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Infer a tight-fitting output image size from the passed tiles in MergeTilesToImageInvocation.

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Ryan Dick 2023-11-27 14:07:38 -05:00 committed by Kent Keirsey
parent b19ed36b43
commit 32da359ba5
1 changed files with 8 additions and 3 deletions
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11
invokeai/app/invocations/tiles.py
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@ -127,8 +127,6 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Merge multiple tile images into a single image."""
# Inputs
image_width: int = InputField(ge=1, description="The width of the output image, in pixels.")
image_height: int = InputField(ge=1, description="The height of the output image, in pixels.")
tiles_with_images: list[TileWithImage] = InputField(description="A list of tile images with tile properties.")
blend_amount: int = InputField(
ge=0,
@ -139,6 +137,13 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
images = [twi.image for twi in self.tiles_with_images]
tiles = [twi.tile for twi in self.tiles_with_images]
# Infer the output image dimensions from the max/min tile limits.
height = 0
width = 0
for tile in tiles:
height = max(height, tile.coords.bottom)
width = max(width, tile.coords.right)
# Get all tile images for processing.
# TODO(ryand): It pains me that we spend time PNG decoding each tile from disk when they almost certainly
# existed in memory at an earlier point in the graph.
@ -152,7 +157,7 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
# Check the first tile to determine how many image channels are expected in the output.
channels = tile_np_images[0].shape[-1]
dtype = tile_np_images[0].dtype
np_image = np.zeros(shape=(self.image_height, self.image_width, channels), dtype=dtype)
np_image = np.zeros(shape=(height, width, channels), dtype=dtype)
merge_tiles_with_linear_blending(
dst_image=np_image, tiles=tiles, tile_images=tile_np_images, blend_amount=self.blend_amount