Fixed Scale ratio problem

1 files changed, 11 insertions, 14 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 0246f5dd..d9b0e0e7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -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")