Fix opencv variant collision and PyTorch 2.6 torch.load

models/denoise-nafnet/convert.py

@@ -49,7 +49,10 @@ def load_nafnet_model(config_path, checkpoint_path):
         dec_blk_nums=network_g.get('dec_blk_nums', [2, 2, 2, 2])
     )
 
-    checkpoint = torch.load(checkpoint_path, map_location='cpu')
+    # weights_only=False: PyTorch 2.6 flipped this default to True, which
+    # rejects the NAFNet checkpoint (it pickles non-tensor objects). The
+    # checkpoint is a trusted file we ship, so full unpickling is safe.
+    checkpoint = torch.load(checkpoint_path, map_location='cpu', weights_only=False)
     if 'params' in checkpoint:
         state_dict = checkpoint['params']
     elif 'params_ema' in checkpoint:

models/mask-object-segnext-b2hq/convert.py

@@ -217,7 +217,10 @@ def to_numpy(tensor):
 
 def load_segnext_model(checkpoint_path):
     """Load SegNext model from checkpoint."""
-    state_dict = torch.load(checkpoint_path, map_location="cpu")
+    # weights_only=False: checkpoint stores a non-tensor "config" object
+    # alongside the weights, which PyTorch 2.6's weights_only=True default
+    # rejects. Trusted file we ship, so full unpickling is safe.
+    state_dict = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
     model = load_model(state_dict["config"])
     model.load_state_dict(state_dict["state_dict"], strict=True)
     for param in model.parameters():

models/upscale-bsrgan/convert.py

@@ -154,7 +154,10 @@ def convert(checkpoint, output, scale, height=256, width=256,
 
     print(f"Loading BSRGAN model (scale={scale})...")
     model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=scale)
-    state_dict = torch.load(checkpoint, map_location='cpu')
+    # weights_only=False for consistency with the other converters; this
+    # is a trusted checkpoint we ship. PyTorch 2.6 made weights_only=True
+    # the default, which can reject checkpoints carrying non-tensor data.
+    state_dict = torch.load(checkpoint, map_location='cpu', weights_only=False)
     model.load_state_dict(state_dict, strict=True)
     model.eval()
 

pyproject.toml

@@ -21,7 +21,12 @@ core = [
     "onnx>=1.10.0",
     "onnxruntime",
     "onnxsim",
-    "opencv-python",
+    # headless, not opencv-python: albumentations/albucore pull
+    # opencv-python-headless, and both variants install into the same
+    # cv2/ namespace. Two of them race over cv2/__init__.py and CI ends
+    # up with a half-written package (cv2.imread missing). We use no GUI
+    # calls, so headless is the single consistent variant.
+    "opencv-python-headless",
 ]
 nafnet = [
     {include-group = "core"},
@@ -87,6 +92,15 @@ eval = [
     "scipy",
 ]
 
+[tool.uv]
+# mmcv/mmengine require opencv-python while albumentations/albucore
+# require opencv-python-headless; both install into the same cv2/
+# namespace and race over cv2/__init__.py, leaving a half-written
+# package on CI (cv2.imread missing). Force the headless variant
+# everywhere — mmcv imports cv2 too and we use no GUI calls — so the
+# shared venv only ever has one opencv.
+override-dependencies = ["opencv-python; sys_platform == 'never'"]
+
 [tool.uv.extra-build-dependencies]
 mmcv = ["setuptools<71"]