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Add a center crop option to latent upscale node.

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comfyanonymous 2023-01-24 17:26:11 -05:00
parent c1eac7bab2
commit 463d0d0828
1 changed files with 19 additions and 3 deletions
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22
nodes.py
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@ -115,17 +115,33 @@ class EmptyLatentImage:
class LatentUpscale: class LatentUpscale:
upscale_methods = ["nearest-exact", "bilinear", "area"] upscale_methods = ["nearest-exact", "bilinear", "area"]
crop_methods = ["disabled", "center"]
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,),
"width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}), "width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
"height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),}} "height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
"crop": (s.crop_methods,)}}
RETURN_TYPES = ("LATENT",) RETURN_TYPES = ("LATENT",)
FUNCTION = "upscale" FUNCTION = "upscale"
def upscale(self, samples, upscale_method, width, height): def upscale(self, samples, upscale_method, width, height, crop):
s = torch.nn.functional.interpolate(samples, size=(height // 8, width // 8), mode=upscale_method) if crop == "center":
old_width = samples.shape[3]
old_height = samples.shape[2]
old_aspect = old_width / old_height
new_aspect = width / height
x = 0
y = 0
if old_aspect > new_aspect:
x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
elif old_aspect < new_aspect:
y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
s = samples[:,:,y:old_height-y,x:old_width-x]
else:
s = samples
s = torch.nn.functional.interpolate(s, size=(height // 8, width // 8), mode=upscale_method)
return (s,) return (s,)
class KSampler: class KSampler: