make it possible to use checkpoints of different types (SD1, SDXL) in first...

make it possible to use checkpoints of different types (SD1, SDXL) in first and second pass of hires fix

modules/processing.py

@@ -1060,16 +1060,21 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         if not self.enable_hr:
             return samples
 
+        if self.latent_scale_mode is None:
+            decoded_samples = decode_first_stage(self.sd_model, samples)
+        else:
+            decoded_samples = None
+
         current = shared.sd_model.sd_checkpoint_info
         try:
             if self.hr_checkpoint_info is not None:
                 sd_models.reload_model_weights(info=self.hr_checkpoint_info)
 
-            return self.sample_hr_pass(samples, seeds, subseeds, subseed_strength, prompts)
+            return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
         finally:
             sd_models.reload_model_weights(info=current)
 
-    def sample_hr_pass(self, samples, seeds, subseeds, subseed_strength, prompts):
+    def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
         self.is_hr_pass = True
 
         target_width = self.hr_upscale_to_x
@@ -1100,7 +1105,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 image_conditioning = self.txt2img_image_conditioning(samples)
         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 = []