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Revert LatentComposite.

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comfyanonymous 2023-04-14 00:14:35 -04:00
parent fed4a70b8e
commit 1a7cda715b
1 changed files with 31 additions and 51 deletions
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74
nodes.py
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@ -578,64 +578,44 @@ class LatentFlip:
class LatentComposite: class LatentComposite:
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
return { return {"required": { "samples_to": ("LATENT",),
"required": {
"samples_to": ("LATENT",),
"samples_from": ("LATENT",), "samples_from": ("LATENT",),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"feather": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "feather": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
} }}
}
RETURN_TYPES = ("LATENT",) RETURN_TYPES = ("LATENT",)
FUNCTION = "composite" FUNCTION = "composite"
CATEGORY = "latent" CATEGORY = "latent"
def composite(self, samples_to, samples_from, x, y, feather): def composite(self, samples_to, samples_from, x, y, composite_method="normal", feather=0):
output = samples_to.copy() x = x // 8
destination = samples_to["samples"].clone() y = y // 8
source = samples_from["samples"]
left, top = (x // 8, y // 8)
right, bottom = (left + source.shape[3], top + source.shape[2],)
feather = feather // 8 feather = feather // 8
samples_out = samples_to.copy()
s = samples_to["samples"].clone()
samples_to = samples_to["samples"]
samples_from = samples_from["samples"]
if feather == 0:
s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]
else:
samples_from = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]
mask = torch.ones_like(samples_from)
for t in range(feather):
if y != 0:
mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))
if y + samples_from.shape[2] < samples_to.shape[2]:
mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))
# calculate the bounds of the source that will be overlapping the destination if x != 0:
# this prevents the source trying to overwrite latent pixels that are out of bounds mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))
# of the destination if x + samples_from.shape[3] < samples_to.shape[3]:
visible_width, visible_height = (destination.shape[3] - left, destination.shape[2] - top,) mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))
rev_mask = torch.ones_like(mask) - mask
mask = torch.ones_like(source) s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] * mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] * rev_mask
samples_out["samples"] = s
for f in range(feather): return (samples_out,)
feather_rate = (f + 1.0) / feather
if left > 0:
mask[:, :, :, f] *= feather_rate
if right < destination.shape[3] - 1:
mask[:, :, :, -f] *= feather_rate
if top > 0:
mask[:, :, f, :] *= feather_rate
if bottom < destination.shape[2] - 1:
mask[:, :, -f, :] *= feather_rate
mask = mask[:, :, :visible_height, :visible_width]
inverse_mask = torch.ones_like(mask) - mask
source_portion = mask * source[:, :, :visible_height, :visible_width]
destination_portion = inverse_mask * destination[:, :, top:bottom, left:right]
destination[:, :, top:bottom, left:right] = source_portion + destination_portion
output["samples"] = destination
return (output,)
class LatentCrop: class LatentCrop:
@classmethod @classmethod