fix many resolution problems

.gitignore

@@ -12,6 +12,7 @@ lena_result.png
 lena_test.py
 user_path_config.txt
 build_chb.py
+experiment.py
 /modules/*.png
 /repositories
 /venv

fooocus_version.py

@@ -1 +1 @@
-version = '2.1.712'
+version = '2.1.714'

modules/async_worker.py

@@ -29,7 +29,8 @@ def worker():
     from modules.sdxl_styles import apply_style, apply_wildcards, 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, image_is_generated_in_current_ui, make_sure_that_image_is_not_too_large
+    from modules.util import join_prompts, remove_empty_str, HWC3, resize_image, \
+        get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil
     from modules.upscaler import perform_upscale
 
     try:
@@ -287,11 +288,6 @@ def worker():
             progressbar(13, 'Image processing ...')
 
         if 'vary' in goals:
-            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'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:
@@ -299,7 +295,16 @@ def worker():
             if advanced_parameters.overwrite_vary_strength > 0:
                 denoising_strength = advanced_parameters.overwrite_vary_strength
 
-            uov_input_image = make_sure_that_image_is_not_too_large(uov_input_image)
+            shape_ceil = get_image_shape_ceil(uov_input_image)
+            if shape_ceil < 1024:
+                print(f'[Vary] Image is resized because it is too small.')
+                shape_ceil = 1024
+            elif shape_ceil > 2048:
+                print(f'[Vary] Image is resized because it is too big.')
+                shape_ceil = 2048
+
+            uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil)
+
             initial_pixels = core.numpy_to_pytorch(uov_input_image)
             progressbar(13, 'VAE encoding ...')
             initial_latent = core.encode_vae(vae=pipeline.final_vae, pixels=initial_pixels)
@@ -324,18 +329,12 @@ def worker():
             else:
                 f = 1.0
 
-            width_f = int(width * f)
+            shape_ceil = get_shape_ceil(H * f, W * f)
-            height_f = int(height * f)
+            if shape_ceil < 1024:
-
+                print(f'[Upscale] Image is resized because it is too small.')
-            if image_is_generated_in_current_ui(uov_input_image, ui_width=width_f, ui_height=height_f):
+                shape_ceil = 1024
-                uov_input_image = resize_image(uov_input_image, width=int(W * f), height=int(H * f))
+            uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil)
-                print(f'Processing images generated by Fooocus.')
+            image_is_super_large = shape_ceil > 2800
-            else:
-                uov_input_image = resize_image(uov_input_image, width=width_f, height=height_f)
-                print(f'Resolution corrected - users are uploading their own images.')
-
-            H, W, C = uov_input_image.shape
-            image_is_super_large = H * W > 2800 * 2800
 
             if 'fast' in uov_method:
                 direct_return = True

modules/inpaint_worker.py

@@ -3,7 +3,7 @@ import numpy as np
 import modules.default_pipeline as pipeline
 
 from PIL import Image, ImageFilter
-from modules.util import resample_image
+from modules.util import resample_image, set_image_shape_ceil
 
 
 inpaint_head = None
@@ -148,12 +148,10 @@ class InpaintWorker:
         self.interested_image = image[a:b, c:d]
 
         # resize to make images ready for diffusion
+        self.interested_image = set_image_shape_ceil(self.interested_image, 1024)
         H, W, C = self.interested_image.shape
-        k = ((1024.0 ** 2.0) / float(H * W)) ** 0.5
+
-        H = int(np.ceil(float(H) * k / 16.0)) * 16
-        W = int(np.ceil(float(W) * k / 16.0)) * 16
         self.interested_mask = up255(resample_image(self.interested_mask, W, H), t=127)
-        self.interested_image = resample_image(self.interested_image, W, H)
         self.interested_fill = fooocus_fill(self.interested_image, self.interested_mask)
 
         # soft pixels

modules/util.py

@@ -1,30 +1,12 @@
 import numpy as np
 import datetime
 import random
+import math
 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)
 
 
@@ -92,16 +74,22 @@ def resize_image(im, width, height, resize_mode=1):
     return np.array(res)
 
 
-def make_sure_that_image_is_not_too_large(x):
+def get_shape_ceil(h, w):
-    H, W, C = x.shape
+    return math.ceil(((h * w) ** 0.5) / 64.0) * 64.0
-    k = float(2048 * 2048) / float(H * W)
+
-    k = k ** 0.5
+
-    if k < 1:
+def get_image_shape_ceil(im):
-        H_new = int(H * k)
+    H, W, _ = im.shape
-        W_new = int(W * k)
+    return get_shape_ceil(H, W)
-        print(f'Image is too large - resizing from ({H}, {W}) to ({H_new}, {W_new}).')
+
-        x = resize_image(x, width=W_new, height=H_new, resize_mode=0)
+
-    return x
+def set_image_shape_ceil(im, shape_ceil):
+    H, W, _ = im.shape
+    shape_ceil_before = get_shape_ceil(H, W)
+    k = float(shape_ceil) / shape_ceil_before
+    H = int(round(float(H) * k / 64.0) * 64)
+    W = int(round(float(W) * k / 64.0) * 64)
+    return resample_image(im, width=W, height=H)
 
 
 def HWC3(x):