Option to use advanced upscalers with normal img2img

modules/images.py

@@ -209,8 +209,16 @@ def draw_prompt_matrix(im, width, height, all_prompts):
 
 
 def resize_image(resize_mode, im, width, height):
+    def resize(im, w, h):
+        if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
+            return im.resize((w, h), resample=LANCZOS)
+
+        upscaler = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img][0]
+        return upscaler.upscale(im, w, h)
+
     if resize_mode == 0:
-        res = im.resize((width, height), resample=LANCZOS)
+        res = resize(im, width, height)
+
     elif resize_mode == 1:
         ratio = width / height
         src_ratio = im.width / im.height
@@ -218,9 +226,10 @@ def resize_image(resize_mode, im, width, height):
         src_w = width if ratio > src_ratio else im.width * height // im.height
         src_h = height if ratio <= src_ratio else im.height * width // im.width
 
-        resized = im.resize((src_w, src_h), resample=LANCZOS)
+        resized = resize(im, src_w, src_h)
         res = Image.new("RGB", (width, height))
         res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
+
     else:
         ratio = width / height
         src_ratio = im.width / im.height
@@ -228,7 +237,7 @@ def resize_image(resize_mode, im, width, height):
         src_w = width if ratio < src_ratio else im.width * height // im.height
         src_h = height if ratio >= src_ratio else im.height * width // im.width
 
-        resized = im.resize((src_w, src_h), resample=LANCZOS)
+        resized = resize(im, src_w, src_h)
         res = Image.new("RGB", (width, height))
         res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
 

modules/processing.py

@@ -462,7 +462,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         else:
             decoded_samples = self.sd_model.decode_first_stage(samples)
 
-            if opts.upscaler_for_hires_fix is None or opts.upscaler_for_hires_fix == "None":
+            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:
                 lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
@@ -472,7 +472,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                     x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                     x_sample = x_sample.astype(np.uint8)
                     image = Image.fromarray(x_sample)
-                    upscaler = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_hires_fix][0]
+                    upscaler = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img][0]
                     image = upscaler.upscale(image, self.width, self.height)
                     image = np.array(image).astype(np.float32) / 255.0
                     image = np.moveaxis(image, 2, 0)

modules/shared.py

@@ -168,7 +168,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "ldsr_pre_down": OptionInfo(1, "LDSR Pre-process downssample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
     "ldsr_post_down": OptionInfo(1, "LDSR Post-process down-sample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
 
-    "upscaler_for_hires_fix": OptionInfo(None, "Upscaler for highres. fix", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
+    "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {