bring back scale latent option in settings

1 files changed, 4 insertions, 4 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 7e2a416d..b9a1660e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -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()