lnyan commited on
Commit
c9b1627
1 Parent(s): cefe0d3

Update app

Browse files
Files changed (1) hide show
  1. utils.py +2 -50
utils.py CHANGED
@@ -142,57 +142,9 @@ def mean_fill(img, mask):
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  img[mask < 1] = avg
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  return img, mask
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- """
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- Apache-2.0 license
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- https://github.com/hafriedlander/stable-diffusion-grpcserver/blob/main/sdgrpcserver/services/generate.py
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- https://github.com/parlance-zz/g-diffuser-bot/tree/g-diffuser-bot-beta2
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- _handleImageAdjustment
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- """
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- try:
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- from sd_grpcserver.sdgrpcserver import images
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- import torch
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- from math import sqrt
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- def handleImageAdjustment(array, adjustments):
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- tensor = images.fromPIL(Image.fromarray(array))
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- for adjustment in adjustments:
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- which = adjustment[0]
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-
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- if which == "blur":
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- sigma = adjustment[1]
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- direction = adjustment[2]
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-
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- if direction == "DOWN" or direction == "UP":
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- orig = tensor
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- repeatCount=256
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- sigma /= sqrt(repeatCount)
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-
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- for _ in range(repeatCount):
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- tensor = images.gaussianblur(tensor, sigma)
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- if direction == "DOWN":
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- tensor = torch.minimum(tensor, orig)
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- else:
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- tensor = torch.maximum(tensor, orig)
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- else:
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- tensor = images.gaussianblur(tensor, adjustment.blur.sigma)
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- elif which == "invert":
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- tensor = images.invert(tensor)
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- elif which == "levels":
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- tensor = images.levels(tensor, adjustment[1], adjustment[2], adjustment[3], adjustment[4])
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- elif which == "channels":
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- tensor = images.channelmap(tensor, [adjustment.channels.r, adjustment.channels.g, adjustment.channels.b, adjustment.channels.a])
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- elif which == "rescale":
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- self.unimp("Rescale")
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- elif which == "crop":
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- tensor = images.crop(tensor, adjustment.crop.top, adjustment.crop.left, adjustment.crop.height, adjustment.crop.width)
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- return np.array(images.toPIL(tensor)[0])
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- except:
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- pass
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-
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  def g_diffuser(img,mask):
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- adjustments=[["blur",32,"UP"],["level",0,0.05,0,1]]
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- mask=handleImageAdjustment(mask,adjustments)
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- out_mask=handleImageAdjustment(mask,adjustments)
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- return img, mask, out_mask
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  def dummy_fill(img,mask):
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  return img,mask
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  functbl = {
 
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  img[mask < 1] = avg
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  return img, mask
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  def g_diffuser(img,mask):
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+ return img, mask
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+
 
 
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  def dummy_fill(img,mask):
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  return img,mask
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  functbl = {