improve resolution handling (#396)

* improve resolution handling

* improve resolution handling

* improve resolution handling

* improve resolution handling

fooocus_version.py

@@ -1 +1 @@
-version = '2.0.51'
+version = '2.0.52'

modules/async_worker.py

@@ -23,7 +23,7 @@ def worker():
     from modules.sdxl_styles import apply_style, aspect_ratios, fooocus_expansion
     from modules.private_logger import log
     from modules.expansion import safe_str
-    from modules.util import join_prompts, remove_empty_str, HWC3, resize_image
+    from modules.util import join_prompts, remove_empty_str, HWC3, resize_image, image_is_generated_in_current_ui
     from modules.upscaler import perform_upscale
 
     try:
@@ -82,11 +82,12 @@ def worker():
             progressbar(0, 'Image processing ...')
             if uov_method != flags.disabled and uov_input_image is not None:
                 uov_input_image = HWC3(uov_input_image)
-                H, W, C = uov_input_image.shape
                 if 'vary' in uov_method:
-                    if H * W + 8 < width * height or float(abs(H * width - W * height)) > 1.5 * float(max(H, W, width, height)):
+                    if not image_is_generated_in_current_ui(uov_input_image, ui_width=width, ui_height=height):
                         uov_input_image = resize_image(uov_input_image, width=width, height=height)
-                        print(f'Aspect ratio corrected - users are uploading their own images.')
+                        print(f'Resolution corrected - users are uploading their own images.')
+                    else:
+                        print(f'Processing images generated by Fooocus.')
                     if 'subtle' in uov_method:
                         denoising_strength = 0.5
                     if 'strong' in uov_method:
@@ -99,6 +100,14 @@ def worker():
                     height = H * 8
                     print(f'Final resolution is {str((height, width))}.')
                 elif 'upscale' in uov_method:
+                    H, W, C = uov_input_image.shape
+                    progressbar(0, f'Upscaling image from {str((H, W))} ...')
+
+                    uov_input_image = core.numpy_to_pytorch(uov_input_image)
+                    uov_input_image = perform_upscale(uov_input_image)
+                    uov_input_image = core.pytorch_to_numpy(uov_input_image)[0]
+                    print(f'Image upscaled.')
+
                     if '1.5x' in uov_method:
                         f = 1.5
                     elif '2x' in uov_method:
@@ -106,25 +115,31 @@ def worker():
                     else:
                         f = 1.0
 
-                    width = int(W * f)
+                    width_f = int(width * f)
-                    height = int(H * f)
+                    height_f = int(height * f)
-                    image_is_super_large = width * height > 2800 * 2800
-                    progressbar(0, f'Upscaling image from {str((H, W))} to {str((height, width))}...')
 
-                    uov_input_image = core.numpy_to_pytorch(uov_input_image)
+                    if image_is_generated_in_current_ui(uov_input_image, ui_width=width_f, ui_height=height_f):
-                    uov_input_image = perform_upscale(uov_input_image)
+                        uov_input_image = resize_image(uov_input_image, width=int(W * f), height=int(H * f))
-                    uov_input_image = core.pytorch_to_numpy(uov_input_image)[0]
+                        print(f'Processing images generated by Fooocus.')
-                    uov_input_image = resize_image(uov_input_image, width=width, height=height)
+                    else:
-                    print(f'Image upscaled.')
+                        uov_input_image = resize_image(uov_input_image, width=width_f, height=height_f)
+                        print(f'Resolution corrected - users are uploading their own images.')
 
-                    if 'fast' in uov_method or image_is_super_large:
+                    H, W, C = uov_input_image.shape
-                        if 'fast' not in uov_method:
+                    image_is_super_large = H * W > 2800 * 2800
-                            print('Image is too large. Directly returned the SR image. '
+
-                                  'Usually directly return SR image at 4K resolution '
+                    if 'fast' in uov_method:
-                                  'yields better results than SDXL diffusion.')
+                        direct_return = True
-                        d = [
+                    elif image_is_super_large:
-                            ('Upscale (Fast)', '2x'),
+                        print('Image is too large. Directly returned the SR image. '
-                        ]
+                              'Usually directly return SR image at 4K resolution '
+                              'yields better results than SDXL diffusion.')
+                        direct_return = True
+                    else:
+                        direct_return = False
+
+                    if direct_return:
+                        d = [('Upscale (Fast)', '2x')]
                         log(uov_input_image, d, single_line_number=1)
                         outputs.append(['results', [uov_input_image]])
                         return
@@ -135,6 +150,7 @@ def worker():
                     switch = int(steps * 0.67)
                     initial_pixels = core.numpy_to_pytorch(uov_input_image)
                     progressbar(0, 'VAE encoding ...')
+
                     initial_latent = core.encode_vae(vae=pipeline.xl_base_patched.vae, pixels=initial_pixels, tiled=True)
                     B, C, H, W = initial_latent['samples'].shape
                     width = W * 8

modules/util.py

@@ -6,6 +6,25 @@ import os
 from PIL import Image
 
 
+def image_is_generated_in_current_ui(image, ui_width, ui_height):
+    H, W, C = image.shape
+    
+    if H < ui_height:
+        return False
+
+    if W < ui_width:
+        return False
+
+    k1 = float(H) / float(W)
+    k2 = float(ui_height) / float(ui_width)
+    d = abs(k1 - k2)
+
+    if d > 0.01:
+        return False
+
+    return True
+
+
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)