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Fixed Scale ratio problem

This commit is contained in:
Buckzor 2022-10-13 22:23:22 +01:00 committed by AUTOMATIC1111
parent 40d1c6e423
commit b382de2d77
1 changed files with 11 additions and 14 deletions
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25
modules/processing.py
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@ -522,15 +522,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
else:
state.job_count = state.job_count * 2
#desired_pixel_count = self.firstphase_width * self.firstphase_height
#actual_pixel_count = self.width * self.height
#scale = math.sqrt(desired_pixel_count / actual_pixel_count)
#self.firstphase_width = math.ceil(scale * self.width / 64) * 64
#self.firstphase_height = math.ceil(scale * self.height / 64) * 64
#self.firstphase_width_truncated = int(scale * self.width)
#self.firstphase_height_truncated = int(scale * self.height)
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
@ -544,17 +535,23 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
truncate_x = 0
truncate_y = 0
width_ratio = self.width/self.firstphase_width
height_ratio = self.height/self.firstphase_height
if self.crop_scale:
if self.width/self.firstphase_width > self.height/self.firstphase_height:
if width_ratio > height_ratio:
#Crop to landscape
truncate_y = (self.width - self.firstphase_width)//2 // opt_f
truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f)
elif self.width/self.firstphase_width < self.height/self.firstphase_height:
elif width_ratio < height_ratio:
#Crop to portrait
truncate_x = (self.height - self.firstphase_height)//2 // opt_f
truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f)
samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
if self.scale_latent:
samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")