Polish imports and modify asserts to raise proper errors with messages.

comfy_extras/void_noise_warp.py

@@ -19,6 +19,7 @@ from typing import Optional
 import torch
 import torch.nn.functional as F
 from einops import rearrange
+from torchvision.models.optical_flow import raft_large
 
 import comfy.model_management
 
@@ -35,7 +36,10 @@ def _torch_resize_chw(image, size, interp, copy=True):
     the requested size matches the input, returns the input tensor as is
     (faster but callers must not mutate the result).
     """
-    assert image.ndim == 3, image.shape
+    if image.ndim != 3:
+        raise ValueError(
+            f"_torch_resize_chw expects a 3D CHW tensor, got shape {tuple(image.shape)}"
+        )
     _, in_h, in_w = image.shape
     if isinstance(size, (int, float)) and not isinstance(size, bool):
         new_h = max(1, int(in_h * size))
@@ -59,8 +63,14 @@ def _torch_remap_relative(image, dx, dy, interp="bilinear"):
     Equivalent to ``rp.torch_remap_image(image, dx, dy, relative=True, interp=interp)``
     for ``interp`` in {"bilinear", "nearest"}.  Out-of-bounds samples are 0.
     """
-    assert image.ndim == 3
-    assert dx.shape == dy.shape
+    if image.ndim != 3:
+        raise ValueError(
+            f"_torch_remap_relative expects a 3D CHW tensor, got shape {tuple(image.shape)}"
+        )
+    if dx.shape != dy.shape:
+        raise ValueError(
+            f"_torch_remap_relative: dx and dy must match, got {tuple(dx.shape)} vs {tuple(dy.shape)}"
+        )
     _, h, w = image.shape
 
     x_abs = dx + torch.arange(w, device=dx.device, dtype=dx.dtype)
@@ -82,9 +92,16 @@ def _torch_scatter_add_relative(image, dx, dy):
     Equivalent to ``rp.torch_scatter_add_image(image, dx, dy, relative=True,
     interp='floor')``.  Out-of-bounds targets are dropped.
     """
-    assert image.ndim == 3
+    if image.ndim != 3:
+        raise ValueError(
+            f"_torch_scatter_add_relative expects a 3D CHW tensor, got shape {tuple(image.shape)}"
+        )
     in_c, in_h, in_w = image.shape
-    assert dx.shape == dy.shape == (in_h, in_w)
+    if dx.shape != (in_h, in_w) or dy.shape != (in_h, in_w):
+        raise ValueError(
+            f"_torch_scatter_add_relative: dx/dy must be ({in_h}, {in_w}), "
+            f"got dx={tuple(dx.shape)} dy={tuple(dy.shape)}"
+        )
 
     x = dx.long() + torch.arange(in_w, device=dx.device, dtype=torch.long)
     y = dy.long() + torch.arange(in_h, device=dy.device, dtype=torch.long)[:, None]
@@ -185,11 +202,20 @@ def warp_state(state, flow):
     ``state`` has shape ``(3+c, h, w)`` (= dx, dy, ω, c noise channels).
     ``flow`` has shape ``(2, h, w)`` (= dx, dy).
     """
-    assert flow.device == state.device
-    assert flow.ndim == 3 and flow.shape[0] == 2
-    assert state.ndim == 3
+    if flow.device != state.device:
+        raise ValueError(
+            f"warp_state: flow and state must be on the same device, "
+            f"got flow={flow.device} state={state.device}"
+        )
+    if state.ndim != 3:
+        raise ValueError(
+            f"warp_state: state must be 3D (3+C, H, W), got shape {tuple(state.shape)}"
+        )
     xyoc, h, w = state.shape
-    assert flow.shape == (2, h, w)
+    if flow.shape != (2, h, w):
+        raise ValueError(
+            f"warp_state: flow must have shape (2, {h}, {w}), got {tuple(flow.shape)}"
+        )
     device = state.device
 
     x_ch, y_ch = 0, 1
@@ -198,8 +224,12 @@ def warp_state(state, flow):
     w_ch = 2       # state[w_ch] = ω
     c = xyoc - xyw
     oc = xyoc - xy
-    assert c > 0, "state has no noise channels"
-    assert (state[w_ch] > 0).all(), "all weights must be > 0"
+    if c <= 0:
+        raise ValueError(
+            f"warp_state: state has no noise channels (expected 3+C with C>0, got {xyoc} channels)"
+        )
+    if not (state[w_ch] > 0).all():
+        raise ValueError("warp_state: all weights in state[2] must be > 0")
 
     grid = xy_meshgrid_like_image(state)
 
@@ -267,7 +297,10 @@ class NoiseWarper:
     """
 
     def __init__(self, c, h, w, device, dtype=torch.float32):
-        assert c > 0 and h > 0 and w > 0
+        if c <= 0 or h <= 0 or w <= 0:
+            raise ValueError(
+                f"NoiseWarper: c/h/w must all be positive, got c={c} h={h} w={w}"
+            )
         self.c = c
         self.h = h
         self.w = w
@@ -287,7 +320,10 @@ class NoiseWarper:
         return n * weights / (weights ** 2).sqrt()
 
     def __call__(self, dx, dy):
-        assert dx.shape == dy.shape
+        if dx.shape != dy.shape:
+            raise ValueError(
+                f"NoiseWarper: dx and dy must match, got {tuple(dx.shape)} vs {tuple(dy.shape)}"
+            )
         flow = torch.stack([dx, dy]).to(self.device, self.dtype)
         _, oflowh, ofloww = flow.shape
 
@@ -312,8 +348,6 @@ class RaftOpticalFlow:
     """Torchvision RAFT-large wrapper.  ``__call__`` returns a (2, H, W) flow."""
 
     def __init__(self, device=None):
-        from torchvision.models.optical_flow import raft_large
-
         if device is None:
             device = comfy.model_management.get_torch_device()
         device = torch.device(device) if not isinstance(device, torch.device) else device
@@ -334,7 +368,11 @@ class RaftOpticalFlow:
 
     def __call__(self, from_image, to_image):
         """``from_image``, ``to_image``: CHW float tensors in [0, 1]."""
-        assert from_image.shape == to_image.shape
+        if from_image.shape != to_image.shape:
+            raise ValueError(
+                f"RaftOpticalFlow: from_image and to_image must match, "
+                f"got {tuple(from_image.shape)} vs {tuple(to_image.shape)}"
+            )
         _, h, w = from_image.shape
         with torch.no_grad():
             img1 = self._preprocess(from_image)
@@ -385,9 +423,20 @@ def get_noise_from_video(
     Returns:
         ``(T, H', W', noise_channels)`` float32 noise tensor on ``device``.
     """
-    assert isinstance(resize_flow, int) and resize_flow >= 1, resize_flow
-    assert video_frames.ndim == 4 and video_frames.shape[-1] == 3, video_frames.shape
-    assert video_frames.dtype == torch.uint8, video_frames.dtype
+    if not isinstance(resize_flow, int) or resize_flow < 1:
+        raise ValueError(
+            f"get_noise_from_video: resize_flow must be a positive int, got {resize_flow!r}"
+        )
+    if video_frames.ndim != 4 or video_frames.shape[-1] != 3:
+        raise ValueError(
+            "get_noise_from_video: video_frames must have shape (T, H, W, 3), "
+            f"got {tuple(video_frames.shape)}"
+        )
+    if video_frames.dtype != torch.uint8:
+        raise TypeError(
+            "get_noise_from_video: video_frames must be uint8 in [0, 255], "
+            f"got dtype {video_frames.dtype}"
+        )
 
     if device is None:
         device = comfy.model_management.get_torch_device()