Merge ad8ab17b0c2f3f21adbf8396d79628d9395ddbf6 into 82bf9a373023f2317a7d30c28d9064ffe016e2c4

modules/shared_options.py

@@ -105,6 +105,7 @@ options_templates.update(options_section(('upscaling', "Upscaling", "postprocess
     "DAT_tile_overlap": OptionInfo(8, "Tile overlap for DAT upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in shared.sd_upscalers]}),
     "set_scale_by_when_changing_upscaler": OptionInfo(False, "Automatically set the Scale by factor based on the name of the selected Upscaler."),
+    "upscaler_fast_prescale_threshold": OptionInfo(1.0, "Maximum threshold for fast pre-scaling of very small images.", gr.Slider, {"minimum": 1, "maximum": 8, "step": 0.01}).info("The maximum scale difference for performing fast Lanczos pre-scaling before the first upscaling step. This can prevent expensive additional steps if the source image is very small. 1 = disabled; 1.25-3.0 recommended"),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration", "postprocessing"), {

modules/upscaler.py

@@ -6,6 +6,7 @@ from PIL import Image
 
 import modules.shared
 from modules import modelloader, shared
+import math
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
 NEAREST = (Image.Resampling.NEAREST if hasattr(Image, 'Resampling') else Image.NEAREST)
@@ -56,6 +57,38 @@ class Upscaler:
         dest_w = int((img.width * scale) // 8 * 8)
         dest_h = int((img.height * scale) // 8 * 8)
 
+        # Attempt a cheap resize of the source image, if it falls below the fixed scaling size of the upscaling model.
+        # We resize the image by the smallest amount necessary for the fixed scaling to meet the target dimensions.
+        prescale_threshold = modules.shared.opts.upscaler_fast_prescale_threshold
+        if prescale_threshold > 1 and self.name and self.name not in ["Nearest", "Lanczos"]:
+            # Get the matching upscaler
+            upscaler_data = next((x for x in self.scalers if x.data_path == selected_model), None)
+
+            if upscaler_data is not None:
+                upscaler_scale = upscaler_data.scale
+                if scale > upscaler_scale:
+
+                    # Calculate the minimum intermediate dimensions.
+                    min_intermediate_w = math.ceil(dest_w / upscaler_scale)
+                    min_intermediate_h = math.ceil(dest_h / upscaler_scale)
+
+                    # Preserve aspect ratio and make sure any adjustments don't drop us below the
+                    # minimum scaling needed.
+                    aspect_ratio = img.width / img.height
+
+                    intermediate_w = max(min_intermediate_w, int(math.ceil(min_intermediate_h * aspect_ratio)))
+                    intermediate_h = max(min_intermediate_h, int(math.ceil(min_intermediate_w / aspect_ratio)))
+
+                    if intermediate_w / aspect_ratio > intermediate_h:
+                        intermediate_w = int(math.ceil(intermediate_h * aspect_ratio))
+                    else:
+                        intermediate_h = int(math.ceil(intermediate_w / aspect_ratio))
+
+                    scale_diff = max(intermediate_w / img.width, intermediate_h / img.height)
+
+                    if scale_diff <= prescale_threshold:
+                        img = img.resize((intermediate_w, intermediate_h), resample=LANCZOS)
+
         for i in range(3):
             if img.width >= dest_w and img.height >= dest_h and (i > 0 or scale != 1):
                 break