Update Salia_Croppytools.py

Salia_Croppytools.py

@@ -1,553 +1,553 @@
-import os
-from typing import Tuple
-
-import torch
-import torch.nn.functional as F
-import numpy as np
-from PIL import Image
-
-
-# Salia utils (same style as your loader node)
-try:
-    from ..utils.io import list_pngs, load_image_from_assets, file_hash, safe_path
-except Exception:
-    # Fallback if you placed this file in a different package depth
-    try:
-        from .utils.io import list_pngs, load_image_from_assets, file_hash, safe_path
-    except Exception as e:
-        _UTILS_IMPORT_ERR = e
-
-        def _missing(*args, **kwargs):
-            raise ImportError(
-                "Could not import Salia utils (list_pngs/load_image_from_assets/file_hash/safe_path). "
-                "Place this node file in the same package layout as your other Salia nodes.\n"
-                f"Original import error: {_UTILS_IMPORT_ERR}"
-            )
-
-        list_pngs = _missing
-        load_image_from_assets = _missing
-        file_hash = _missing
-        safe_path = _missing
-
-
-# -----------------------------
-# Helpers (IMAGE)
-# -----------------------------
-
-def _as_image(img: torch.Tensor) -> torch.Tensor:
-    # ComfyUI IMAGE is usually [B,H,W,C]
-    if not isinstance(img, torch.Tensor):
-        raise TypeError("IMAGE must be a torch.Tensor")
-    if img.dim() != 4:
-        raise ValueError(f"Expected IMAGE shape [B,H,W,C], got {tuple(img.shape)}")
-    if img.shape[-1] not in (3, 4):
-        raise ValueError(f"Expected IMAGE channels 3 (RGB) or 4 (RGBA), got C={img.shape[-1]}")
-    return img
-
-
-def _crop_with_padding(image: torch.Tensor, x: int, y: int, w: int, h: int) -> torch.Tensor:
-    """
-    Crops [x,y] top-left, size w*h. If out of bounds, pads with zeros.
-    image: [B,H,W,C]
-    returns: [B,h,w,C]
-    """
-    image = _as_image(image)
-    B, H, W, C = image.shape
-    w = max(1, int(w))
-    h = max(1, int(h))
-    x = int(x)
-    y = int(y)
-
-    out = torch.zeros((B, h, w, C), device=image.device, dtype=image.dtype)
-
-    # intersection in source
-    x0s = max(0, x)
-    y0s = max(0, y)
-    x1s = min(W, x + w)
-    y1s = min(H, y + h)
-
-    if x1s <= x0s or y1s <= y0s:
-        return out
-
-    # destination offsets
-    x0d = x0s - x
-    y0d = y0s - y
-    x1d = x0d + (x1s - x0s)
-    y1d = y0d + (y1s - y0s)
-
-    out[:, y0d:y1d, x0d:x1d, :] = image[:, y0s:y1s, x0s:x1s, :]
-    return out
-
-
-def _ensure_rgba(img: torch.Tensor) -> torch.Tensor:
-    """
-    img: [B,H,W,C] where C is 3 or 4
-    returns RGBA [B,H,W,4]
-    """
-    img = _as_image(img)
-    if img.shape[-1] == 4:
-        return img
-    # RGB -> RGBA with alpha=1
-    B, H, W, _ = img.shape
-    alpha = torch.ones((B, H, W, 1), device=img.device, dtype=img.dtype)
-    return torch.cat([img, alpha], dim=-1)
-
-
-def _alpha_over_region(overlay: torch.Tensor, canvas: torch.Tensor, x: int, y: int) -> torch.Tensor:
-    """
-    Places overlay at canvas pixel position (x,y) top-left corner.
-    Supports RGB/RGBA for both. Uses alpha-over if overlay has alpha or canvas has alpha.
-    Returns same channel count as canvas (3->3, 4->4).
-    """
-    overlay = _as_image(overlay)
-    canvas = _as_image(canvas)
-
-    # Simple batch handling (Comfy usually matches batches, but allow 1->N)
-    if overlay.shape[0] != canvas.shape[0]:
-        if overlay.shape[0] == 1 and canvas.shape[0] > 1:
-            overlay = overlay.expand(canvas.shape[0], *overlay.shape[1:])
-        elif canvas.shape[0] == 1 and overlay.shape[0] > 1:
-            canvas = canvas.expand(overlay.shape[0], *canvas.shape[1:])
-        else:
-            raise ValueError(f"Batch mismatch: overlay {overlay.shape[0]} vs canvas {canvas.shape[0]}")
-
-    B, Hc, Wc, Cc = canvas.shape
-    _, Ho, Wo, _ = overlay.shape
-
-    x = int(x)
-    y = int(y)
-
-    out = canvas.clone()
-
-    # intersection on canvas
-    x0c = max(0, x)
-    y0c = max(0, y)
-    x1c = min(Wc, x + Wo)
-    y1c = min(Hc, y + Ho)
-
-    if x1c <= x0c or y1c <= y0c:
-        return out
-
-    # corresponding region on overlay
-    x0o = x0c - x
-    y0o = y0c - y
-    x1o = x0o + (x1c - x0c)
-    y1o = y0o + (y1c - y0c)
-
-    canvas_region = out[:, y0c:y1c, x0c:x1c, :]
-    overlay_region = overlay[:, y0o:y1o, x0o:x1o, :]
-
-    # Convert both regions to RGBA for compositing
-    canvas_rgba = _ensure_rgba(canvas_region)
-    overlay_rgba = _ensure_rgba(overlay_region)
-
-    over_rgb = overlay_rgba[..., :3].clamp(0.0, 1.0)
-    over_a = overlay_rgba[..., 3:4].clamp(0.0, 1.0)
-
-    under_rgb = canvas_rgba[..., :3].clamp(0.0, 1.0)
-    under_a = canvas_rgba[..., 3:4].clamp(0.0, 1.0)
-
-    # Premultiplied alpha composite: out = over + under*(1-over_a)
-    over_pm = over_rgb * over_a
-    under_pm = under_rgb * under_a
-
-    out_a = over_a + under_a * (1.0 - over_a)
-    out_pm = over_pm + under_pm * (1.0 - over_a)
-
-    eps = 1e-6
-    out_rgb = torch.where(out_a > eps, out_pm / (out_a + eps), torch.zeros_like(out_pm))
-    out_rgb = out_rgb.clamp(0.0, 1.0)
-    out_a = out_a.clamp(0.0, 1.0)
-
-    if Cc == 3:
-        out[:, y0c:y1c, x0c:x1c, :] = out_rgb
-    else:
-        out[:, y0c:y1c, x0c:x1c, :] = torch.cat([out_rgb, out_a], dim=-1)
-
-    return out
-
-
-# -----------------------------
-# RMBG EXACT MASK COMBINE LOGIC (copied solution)
-# -----------------------------
-
-class _AILab_MaskCombiner_Exact:
-    def combine_masks(self, mask_1, mode="combine", mask_2=None, mask_3=None, mask_4=None):
-        try:
-            masks = [m for m in [mask_1, mask_2, mask_3, mask_4] if m is not None]
-
-            if len(masks) <= 1:
-                return (masks[0] if masks else torch.zeros((1, 64, 64), dtype=torch.float32),)
-
-            ref_shape = masks[0].shape
-            masks = [self._resize_if_needed(m, ref_shape) for m in masks]
-
-            if mode == "combine":
-                result = torch.maximum(masks[0], masks[1])
-                for mask in masks[2:]:
-                    result = torch.maximum(result, mask)
-            elif mode == "intersection":
-                result = torch.minimum(masks[0], masks[1])
-            else:
-                result = torch.abs(masks[0] - masks[1])
-
-            return (torch.clamp(result, 0, 1),)
-        except Exception as e:
-            print(f"Error in combine_masks: {str(e)}")
-            print(f"Mask shapes: {[m.shape for m in masks]}")
-            raise e
-
-    def _resize_if_needed(self, mask, target_shape):
-        try:
-            if mask.shape == target_shape:
-                return mask
-
-            if len(mask.shape) == 2:
-                mask = mask.unsqueeze(0)
-            elif len(mask.shape) == 4:
-                mask = mask.squeeze(1)
-
-            target_height = target_shape[-2] if len(target_shape) >= 2 else target_shape[0]
-            target_width = target_shape[-1] if len(target_shape) >= 2 else target_shape[1]
-
-            resized_masks = []
-            for i in range(mask.shape[0]):
-                mask_np = mask[i].cpu().numpy()
-                img = Image.fromarray((mask_np * 255).astype(np.uint8))
-                img_resized = img.resize((target_width, target_height), Image.LANCZOS)
-                mask_resized = np.array(img_resized).astype(np.float32) / 255.0
-                resized_masks.append(torch.from_numpy(mask_resized))
-
-            return torch.stack(resized_masks)
-
-        except Exception as e:
-            print(f"Error in _resize_if_needed: {str(e)}")
-            print(f"Input mask shape: {mask.shape}, Target shape: {target_shape}")
-            raise e
-
-
-# -----------------------------
-# 1) Cropout_Square_From_IMG
-# -----------------------------
-
-class Cropout_Square_From_IMG:
-    CATEGORY = "image/salia"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "img": ("IMAGE",),
-                "x": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
-                "y": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
-                "square_size": ("INT", {"default": 512, "min": 1, "max": 16384, "step": 1}),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("image",)
-    FUNCTION = "run"
-
-    def run(self, img, x, y, square_size):
-        cropped = _crop_with_padding(img, x, y, square_size, square_size)
-        return (cropped,)
-
-
-# -----------------------------
-# 2) Cropout_Rect_From_IMG
-# -----------------------------
-
-class Cropout_Rect_From_IMG:
-    CATEGORY = "image/salia"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "img": ("IMAGE",),
-                "x": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
-                "y": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
-                "width": ("INT", {"default": 512, "min": 1, "max": 16384, "step": 1}),
-                "height": ("INT", {"default": 512, "min": 1, "max": 16384, "step": 1}),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("image",)
-    FUNCTION = "run"
-
-    def run(self, img, x, y, width, height):
-        cropped = _crop_with_padding(img, x, y, width, height)
-        return (cropped,)
-
-
-# -----------------------------
-# 3) Paste_rect_to_img
-# -----------------------------
-
-class Paste_rect_to_img:
-    CATEGORY = "image/salia"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "overlay": ("IMAGE",),
-                "canvas": ("IMAGE",),
-                "x": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
-                "y": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("image",)
-    FUNCTION = "run"
-
-    def run(self, overlay, canvas, x, y):
-        out = _alpha_over_region(overlay, canvas, x, y)
-        return (out,)
-
-
-# -----------------------------
-# 4) Combine_2_masks  (RMBG exact: torch.maximum + PIL resize)
-# -----------------------------
-
-class Combine_2_masks:
-    CATEGORY = "mask/salia"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {"required": {"maskA": ("MASK",), "maskB": ("MASK",)}}
-
-    RETURN_TYPES = ("MASK",)
-    RETURN_NAMES = ("mask",)
-    FUNCTION = "run"
-
-    def run(self, maskA, maskB):
-        combiner = _AILab_MaskCombiner_Exact()
-        out, = combiner.combine_masks(maskA, mode="combine", mask_2=maskB)
-        return (out,)
-
-
-# -----------------------------
-# 5) Combine_2_masks_invert_1 (invert A then RMBG combine)
-# -----------------------------
-
-class Combine_2_masks_invert_1:
-    CATEGORY = "mask/salia"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {"required": {"maskA": ("MASK",), "maskB": ("MASK",)}}
-
-    RETURN_TYPES = ("MASK",)
-    RETURN_NAMES = ("mask",)
-    FUNCTION = "run"
-
-    def run(self, maskA, maskB):
-        combiner = _AILab_MaskCombiner_Exact()
-        maskA = 1.0 - maskA
-        out, = combiner.combine_masks(maskA, mode="combine", mask_2=maskB)
-        return (out,)
-
-
-# -----------------------------
-# 6) Combine_2_masks_inverse
-# invert both, combine, invert result (RMBG max logic)
-# -----------------------------
-
-class Combine_2_masks_inverse:
-    CATEGORY = "mask/salia"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {"required": {"maskA": ("MASK",), "maskB": ("MASK",)}}
-
-    RETURN_TYPES = ("MASK",)
-    RETURN_NAMES = ("mask",)
-    FUNCTION = "run"
-
-    def run(self, maskA, maskB):
-        combiner = _AILab_MaskCombiner_Exact()
-        maskA = 1.0 - maskA
-        maskB = 1.0 - maskB
-        combined, = combiner.combine_masks(maskA, mode="combine", mask_2=maskB)
-        out = 1.0 - combined
-        out = torch.clamp(out, 0, 1)
-        return (out,)
-
-
-# -----------------------------
-# 7) combine_masks_with_loaded (RMBG exact combine)
-# -----------------------------
-
-class combine_masks_with_loaded:
-    CATEGORY = "mask/salia"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        choices = list_pngs() or ["<no pngs found>"]
-        return {
-            "required": {
-                "mask": ("MASK",),
-                "image": (choices, {}),
-            }
-        }
-
-    RETURN_TYPES = ("MASK",)
-    RETURN_NAMES = ("mask",)
-    FUNCTION = "run"
-
-    def run(self, mask, image):
-        if image == "<no pngs found>":
-            raise FileNotFoundError("No PNGs in assets/images")
-
-        _img, loaded_mask = load_image_from_assets(image)
-
-        combiner = _AILab_MaskCombiner_Exact()
-        out, = combiner.combine_masks(mask, mode="combine", mask_2=loaded_mask)
-        return (out,)
-
-    @classmethod
-    def IS_CHANGED(cls, mask, image):
-        if image == "<no pngs found>":
-            return image
-        return file_hash(image)
-
-    @classmethod
-    def VALIDATE_INPUTS(cls, mask, image):
-        if image == "<no pngs found>":
-            return "No PNGs in assets/images"
-        try:
-            path = safe_path(image)
-        except Exception as e:
-            return str(e)
-        if not os.path.isfile(path):
-            return f"File not found in assets/images: {image}"
-        return True
-
-
-# -----------------------------
-# 8) NEW: invert input mask, combine with loaded mask, apply to image alpha, paste on canvas
-# -----------------------------
-
-class apply_segment:
-    CATEGORY = "image/salia"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        choices = list_pngs() or ["<no pngs found>"]
-        return {
-            "required": {
-                "mask": ("MASK",),
-                "image": (choices, {}),          # dropdown asset (used ONLY for loaded mask)
-                "img": ("IMAGE",),               # the image to receive final_mask as alpha (overlay source)
-                "canvas": ("IMAGE",),            # destination
-                "x": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
-                "y": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("image",)
-    FUNCTION = "run"
-
-    def run(self, mask, image, img, canvas, x, y):
-        if image == "<no pngs found>":
-            raise FileNotFoundError("No PNGs in assets/images")
-
-        combiner = _AILab_MaskCombiner_Exact()
-
-        # Load asset mask (do NOT invert)
-        _img_asset, loaded_mask = load_image_from_assets(image)
-
-        # Invert input mask, then combine with loaded mask (RMBG exact combine => maximum)
-        inv_mask = 1.0 - mask
-        final_mask, = combiner.combine_masks(inv_mask, mode="combine", mask_2=loaded_mask)
-
-        # Apply final_mask as alpha to input image -> final_overlay (RGBA)
-        img = _as_image(img)
-        B, H, W, C = img.shape
-
-        # Resize final_mask to match img H/W if needed (uses RMBG exact resize helper)
-        # (target_shape must look like a mask shape [B,H,W], but resize keeps its own batch count)
-        final_mask_resized = combiner._resize_if_needed(final_mask, (final_mask.shape[0], H, W))
-
-        # Batch match (simple 1->N expansion only)
-        if final_mask_resized.shape[0] != B:
-            if final_mask_resized.shape[0] == 1 and B > 1:
-                final_mask_resized = final_mask_resized.expand(B, H, W)
-            elif B == 1 and final_mask_resized.shape[0] > 1:
-                img = img.expand(final_mask_resized.shape[0], *img.shape[1:])
-                B = img.shape[0]
-            else:
-                raise ValueError(f"Batch mismatch: img batch={B}, final_mask batch={final_mask_resized.shape[0]}")
-
-        if C == 3:
-            # RGB -> RGBA with alpha = final_mask
-            alpha = final_mask_resized.to(device=img.device, dtype=img.dtype)
-            final_overlay = torch.cat([img, alpha.unsqueeze(-1)], dim=-1)
-        else:
-            # RGBA: combine existing alpha with final_mask using RMBG combine (maximum)
-            rgb = img[..., :3]
-            alpha_img = img[..., 3]  # [B,H,W]
-
-            # RMBG combine uses PIL-resize sometimes, so keep combine inputs on CPU
-            a1 = alpha_img.detach().cpu()
-            a2 = final_mask_resized.detach().cpu()
-            combined_alpha, = combiner.combine_masks(a1, mode="combine", mask_2=a2)
-
-            combined_alpha = combined_alpha.to(device=img.device, dtype=img.dtype)
-            final_overlay = torch.cat([rgb, combined_alpha.unsqueeze(-1)], dim=-1)
-
-        # Paste final_overlay onto canvas at (x,y)
-        canvas = _as_image(canvas)
-        final_overlay = final_overlay.to(device=canvas.device, dtype=canvas.dtype)
-
-        out = _alpha_over_region(final_overlay, canvas, x, y)
-        return (out,)
-
-    @classmethod
-    def IS_CHANGED(cls, mask, image, img, canvas, x, y):
-        if image == "<no pngs found>":
-            return image
-        return file_hash(image)
-
-    @classmethod
-    def VALIDATE_INPUTS(cls, mask, image, img, canvas, x, y):
-        if image == "<no pngs found>":
-            return "No PNGs in assets/images"
-        try:
-            path = safe_path(image)
-        except Exception as e:
-            return str(e)
-        if not os.path.isfile(path):
-            return f"File not found in assets/images: {image}"
-        return True
-
-
-# -----------------------------
-# Node mappings
-# -----------------------------
-
-NODE_CLASS_MAPPINGS = {
-    "Cropout_Square_From_IMG": Cropout_Square_From_IMG,
-    "Cropout_Rect_From_IMG": Cropout_Rect_From_IMG,
-    "Paste_rect_to_img": Paste_rect_to_img,
-    "Combine_2_masks": Combine_2_masks,
-    "Combine_2_masks_invert_1": Combine_2_masks_invert_1,
-    "Combine_2_masks_inverse": Combine_2_masks_inverse,
-    "combine_masks_with_loaded": combine_masks_with_loaded,
-    "apply_segment": apply_segment,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "Cropout_Square_From_IMG": "Cropout_Square_From_IMG",
-    "Cropout_Rect_From_IMG": "Cropout_Rect_From_IMG",
-    "Paste_rect_to_img": "Paste_rect_to_img",
-    "Combine_2_masks": "Combine_2_masks",
-    "Combine_2_masks_invert_1": "Combine_2_masks_invert_1",
-    "Combine_2_masks_inverse": "Combine_2_masks_inverse",
-    "combine_masks_with_loaded": "combine_masks_with_loaded",
-    "apply_segment": "apply_segment",
+import os
+from typing import Tuple
+
+import torch
+import torch.nn.functional as F
+import numpy as np
+from PIL import Image
+
+
+# Salia utils (same style as your loader node)
+try:
+    from ..utils.io import list_pngs, load_image_from_assets, file_hash, safe_path
+except Exception:
+    # Fallback if you placed this file in a different package depth
+    try:
+        from .utils.io import list_pngs, load_image_from_assets, file_hash, safe_path
+    except Exception as e:
+        _UTILS_IMPORT_ERR = e
+
+        def _missing(*args, **kwargs):
+            raise ImportError(
+                "Could not import Salia utils (list_pngs/load_image_from_assets/file_hash/safe_path). "
+                "Place this node file in the same package layout as your other Salia nodes.\n"
+                f"Original import error: {_UTILS_IMPORT_ERR}"
+            )
+
+        list_pngs = _missing
+        load_image_from_assets = _missing
+        file_hash = _missing
+        safe_path = _missing
+
+
+# -----------------------------
+# Helpers (IMAGE)
+# -----------------------------
+
+def _as_image(img: torch.Tensor) -> torch.Tensor:
+    # ComfyUI IMAGE is usually [B,H,W,C]
+    if not isinstance(img, torch.Tensor):
+        raise TypeError("IMAGE must be a torch.Tensor")
+    if img.dim() != 4:
+        raise ValueError(f"Expected IMAGE shape [B,H,W,C], got {tuple(img.shape)}")
+    if img.shape[-1] not in (3, 4):
+        raise ValueError(f"Expected IMAGE channels 3 (RGB) or 4 (RGBA), got C={img.shape[-1]}")
+    return img
+
+
+def _crop_with_padding(image: torch.Tensor, x: int, y: int, w: int, h: int) -> torch.Tensor:
+    """
+    Crops [x,y] top-left, size w*h. If out of bounds, pads with zeros.
+    image: [B,H,W,C]
+    returns: [B,h,w,C]
+    """
+    image = _as_image(image)
+    B, H, W, C = image.shape
+    w = max(1, int(w))
+    h = max(1, int(h))
+    x = int(x)
+    y = int(y)
+
+    out = torch.zeros((B, h, w, C), device=image.device, dtype=image.dtype)
+
+    # intersection in source
+    x0s = max(0, x)
+    y0s = max(0, y)
+    x1s = min(W, x + w)
+    y1s = min(H, y + h)
+
+    if x1s <= x0s or y1s <= y0s:
+        return out
+
+    # destination offsets
+    x0d = x0s - x
+    y0d = y0s - y
+    x1d = x0d + (x1s - x0s)
+    y1d = y0d + (y1s - y0s)
+
+    out[:, y0d:y1d, x0d:x1d, :] = image[:, y0s:y1s, x0s:x1s, :]
+    return out
+
+
+def _ensure_rgba(img: torch.Tensor) -> torch.Tensor:
+    """
+    img: [B,H,W,C] where C is 3 or 4
+    returns RGBA [B,H,W,4]
+    """
+    img = _as_image(img)
+    if img.shape[-1] == 4:
+        return img
+    # RGB -> RGBA with alpha=1
+    B, H, W, _ = img.shape
+    alpha = torch.ones((B, H, W, 1), device=img.device, dtype=img.dtype)
+    return torch.cat([img, alpha], dim=-1)
+
+
+def _alpha_over_region(overlay: torch.Tensor, canvas: torch.Tensor, x: int, y: int) -> torch.Tensor:
+    """
+    Places overlay at canvas pixel position (x,y) top-left corner.
+    Supports RGB/RGBA for both. Uses alpha-over if overlay has alpha or canvas has alpha.
+    Returns same channel count as canvas (3->3, 4->4).
+    """
+    overlay = _as_image(overlay)
+    canvas = _as_image(canvas)
+
+    # Simple batch handling (Comfy usually matches batches, but allow 1->N)
+    if overlay.shape[0] != canvas.shape[0]:
+        if overlay.shape[0] == 1 and canvas.shape[0] > 1:
+            overlay = overlay.expand(canvas.shape[0], *overlay.shape[1:])
+        elif canvas.shape[0] == 1 and overlay.shape[0] > 1:
+            canvas = canvas.expand(overlay.shape[0], *canvas.shape[1:])
+        else:
+            raise ValueError(f"Batch mismatch: overlay {overlay.shape[0]} vs canvas {canvas.shape[0]}")
+
+    B, Hc, Wc, Cc = canvas.shape
+    _, Ho, Wo, _ = overlay.shape
+
+    x = int(x)
+    y = int(y)
+
+    out = canvas.clone()
+
+    # intersection on canvas
+    x0c = max(0, x)
+    y0c = max(0, y)
+    x1c = min(Wc, x + Wo)
+    y1c = min(Hc, y + Ho)
+
+    if x1c <= x0c or y1c <= y0c:
+        return out
+
+    # corresponding region on overlay
+    x0o = x0c - x
+    y0o = y0c - y
+    x1o = x0o + (x1c - x0c)
+    y1o = y0o + (y1c - y0c)
+
+    canvas_region = out[:, y0c:y1c, x0c:x1c, :]
+    overlay_region = overlay[:, y0o:y1o, x0o:x1o, :]
+
+    # Convert both regions to RGBA for compositing
+    canvas_rgba = _ensure_rgba(canvas_region)
+    overlay_rgba = _ensure_rgba(overlay_region)
+
+    over_rgb = overlay_rgba[..., :3].clamp(0.0, 1.0)
+    over_a = overlay_rgba[..., 3:4].clamp(0.0, 1.0)
+
+    under_rgb = canvas_rgba[..., :3].clamp(0.0, 1.0)
+    under_a = canvas_rgba[..., 3:4].clamp(0.0, 1.0)
+
+    # Premultiplied alpha composite: out = over + under*(1-over_a)
+    over_pm = over_rgb * over_a
+    under_pm = under_rgb * under_a
+
+    out_a = over_a + under_a * (1.0 - over_a)
+    out_pm = over_pm + under_pm * (1.0 - over_a)
+
+    eps = 1e-6
+    out_rgb = torch.where(out_a > eps, out_pm / (out_a + eps), torch.zeros_like(out_pm))
+    out_rgb = out_rgb.clamp(0.0, 1.0)
+    out_a = out_a.clamp(0.0, 1.0)
+
+    if Cc == 3:
+        out[:, y0c:y1c, x0c:x1c, :] = out_rgb
+    else:
+        out[:, y0c:y1c, x0c:x1c, :] = torch.cat([out_rgb, out_a], dim=-1)
+
+    return out
+
+
+# -----------------------------
+# RMBG EXACT MASK COMBINE LOGIC (copied solution)
+# -----------------------------
+
+class _AILab_MaskCombiner_Exact:
+    def combine_masks(self, mask_1, mode="combine", mask_2=None, mask_3=None, mask_4=None):
+        try:
+            masks = [m for m in [mask_1, mask_2, mask_3, mask_4] if m is not None]
+
+            if len(masks) <= 1:
+                return (masks[0] if masks else torch.zeros((1, 64, 64), dtype=torch.float32),)
+
+            ref_shape = masks[0].shape
+            masks = [self._resize_if_needed(m, ref_shape) for m in masks]
+
+            if mode == "combine":
+                result = torch.maximum(masks[0], masks[1])
+                for mask in masks[2:]:
+                    result = torch.maximum(result, mask)
+            elif mode == "intersection":
+                result = torch.minimum(masks[0], masks[1])
+            else:
+                result = torch.abs(masks[0] - masks[1])
+
+            return (torch.clamp(result, 0, 1),)
+        except Exception as e:
+            print(f"Error in combine_masks: {str(e)}")
+            print(f"Mask shapes: {[m.shape for m in masks]}")
+            raise e
+
+    def _resize_if_needed(self, mask, target_shape):
+        try:
+            if mask.shape == target_shape:
+                return mask
+
+            if len(mask.shape) == 2:
+                mask = mask.unsqueeze(0)
+            elif len(mask.shape) == 4:
+                mask = mask.squeeze(1)
+
+            target_height = target_shape[-2] if len(target_shape) >= 2 else target_shape[0]
+            target_width = target_shape[-1] if len(target_shape) >= 2 else target_shape[1]
+
+            resized_masks = []
+            for i in range(mask.shape[0]):
+                mask_np = mask[i].cpu().numpy()
+                img = Image.fromarray((mask_np * 255).astype(np.uint8))
+                img_resized = img.resize((target_width, target_height), Image.LANCZOS)
+                mask_resized = np.array(img_resized).astype(np.float32) / 255.0
+                resized_masks.append(torch.from_numpy(mask_resized))
+
+            return torch.stack(resized_masks)
+
+        except Exception as e:
+            print(f"Error in _resize_if_needed: {str(e)}")
+            print(f"Input mask shape: {mask.shape}, Target shape: {target_shape}")
+            raise e
+
+
+# -----------------------------
+# 1) Cropout_Square_From_IMG
+# -----------------------------
+
+class Cropout_Square_From_IMG:
+    CATEGORY = "image/salia"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "img": ("IMAGE",),
+                "x": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
+                "y": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
+                "square_size": ("INT", {"default": 512, "min": 1, "max": 16384, "step": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "run"
+
+    def run(self, img, x, y, square_size):
+        cropped = _crop_with_padding(img, x, y, square_size, square_size)
+        return (cropped,)
+
+
+# -----------------------------
+# 2) Cropout_Rect_From_IMG
+# -----------------------------
+
+class Cropout_Rect_From_IMG:
+    CATEGORY = "image/salia"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "img": ("IMAGE",),
+                "x": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
+                "y": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
+                "width": ("INT", {"default": 512, "min": 1, "max": 16384, "step": 1}),
+                "height": ("INT", {"default": 512, "min": 1, "max": 16384, "step": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "run"
+
+    def run(self, img, x, y, width, height):
+        cropped = _crop_with_padding(img, x, y, width, height)
+        return (cropped,)
+
+
+# -----------------------------
+# 3) Paste_rect_to_img
+# -----------------------------
+
+class Paste_rect_to_img:
+    CATEGORY = "image/salia"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "overlay": ("IMAGE",),
+                "canvas": ("IMAGE",),
+                "x": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
+                "y": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "run"
+
+    def run(self, overlay, canvas, x, y):
+        out = _alpha_over_region(overlay, canvas, x, y)
+        return (out,)
+
+
+# -----------------------------
+# 4) Combine_2_masks  (RMBG exact: torch.maximum + PIL resize)
+# -----------------------------
+
+class Combine_2_masks:
+    CATEGORY = "mask/salia"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {"maskA": ("MASK",), "maskB": ("MASK",)}}
+
+    RETURN_TYPES = ("MASK",)
+    RETURN_NAMES = ("mask",)
+    FUNCTION = "run"
+
+    def run(self, maskA, maskB):
+        combiner = _AILab_MaskCombiner_Exact()
+        out, = combiner.combine_masks(maskA, mode="combine", mask_2=maskB)
+        return (out,)
+
+
+# -----------------------------
+# 5) Combine_2_masks_invert_1 (invert A then RMBG combine)
+# -----------------------------
+
+class Combine_2_masks_invert_1:
+    CATEGORY = "mask/salia"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {"maskA": ("MASK",), "maskB": ("MASK",)}}
+
+    RETURN_TYPES = ("MASK",)
+    RETURN_NAMES = ("mask",)
+    FUNCTION = "run"
+
+    def run(self, maskA, maskB):
+        combiner = _AILab_MaskCombiner_Exact()
+        maskA = 1.0 - maskA
+        out, = combiner.combine_masks(maskA, mode="combine", mask_2=maskB)
+        return (out,)
+
+
+# -----------------------------
+# 6) Combine_2_masks_inverse
+# invert both, combine, invert result (RMBG max logic)
+# -----------------------------
+
+class Combine_2_masks_inverse:
+    CATEGORY = "mask/salia"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {"maskA": ("MASK",), "maskB": ("MASK",)}}
+
+    RETURN_TYPES = ("MASK",)
+    RETURN_NAMES = ("mask",)
+    FUNCTION = "run"
+
+    def run(self, maskA, maskB):
+        combiner = _AILab_MaskCombiner_Exact()
+        maskA = 1.0 - maskA
+        maskB = 1.0 - maskB
+        combined, = combiner.combine_masks(maskA, mode="combine", mask_2=maskB)
+        out = 1.0 - combined
+        out = torch.clamp(out, 0, 1)
+        return (out,)
+
+
+# -----------------------------
+# 7) combine_masks_with_loaded (RMBG exact combine)
+# -----------------------------
+
+class combine_masks_with_loaded:
+    CATEGORY = "mask/salia"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        choices = list_pngs() or ["<no pngs found>"]
+        return {
+            "required": {
+                "mask": ("MASK",),
+                "image": (choices, {}),
+            }
+        }
+
+    RETURN_TYPES = ("MASK",)
+    RETURN_NAMES = ("mask",)
+    FUNCTION = "run"
+
+    def run(self, mask, image):
+        if image == "<no pngs found>":
+            raise FileNotFoundError("No PNGs in assets/images")
+
+        _img, loaded_mask = load_image_from_assets(image)
+
+        combiner = _AILab_MaskCombiner_Exact()
+        out, = combiner.combine_masks(mask, mode="combine", mask_2=1.0-loaded_mask)
+        return (out,)
+
+    @classmethod
+    def IS_CHANGED(cls, mask, image):
+        if image == "<no pngs found>":
+            return image
+        return file_hash(image)
+
+    @classmethod
+    def VALIDATE_INPUTS(cls, mask, image):
+        if image == "<no pngs found>":
+            return "No PNGs in assets/images"
+        try:
+            path = safe_path(image)
+        except Exception as e:
+            return str(e)
+        if not os.path.isfile(path):
+            return f"File not found in assets/images: {image}"
+        return True
+
+
+# -----------------------------
+# 8) NEW: invert input mask, combine with loaded mask, apply to image alpha, paste on canvas
+# -----------------------------
+
+class apply_segment:
+    CATEGORY = "image/salia"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        choices = list_pngs() or ["<no pngs found>"]
+        return {
+            "required": {
+                "mask": ("MASK",),
+                "image": (choices, {}),          # dropdown asset (used ONLY for loaded mask)
+                "img": ("IMAGE",),               # the image to receive final_mask as alpha (overlay source)
+                "canvas": ("IMAGE",),            # destination
+                "x": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
+                "y": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "run"
+
+    def run(self, mask, image, img, canvas, x, y):
+        if image == "<no pngs found>":
+            raise FileNotFoundError("No PNGs in assets/images")
+
+        combiner = _AILab_MaskCombiner_Exact()
+
+        # Load asset mask (do NOT invert)
+        _img_asset, loaded_mask = load_image_from_assets(image)
+
+        # Invert input mask, then combine with loaded mask (RMBG exact combine => maximum)
+        inv_mask = 1.0 - mask
+        final_mask, = combiner.combine_masks(inv_mask, mode="combine", mask_2=loaded_mask)
+
+        # Apply final_mask as alpha to input image -> final_overlay (RGBA)
+        img = _as_image(img)
+        B, H, W, C = img.shape
+
+        # Resize final_mask to match img H/W if needed (uses RMBG exact resize helper)
+        # (target_shape must look like a mask shape [B,H,W], but resize keeps its own batch count)
+        final_mask_resized = combiner._resize_if_needed(final_mask, (final_mask.shape[0], H, W))
+
+        # Batch match (simple 1->N expansion only)
+        if final_mask_resized.shape[0] != B:
+            if final_mask_resized.shape[0] == 1 and B > 1:
+                final_mask_resized = final_mask_resized.expand(B, H, W)
+            elif B == 1 and final_mask_resized.shape[0] > 1:
+                img = img.expand(final_mask_resized.shape[0], *img.shape[1:])
+                B = img.shape[0]
+            else:
+                raise ValueError(f"Batch mismatch: img batch={B}, final_mask batch={final_mask_resized.shape[0]}")
+
+        if C == 3:
+            # RGB -> RGBA with alpha = final_mask
+            alpha = final_mask_resized.to(device=img.device, dtype=img.dtype)
+            final_overlay = torch.cat([img, alpha.unsqueeze(-1)], dim=-1)
+        else:
+            # RGBA: combine existing alpha with final_mask using RMBG combine (maximum)
+            rgb = img[..., :3]
+            alpha_img = img[..., 3]  # [B,H,W]
+
+            # RMBG combine uses PIL-resize sometimes, so keep combine inputs on CPU
+            a1 = alpha_img.detach().cpu()
+            a2 = final_mask_resized.detach().cpu()
+            combined_alpha, = combiner.combine_masks(a1, mode="combine", mask_2=a2)
+
+            combined_alpha = combined_alpha.to(device=img.device, dtype=img.dtype)
+            final_overlay = torch.cat([rgb, combined_alpha.unsqueeze(-1)], dim=-1)
+
+        # Paste final_overlay onto canvas at (x,y)
+        canvas = _as_image(canvas)
+        final_overlay = final_overlay.to(device=canvas.device, dtype=canvas.dtype)
+
+        out = _alpha_over_region(final_overlay, canvas, x, y)
+        return (out,)
+
+    @classmethod
+    def IS_CHANGED(cls, mask, image, img, canvas, x, y):
+        if image == "<no pngs found>":
+            return image
+        return file_hash(image)
+
+    @classmethod
+    def VALIDATE_INPUTS(cls, mask, image, img, canvas, x, y):
+        if image == "<no pngs found>":
+            return "No PNGs in assets/images"
+        try:
+            path = safe_path(image)
+        except Exception as e:
+            return str(e)
+        if not os.path.isfile(path):
+            return f"File not found in assets/images: {image}"
+        return True
+
+
+# -----------------------------
+# Node mappings
+# -----------------------------
+
+NODE_CLASS_MAPPINGS = {
+    "Cropout_Square_From_IMG": Cropout_Square_From_IMG,
+    "Cropout_Rect_From_IMG": Cropout_Rect_From_IMG,
+    "Paste_rect_to_img": Paste_rect_to_img,
+    "Combine_2_masks": Combine_2_masks,
+    "Combine_2_masks_invert_1": Combine_2_masks_invert_1,
+    "Combine_2_masks_inverse": Combine_2_masks_inverse,
+    "combine_masks_with_loaded": combine_masks_with_loaded,
+    "apply_segment": apply_segment,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "Cropout_Square_From_IMG": "Cropout_Square_From_IMG",
+    "Cropout_Rect_From_IMG": "Cropout_Rect_From_IMG",
+    "Paste_rect_to_img": "Paste_rect_to_img",
+    "Combine_2_masks": "Combine_2_masks",
+    "Combine_2_masks_invert_1": "Combine_2_masks_invert_1",
+    "Combine_2_masks_inverse": "Combine_2_masks_inverse",
+    "combine_masks_with_loaded": "combine_masks_with_loaded",
+    "apply_segment": "apply_segment",
 }