1,9 +1,11 @@
 # SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
 # <!-- // /*  d a r k s h a p e s */ -->
 # ruff: noqa
-from negate.datasets import build_datasets, dataset_to_nparray, generate_dataset
+
+from negate.config import negate_options as negate_opt
+from negate.datasets import build_datasets, generate_dataset
 from negate.extract import FeatureExtractor, DeviceName, features, VAEModel
 from negate.train import TrainResult, grade, generate_datestamp_path, datestamped_folder, get_time, model_path
 from negate.track import in_console, on_graph
-from negate.save import save_model, save_to_onnx
+from negate.save import save_models, save_to_onnx
 from negate.residuals import Residual
diff --git a/negate/__main__.py b/negate/__main__.py
index feaf455..530df82 100644
--- a/negate/__main__.py
+++ b/negate/__main__.py
@@ -1,81 +1,115 @@
 # SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
 # <!-- // /*  d a r k s h a p e s */ -->
 
+"""Negate CLI entry point for training and inference.\n
+:returns: None."""
+
 from pathlib import Path
 from typing import Any
 
 import numpy as np
 
-from negate import TrainResult, VAEModel, build_datasets, datestamped_folder, features, generate_dataset, grade, in_console, model_path, on_graph, save_to_onnx
+from negate import (
+    TrainResult,
+    VAEModel,
+    build_datasets,
+    negate_opt,
+    datestamped_folder,
+    features,
+    generate_dataset,
+    generate_datestamp_path,
+    grade,
+    in_console,
+    model_path,
+    on_graph,
+    save_models,
+    save_to_onnx,
+)
+
+
+def run_native(features_array) -> np.ndarray:
+    """Run inference using XGBoost with PCA pre-processing.\n
+    :param features_array: Feature array.\n
+    :param scale_pos_weight: Weight for positive class.\n
+    :return: Prediction array."""
+    import pickle
 
+    import xgboost as xgb
 
-def evaluate(prediction: np.ndarray, ground_truth: np.ndarray) -> None:
-    """Print accuracy and class distribution.\n
-    :param prediction: Model outputs (0 = genuine, 1 = synthetic).\n
-    :param ground_truth: Ground-truth labels.\n
-    :return: None."""
+    model_file_path_named = model_path / "negate.ubj"
 
-    prediction = prediction.astype(int)
-    ground_truth = ground_truth.astype(int)
+    if not model_file_path_named.exists():
+        raise FileNotFoundError(f"Model file not found: {str(model_file_path_named)}. Please run 'train' first to create the model.")
+    else:
+        model_file_path_named = str(model_file_path_named)
 
-    acc = float(np.mean(prediction == ground_truth))
+    pca_file_path_named = model_path / "negate_pca.pkl"
+    with open(pca_file_path_named, "rb") as pca_file:
+        pca = pickle.load(pca_file)
 
-    print(f"Accuracy: {acc:.2%}")
+    features_pca = pca.transform(features_array)
 
+    model = xgb.Booster()
+    model.load_model(model_file_path_named)
+
+    result = model.predict(xgb.DMatrix(features_pca))
+
+    return result
 
-def predict(image_path: Path, vae_type: VAEModel, metadata: dict[str, Any], true_label: int | None = None) -> np.ndarray:
-    """Predict synthetic or original for given image. (0 = genuine, 1 = synthetic)\n
-    :param image_path: Path to image file or folder.
-    :param vae_type: VAE model to use for feature extraction.
-    :return: Prediction array."""
 
+def run_onnx(features_array) -> Any:
+    """Run inference using ONNX Runtime with PCA pre-processing.\n
+    :param features_array: Feature array.\n
+    :return: Prediction array."""
     import onnxruntime as ort
-    from datasets import Dataset
-    from onnxruntime import SparseTensor
     from onnxruntime.capi.onnxruntime_pybind11_state import Fail as ONNXRuntimeError
     from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument
 
-    pca_file_path_named = model_path / "negate_pca.onnx"
     model_file_path_named = model_path / "negate.onnx"
     if not model_file_path_named.exists():
         raise FileNotFoundError(f"Model file not found: {str(model_file_path_named)}. Please run 'train' first to create the model.")
     else:
         model_file_path_named = str(model_file_path_named)
-    print(f"""{"Evaluation" if true_label is not None else "Detection"} selected.
-Checking path '{image_path}' with {vae_type.value}""")
-
-    dataset: Dataset = generate_dataset(image_path)
-    features_dataset: Dataset = features(dataset, vae_type)
-    features_array = np.array(features_dataset["features"]).astype(np.float32)  # type: ignore[arg-type]
 
-    session_pca = ort.InferenceSession(pca_file_path_named)
-    input_name_pca = session_pca.get_inputs()[0].name
-    features_pca = session_pca.run(None, {input_name_pca: features_array})[0]
+    # pca_file_path_named = model_path / "negate_pca.onnx"
+    # session_pca = ort.InferenceSession(pca_file_path_named)
+    # input_name_pca = session_pca.get_inputs()[0].name
+    # features_pca = session_pca.run(None, {input_name_pca: features_array})[0]
 
-    scale_pos_weight = metadata["scale_pos_weight"]
-    print(metadata["scale_pos_weight"])
-    scale_pos_weight = float(scale_pos_weight)
-    input_name = ort.get_available_providers()[0]
-    features_model = features_pca.astype(np.float32)
+    # input_name = ort.get_available_providers()[0]
+    features_model = features_array.astype(np.float32)  # type: ignore
 
     session = ort.InferenceSession(model_file_path_named)
     print(f"Model '{model_file_path_named}' loaded.")
     input_name = session.get_inputs()[0].name
     try:
-        result: SparseTensor = session.run(None, {input_name: features_model})[0]  # type: ignore
-        print(result)
+        result = session.run(None, {input_name: features_model})[0]  # type: ignore
+        return result
     except (InvalidArgument, ONNXRuntimeError) as error_log:
-        match error_log:
-            case _ if "Expected: 16384" in str(error_log):
-                print(f"Training feature extractor does not match current feature extractor{vae_type.value} : {error_log}")
-                predict(image_path, VAEModel.MITSUA_FP16, true_label)
-            case _ if "Expected: 65536" in str(error_log):
-                print(f"Training feature extractor does not match current feature extractor {vae_type.value} : {error_log}")
-                predict(image_path, VAEModel.FLUX2_FP32, true_label)
-            case _ if "Expected: 131072" in str(error_log):
-                print(f"Training feature extractor does not match current feature extractor {vae_type.value} : {error_log}")
-                predict(image_path, VAEModel.GLM_BF16, true_label)
+        import sys
+
+        print(error_log)
+        sys.exit()
+
+
+def predict(image_path: Path, vae_type: VAEModel, true_label: int | None = None) -> np.ndarray:
+    """Predict synthetic or original for given image. (0 = genuine, 1 = synthetic)\n
+    :param image_path: Path to image file or folder.
+    :param vae_type: VAE model to use for feature extraction.
+    :return: Prediction array."""
+    from datasets import Dataset
 
+    print(f"""{"Evaluation" if true_label is not None else "Detection"} selected.
+Checking path '{image_path}' with {vae_type.value}""")
+
+    dataset: Dataset = generate_dataset(image_path)
+    features_dataset: Dataset = features(dataset, vae_type)
+    features_array = np.array(features_dataset["features"], dtype=np.float32)  # type: ignore[arg-type]
+
+    result = run_onnx(features_array) if negate_opt.use_onnx else run_native(features_array)
+
+    thresh = 0.5
+    predictions = (result > thresh).astype(int)
     match true_label:
         case None:
             for prediction in result:  # type: ignore
@@ -84,14 +118,16 @@ def predict(image_path: Path, vae_type: VAEModel, metadata: dict[str, Any], true
                 else:
                     print("image is SYNTHETIC")
         case _:
-            evaluate(result, np.array([true_label]))  # type: ignore
+            ground_truth = np.full(predictions.shape, true_label, dtype=int)
+            acc = float(np.mean(predictions == ground_truth))
+            print(f"Accuracy: {acc:.2%}")
 
-    return result  # type: ignore[return-value]
+    return result, predictions  # type: ignore[return-value]
 
 
-def training_run(vae_type: VAEModel, file_or_folder_path: Path | None = None) -> None:
+def training_run(vae_type: VAEModel, file_or_folder_path: Path | None = None, compare: bool = False) -> None:
     """Train \n
-    # XGB00st\n
+    # xgb00OOst\n
     model using dataset at path.\n
     :param path: Dataset root folder."""
     from datasets import Dataset
@@ -101,6 +137,7 @@ def training_run(vae_type: VAEModel, file_or_folder_path: Path | None = None) ->
     features_dataset: Dataset = features(dataset, vae_type)
     train_result: TrainResult = grade(features_dataset)
     save_to_onnx(train_result)
+    save_models(train_result, compare)
     in_console(train_result, vae_type)
     on_graph(train_result)
 
@@ -121,9 +158,10 @@ def main() -> None:
         "-m",
         "--model",
         choices=[m.value for m in VAEModel],
-        default=VAEModel.GLM_BF16.value,
-        help="Change the VAE model to use for training to a supported HuggingFace repo. Accuracy and memory use decrease from left to right",
+        default=negate_opt.default_vae,
+        help=f"Change the VAE model to use for training to a supported HuggingFace repo (default {negate_opt.default_vae}). Accuracy and memory use decrease from left to right",
     )
+
     check_parser = subparsers.add_parser(
         "check",
         help="Check whether an image at the provided path is synthetic or original.",
@@ -132,7 +170,7 @@ def main() -> None:
     label_grp = check_parser.add_mutually_exclusive_group()
     label_grp.add_argument("-s", "--synthetic", action="store_const", const=1, dest="label", help="Mark image as synthetic (label = 1) for evaluation.")
     label_grp.add_argument("-g", "--genuine", action="store_const", const=0, dest="label", help="Mark image as genuine (label = 0) for evaluation.")
-
+    subparsers.add_parser("compare", help="Run extraction and training using all possible VAE.")
     args = parser.parse_args(argv[1:])
 
     match args.cmd:
@@ -157,7 +195,11 @@ def main() -> None:
             with open(results_file_path) as result_metadata:
                 train_metadata = json.load(result_metadata)
             vae_type = VAEModel(train_metadata["vae_type"])
-            predict(Path(args.path), vae_type=vae_type, metadata=train_metadata, true_label=args.label)
+            predict(Path(args.path), vae_type=vae_type, true_label=args.label)
+        case "compare":
+            for model in VAEModel:
+                _regenerate = generate_datestamp_path("test")
+                training_run(vae_type=VAEModel(model.value), compare=True)
         case _:
             raise NotImplementedError
 
diff --git a/negate/ae.py b/negate/ae.py
deleted file mode 100644
index a05cc43..0000000
--- a/negate/ae.py
+++ /dev/null
@@ -1,285 +0,0 @@
-# Take from FLUX
-
-# changes
-# + head dim, num_heads
-# ~ 64 ch
-# ~ forward std dev fixed 0.1
-# ~ b (h d) x y -> b h (x y) d
-# - middle block processing hs and append
-# - upscale
-# - scale.shift
-
-import math
-
-import torch
-import torch.nn.functional as F
-
-from einops import rearrange
-from torch import Tensor, nn
-from safetensors.torch import load_file
-
-
-def swish(x: Tensor) -> Tensor:
-    return x * torch.sigmoid(x)
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels: int):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.head_dim = 64
-        self.num_heads = in_channels // self.head_dim
-        self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-        self.qkv = nn.Conv2d(in_channels, in_channels * 3, kernel_size=1, bias=False)
-        self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1, bias=False)
-        nn.init.normal_(self.proj_out.weight, std=0.2 / math.sqrt(in_channels))
-
-    def attention(self, h_: Tensor) -> Tensor:
-        h_ = self.norm(h_)
-        qkv = self.qkv(h_)
-        q, k, v = qkv.chunk(3, dim=1)
-        b, c, h, w = q.shape
-        q = rearrange(q, "b (h d) x y -> b h (x y) d", h=self.num_heads, d=self.head_dim)
-        k = rearrange(k, "b (h d) x y -> b h (x y) d", h=self.num_heads, d=self.head_dim)
-        v = rearrange(v, "b (h d) x y -> b h (x y) d", h=self.num_heads, d=self.head_dim)
-        h_ = F.scaled_dot_product_attention(q, k, v)
-        h_ = rearrange(h_, "b h (x y) d -> b (h d) x y", x=h, y=w)
-        return h_
-
-    def forward(self, x: Tensor) -> Tensor:
-        return x + self.proj_out(self.attention(x))
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, in_channels: int, out_channels: int):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
-        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        if self.in_channels != self.out_channels:
-            self.nin_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
-    def forward(self, x):
-        h = x
-        h = self.norm1(h)
-        h = swish(h)
-        h = self.conv1(h)
-        h = self.norm2(h)
-        h = swish(h)
-        h = self.conv2(h)
-        if self.in_channels != self.out_channels:
-            x = self.nin_shortcut(x)
-        return x + h
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels: int):
-        super().__init__()
-        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
-
-    def forward(self, x: Tensor):
-        pad = (0, 1, 0, 1)
-        x = nn.functional.pad(x, pad, mode="constant", value=0)
-        x = self.conv(x)
-        return x
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels: int):
-        super().__init__()
-        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
-
-    def forward(self, x: Tensor):
-        x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        x = self.conv(x)
-        return x
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        resolution: int,
-        in_channels: int,
-        ch: int,
-        ch_mult: list[int],
-        num_res_blocks: int,
-        z_channels: int,
-    ):
-        super().__init__()
-        self.ch = ch
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.conv_in = nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1)
-        curr_res = resolution
-        in_ch_mult = (1,) + tuple(ch_mult)
-        self.in_ch_mult = in_ch_mult
-        self.down = nn.ModuleList()
-        block_in = self.ch
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch * in_ch_mult[i_level]
-            block_out = ch * ch_mult[i_level]
-            for _ in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
-                block_in = block_out
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions - 1:
-                down.downsample = Downsample(block_in)
-                curr_res = curr_res // 2
-            self.down.append(down)
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
-        self.mid.attn_1 = AttnBlock(block_in)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
-        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
-        self.conv_out = nn.Conv2d(block_in, z_channels, kernel_size=3, stride=1, padding=1)
-
-    def forward(self, x: Tensor) -> Tensor:
-        h = self.conv_in(x)
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](h)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-            if i_level != self.num_resolutions - 1:
-                h = self.down[i_level].downsample(h)
-        h = self.mid.block_1(h)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h)
-        h = self.norm_out(h)
-        h = swish(h)
-        h = self.conv_out(h)
-        return h
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        ch: int,
-        out_ch: int,
-        ch_mult: list[int],
-        num_res_blocks: int,
-        in_channels: int,
-        resolution: int,
-        z_channels: int,
-    ):
-        super().__init__()
-        self.ch = ch
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.ffactor = 2 ** (self.num_resolutions - 1)
-        block_in = ch * ch_mult[self.num_resolutions - 1]
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        self.z_shape = (1, z_channels, curr_res, curr_res)
-        self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1)
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
-        self.mid.attn_1 = AttnBlock(block_in)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch * ch_mult[i_level]
-            for _ in range(self.num_res_blocks + 1):
-                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
-                block_in = block_out
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in)
-                curr_res = curr_res * 2
-            self.up.insert(0, up)
-        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
-        self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
-
-    def forward(self, z: Tensor) -> Tensor:
-        h = self.conv_in(z)
-        h = self.mid.block_1(h)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h)
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.up[i_level].block[i_block](h)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-        h = self.norm_out(h)
-        h = swish(h)
-        h = self.conv_out(h)
-        return h
-
-
-class DiagonalGaussian(nn.Module):
-    def __init__(self, sample: bool = True, chunk_dim: int = 1):
-        super().__init__()
-        self.sample = sample
-        self.chunk_dim = chunk_dim
-
-    def forward(self, z: Tensor) -> Tensor:
-        mean = z
-        if self.sample:
-            std = 0.1
-            return mean * (1 + std * torch.randn_like(mean))
-        else:
-            return mean
-
-
-class VAE(nn.Module):
-    def __init__(self, resolution, in_channels, ch, out_ch, ch_mult, num_res_blocks, z_channels):
-        super().__init__()
-        self.encoder = Encoder(
-            resolution=resolution,
-            in_channels=in_channels,
-            ch=ch,
-            ch_mult=ch_mult,
-            num_res_blocks=num_res_blocks,
-            z_channels=z_channels,
-        )
-        self.decoder = Decoder(
-            resolution=resolution,
-            in_channels=in_channels,
-            ch=ch,
-            out_ch=out_ch,
-            ch_mult=ch_mult,
-            num_res_blocks=num_res_blocks,
-            z_channels=z_channels,
-        )
-        self.reg = DiagonalGaussian()
-
-    def forward(self, x: Tensor) -> Tensor:
-        z = self.encoder(x)
-        z_s = self.reg(z)
-        decz = self.decoder(z_s)
-        return decz, z
-
-
-if __name__ == "__main__":
-    vae = VAE(
-        resolution=256,
-        in_channels=3,
-        ch=64,
-        out_ch=3,
-        ch_mult=[1, 2, 4, 4],
-        num_res_blocks=2,
-        z_channels=16,
-    )
-    device = "cuda" if torch.cuda.is_available() else "mps" if torch.mps.is_available() else "cpu"
-    vae.eval().to(device)
-    x = torch.randn(1, 3, 256, 256).to(device)
-    decz, z = vae(x)
-    print(decz.shape, z.shape)
diff --git a/negate/config.py b/negate/config.py
new file mode 100644
index 0000000..7d9c4f1
--- /dev/null
+++ b/negate/config.py
@@ -0,0 +1,52 @@
+from typing import NamedTuple
+
+
+class NegateConfig(NamedTuple):
+    """YAML config values.\n
+    :param patch_size: Patch width for residuals.\n
+    :param top_k: Number of patches.\n
+    :param vae_tiling: Enable tiling.\n
+    :param vae_slicing: Enable slicing.\n
+    :param use_onnx: Use ONNX for inference.\n
+    :param use_gpu: Use GPU if available.\n
+    :return: Config instance."""  # noqa: D401
+
+    batch_size: int
+    cache_features: bool
+    default_vae: str
+    dtype: str
+    n_components: float
+    num_boost_round: int
+    patch_size: int
+    top_k: int
+    use_onnx: bool
+    vae_slicing: bool
+    vae_tiling: bool
+    early_stopping_rounds: int
+    colsample_bytree: float
+    eval_metric: list
+    learning_rate: float
+    max_depth: int
+    objective: str
+    subsample: float
+    scale_pos_weight: float | None
+    seed: int
+
+
+def load_config_options() -> NegateConfig:
+    """Load YAML configuration options.\n
+    :return: Config dict."""
+
+    from pathlib import Path
+
+    import yaml
+
+    config_path = Path(__file__).parent.parent / "config" / "config.yaml"
+    with open(config_path, "r") as config_file:
+        data = yaml.safe_load(config_file)
+    train_cfg = data.pop("train", {})
+    data.update(train_cfg)
+    return NegateConfig(**data)
+
+
+negate_options = load_config_options()
diff --git a/negate/datasets.py b/negate/datasets.py
index 86a3be8..ca9f0ab 100644
--- a/negate/datasets.py
+++ b/negate/datasets.py
@@ -3,7 +3,6 @@
 
 from pathlib import Path
 
-import numpy as np
 from datasets import Dataset, Image, concatenate_datasets, load_dataset
 
 
@@ -91,19 +90,3 @@ def build_datasets(input_folder: Path | None = None) -> Dataset:
 
     dataset = concatenate_datasets([slice_dataset, rnd_synthetic_dataset, original_dataset])
     return dataset
-
-
-def dataset_to_nparray(dataset: Dataset, column_names: list[str] | None = None) -> np.ndarray:
-    """Convert Dataset to ndarray.\n
-    :param dataset: HuggingFace Dataset of images.
-    :param columns: Columns to keep. If None all columns are used.
-    :return: Array of shape (n_samples, n_features) or (n_samples,) if a single column."""
-
-    if column_names is None:
-        column_names = dataset.column_names
-
-    data = {name: dataset[name] for name in column_names}
-
-    if len(column_names) == 1:
-        return np.array(data[column_names[0]])
-    return np.vstack([np.array(data[name]) for name in column_names]).T
diff --git a/negate/extract.py b/negate/extract.py
index 7b5c72f..49622b3 100644
--- a/negate/extract.py
+++ b/negate/extract.py
@@ -1,24 +1,26 @@
 # SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
 # <!-- // /*  d a r k s h a p e s */ -->
 
-from enum import Enum
 from dataclasses import dataclass
+from enum import Enum
+from pathlib import Path
+
 from datasets import Dataset
 from PIL.Image import Image
 
+from negate.config import negate_options as negate_opt
+
 
 class VAEModel(str, Enum):
     """Choose the name and size of the VAE model to use for extraction."""
 
     SANA_FP32 = "exdysa/dc-ae-f32c32-sana-1.1-diffusers"
-    SANA_FP16 = "exdysa/dc-ae-f32c32-sana-1.1-diffusers"
-    AURAEQUI_BF16 = "exdysa/AuraEquiVAE-SAFETENSORS"
-    GLM_BF16 = "zai-org/GLM-Image"
-    FLUX2_FP32 = "black-forest-labs/FLUX.2-dev"
-    FLUX2_FP16 = "black-forest-labs/FLUX.2-klein-4B"
-    FLUX1_FP32 = "Tongyi-MAI/Z-Image"
-    FLUX1_FP16 = "Freepik/F-Lite-Texture"
-    MITSUA_FP16 = "exdysa/mitsua-vae-SAFETENSORS"
+    SANA_FP16 = "exdysa/dc-ae-f32c32-sana-1.1-diffusers"  # dc_ae 'accuracy': 0.8235294117647058,
+    FLUX2_FP32 = "black-forest-labs/FLUX.2-dev"  # f2 dev 'accuracy': 0.9313725490196079,
+    FLUX2_FP16 = "black-forest-labs/FLUX.2-klein-4B"  # f2 klein 'accuracy': 0.9215686274509803,
+    FLUX1_FP32 = "Tongyi-MAI/Z-Image"  # zimage 'accuracy': 0.9411764705882353,
+    FLUX1_FP16 = "Freepik/F-Lite-Texture"  # flite 'accuracy': 0.9509803921568627,
+    MITSUA_FP16 = "exdysa/mitsua-vae-SAFETENSORS"  # mitsua 'accuracy': 0.9509803921568627,
 
 
 @dataclass
@@ -29,8 +31,6 @@ class VAEInfo:
 
 MODEL_MAP = {
     VAEModel.MITSUA_FP16: VAEInfo(VAEModel.MITSUA_FP16, "autoencoders.autoencoder_kl.AutoencoderKL"),
-    VAEModel.AURAEQUI_BF16: VAEInfo(VAEModel.AURAEQUI_BF16, "ae.VAE"),
-    VAEModel.GLM_BF16: VAEInfo(VAEModel.GLM_BF16, "autoencoders.autoencoder_kl.AutoencoderKL"),
     VAEModel.FLUX1_FP32: VAEInfo(VAEModel.FLUX1_FP32, "autoencoders.autoencoder_kl.AutoencoderKL"),
     VAEModel.FLUX1_FP16: VAEInfo(VAEModel.FLUX1_FP16, "autoencoders.autoencoder_kl.AutoencoderKL"),
     VAEModel.FLUX2_FP32: VAEInfo(VAEModel.FLUX2_FP32, "autoencoders.autoencoder_kl_flux2.AutoencoderKLFlux2"),
@@ -66,50 +66,37 @@ def __init__(self, vae_type: VAEModel, device: DeviceName, dtype: torch.dtype) -
         :param device: Target device.
         :param dtype: Data type for tensors."""
 
-        from diffusers.models.autoencoders.vae import AutoencoderMixin
-        from negate import Residual, ae  # `B̴̨̒e̷w̷͇̃ȁ̵͈r̸͔͛ę̵͂ ̷̫̚t̵̻̐h̶̜͒ȩ̸̋ ̵̪̄ő̷̦ù̵̥r̷͇̂o̷̫͑b̷̲͒ò̷̫r̴̢͒ô̵͍s̵̩̈́` #type: ignore
+        from negate import Residual  # `B̴̨̒e̷w̷͇̃ȁ̵͈r̸͔͛ę̵͂ ̷̫̚t̵̻̐h̶̜͒ȩ̸̋ ̵̪̄ő̷̦ù̵̥r̷͇̂o̷̫͑b̷̲͒ò̷̫r̴̢͒ô̵͍s̵̩̈́` #type: ignore
 
+        self.residual_transform = Residual(patch_size=negate_opt.patch_size, top_k=negate_opt.top_k)
         self.device = device.value
         self.dtype = dtype
         self.model: VAEInfo = MODEL_MAP[vae_type]
-        self.vae: AutoencoderMixin | ae.VAE | None = None
-        self.residual_transform = Residual()
-        if self.vae is None:
+        if not hasattr(self, "vae"):
             self.create_vae()
 
-    def aura_equi_vae(self, vae_path: str):
-        """Processing specifically for AuraEquiVAE
-        :param vae_path: The path to the VAE model directory"""
-        import os
-        from negate.ae import VAE
-        from safetensors.torch import load_file
-
-        vae = VAE(resolution=256, in_channels=3, ch=256, out_ch=3, ch_mult=[1, 2, 4, 4], num_res_blocks=2, z_channels=16).to(self.device).bfloat16()
-        vae_file = os.path.join(vae_path, "vae_epoch_3_step_49501_bf16.safetensors")
-        state_dict = load_file(vae_file)
-        vae.load_state_dict(state_dict)
-        return vae
-
     def create_vae(self):
         """Download and load the VAE from the model repo."""
 
         import os
 
         from diffusers.models import autoencoders
-        from huggingface_hub.errors import LocalEntryNotFoundError
         from huggingface_hub import snapshot_download
+        from huggingface_hub.errors import LocalEntryNotFoundError
+
+        if negate_opt.vae_tiling:
+            self.vae.enable_tiling()
+        if negate_opt.vae_slicing:
+            self.vae.enable_slicing()
 
         autoencoder_cls = getattr(autoencoders, self.model.module.split(".")[-1], None)
         try:
-            vae_model = autoencoder_cls.from_pretrained(self.model.enum.value, torch_dtype=self.dtype, local_files_only=True).to(self.device)
+            vae_model = autoencoder_cls.from_pretrained(self.model.enum.value, torch_dtype=self.dtype, local_files_only=True).to(self.device)  # type: ignore
         except (LocalEntryNotFoundError, OSError, AttributeError):
             print("Downloading model...")
         vae_path: str = snapshot_download(self.model.enum.value, allow_patterns=["vae/*"])  # type: ignore
         vae_path = os.path.join(vae_path, "vae")
-        if self.model.enum == VAEModel.AURAEQUI_BF16:
-            vae_model = self.aura_equi_vae(vae_path)
-        else:
-            vae_model = autoencoder_cls.from_pretrained(vae_path, torch_dtype=self.dtype, local_files_only=True).to(self.device)
+        vae_model = autoencoder_cls.from_pretrained(vae_path, torch_dtype=self.dtype, local_files_only=True).to(self.device)  # type: ignore
 
         vae_model.eval()
         self.vae = vae_model
@@ -128,17 +115,12 @@ def _extract_special(self, batch: "torch.Tensor", image: Image):
         :param img: Original PIL image.
         :return: NumPy mean latent."""
 
-        from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
         import torch
+        from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
         from torch import Tensor
 
-        if self.model.enum == VAEModel.AURAEQUI_BF16:
-            latent: Tensor = self.vae.encoder(batch)
-            latent = latent.clamp(-8.0, 8.0)
-            mean = torch.mean(latent, dim=0).cpu().float()
-        else:
-            latent: Tensor = self.vae.encode(batch)  # type: ignore
-            mean = torch.mean(latent.latent, dim=0).cpu().float()  # type: ignore
+        latent: Tensor = self.vae.encode(batch)  # type: ignore
+        mean = torch.mean(latent.latent, dim=0).cpu().float()  # type: ignore
 
         logvar = torch.zeros_like(mean).cpu().float()
         params = torch.cat([mean, logvar], dim=1)
@@ -160,13 +142,13 @@ def batch_extract(self, dataset: Dataset):
         for image in dataset["image"]:
             rgb = image.convert("RGB")
             col = self.transform(rgb)
-            for patches in self.residual_transform.crop_select(image, size=768, top_k=1):
+            for patches in self.residual_transform.crop_select(image):
                 patch_stack.append(self.transform(patches.convert("RGB")))
 
             batch = torch.stack([col, *patch_stack]).to(self.device, self.dtype)
             with torch.no_grad():
                 match self.model.enum:
-                    case VAEModel.SANA_FP32 | VAEModel.SANA_FP16 | VAEModel.AURAEQUI_BF16:
+                    case VAEModel.SANA_FP32 | VAEModel.SANA_FP16:
                         mean_latent = self._extract_special(batch, image)
                     case _:
                         mean_latent = self._extract_generic(batch)
@@ -198,6 +180,30 @@ def __exit__(self, exc_type, exc, tb) -> None:
             self.cleanup()
 
 
+def feature_cache(dataset: Dataset, vae_type: VAEModel) -> Path:
+    """Generate cache filename based on dataset fingerprint and VAE model.\n
+    Dataset fingerprint automatically changes when data changes.
+
+    :param dataset: The incoming dataset to be processed.
+    :param vae_type: The VAE model typeselectedfor feature extraction.
+    :returns: Location to cache results of feature extraction"""
+
+    import hashlib
+
+    cache_dir = Path(".cache/features")  # <chud> was here
+    cache_dir.mkdir(parents=True, exist_ok=True)
+
+    dataset_hash = dataset._fingerprint if hasattr(dataset, "_fingerprint") else hashlib.md5(str(dataset).encode()).hexdigest()[:8]
+    vae_name = vae_type.value.split("/")[-1].replace("-", "_")
+    cache_file = cache_dir / f"features_{vae_name}_{dataset_hash}.arrow"
+
+    if cache_file.exists():
+        print(f"Using cached features from {cache_file}")
+    else:
+        print(f"Extracting features (will cache to {cache_file})")
+    return cache_file
+
+
 def features(dataset: Dataset, vae_type: VAEModel) -> Dataset:
     """Generate a feature dataset from images.\n
     :param dataset: Dataset containing images.
@@ -207,17 +213,23 @@ def features(dataset: Dataset, vae_type: VAEModel) -> Dataset:
     import torch
 
     device = DeviceName.CUDA if torch.cuda.is_available() else DeviceName.MPS if torch.mps.is_available() else DeviceName.CPU
-    dtype = torch.bfloat16
 
-    # <chud> was here
+    dtype = getattr(torch, negate_opt.dtype)
+    kwargs = {}
+    match negate_opt:
+        case opt if opt.batch_size > 0:
+            kwargs.setdefault("batch_size", opt.batch_size)
+        case opt if opt.cache_features:
+            cache_file_name = str(feature_cache(dataset, vae_type))
+            kwargs.setdefault("cache_file_name", cache_file_name)
 
     with FeatureExtractor(vae_type, device, dtype) as extractor:
         features_dataset = dataset.map(
             extractor.batch_extract,
-            batched=True,
-            batch_size=4,
+            batched=negate_opt.batch_size > 0,
             remove_columns=["image"],
             desc="Extracting features...",
+            **kwargs,
         )
     features_dataset.set_format(type="numpy", columns=["features", "label"])
 
diff --git a/negate/residuals.py b/negate/residuals.py
index 726005d..55878f0 100644
--- a/negate/residuals.py
+++ b/negate/residuals.py
@@ -9,12 +9,14 @@
 
 
 class Residual:
-    def __init__(self, dtype: np.typing.DTypeLike = np.float32):
+    def __init__(self, top_k: int, patch_size: int = 512, dtype: np.typing.DTypeLike = np.float32) -> None:
         """Initialize residual class for residual image processing.\n
         :param dtype: dtype for internal numpy conversion.
         return: None."""
 
         self.dtype = dtype
+        self.top_k = top_k
+        self.patch_size = patch_size
 
     def __call__(self, image: Image) -> Image:
         """Create a 3-channel residual from a grayscale image.\n
@@ -128,7 +130,7 @@ def image_from_fourier(self, masked_spectrum: NDArray, fourier_shift: NDArray, m
 
         return fromarray(reconstructed_uint8, mode="L").convert("RGB")
 
-    def mask_patches(self, numeric_image: NDArray, size: int):
+    def mask_patches(self, numeric_image: NDArray):
         """Crop patches and compute freq divergence.\n
         :return: List of (divergence, patch Image).
         :param numeric_image: Image converted into an array.
@@ -137,17 +139,17 @@ def mask_patches(self, numeric_image: NDArray, size: int):
         from PIL.Image import fromarray
 
         metrics: list[tuple[float, Image]] = []
-
+        patch_size = self.patch_size
         h, w = numeric_image.shape
-        nx = (w + size - 1) // size
-        ny = (h + size - 1) // size
+        nx = (w + patch_size - 1) // patch_size
+        ny = (h + patch_size - 1) // patch_size
         for iy in range(ny):
             for ix in range(nx):
-                x0 = ix * size
-                y0 = iy * size
-                patch_arr = numeric_image[y0 : y0 + size, x0 : x0 + size]
-                if patch_arr.shape != (size, size):
-                    pad = np.zeros((size, size), dtype=self.dtype)
+                x0 = ix * patch_size
+                y0 = iy * patch_size
+                patch_arr = numeric_image[y0 : y0 + patch_size, x0 : x0 + patch_size]
+                if patch_arr.shape != (patch_size, patch_size):
+                    pad = np.zeros((patch_size, patch_size), dtype=self.dtype)
                     pad[: patch_arr.shape[0], : patch_arr.shape[1]] = patch_arr
                     patch_arr = pad
 
@@ -163,12 +165,7 @@ def mask_patches(self, numeric_image: NDArray, size: int):
                 metrics.append((div, patch_img))
         return metrics
 
-    def crop_select(
-        self,
-        image: Image,
-        size: int,
-        top_k: int = 5,
-    ) -> list[Image]:
+    def crop_select(self, image: Image) -> list[Image]:
         """Crop image into patches, compute freq-divergence, return most extreme patches.\n
         :param image: PIL image to process.
         :param size: Patch dimension.
@@ -179,11 +176,12 @@ def crop_select(
         gray = image.convert("L")
         numeric_image = np.array(gray, dtype=self.dtype)
 
-        metrics: list[tuple[float, Image]] = self.mask_patches(numeric_image, size=size)
+        metrics: list[tuple[float, Image]] = self.mask_patches(numeric_image)
 
         metrics.sort(key=lambda x: x[0], reverse=True)
 
         chosen: list[Image] = []
+        top_k = self.top_k
         chosen.extend([p for _, p in metrics[:top_k]])  # high diverges
         chosen.extend([p for _, p in metrics[-top_k:]])  # low diverges
 
diff --git a/negate/save.py b/negate/save.py
index 2a2c54a..c31b93f 100644
--- a/negate/save.py
+++ b/negate/save.py
@@ -1,6 +1,9 @@
 # SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
 # <!-- // /*  d a r k s h a p e s */ -->
 
+import shutil
+from pathlib import Path
+
 from negate import TrainResult, generate_datestamp_path, model_path
 
 
@@ -20,27 +23,34 @@ def save_metadata(train_result: TrainResult, file_name: str = "negate") -> str:
     return metadata_file_name
 
 
-def save_model(train_result: TrainResult, file_name: str = "negate") -> None:
+def save_models(train_result: TrainResult, compare: bool, file_name: str = "negate") -> None:
     """Persist a trained model and its PCA transformer.\n
     :param train_result: Training output containing model, PCA and metadata.
     :param file_name: Base name for the files written to the *models* folder.
     :return: None"""
+
     import pickle
 
     from sklearn.decomposition import PCA
     from xgboost import Booster
 
+    datestamp_path = generate_datestamp_path(file_name)
+
     model: Booster = train_result.model
     pca: PCA = train_result.pca
-
-    pca_file_name = generate_datestamp_path(f"{file_name}_pca.pkl")
+    pca_file_name = datestamp_path + "_pca.pkl"
     with open(pca_file_name, "wb") as f:
         pickle.dump(pca, f)
 
-    negate_xgb_file_name = generate_datestamp_path(f"{file_name}.json")
+    negate_xgb_file_name = datestamp_path + ".ubj"
     model.save_model(negate_xgb_file_name)
 
     metadata_file_name = save_metadata(train_result)
+
+    if not compare:
+        for src in (negate_xgb_file_name, pca_file_name):
+            shutil.copy(src, model_path / Path(src).name)  # type: ignore no overloads
+
     print(f"Models saved to disk. {pca_file_name} {negate_xgb_file_name} {metadata_file_name}")
 
 
@@ -49,15 +59,14 @@ def save_to_onnx(train_result: TrainResult, file_name: str = "negate"):
     :param train_result: Training output containing the XGBoost model.
     :param file_name: Base name for the ONNX file."""
 
-    import shutil
-    from pathlib import Path
-
     import onnx
     from skl2onnx import convert_sklearn
     from skl2onnx.common.data_types import FloatTensorType
 
     from negate.to_onnx import DataType, IOShape, ModelInputFormat, ONNXConverter
 
+    datestamp_path = generate_datestamp_path(file_name)
+
     model = train_result.model
     num_features = train_result.feature_matrix.shape[1]
     pca = train_result.pca
@@ -68,14 +77,14 @@ def save_to_onnx(train_result: TrainResult, file_name: str = "negate"):
         name="input",
         format=ModelInputFormat.FORMAT_NONE,  # Used for TensorRT
     )
-    negate_onnx_file_name = generate_datestamp_path(f"{file_name}.onnx")
+    negate_onnx_file_name = datestamp_path + ".onnx"
     onnx_model = ONNXConverter.from_xgboost(model, inputs=[input_shape], opset=12)
     onnx.save(onnx_model, negate_onnx_file_name)
 
     initial_pca_types = [("input", FloatTensorType([None, num_features]))]
     negate_pca_onnx_raw = convert_sklearn(pca, initial_types=initial_pca_types)
     negate_pca_onnx = ONNXConverter.optim_onnx(negate_pca_onnx_raw)  # type: ignore[arg-type]
-    pca_file_name = generate_datestamp_path(f"{file_name}_pca.onnx")
+    pca_file_name = datestamp_path + "_pca.onnx"
     onnx.save(negate_pca_onnx, pca_file_name)
 
     metadata_file_name = save_metadata(train_result)
diff --git a/negate/track.py b/negate/track.py
index 0439a87..e550e7b 100644
--- a/negate/track.py
+++ b/negate/track.py
@@ -2,6 +2,7 @@
 # <!-- // /*  d a r k s h a p e s */ -->
 
 from negate import TrainResult, VAEModel, get_time, generate_datestamp_path, model_path
+import matplotlib.pyplot as plt
 
 
 def in_console(train_result: TrainResult, vae_type: VAEModel) -> None:
@@ -78,11 +79,10 @@ def in_console(train_result: TrainResult, vae_type: VAEModel) -> None:
 def on_graph(train_result: TrainResult) -> None:
     """Save and show PCA variance plots for a trained model.\n
     :param train_result: Result object from training."""
-
-    import matplotlib.pyplot as plt
     import numpy as np
     from numpy.typing import NDArray
     from sklearn.metrics import confusion_matrix
+    import seaborn as sns
 
     X_train: NDArray = train_result.X_train
     X_train_pca = train_result.X_train_pca
@@ -92,73 +92,69 @@ def on_graph(train_result: TrainResult) -> None:
     y_pred = (y_pred_proba > 0.5).astype(int)
 
     pca = train_result.pca
-    plt.figure(figsize=(10, 5))
-    plt.subplot(1, 2, 1)
-    plt.plot(np.cumsum(pca.explained_variance_ratio_), color="aqua")
-    plt.xlabel("Number of Components")
-    plt.ylabel("Cumulative Explained Variance")
-    plt.title("PCA Explained Variance")
-    plt.grid(True)
-
-    plt.subplot(1, 2, 2)
-    plt.bar(range(min(20, len(pca.explained_variance_ratio_))), pca.explained_variance_ratio_[:20], color="aqua")
-    plt.xlabel("Component")
-    plt.ylabel("Explained Variance Ratio")
-    plt.title("First 20 Components")
-    plt.tight_layout()
-    plt.savefig(generate_datestamp_path("score_explained_variance.png"))
-    plt.show()
-
-    cm = confusion_matrix(train_result.y_test, y_pred)
-    fig, ax = plt.subplots()
-    cax = ax.imshow(cm, interpolation="nearest", cmap="Reds")
 
-    ax.set_xticks(np.arange(cm.shape[1]))
-    ax.set_yticks(np.arange(cm.shape[0]))
-    ax.set_xticklabels(["Real", "Synthetic"])
-    ax.set_yticklabels(["Real", "Synthetic"])
-    plt.setp(ax.get_xticklabels(), rotation=45, ha="right")
+    # Create a single figure with 6 subplots (2 rows × 3 columns)
+    fig, axes = plt.subplots(2, 3, figsize=(18, 12))
+    ax_cum = axes[0, 0]
+    ax_bar = axes[0, 1]
+    ax_conf = axes[0, 2]
+    ax_orig = axes[1, 0]
+    ax_pca = axes[1, 1]
+    ax_heat = axes[1, 2]
+
+    # 1. Cumulative explained variance
+    ax_cum.plot(np.cumsum(pca.explained_variance_ratio_), color="aqua")
+    ax_cum.set_xlabel("Number of Components")
+    ax_cum.set_ylabel("Cumulative Explained Variance")
+    ax_cum.set_title("PCA Explained Variance")
+    ax_cum.grid(True)
+
+    # 2. First 20 components
+    ax_bar.bar(
+        range(min(20, len(pca.explained_variance_ratio_))),
+        pca.explained_variance_ratio_[:20],
+        color="aqua",
+    )
+    ax_bar.set_xlabel("Component")
+    ax_bar.set_ylabel("Explained Variance Ratio")
+    ax_bar.set_title("First 20 Components")
 
+    # 3. Confusion matrix
+    cm = confusion_matrix(train_result.y_test, y_pred)
+    cax = ax_conf.imshow(cm, interpolation="nearest", cmap="Reds")
+    ax_conf.set_xticks(np.arange(cm.shape[1]))
+    ax_conf.set_yticks(np.arange(cm.shape[0]))
+    ax_conf.set_xticklabels(["Real", "Synthetic"])
+    ax_conf.set_yticklabels(["Real", "Synthetic"])
+    plt.setp(ax_conf.get_xticklabels(), rotation=45, ha="right")
     for i in range(cm.shape[0]):
         for j in range(cm.shape[1]):
-            ax.text(j, i, cm[i, j], ha="center", va="center", color="black")
-
-    ax.set_xlabel("Predicted")
-    ax.set_ylabel("Actual")
-    ax.set_title("Confusion Matrix")
-    fig.colorbar(cax)
-    plt.savefig(generate_datestamp_path("score_confusion_matrix.png"))
-    plt.show()
-
-    plt.figure(figsize=(10, 5))
-    plt.subplot(1, 2, 1)
-    plt.scatter(X_train[:, 0], X_train[:, 1], c=y_plot, cmap="coolwarm", edgecolor="k")
-    plt.xlabel("Feature 1")
-    plt.ylabel("Feature 2")
-    plt.title("Original Data (First Two Features)")
-    plt.colorbar(label="Prediction")
-
-    plt.subplot(1, 2, 2)
-    plt.scatter(X_train_pca[:, 0], X_train_pca[:, 1], c=y_plot, cmap="coolwarm", edgecolor="k")
-    plt.xlabel("Principal Component 1")
-    plt.ylabel("Principal Component 2")
-    plt.title("PCA Transformed Data")
-    plt.colorbar(label="Prediction")
-    plt.tight_layout()
-    plt.savefig(generate_datestamp_path("pca_transform_map.png"))
-    plt.show()
-
-    import seaborn as sns
-
+            ax_conf.text(j, i, cm[i, j], ha="center", va="center", color="black")
+    ax_conf.set_xlabel("Predicted")
+    ax_conf.set_ylabel("Actual")
+    ax_conf.set_title("Confusion Matrix")
+    fig.colorbar(cax, ax=ax_conf)
+
+    # 4. Original data scatter
+    ax_orig.scatter(X_train[:, 0], X_train[:, 1], c=y_plot, cmap="coolwarm", edgecolor="k")
+    ax_orig.set_xlabel("Feature 1")
+    ax_orig.set_ylabel("Feature 2")
+    ax_orig.set_title("Original Data (First Two Features)")
+    # ax_orig.colorbar(label="Prediction")
+
+    # 5. PCA transformed scatter
+    ax_pca.scatter(X_train_pca[:, 0], X_train_pca[:, 1], c=y_plot, cmap="coolwarm", edgecolor="k")
+    ax_pca.set_xlabel("Principal Component 1")
+    ax_pca.set_ylabel("Principal Component 2")
+    ax_pca.set_title("PCA Transformed Data")
+    # ax_pca.colorbar(label="Prediction")
+
+    # 6. Correlation heatmap
     corr = np.corrcoef(X_train_pca, rowvar=False)
     upper_triangle_mask = np.triu(np.ones_like(corr, dtype=bool))
-
-    # Get actual min/max from the lower triangle (excluding diagonal)
     lower_triangle = corr[np.tril_indices_from(corr, k=-1)]
     vmin = lower_triangle.min()
     vmax = lower_triangle.max()
-
-    figure, ax = plt.subplots(figsize=(12, 10))
     cmap = sns.diverging_palette(20, 230, as_cmap=True)
     sns.heatmap(
         corr,
@@ -170,8 +166,9 @@ def on_graph(train_result: TrainResult) -> None:
         square=True,
         linewidths=0.5,
         cbar_kws={"shrink": 0.5},
+        ax=ax_heat,
     )
-    ax.set_title(f"Feature Correlation Heatmap (PCA Components)\nRange: [{vmin:.3e}, {vmax:.3e}]")
-    plt.tight_layout()
-    figure.savefig(generate_datestamp_path("correlation_heatmap.png"))
-    plt.show()
+    ax_heat.set_title(f"Feature Correlation Heatmap (PCA Components)\nRange: [{vmin:.3e}, {vmax:.3e}]")
+
+    plt.tight_layout(pad=0.5)
+    plt.savefig(generate_datestamp_path("combined_plots.png"))
diff --git a/negate/train.py b/negate/train.py
index 2c4fed1..30dac90 100644
--- a/negate/train.py
+++ b/negate/train.py
@@ -11,24 +11,31 @@
 from sklearn.decomposition import PCA
 from xgboost import Booster
 
+from negate.config import negate_options as negate_opt
+
 get_time = lambda: datetime.now().strftime("%Y%m%d_%H%M%S")
 datestamped_folder = Path("models", get_time())
-generate_datestamp_path = lambda file_name: str(datestamped_folder / file_name)
 model_path = Path(__file__).parent.parent / "models"
 
 
+def generate_datestamp_path(file_name) -> str:
+    datestamped_folder.mkdir(parents=True, exist_ok=True)
+    generated_path = str(datestamped_folder / file_name)
+    return generated_path
+
+
 @dataclass
 class TrainingParameters:
     """Container holding main model parameters"""
 
-    colsample_bytree: float = 0.8
-    eval_metric: list = field(default_factory=lambda: ["logloss", "aucpr"])
-    learning_rate: float = 0.1
-    max_depth: int = 4
-    objective: str = "binary:logistic"
-    scale_pos_weight: float | None = None
-    seed: int | None = None
-    subsample: float = 0.8
+    seed: int = negate_opt.seed
+    colsample_bytree: float = negate_opt.colsample_bytree
+    eval_metric: list = field(default_factory=lambda: negate_opt.eval_metric)
+    learning_rate: float = negate_opt.learning_rate
+    max_depth: int = negate_opt.max_depth
+    objective: str = negate_opt.objective
+    scale_pos_weight: float | None = negate_opt.scale_pos_weight
+    subsample: float = negate_opt.subsample
 
 
 @dataclass
@@ -41,7 +48,7 @@ class TrainResult:
     model: Booster
     num_features: int
     pca: PCA
-    scale_pos_weight: float
+    scale_pos_weight: float | None
     seed: int
     X_train_pca: NDArray
     X_train: NDArray
@@ -58,26 +65,25 @@ def grade(features_dataset: Dataset) -> TrainResult:
     from numpy.random import default_rng
     from sklearn.model_selection import train_test_split
 
-    feature_matrix = np.array([sample["features"] for sample in features_dataset]).astype(np.float32)  # type: ignore no overloads
+    feature_matrix = np.asarray([sample["features"] for sample in features_dataset], dtype=np.float32)  # type: ignore
     labels = np.array([sample["label"] for sample in features_dataset])  # type: ignore no overloads
 
     rng = default_rng(1)
     random_state = lambda: int(np.round(rng.random() * 0xFFFFFFFF))
     seed = random_state()
-    X_train, X_test, y_train, y_test = train_test_split(feature_matrix, labels, test_size=0.2, stratify=labels, random_state=seed)
+    params = TrainingParameters(
+        seed=seed,
+    )
+    X_train, X_test, y_train, y_test = train_test_split(feature_matrix, labels, test_size=0.2, stratify=labels, random_state=params.seed)
 
-    pca: PCA = PCA(n_components=0.95, random_state=seed)  # dimensionality .95
+    pca: PCA = PCA(n_components=0.95, random_state=params.seed)  # dimensionality .95
     X_train_pca = pca.fit_transform(X_train)
     X_test_pca = pca.transform(X_test)
 
-    scale_pos_weight = np.sum(y_train == 0) / np.sum(y_train == 1)
+    params.scale_pos_weight = np.sum(y_train == 0) / np.sum(y_train == 1)
     d_matrix_train = xgb.DMatrix(X_train_pca, label=y_train)
     d_matrix_test = xgb.DMatrix(X_test_pca, label=y_test)
 
-    params = TrainingParameters(
-        scale_pos_weight=scale_pos_weight,
-        seed=seed,
-    )
     training_parameters = asdict(params)
     evaluation_parameters = [(d_matrix_train, "train"), (d_matrix_test, "test")]
     evaluation_result = {}
@@ -91,11 +97,11 @@ def grade(features_dataset: Dataset) -> TrainResult:
         pca=pca,
         d_matrix_test=d_matrix_test,  # type: ignore
         model=model,
-        scale_pos_weight=scale_pos_weight,
+        scale_pos_weight=params.scale_pos_weight,
         X_train_pca=X_train_pca,
         y_test=y_test,  # type: ignore
         labels=labels,
         feature_matrix=feature_matrix,
-        seed=seed,
+        seed=params.seed,
         num_features=model.num_features(),
     )
diff --git a/results/combined_plots.png b/results/combined_plots.png
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diff --git a/results/results.json b/results/results.json
deleted file mode 100644
index e1d9202..0000000
--- a/results/results.json
+++ /dev/null
@@ -1,20 +0,0 @@
-{
-    "accuracy": "0.9690721649484536",
-    "best_iter": "53",
-    "best_score": "0.9933269449816234",
-    "cumulative:": "[0.07007149 0.12663467 0.15042071 0.17022693 0.18916745 0.20264165\n 0.21424904 0.22467917 0.23394077 0.24301024 0.25173762 0.2600328\n 0.26813537 0.27596053 0.28343022 0.29078788 0.2979155  0.30497494\n 0.3119168  0.31863883 0.3252243  0.33161107 0.33782476 0.34401444\n 0.34997436 0.35590667 0.36177313 0.3675905  0.37325367 0.3787788\n 0.384235   0.38964587 0.39498276 0.40025353 0.40543026 0.41055965\n 0.4156341  0.42061973 0.4255008  0.43036702 0.4351645  0.43992177\n 0.44464865 0.4493351  0.45390958 0.45841444 0.46289667 0.4673559\n 0.4717829  0.47617012 0.48050338 0.4848013  0.489041   0.49326256\n 0.4974451  0.5015809  0.50571084 0.5098176  0.5138784  0.5179276\n 0.52193904 0.525917   0.529865   0.53380066 0.5377231  0.54159486\n 0.5454342  0.54926467 0.5530502  0.556791   0.56052    0.5641965\n 0.5678675  0.5714872  0.57509106 0.57867914 0.5822311  0.5857644\n 0.58928233 0.592793   0.59627086 0.59971887 0.60316205 0.60658395\n 0.6099794  0.61334175 0.61667436 0.619985   0.6232716  0.6265461\n 0.62981224 0.6330606  0.63630044 0.63946843 0.64262515 0.6457716\n 0.6488872  0.65199125 0.65502757 0.6580535  0.6610565  0.6640251\n 0.6669438  0.6698483  0.6727014  0.67552817 0.6783299  0.6810903\n 0.6838292  0.6865456  0.6892405  0.6919044  0.6945346  0.69714165\n 0.69970524 0.70223397 0.70471185 0.7071842  0.7095903  0.7119819\n 0.71437263 0.71673536 0.71906656 0.72137076 0.72366214 0.7259256\n 0.72818136 0.73042464 0.7326233  0.73481643 0.7369742  0.7391216\n 0.74125946 0.7433683  0.74547356 0.7475682  0.74964374 0.75171167\n 0.75376904 0.7558107  0.757825   0.75982887 0.7618149  0.7637917\n 0.76576257 0.767709   0.7696543  0.7715925  0.7735119  0.77542216\n 0.77732575 0.77922314 0.7810983  0.7829713  0.78483254 0.7866745\n 0.7885077  0.7903256  0.7921303  0.7939335  0.7957258  0.7975064\n 0.79927003 0.80102766 0.8027792  0.80452484 0.8062575  0.8079841\n 0.8096921  0.81139535 0.813073   0.8147449  0.8164148  0.8180765\n 0.81972486 0.82136554 0.82299864 0.8246217  0.82623744 0.8278416\n 0.8294405  0.8310295  0.832608   0.83417296 0.8357336  0.83728594\n 0.8388256  0.8403523  0.841871   0.84337974 0.84488434 0.8463729\n 0.8478495  0.8493148  0.850769   0.8522208  0.85365826 0.8550832\n 0.8565004  0.85791075 0.85931104 0.8606994  0.86207896 0.86343545\n 0.86477923 0.86610985 0.86742765 0.86871994 0.8700045  0.8712725\n 0.87253964 0.87379074 0.87502587 0.8762541  0.8774646  0.87864786\n 0.8798055  0.88094205 0.882077   0.88319016 0.884288   0.88536936\n 0.8864456  0.8875099  0.88856953 0.8896209  0.890664   0.8917057\n 0.89274216 0.89376813 0.8947927  0.8958099  0.89682394 0.89783007\n 0.8988324  0.899825   0.9008152  0.90179825 0.90277    0.9037378\n 0.9046995  0.9056541  0.90660703 0.90755624 0.9084997  0.90944076\n 0.91037524 0.91130465 0.91222924 0.9131488  0.91406435 0.9149753\n 0.9158825  0.916784   0.9176836  0.9185801  0.9194655  0.9203469\n 0.9212241  0.92210066 0.92296964 0.9238352  0.92469907 0.92556065\n 0.9264135  0.92726207 0.9281086  0.9289509  0.9297887  0.93062186\n 0.93144906 0.93227077 0.93309164 0.93390757 0.93472147 0.93552625\n 0.93633    0.93713075 0.93792987 0.9387237  0.9395151  0.94030124\n 0.94108176 0.9418612  0.9426338  0.94340473 0.9441706  0.9449316\n 0.94568676 0.9464374  0.9471839  0.947925   0.9486617  0.9493933\n 0.9501213 ]",
-    "explained_var": "0.95012134",
-    "f1_macro": "0.9690194825934206",
-    "f1_weighted": "0.9690853355372118",
-    "feature_shape": "(484, 131072)",
-    "imbalance_ratio": "0.9206349206349206",
-    "label_dist": "{'real': 232, 'synthetic': 252, 'real_pct': np.float64(47.93388429752066), 'synthetic_pct': np.float64(52.066115702479344)}",
-    "labels_shape": "(484,)",
-    "n_components": "0.95",
-    "original_dim": "131072",
-    "pca_dim": "295",
-    "roc_auc": "0.9936061381074169",
-    "scale_pos_weight": "0.9253731343283582",
-    "seed": "2198257139",
-    "timestamp": "20260131_164505"
-}
\ No newline at end of file
diff --git a/results/score_confusion_matrix.png b/results/score_confusion_matrix.png
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diff --git a/results/vae_comparison.txt b/results/vae_comparison.txt
new file mode 100644
index 0000000..0a9f27c
--- /dev/null
+++ b/results/vae_comparison.txt
@@ -0,0 +1,706 @@
+{'accuracy': 0.8235294117647058,
+ 'best_iter': 32,
+ 'best_score': 0.9447935070036866,
+ 'cumulative:': array([0.32039887, 0.37570587, 0.4090894 , 0.436353  , 0.44910192,
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+       0.50377613, 0.5113055 , 0.5186545 , 0.52593285, 0.5329015 ,
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+       0.5712244 , 0.57724464, 0.5830629 , 0.5887853 , 0.59437275,
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+       0.6501189 , 0.6547675 , 0.659283  , 0.6637747 , 0.66820705,
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+       0.69374937, 0.6977799 , 0.70176166, 0.7056897 , 0.709554  ,
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+       0.9216819 , 0.9231232 , 0.9245376 , 0.92593247, 0.92731655,
+       0.92867297, 0.9300219 , 0.9313516 , 0.9326673 , 0.93395793,
+       0.93523085, 0.9364834 , 0.9377117 , 0.93892545, 0.9401313 ,
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+       0.94712096, 0.9482242 , 0.9493191 , 0.95039463], dtype=float32),
+ 'explained_var': np.float32(0.9503943),
+ 'f1_macro': 0.8224371373307543,
+ 'f1_weighted': 0.823256343156218,
+ 'feature_shape': (510, 256),
+ 'imbalance_ratio': np.float64(0.895910780669145),
+ 'label_dist': {'real': 241,
+                'real_pct': np.float64(47.25490196078431),
+                'synthetic': 269,
+                'synthetic_pct': np.float64(52.74509803921569)},
+ 'labels_shape': (510,),
+ 'n_components': np.int64(159),
+ 'original_dim': 256,
+ 'pca_dim': 159,
+ 'roc_auc': 0.9251543209876544,
+ 'scale_pos_weight': np.float64(0.8976744186046511),
+ 'seed': 2198257139,
+ 'timestamp': '20260203_113430',
+ 'vae_type': 'exdysa/dc-ae-f32c32-sana-1.1-diffusers'}
+============================================================
+CLASSIFICATION RESULTS
+============================================================
+              precision    recall  f1-score   support
+
+        Real       0.83      0.79      0.81        48
+   Synthetic       0.82      0.85      0.84        54
+
+    accuracy                           0.82       102
+   macro avg       0.82      0.82      0.82       102
+weighted avg       0.82      0.82      0.82       102
+
+Training selected.
+Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:00<00:00, 69591.90it/s]
+Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 69/69 [00:00<00:00, 50218.11it/s]
+Using local images from /Users/e6d64/Documents/GitHub/darkshapes/negate/assets
+Downloading model...
+Fetching 2 files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 4728.64it/s]
+Download complete: : 0.00B [00:00, ?B/s]                                                                                                  | 0/2 [00:00<?, ?it/s]
+Extracting features...: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 510/510 [25:41<00:00,  3.02s/ examples]
+[0]     train-logloss:0.66487   train-aucpr:0.74907     test-logloss:0.67617    test-aucpr:0.59764
+[20]    train-logloss:0.23519   train-aucpr:0.99974     test-logloss:0.50478    test-aucpr:0.83670
+[35]    train-logloss:0.13846   train-aucpr:1.00000     test-logloss:0.50541    test-aucpr:0.85411
+{'accuracy': 0.7450980392156863,
+ 'best_iter': 25,
+ 'best_score': 0.8661537185695733,
+ 'cumulative:': array([0.02055815, 0.02884234, 0.03609142, 0.04243419, 0.04790204,
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+       0.9193343 , 0.92083275, 0.9223274 , 0.92381275, 0.92529184,
+       0.9267646 , 0.928235  , 0.9296994 , 0.93115574, 0.93260723,
+       0.9340536 , 0.9354985 , 0.9369353 , 0.9383691 , 0.93979937,
+       0.94121546, 0.9426293 , 0.94403976, 0.9454423 , 0.9468432 ,
+       0.9482316 , 0.94961494, 0.9509903 ], dtype=float32),
+ 'explained_var': np.float32(0.9509903),
+ 'f1_macro': 0.7386677177769019,
+ 'f1_weighted': 0.7410790883164461,
+ 'feature_shape': (510, 4096),
+ 'imbalance_ratio': np.float64(0.895910780669145),
+ 'label_dist': {'real': 241,
+                'real_pct': np.float64(47.25490196078431),
+                'synthetic': 269,
+                'synthetic_pct': np.float64(52.74509803921569)},
+ 'labels_shape': (510,),
+ 'n_components': np.int64(368),
+ 'original_dim': 4096,
+ 'pca_dim': 368,
+ 'roc_auc': 0.8541666666666667,
+ 'scale_pos_weight': np.float64(0.8976744186046511),
+ 'seed': 2198257139,
+ 'timestamp': '20260203_120027',
+ 'vae_type': 'exdysa/AuraEquiVAE-SAFETENSORS'}
+============================================================
+CLASSIFICATION RESULTS
+============================================================
+              precision    recall  f1-score   support
+
+        Real       0.79      0.62      0.70        48
+   Synthetic       0.72      0.85      0.78        54
+
+    accuracy                           0.75       102
+   macro avg       0.75      0.74      0.74       102
+weighted avg       0.75      0.75      0.74       102
+
+Training selected.
+Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:00<00:00, 84784.80it/s]
+Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 69/69 [00:00<00:00, 54760.07it/s]
+Using local images from /Users/e6d64/Documents/GitHub/darkshapes/negate/assets
+/Users/e6d64/Documents/GitHub/darkshapes/negate/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py:202: UserWarning: The `local_dir_use_symlinks` argument is deprecated and ignored in `hf_hub_download`. Downloading to a local directory does not use symlinks anymore.
+  warnings.warn(
+Downloading model...
+Fetching 2 files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 6009.03it/s]
+Download complete: : 0.00B [00:00, ?B/s]                                                                                                  | 0/2 [00:00<?, ?it/s]
+Extracting features...: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 510/510 [26:11<00:00,  3.08s/ examples]
+[0]     train-logloss:0.62647   train-aucpr:0.95737     test-logloss:0.64050    test-aucpr:0.87195
+[20]    train-logloss:0.15574   train-aucpr:0.99996     test-logloss:0.23647    test-aucpr:0.99134
+[40]    train-logloss:0.06514   train-aucpr:1.00000     test-logloss:0.15002    test-aucpr:0.99510
+[60]    train-logloss:0.03711   train-aucpr:1.00000     test-logloss:0.12153    test-aucpr:0.99588
+[67]    train-logloss:0.03158   train-aucpr:1.00000     test-logloss:0.11701    test-aucpr:0.99588
+Models saved to disk. models/20260203_112525/negate_pca.pkl models/20260203_112525/negate.ubj models/20260203_112525/negate_metadata.npz
+{'accuracy': 0.9411764705882353,
+ 'best_iter': 57,
+ 'best_score': 0.996251156832072,
+ 'cumulative:': array([0.20879647, 0.3386417 , 0.4146717 , 0.47137022, 0.5054852 ,
+       0.5302627 , 0.5504869 , 0.5703159 , 0.58569634, 0.59944147,
+       0.6108004 , 0.6215837 , 0.6311039 , 0.63990617, 0.6483093 ,
+       0.6555219 , 0.6620954 , 0.6681175 , 0.6740133 , 0.67959386,
+       0.68515986, 0.690452  , 0.69561595, 0.7005858 , 0.70535815,
+       0.7099375 , 0.7142347 , 0.7185122 , 0.72259593, 0.7264934 ,
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+       0.76335084, 0.76625496, 0.7691105 , 0.7719183 , 0.7746487 ,
+       0.7773422 , 0.77997506, 0.7826002 , 0.7851505 , 0.7876596 ,
+       0.7901361 , 0.7925915 , 0.7950067 , 0.7974055 , 0.7997377 ,
+       0.802044  , 0.8042749 , 0.8064862 , 0.80865234, 0.8107985 ,
+       0.81291693, 0.8149864 , 0.81704867, 0.8190719 , 0.82105535,
+       0.8230374 , 0.82498145, 0.8268952 , 0.828792  , 0.83066714,
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+       0.8414002 , 0.84311235, 0.8448    , 0.8464697 , 0.8481086 ,
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+       0.864932  , 0.86634773, 0.8677544 , 0.8691542 , 0.8705392 ,
+       0.8719003 , 0.87325615, 0.87460625, 0.87594   , 0.87725735,
+       0.87856233, 0.8798534 , 0.88111615, 0.8823716 , 0.88360524,
+       0.88482463, 0.8860307 , 0.88721526, 0.88839686, 0.88955784,
+       0.89070636, 0.89184153, 0.8929674 , 0.89408195, 0.89518636,
+       0.89627606, 0.89735717, 0.8984263 , 0.8994889 , 0.9005403 ,
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+       0.9065944 , 0.9075694 , 0.9085313 , 0.90948737, 0.9104275 ,
+       0.9113511 , 0.91227067, 0.91318303, 0.91409117, 0.9149876 ,
+       0.9158632 , 0.91673565, 0.91759396, 0.9184469 , 0.9192917 ,
+       0.9201239 , 0.92095304, 0.9217681 , 0.9225756 , 0.9233801 ,
+       0.92417216, 0.92495716, 0.92573744, 0.92651385, 0.9272718 ,
+       0.92802405, 0.9287667 , 0.9295021 , 0.93023634, 0.9309611 ,
+       0.93168277, 0.9323967 , 0.9331029 , 0.93380624, 0.934504  ,
+       0.9351974 , 0.93588746, 0.93656653, 0.9372292 , 0.93788177,
+       0.9385266 , 0.93916875, 0.93980736, 0.94043964, 0.9410696 ,
+       0.9416923 , 0.9423115 , 0.94292593, 0.9435318 , 0.9441331 ,
+       0.94472915, 0.9453196 , 0.9459045 , 0.9464854 , 0.9470562 ,
+       0.94762206, 0.94818544, 0.94874233, 0.9492953 , 0.94984376,
+       0.95038766], dtype=float32),
+ 'explained_var': np.float32(0.950387),
+ 'f1_macro': 0.941085868309588,
+ 'f1_weighted': 0.9412217717275589,
+ 'feature_shape': (510, 65536),
+ 'imbalance_ratio': np.float64(0.895910780669145),
+ 'label_dist': {'real': 241,
+                'real_pct': np.float64(47.25490196078431),
+                'synthetic': 269,
+                'synthetic_pct': np.float64(52.74509803921569)},
+ 'labels_shape': (510,),
+ 'n_components': np.int64(186),
+ 'original_dim': 65536,
+ 'pca_dim': 186,
+ 'roc_auc': 0.9953703703703703,
+ 'scale_pos_weight': np.float64(0.8976744186046511),
+ 'seed': 2198257139,
+ 'timestamp': '20260203_122656',
+ 'vae_type': 'zai-org/GLM-Image'}
+============================================================
+CLASSIFICATION RESULTS
+============================================================
+              precision    recall  f1-score   support
+
+        Real       0.92      0.96      0.94        48
+   Synthetic       0.96      0.93      0.94        54
+
+    accuracy                           0.94       102
+   macro avg       0.94      0.94      0.94       102
+weighted avg       0.94      0.94      0.94       102
+
+Training selected.
+Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:00<00:00, 69782.95it/s]
+Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 69/69 [00:00<00:00, 61707.24it/s]
+Using local images from /Users/e6d64/Documents/GitHub/darkshapes/negate/assets
+/Users/e6d64/Documents/GitHub/darkshapes/negate/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py:202: UserWarning: The `local_dir_use_symlinks` argument is deprecated and ignored in `hf_hub_download`. Downloading to a local directory does not use symlinks anymore.
+  warnings.warn(
+Downloading model...
+Fetching 2 files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 17260.51it/s]
+Download complete: : 0.00B [00:00, ?B/s]                                                                                                  | 0/2 [00:00<?, ?it/s]
+Extracting features...: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 510/510 [08:41<00:00,  1.02s/ examples]
+[0]     train-logloss:0.61810   train-aucpr:0.98933     test-logloss:0.62796    test-aucpr:0.92697
+[20]    train-logloss:0.13859   train-aucpr:1.00000     test-logloss:0.20699    test-aucpr:0.99324
+[36]    train-logloss:0.06291   train-aucpr:1.00000     test-logloss:0.13483    test-aucpr:0.99503
+Models saved to disk. models/20260203_112525/negate_pca.pkl models/20260203_112525/negate.ubj models/20260203_112525/negate_metadata.npz
+{'accuracy': 0.9313725490196079,
+ 'best_iter': 26,
+ 'best_score': 0.9954208026330233,
+ 'cumulative:': array([0.06285927, 0.11291818, 0.13728824, 0.1586758 , 0.17771533,
+       0.19297539, 0.20477706, 0.21612123, 0.22722429, 0.2359885 ,
+       0.24447197, 0.252809  , 0.26081258, 0.2684782 , 0.27584013,
+       0.28302342, 0.28993875, 0.29675046, 0.30340844, 0.30984122,
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+       0.685529  , 0.68802947, 0.690477  , 0.6929141 , 0.69532317,
+       0.69771343, 0.70008063, 0.7024182 , 0.7047432 , 0.7070309 ,
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+       0.7203824 , 0.7225331 , 0.7246764 , 0.7268073 , 0.72891855,
+       0.7310196 , 0.73310447, 0.735164  , 0.7372028 , 0.7392382 ,
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+       0.75119084, 0.75314784, 0.7550898 , 0.757028  , 0.75895256,
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+       0.80509126, 0.80674744, 0.80838645, 0.81001496, 0.81163377,
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+       0.8707816 , 0.8719753 , 0.87315536, 0.8743068 , 0.87545204,
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+       0.90618485, 0.9070837 , 0.9079783 , 0.9088704 , 0.9097565 ,
+       0.9106375 , 0.9115115 , 0.9123828 , 0.91325104, 0.9141119 ,
+       0.9149703 , 0.9158212 , 0.9166695 , 0.91751236, 0.9183534 ,
+       0.91919297, 0.92002803, 0.92086005, 0.9216871 , 0.9225039 ,
+       0.92331886, 0.92412996, 0.924939  , 0.92574203, 0.9265428 ,
+       0.9273386 , 0.9281331 , 0.9289214 , 0.9297087 , 0.9304923 ,
+       0.9312715 , 0.93204725, 0.93281615, 0.93358386, 0.9343488 ,
+       0.9351119 , 0.93587106, 0.93662995, 0.9373801 , 0.9381281 ,
+       0.93887377, 0.9396105 , 0.94034475, 0.94107735, 0.94180685,
+       0.9425319 , 0.94325286, 0.94397074, 0.9446846 , 0.9453965 ,
+       0.94610393, 0.9468083 , 0.947512  , 0.94821244, 0.94890994,
+       0.9496051 , 0.95029515], dtype=float32),
+ 'explained_var': np.float32(0.9502946),
+ 'f1_macro': 0.9313131313131313,
+ 'f1_weighted': 0.9314319667260843,
+ 'feature_shape': (510, 131072),
+ 'imbalance_ratio': np.float64(0.895910780669145),
+ 'label_dist': {'real': 241,
+                'real_pct': np.float64(47.25490196078431),
+                'synthetic': 269,
+                'synthetic_pct': np.float64(52.74509803921569)},
+ 'labels_shape': (510,),
+ 'n_components': np.int64(312),
+ 'original_dim': 131072,
+ 'pca_dim': 312,
+ 'roc_auc': 0.9945987654320988,
+ 'scale_pos_weight': np.float64(0.8976744186046511),
+ 'seed': 2198257139,
+ 'timestamp': '20260203_123602',
+ 'vae_type': 'black-forest-labs/FLUX.2-dev'}
+============================================================
+CLASSIFICATION RESULTS
+============================================================
+              precision    recall  f1-score   support
+
+        Real       0.90      0.96      0.93        48
+   Synthetic       0.96      0.91      0.93        54
+
+    accuracy                           0.93       102
+   macro avg       0.93      0.93      0.93       102
+weighted avg       0.93      0.93      0.93       102
+
+Training selected.
+Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:00<00:00, 93372.75it/s]
+Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 69/69 [00:00<00:00, 76239.98it/s]
+Using local images from /Users/e6d64/Documents/GitHub/darkshapes/negate/assets
+/Users/e6d64/Documents/GitHub/darkshapes/negate/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py:202: UserWarning: The `local_dir_use_symlinks` argument is deprecated and ignored in `hf_hub_download`. Downloading to a local directory does not use symlinks anymore.
+  warnings.warn(
+Downloading model...
+Fetching 2 files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 7250.31it/s]
+Download complete: : 0.00B [00:00, ?B/s]                                                                                                  | 0/2 [00:00<?, ?it/s]
+Extracting features...: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 510/510 [08:44<00:00,  1.03s/ examples]
+[0]     train-logloss:0.61898   train-aucpr:0.98211     test-logloss:0.63104    test-aucpr:0.87741
+[20]    train-logloss:0.14185   train-aucpr:1.00000     test-logloss:0.21575    test-aucpr:0.98967
+[40]    train-logloss:0.05353   train-aucpr:1.00000     test-logloss:0.14045    test-aucpr:0.99580
+Models saved to disk. models/20260203_112525/negate_pca.pkl models/20260203_112525/negate.ubj models/20260203_112525/negate_metadata.npz
+{'accuracy': 0.9215686274509803,
+ 'best_iter': 30,
+ 'best_score': 0.9958026412849259,
+ 'cumulative:': array([0.06283535, 0.11288635, 0.13720979, 0.15859109, 0.17762668,
+       0.19287013, 0.20466967, 0.21599604, 0.22709799, 0.23583564,
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+       0.5454022 , 0.54902005, 0.55263424, 0.55621374, 0.55978453,
+       0.5633425 , 0.56683916, 0.57031655, 0.5737854 , 0.5772145 ,
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+       0.7965016 , 0.7981966 , 0.799882  , 0.80156314, 0.80323094,
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+       0.8643813 , 0.86566365, 0.8669406 , 0.86820877, 0.869468  ,
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+       0.8818472 , 0.88289326, 0.88393813, 0.88497025, 0.88599724,
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+       0.9190968 , 0.9199313 , 0.9207629 , 0.9215907 , 0.92241013,
+       0.9232291 , 0.92404   , 0.9248475 , 0.92565036, 0.92645055,
+       0.92724854, 0.9280442 , 0.9288347 , 0.9296248 , 0.9304113 ,
+       0.9311932 , 0.9319712 , 0.9327439 , 0.9335141 , 0.934281  ,
+       0.9350443 , 0.9358039 , 0.93655974, 0.9373112 , 0.93806195,
+       0.9388039 , 0.9395436 , 0.94027805, 0.9410107 , 0.9417423 ,
+       0.94246936, 0.94319314, 0.9439121 , 0.94462734, 0.94534004,
+       0.9460497 , 0.9467567 , 0.9474617 , 0.9481653 , 0.9488599 ,
+       0.9495511 , 0.9502404 ], dtype=float32),
+ 'explained_var': np.float32(0.95024014),
+ 'f1_macro': 0.9215384615384616,
+ 'f1_weighted': 0.9216289592760182,
+ 'feature_shape': (510, 131072),
+ 'imbalance_ratio': np.float64(0.895910780669145),
+ 'label_dist': {'real': 241,
+                'real_pct': np.float64(47.25490196078431),
+                'synthetic': 269,
+                'synthetic_pct': np.float64(52.74509803921569)},
+ 'labels_shape': (510,),
+ 'n_components': np.int64(312),
+ 'original_dim': 131072,
+ 'pca_dim': 312,
+ 'roc_auc': 0.9953703703703705,
+ 'scale_pos_weight': np.float64(0.8976744186046511),
+ 'seed': 2198257139,
+ 'timestamp': '20260203_124511',
+ 'vae_type': 'black-forest-labs/FLUX.2-klein-4B'}
+============================================================
+CLASSIFICATION RESULTS
+============================================================
+              precision    recall  f1-score   support
+
+        Real       0.88      0.96      0.92        48
+   Synthetic       0.96      0.89      0.92        54
+
+    accuracy                           0.92       102
+   macro avg       0.92      0.92      0.92       102
+weighted avg       0.92      0.92      0.92       102
+
+Training selected.
+Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:00<00:00, 66597.40it/s]
+Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 69/69 [00:00<00:00, 54543.34it/s]
+Using local images from /Users/e6d64/Documents/GitHub/darkshapes/negate/assets
+/Users/e6d64/Documents/GitHub/darkshapes/negate/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py:202: UserWarning: The `local_dir_use_symlinks` argument is deprecated and ignored in `hf_hub_download`. Downloading to a local directory does not use symlinks anymore.
+  warnings.warn(
+Downloading model...
+Fetching 2 files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 15505.74it/s]
+Download complete: : 0.00B [00:00, ?B/s]                                                                                                  | 0/2 [00:00<?, ?it/s]
+[0]     train-logloss:0.62809   train-aucpr:0.94959     test-logloss:0.64206    test-aucpr:0.84993
+[20]    train-logloss:0.17283   train-aucpr:0.99993     test-logloss:0.28203    test-aucpr:0.97717
+[40]    train-logloss:0.07873   train-aucpr:1.00000     test-logloss:0.20260    test-aucpr:0.98703
+[60]    train-logloss:0.04503   train-aucpr:1.00000     test-logloss:0.17492    test-aucpr:0.99032
+[73]    train-logloss:0.03418   train-aucpr:1.00000     test-logloss:0.16074    test-aucpr:0.99052
+Models saved to disk. models/20260203_112525/negate_pca.pkl models/20260203_112525/negate.ubj models/20260203_112525/negate_metadata.npz
+{'accuracy': 0.9411764705882353,
+ 'best_iter': 63,
+ 'best_score': 0.9908070353683929,
+ 'cumulative:': array([0.5008355 , 0.63640136, 0.68778014, 0.72649044, 0.75550836,
+       0.7684484 , 0.7786998 , 0.7885367 , 0.79660636, 0.80353564,
+       0.8098056 , 0.8152508 , 0.82045627, 0.8250844 , 0.82885593,
+       0.8325481 , 0.83600926, 0.8392975 , 0.8423744 , 0.84541565,
+       0.8481323 , 0.8507303 , 0.8532711 , 0.8556661 , 0.8580119 ,
+       0.86019945, 0.8622937 , 0.86432195, 0.86626065, 0.8681801 ,
+       0.87004316, 0.87187433, 0.87361467, 0.87530494, 0.87697035,
+       0.8785808 , 0.8801614 , 0.88168526, 0.8831797 , 0.8846485 ,
+       0.886067  , 0.88746154, 0.88884234, 0.89019233, 0.8915195 ,
+       0.89281416, 0.8940819 , 0.8953103 , 0.8965237 , 0.8977171 ,
+       0.89890033, 0.9000596 , 0.9011944 , 0.90232074, 0.9034281 ,
+       0.904525  , 0.90560603, 0.90664774, 0.9076754 , 0.90867853,
+       0.90965617, 0.9106186 , 0.9115664 , 0.9125019 , 0.9134263 ,
+       0.91434526, 0.91524816, 0.9161491 , 0.9170418 , 0.9179193 ,
+       0.91878617, 0.91963637, 0.9204788 , 0.921309  , 0.92213446,
+       0.9229524 , 0.9237518 , 0.92454475, 0.9253306 , 0.9261072 ,
+       0.92687106, 0.92762697, 0.92836344, 0.92909807, 0.9298206 ,
+       0.9305429 , 0.9312516 , 0.93195695, 0.9326598 , 0.93335205,
+       0.9340291 , 0.93470174, 0.9353716 , 0.9360266 , 0.93667686,
+       0.93731767, 0.93794745, 0.9385711 , 0.9391894 , 0.93980366,
+       0.9404115 , 0.9410132 , 0.9416146 , 0.94220525, 0.9427905 ,
+       0.9433726 , 0.9439459 , 0.94451153, 0.9450719 , 0.9456304 ,
+       0.9461857 , 0.94673634, 0.94728565, 0.9478211 , 0.94834334,
+       0.94886273, 0.9493755 , 0.9498856 , 0.95038706], dtype=float32),
+ 'explained_var': np.float32(0.95038724),
+ 'f1_macro': 0.9411538461538461,
+ 'f1_weighted': 0.9412217194570136,
+ 'feature_shape': (510, 65536),
+ 'imbalance_ratio': np.float64(0.895910780669145),
+ 'label_dist': {'real': 241,
+                'real_pct': np.float64(47.25490196078431),
+                'synthetic': 269,
+                'synthetic_pct': np.float64(52.74509803921569)},
+ 'labels_shape': (510,),
+ 'n_components': np.int64(119),
+ 'original_dim': 65536,
+ 'pca_dim': 119,
+ 'roc_auc': 0.9891975308641976,
+ 'scale_pos_weight': np.float64(0.8976744186046511),
+ 'seed': 2198257139,
+ 'timestamp': '20260203_124533',
+ 'vae_type': 'Tongyi-MAI/Z-Image'}
+============================================================
+CLASSIFICATION RESULTS
+============================================================
+              precision    recall  f1-score   support
+
+        Real       0.90      0.98      0.94        48
+   Synthetic       0.98      0.91      0.94        54
+
+    accuracy                           0.94       102
+   macro avg       0.94      0.94      0.94       102
+weighted avg       0.94      0.94      0.94       102
+
+Training selected.
+Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:00<00:00, 87719.42it/s]
+Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 69/69 [00:00<00:00, 94515.67it/s]
+Using local images from /Users/e6d64/Documents/GitHub/darkshapes/negate/assets
+/Users/e6d64/Documents/GitHub/darkshapes/negate/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py:202: UserWarning: The `local_dir_use_symlinks` argument is deprecated and ignored in `hf_hub_download`. Downloading to a local directory does not use symlinks anymore.
+  warnings.warn(
+Downloading model...
+Fetching 2 files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 8886.24it/s]
+Download complete: : 0.00B [00:00, ?B/s]                                                                                                  | 0/2 [00:00<?, ?it/s]
+Extracting features...: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 510/510 [08:47<00:00,  1.03s/ examples]
+[0]     train-logloss:0.62834   train-aucpr:0.94960     test-logloss:0.64082    test-aucpr:0.85137
+[20]    train-logloss:0.17378   train-aucpr:0.99998     test-logloss:0.28203    test-aucpr:0.97173
+[40]    train-logloss:0.07873   train-aucpr:1.00000     test-logloss:0.19541    test-aucpr:0.98613
+[60]    train-logloss:0.04478   train-aucpr:1.00000     test-logloss:0.16044    test-aucpr:0.99079
+[80]    train-logloss:0.03019   train-aucpr:1.00000     test-logloss:0.14220    test-aucpr:0.99216
+[86]    train-logloss:0.02748   train-aucpr:1.00000     test-logloss:0.14044    test-aucpr:0.99261
+Models saved to disk. models/20260203_112525/negate_pca.pkl models/20260203_112525/negate.ubj models/20260203_112525/negate_metadata.npz
+{'accuracy': 0.9509803921568627,
+ 'best_iter': 76,
+ 'best_score': 0.9932688530737622,
+ 'cumulative:': array([0.5008354 , 0.6364009 , 0.68777966, 0.72648996, 0.7555079 ,
+       0.7684479 , 0.7786994 , 0.7885362 , 0.7966058 , 0.80353487,
+       0.80980486, 0.81525004, 0.82045555, 0.82508373, 0.8288553 ,
+       0.83254737, 0.83600855, 0.8392968 , 0.8423738 , 0.845415  ,
+       0.84813154, 0.8507295 , 0.8532704 , 0.8556654 , 0.8580112 ,
+       0.86019874, 0.86229306, 0.86432123, 0.86626   , 0.8681795 ,
+       0.87004256, 0.87187386, 0.8736142 , 0.8753045 , 0.87697   ,
+       0.87858045, 0.88016105, 0.88168484, 0.88317925, 0.884648  ,
+       0.8860665 , 0.8874611 , 0.8888419 , 0.890192  , 0.8915192 ,
+       0.89281386, 0.8940816 , 0.8953099 , 0.89652336, 0.89771676,
+       0.8988999 , 0.9000591 , 0.9011939 , 0.9023203 , 0.9034276 ,
+       0.90452456, 0.9056057 , 0.9066474 , 0.90767485, 0.908678  ,
+       0.90965563, 0.910618  , 0.9115657 , 0.9125013 , 0.9134257 ,
+       0.91434467, 0.9152475 , 0.9161484 , 0.91704106, 0.9179186 ,
+       0.9187855 , 0.9196357 , 0.92047817, 0.92130834, 0.92213386,
+       0.9229519 , 0.92375124, 0.92454416, 0.92533004, 0.92610663,
+       0.92687047, 0.9276265 , 0.92836297, 0.9290976 , 0.9298201 ,
+       0.9305424 , 0.93125117, 0.93195647, 0.9326593 , 0.9333516 ,
+       0.9340287 , 0.93470126, 0.9353711 , 0.93602604, 0.9366763 ,
+       0.9373172 , 0.9379469 , 0.93857056, 0.9391889 , 0.9398032 ,
+       0.94041103, 0.94101286, 0.9416143 , 0.9422049 , 0.9427902 ,
+       0.94337237, 0.94394565, 0.94451123, 0.9450716 , 0.9456301 ,
+       0.94618547, 0.9467361 , 0.9472854 , 0.9478209 , 0.9483433 ,
+       0.9488627 , 0.9493754 , 0.9498855 , 0.95038694], dtype=float32),
+ 'explained_var': np.float32(0.9503869),
+ 'f1_macro': 0.950937950937951,
+ 'f1_weighted': 0.9510228333757746,
+ 'feature_shape': (510, 65536),
+ 'imbalance_ratio': np.float64(0.895910780669145),
+ 'label_dist': {'real': 241,
+                'real_pct': np.float64(47.25490196078431),
+                'synthetic': 269,
+                'synthetic_pct': np.float64(52.74509803921569)},
+ 'labels_shape': (510,),
+ 'n_components': np.int64(119),
+ 'original_dim': 65536,
+ 'pca_dim': 119,
+ 'roc_auc': 0.9918981481481481,
+ 'scale_pos_weight': np.float64(0.8976744186046511),
+ 'seed': 2198257139,
+ 'timestamp': '20260203_125444',
+ 'vae_type': 'Freepik/F-Lite-Texture'}
+============================================================
+CLASSIFICATION RESULTS
+============================================================
+              precision    recall  f1-score   support
+
+        Real       0.92      0.98      0.95        48
+   Synthetic       0.98      0.93      0.95        54
+
+    accuracy                           0.95       102
+   macro avg       0.95      0.95      0.95       102
+weighted avg       0.95      0.95      0.95       102
+
+Training selected.
+Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:00<00:00, 79777.54it/s]
+Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 69/69 [00:00<00:00, 60787.01it/s]
+Using local images from /Users/e6d64/Documents/GitHub/darkshapes/negate/assets
+/Users/e6d64/Documents/GitHub/darkshapes/negate/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py:202: UserWarning: The `local_dir_use_symlinks` argument is deprecated and ignored in `hf_hub_download`. Downloading to a local directory does not use symlinks anymore.
+  warnings.warn(
+Downloading model...
+Fetching 2 files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 4536.84it/s]
+Download complete: : 0.00B [00:00, ?B/s]                                                                                                  | 0/2 [00:00<?, ?it/s]
+Extracting features...: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 510/510 [08:54<00:00,  1.05s/ examples]
+[0]     train-logloss:0.62998   train-aucpr:0.95053     test-logloss:0.64407    test-aucpr:0.88450
+[20]    train-logloss:0.18019   train-aucpr:0.99960     test-logloss:0.28092    test-aucpr:0.97840
+[40]    train-logloss:0.08195   train-aucpr:1.00000     test-logloss:0.20554    test-aucpr:0.98489
+[60]    train-logloss:0.04690   train-aucpr:1.00000     test-logloss:0.17432    test-aucpr:0.98614
+[64]    train-logloss:0.04257   train-aucpr:1.00000     test-logloss:0.16912    test-aucpr:0.98586
+Models saved to disk. models/20260203_112525/negate_pca.pkl models/20260203_112525/negate.ubj models/20260203_112525/negate_metadata.npz
+{'accuracy': 0.9509803921568627,
+ 'best_iter': 54,
+ 'best_score': 0.9865258090377969,
+ 'cumulative:': array([0.5197475 , 0.6669655 , 0.71532583, 0.7617874 , 0.77750546,
+       0.7904844 , 0.80144316, 0.8092131 , 0.8166597 , 0.82311094,
+       0.82884383, 0.8342817 , 0.83889663, 0.84310114, 0.84682804,
+       0.85042995, 0.853673  , 0.8567688 , 0.8596666 , 0.86252415,
+       0.8652963 , 0.8678735 , 0.87030214, 0.8724764 , 0.8745751 ,
+       0.8766644 , 0.87866265, 0.8806406 , 0.88251334, 0.88435894,
+       0.88618684, 0.88789296, 0.88958776, 0.8912303 , 0.8928259 ,
+       0.8943444 , 0.89583105, 0.8972746 , 0.89869374, 0.9000952 ,
+       0.90144956, 0.9027784 , 0.9040629 , 0.905308  , 0.9065057 ,
+       0.90768164, 0.90883017, 0.90994704, 0.91106147, 0.91213536,
+       0.91320276, 0.9142565 , 0.9152808 , 0.91628534, 0.91725606,
+       0.9182235 , 0.91917   , 0.9201018 , 0.9210191 , 0.921911  ,
+       0.92279804, 0.9236706 , 0.9245281 , 0.92537785, 0.9262083 ,
+       0.9270306 , 0.92783684, 0.9286329 , 0.92941856, 0.9301993 ,
+       0.9309713 , 0.9317353 , 0.93248296, 0.93322074, 0.93394005,
+       0.93465006, 0.9353533 , 0.9360513 , 0.9367425 , 0.9374229 ,
+       0.9380933 , 0.93875724, 0.9394095 , 0.9400594 , 0.9406987 ,
+       0.94132555, 0.9419424 , 0.94255537, 0.9431586 , 0.9437503 ,
+       0.9443365 , 0.94491804, 0.94549674, 0.9460677 , 0.94662935,
+       0.94718796, 0.9477362 , 0.9482797 , 0.94881546, 0.9493444 ,
+       0.9498675 , 0.95038867], dtype=float32),
+ 'explained_var': np.float32(0.95038867),
+ 'f1_macro': 0.950748430709802,
+ 'f1_weighted': 0.9509472548072826,
+ 'feature_shape': (510, 16384),
+ 'imbalance_ratio': np.float64(0.895910780669145),
+ 'label_dist': {'real': 241,
+                'real_pct': np.float64(47.25490196078431),
+                'synthetic': 269,
+                'synthetic_pct': np.float64(52.74509803921569)},
+ 'labels_shape': (510,),
+ 'n_components': np.int64(102),
+ 'original_dim': 16384,
+ 'pca_dim': 102,
+ 'roc_auc': 0.9837962962962963,
+ 'scale_pos_weight': np.float64(0.8976744186046511),
+ 'seed': 2198257139,
+ 'timestamp': '20260203_130400',
+ 'vae_type': 'exdysa/mitsua-vae-SAFETENSORS'}
+============================================================
+CLASSIFICATION RESULTS
+============================================================
+              precision    recall  f1-score   support
+
+        Real       0.96      0.94      0.95        48
+   Synthetic       0.95      0.96      0.95        54
+
+    accuracy                           0.95       102
+   macro avg       0.95      0.95      0.95       102
+weighted avg       0.95      0.95      0.95       102
+
diff --git a/tests/test_import.py b/tests/test_import.py
index 0371ad1..9d7c330 100644
--- a/tests/test_import.py
+++ b/tests/test_import.py
@@ -4,16 +4,20 @@
 
 def test_main_import():
     from negate import (
-        build_datasets,
-        generate_dataset,
-        dataset_to_nparray,
         FeatureExtractor,
         DeviceName,
-        features,
         TrainResult,
+        VAEModel,
+        build_datasets,
+        config_settings,
+        datestamped_folder,
+        features,
+        generate_dataset,
+        generate_datestamp_path,
         grade,
         in_console,
+        model_path,
         on_graph,
-        save_model,
+        save_models,
         save_to_onnx,
     )
diff --git a/tests/test_vae_map.py b/tests/test_vae_map.py
index ca4af15..de2fec2 100644
--- a/tests/test_vae_map.py
+++ b/tests/test_vae_map.py
@@ -2,7 +2,6 @@
 # <!-- // /*  d a r k s h a p e s */ -->
 
 from negate.extract import VAEModel, MODEL_MAP
-import pytest
 
 
 def test_vae_model_map_entries() -> None:

From a3f76e823c16735e62690dce5547f38efd584838 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=CB=B6=F0=9D=9E=A2=E2=A4=AC=E2=AB=92=E2=B5=96s=E1=90=BC?=
 =?UTF-8?q?=CB=B6?= <91800957+exdysa@users.noreply.github.com>
Date: Tue, 3 Feb 2026 21:51:19 -0500
Subject: [PATCH 4/4] ~patch import

---
 negate/__main__.py   | 2 +-
 tests/test_import.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/negate/__main__.py b/negate/__main__.py
index 530df82..86424a7 100644
--- a/negate/__main__.py
+++ b/negate/__main__.py
@@ -13,7 +13,6 @@
     TrainResult,
     VAEModel,
     build_datasets,
-    negate_opt,
     datestamped_folder,
     features,
     generate_dataset,
@@ -21,6 +20,7 @@
     grade,
     in_console,
     model_path,
+    negate_opt,
     on_graph,
     save_models,
     save_to_onnx,
diff --git a/tests/test_import.py b/tests/test_import.py
index 9d7c330..13811ef 100644
--- a/tests/test_import.py
+++ b/tests/test_import.py
@@ -9,7 +9,7 @@ def test_main_import():
         TrainResult,
         VAEModel,
         build_datasets,
-        config_settings,
+        negate_opt,
         datestamped_folder,
         features,
         generate_dataset,