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Use torch for extract, split, merge

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missionfloyd 2023-04-11 20:17:00 -06:00 committed by GitHub
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1 changed files with 11 additions and 33 deletions
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comfy_extras/nodes_post_processing.py
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@ -326,9 +326,9 @@ class GetChannel:
"required": { "required": {
"image": ("IMAGE",), "image": ("IMAGE",),
"channel": ([ "channel": ([
"Red", "red",
"Green", "green",
"Blue", "blue",
],), ],),
}, },
} }
@ -341,16 +341,11 @@ class GetChannel:
def getchannel(self, image: torch.Tensor, channel: str): def getchannel(self, image: torch.Tensor, channel: str):
batch_size, height, width, _ = image.shape batch_size, height, width, _ = image.shape
result = torch.zeros_like(image) result = torch.zeros_like(image)
channels = ["red", "green", "blue"]
for b in range(batch_size): for b in range(batch_size):
tensor_image = image[b] channel_out = image[b, :, :, channels.index(channel)]
img = (tensor_image * 255).to(torch.uint8).numpy() result[b] = channel_out[:, :, None].expand(-1, -1, 3)
pil_image = Image.fromarray(img, mode='RGB')
output_image = pil_image.getchannel(channel[0])
output_array = torch.tensor(np.array(output_image.convert("RGB"))).float() / 255
result[b] = output_array
return (result,) return (result,)
@ -380,16 +375,8 @@ class Split:
result_b = torch.zeros_like(image) result_b = torch.zeros_like(image)
for b in range(batch_size): for b in range(batch_size):
tensor_image = image[b] channels = torch.chunk(image[b], 3, 2)
img = (tensor_image * 255).to(torch.uint8).numpy() result_r[b], result_g[b], result_b[b] = [x.expand(-1, -1, 3) for x in channels]
pil_image = Image.fromarray(img, mode='RGB')
output_r, output_g, output_b = pil_image.split()
output_array_r = torch.tensor(np.array(output_r.convert("RGB"))).float() / 255
output_array_g = torch.tensor(np.array(output_g.convert("RGB"))).float() / 255
output_array_b = torch.tensor(np.array(output_b.convert("RGB"))).float() / 255
result_r[b], result_g[b], result_b[b] = output_array_r, output_array_g, output_array_b
return (result_r, result_g, result_b) return (result_r, result_g, result_b)
@ -415,19 +402,10 @@ class Merge:
def merge(self, red: torch.Tensor, green: torch.Tensor, blue: torch.Tensor): def merge(self, red: torch.Tensor, green: torch.Tensor, blue: torch.Tensor):
batch_size, height, width, _ = red.shape batch_size, height, width, _ = red.shape
result = torch.zeros_like(red) result = torch.zeros_like(red)
images = [red, green, blue]
for b in range(batch_size): for b in range(batch_size):
img_r = (red[b] * 255).to(torch.uint8).numpy() for i in range(3):
img_g = (green[b] * 255).to(torch.uint8).numpy() result[b, :, :, i] = images[i][b, :, :, 0]
img_b = (blue[b] * 255).to(torch.uint8).numpy()
pil_image_r = Image.fromarray(img_r, mode='RGB').convert("L")
pil_image_g = Image.fromarray(img_g, mode='RGB').convert("L")
pil_image_b = Image.fromarray(img_b, mode='RGB').convert("L")
output_image = Image.merge("RGB", (pil_image_r, pil_image_g, pil_image_b))
output_array = torch.tensor(np.array(output_image.convert("RGB"))).float() / 255
result[b] = output_array
return (result,) return (result,)