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Some cleanups to the load image node. (#13677)

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comfyanonymous 2026-05-02 17:34:27 -07:00 committed by GitHub
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commit ef6722f6be
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1 changed files with 10 additions and 19 deletions
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nodes.py
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@ -1694,26 +1694,27 @@ class LoadImage:
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "load_image"
def load_image(self, image):
image_path = folder_paths.get_annotated_filepath(image)
dtype = comfy.model_management.intermediate_dtype()
device = comfy.model_management.intermediate_device()
components = InputImpl.VideoFromFile(image_path).get_components()
if components.images.shape[0] > 0:
return (components.images, 1.0 - components.alpha[..., -1] if components.alpha is not None else torch.zeros((components.images.shape[0], 64, 64), dtype=torch.float32, device="cpu"))
return (components.images.to(device=device, dtype=dtype), (1.0 - components.alpha[..., -1]).to(device=device, dtype=dtype) if components.alpha is not None else torch.zeros((components.images.shape[0], 64, 64), dtype=dtype, device=device))
# This code is left here to handle animated webp which pyav does not support loading
img = node_helpers.pillow(Image.open, image_path)
output_images = []
output_masks = []
w, h = None, None
dtype = comfy.model_management.intermediate_dtype()
for i in ImageSequence.Iterator(img):
i = node_helpers.pillow(ImageOps.exif_transpose, i)
if i.mode == 'I':
i = i.point(lambda i: i * (1 / 255))
image = i.convert("RGB")
if len(output_images) == 0:
@ -1728,25 +1729,15 @@ class LoadImage:
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
elif i.mode == 'P' and 'transparency' in i.info:
mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
else:
mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
output_images.append(image.to(dtype=dtype))
output_masks.append(mask.unsqueeze(0).to(dtype=dtype))
if img.format == "MPO":
break # ignore all frames except the first one for MPO format
output_image = torch.cat(output_images, dim=0)
output_mask = torch.cat(output_masks, dim=0)
if len(output_images) > 1:
output_image = torch.cat(output_images, dim=0)
output_mask = torch.cat(output_masks, dim=0)
else:
output_image = output_images[0]
output_mask = output_masks[0]
return (output_image, output_mask)
return (output_image.to(device=device, dtype=dtype), output_mask.to(device=device, dtype=dtype))
@classmethod
def IS_CHANGED(s, image):