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Composite images of different sizes

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resample option is used when scaling mask
This commit is contained in:
missionfloyd 2023-04-13 21:54:41 -06:00
parent 46d0fa6424
commit a20163eb84
1 changed files with 17 additions and 9 deletions
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26
comfy_extras/nodes_post_processing.py
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@ -266,11 +266,7 @@ class Rotate:
"max": 360, "max": 360,
"step": 0.1 "step": 0.1
}), }),
"resample": ([ "resample": (["nearest neighbor", "bilinear", "bicubic"],),
"Nearest Neighbor",
"Bilinear",
"Bicubic",
],),
"expand": (["disabled", "enabled"],), "expand": (["disabled", "enabled"],),
"center_x": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION}), "center_x": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION}),
"center_y": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION}), "center_y": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION}),
@ -290,9 +286,9 @@ class Rotate:
batch_size, height, width, _ = image.shape batch_size, height, width, _ = image.shape
resamplers = { resamplers = {
"Nearest Neighbor": Image.Resampling.NEAREST, "nearest neighbor": Image.Resampling.NEAREST,
"Bilinear": Image.Resampling.BILINEAR, "bilinear": Image.Resampling.BILINEAR,
"Bicubic": Image.Resampling.BICUBIC, "bicubic": Image.Resampling.BICUBIC,
} }
tensor_image = image[0] tensor_image = image[0]
@ -430,6 +426,7 @@ class Composite:
"image_b": ("IMAGE",), "image_b": ("IMAGE",),
"x": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION}), "x": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION}),
"y": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION}), "y": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION}),
"resample": (["nearest neighbor", "box", "bilinear", "bicubic", "hamming", "lanczos"],),
}, },
"optional": { "optional": {
"mask": ("MASK",), "mask": ("MASK",),
@ -441,7 +438,16 @@ class Composite:
CATEGORY = "image/postprocessing" CATEGORY = "image/postprocessing"
def composite(self, image_a: torch.Tensor, image_b: torch.Tensor, x: int, y: int, mask: torch.Tensor = None): def composite(self, image_a: torch.Tensor, image_b: torch.Tensor, x: int, y: int, resample: str, mask: torch.Tensor = None):
resamplers = {
"nearest neighbor": Image.Resampling.NEAREST,
"bilinear": Image.Resampling.BILINEAR,
"bicubic": Image.Resampling.BICUBIC,
"box": Image.Resampling.BOX,
"hamming": Image.Resampling.HAMMING,
"lanczos": Image.Resampling.LANCZOS,
}
batch_size, height, width, _ = image_a.shape batch_size, height, width, _ = image_a.shape
result = torch.zeros_like(image_a) result = torch.zeros_like(image_a)
@ -455,6 +461,8 @@ class Composite:
pil_image_a = Image.fromarray(img_a, mode='RGB') pil_image_a = Image.fromarray(img_a, mode='RGB')
pil_image_b = Image.fromarray(img_b, mode='RGB') pil_image_b = Image.fromarray(img_b, mode='RGB')
pil_image_mask = Image.fromarray(img_mask, mode='L') pil_image_mask = Image.fromarray(img_mask, mode='L')
if pil_image_mask.size != pil_image_b.size:
pil_image_mask = pil_image_mask.resize(pil_image_b.size, resamplers[resample])
pil_image_a.paste(pil_image_b, (x, y), pil_image_mask) pil_image_a.paste(pil_image_b, (x, y), pil_image_mask)