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Rename inputs

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This commit is contained in:
missionfloyd 2023-04-13 22:01:14 -06:00
parent a20163eb84
commit 2bd435c25c
1 changed files with 15 additions and 15 deletions
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30
comfy_extras/nodes_post_processing.py
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@ -367,7 +367,7 @@ class Split:
} }
RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE") RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE")
RETURN_NAMES = ("Red", "Green", "Blue") RETURN_NAMES = ("red", "green", "blue")
FUNCTION = "split" FUNCTION = "split"
@ -422,8 +422,8 @@ class Composite:
def INPUT_TYPES(s): def INPUT_TYPES(s):
return { return {
"required": { "required": {
"image_a": ("IMAGE",), "base_image": ("IMAGE",),
"image_b": ("IMAGE",), "overlay_image": ("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"],), "resample": (["nearest neighbor", "box", "bilinear", "bicubic", "hamming", "lanczos"],),
@ -438,7 +438,7 @@ class Composite:
CATEGORY = "image/postprocessing" CATEGORY = "image/postprocessing"
def composite(self, image_a: torch.Tensor, image_b: torch.Tensor, x: int, y: int, resample: str, mask: torch.Tensor = None): def composite(self, base_image: torch.Tensor, overlay_image: torch.Tensor, x: int, y: int, resample: str, mask: torch.Tensor = None):
resamplers = { resamplers = {
"nearest neighbor": Image.Resampling.NEAREST, "nearest neighbor": Image.Resampling.NEAREST,
"bilinear": Image.Resampling.BILINEAR, "bilinear": Image.Resampling.BILINEAR,
@ -448,25 +448,25 @@ class Composite:
"lanczos": Image.Resampling.LANCZOS, "lanczos": Image.Resampling.LANCZOS,
} }
batch_size, height, width, _ = image_a.shape batch_size, height, width, _ = base_image.shape
result = torch.zeros_like(image_a) result = torch.zeros_like(base_image)
for b in range(batch_size): for b in range(batch_size):
if mask is None: if mask is None:
mask = torch.ones(image_b.shape[1:3]) mask = torch.ones(overlay_image.shape[1:3])
img_a = (image_a[b] * 255).to(torch.uint8).numpy() img_a = (base_image[b] * 255).to(torch.uint8).numpy()
img_b = (image_b[b] * 255).to(torch.uint8).numpy() img_b = (overlay_image[b] * 255).to(torch.uint8).numpy()
img_mask = (mask * 255).to(torch.uint8).numpy() img_mask = (mask * 255).to(torch.uint8).numpy()
pil_image_a = Image.fromarray(img_a, mode='RGB') pil_base_image = Image.fromarray(img_a, mode='RGB')
pil_image_b = Image.fromarray(img_b, mode='RGB') pil_overlay_image = 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: if pil_image_mask.size != pil_overlay_image.size:
pil_image_mask = pil_image_mask.resize(pil_image_b.size, resamplers[resample]) pil_image_mask = pil_image_mask.resize(pil_overlay_image.size, resamplers[resample])
pil_image_a.paste(pil_image_b, (x, y), pil_image_mask) pil_base_image.paste(pil_overlay_image, (x, y), pil_image_mask)
output_array = torch.tensor(np.array(pil_image_a.convert("RGB"))).float() / 255 output_array = torch.tensor(np.array(pil_base_image.convert("RGB"))).float() / 255
result[b] = output_array result[b] = output_array
return (result,) return (result,)