146	        # For single mask, expand it to match size of image batch size.
147	        if mask.shape[0] == 1:
148	            mask = mask.repeat(out.shape[0], 1, 1)
149	
150	        assert mask.ndim == 3, f"Mask should have shape [B, H, W]. {mask.shape}"
151	        assert out.ndim == 4, f"Image should have shsape [B, C, H, W]. {out.shape}"
152	        assert out.shape[-2:] == mask.shape[-2:], f"{out.shape[-2:]} != {mask.shape[-2:]}"
153	        assert out.shape[0] == mask.shape[0], f"{out.shape[0]} != {mask.shape[0]}"

147	        if mask.shape[0] == 1:
148	            mask = mask.repeat(out.shape[0], 1, 1)
149	
150	        assert mask.ndim == 3, f"Mask should have shape [B, H, W]. {mask.shape}"
151	        assert out.ndim == 4, f"Image should have shsape [B, C, H, W]. {out.shape}"
152	        assert out.shape[-2:] == mask.shape[-2:], f"{out.shape[-2:]} != {mask.shape[-2:]}"
153	        assert out.shape[0] == mask.shape[0], f"{out.shape[0]} != {mask.shape[0]}"
154	        # Apply each mask in the batch to its corresponding image's alpha channel

148	            mask = mask.repeat(out.shape[0], 1, 1)
149	
150	        assert mask.ndim == 3, f"Mask should have shape [B, H, W]. {mask.shape}"
151	        assert out.ndim == 4, f"Image should have shsape [B, C, H, W]. {out.shape}"
152	        assert out.shape[-2:] == mask.shape[-2:], f"{out.shape[-2:]} != {mask.shape[-2:]}"
153	        assert out.shape[0] == mask.shape[0], f"{out.shape[0]} != {mask.shape[0]}"
154	        # Apply each mask in the batch to its corresponding image's alpha channel
155	        for i in range(out.shape[0]):

149	
150	        assert mask.ndim == 3, f"Mask should have shape [B, H, W]. {mask.shape}"
151	        assert out.ndim == 4, f"Image should have shsape [B, C, H, W]. {out.shape}"
152	        assert out.shape[-2:] == mask.shape[-2:], f"{out.shape[-2:]} != {mask.shape[-2:]}"
153	        assert out.shape[0] == mask.shape[0], f"{out.shape[0]} != {mask.shape[0]}"
154	        # Apply each mask in the batch to its corresponding image's alpha channel
155	        for i in range(out.shape[0]):
156	            out[i, 3, :, :] = mask[i]

54	        current = self
55	        while current is not None:
56	            result.append(current)
57	            current = current.previous
58	        assert len(result) > 1, "At least 2 regions are required."
59	
60	        result = list(reversed(result))
61	        if result[0].mask is None:  # BackgroundRegion

139	        num_conds = len(region_list)
140	
141	        mask = torch.stack([r.mask for r in region_list], dim=0)
142	        mask_sum = mask.sum(dim=0, keepdim=True)
143	        assert mask_sum.sum() > 0, "There are areas that are zero in all masks."
144	        self.mask = mask / mask_sum
145	
146	        self.conds = [r.conditioning[0][0] for r in region_list]

146	        self.conds = [r.conditioning[0][0] for r in region_list]
147	        num_tokens = [cond.shape[1] for cond in self.conds]
148	
149	        def attn2_patch(q: Tensor, k: Tensor, v: Tensor, extra_options: dict):
150	            assert k.mean() == v.mean(), "k and v must be the same."
151	            device, dtype = q.device, q.dtype
152	
153	            if self.conds[0].device != device:

33	        self.init(image, min_tile_size, padding, blending)
34	        return (self,)
35	
36	    def init(self, image: Tensor, min_tile_size: int, padding: int, blending: int):
37	        assert all([x % 8 == 0 for x in image.shape[-3:-1]]), "Image size must be divisible by 8"
38	        assert min_tile_size % 8 == 0, "Tile size must be divisible by 8"
39	        assert blending < padding, "Blending must be smaller than padding"
40	

34	        return (self,)
35	
36	    def init(self, image: Tensor, min_tile_size: int, padding: int, blending: int):
37	        assert all([x % 8 == 0 for x in image.shape[-3:-1]]), "Image size must be divisible by 8"
38	        assert min_tile_size % 8 == 0, "Tile size must be divisible by 8"
39	        assert blending < padding, "Blending must be smaller than padding"
40	
41	        self.image_size = np.array(image.shape[-3:-1])

35	
36	    def init(self, image: Tensor, min_tile_size: int, padding: int, blending: int):
37	        assert all([x % 8 == 0 for x in image.shape[-3:-1]]), "Image size must be divisible by 8"
38	        assert min_tile_size % 8 == 0, "Tile size must be divisible by 8"
39	        assert blending < padding, "Blending must be smaller than padding"
40	
41	        self.image_size = np.array(image.shape[-3:-1])
42	        self.padding = padding

167	    RETURN_TYPES = ("IMAGE",)
168	    FUNCTION = "merge"
169	
170	    def merge(self, image: Tensor, layout: TileLayout, index: int, tile: Tensor):
171	        assert index < layout.total_count, f"Index {index} out of range"
172	        if index == 0:
173	            image = image.clone()
174	        layout.merge(image, index, tile)