Merge branch 'AUTOMATIC1111:master' into master

1 files changed, 6 insertions, 6 deletions

diff --git a/modules/processing.py b/modules/processing.py
index a75b9f84..941ae089 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -140,7 +140,7 @@ class Processed:
         self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override

         self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]

         self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]

-        self.seed = int(self.seed if type(self.seed) != list else self.seed[0])

+        self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1

         self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1

 

         self.all_prompts = all_prompts or [self.prompt]

@@ -528,7 +528,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 firstphase_height_truncated = int(scale * self.height)

 

             else:

-                self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"

 

                 width_ratio = self.width / self.firstphase_width

                 height_ratio = self.height / self.firstphase_height

@@ -540,6 +539,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                     firstphase_width_truncated = self.firstphase_height * self.width / self.height

                     firstphase_height_truncated = self.firstphase_height

 

+            self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"

             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f

             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f

 

@@ -557,11 +557,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 

         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]

 

-        decoded_samples = decode_first_stage(self.sd_model, samples)

+        if opts.use_scale_latent_for_hires_fix:

+            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")

 

-        if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":

-            decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")

         else:

+            decoded_samples = decode_first_stage(self.sd_model, samples)

             lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)

 

             batch_images = []

@@ -578,7 +578,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             decoded_samples = decoded_samples.to(shared.device)

             decoded_samples = 2. * decoded_samples - 1.

 

-        samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))

+            samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))

 

         shared.state.nextjob()