Update downsampling for larger image in no_upscale

library/train_util.py

@@ -123,6 +123,10 @@ class BucketManager():
       self.buckets.append([])
       # print(reso, bucket_id, len(self.buckets))
 
+  def round_to_steps(self, x):
+    x = int(x + .5)
+    return x - x % self.reso_steps
+
   def select_bucket(self, image_width, image_height):
     aspect_ratio = image_width / image_height
     if not self.no_upscale:
@@ -150,7 +154,24 @@ class BucketManager():
         resized_height = self.max_area / resized_width
         assert abs(resized_width / resized_height - aspect_ratio) < 1e-2, "aspect is illegal"
 
-        resized_size = (int(resized_width + .5),  int(resized_height + .5))
+        # リサイズ後の短辺または長辺をreso_steps単位にする：aspect ratioの差が少ないほうを選ぶ
+        # 元のbucketingと同じロジック
+        b_width_rounded = self.round_to_steps(resized_width)
+        b_height_in_wr = self.round_to_steps(b_width_rounded / aspect_ratio)
+        ar_width_rounded = b_width_rounded / b_height_in_wr
+
+        b_height_rounded = self.round_to_steps(resized_height)
+        b_width_in_hr = self.round_to_steps(b_height_rounded * aspect_ratio)
+        ar_height_rounded = b_width_in_hr / b_height_rounded
+
+        # print(b_width_rounded, b_height_in_wr, ar_width_rounded)
+        # print(b_width_in_hr, b_height_rounded, ar_height_rounded)
+
+        if abs(ar_width_rounded - aspect_ratio) < abs(ar_height_rounded - aspect_ratio):
+          resized_size = (b_width_rounded, int(b_width_rounded / aspect_ratio + .5))
+        else:
+          resized_size = (int(b_height_rounded * aspect_ratio + .5), b_height_rounded)
+        # print(resized_size)
       else:
         resized_size = (image_width, image_height)              # リサイズは不要
 
@@ -889,13 +910,14 @@ def debug_dataset(train_dataset, show_input_ids=False):
   k = 0
   for i, example in enumerate(train_dataset):
     if example['latents'] is not None:
-      print("sample has latents from npz file")
+      print(f"sample has latents from npz file: {example['latents'].size()}")
     for j, (ik, cap, lw, iid) in enumerate(zip(example['image_keys'], example['captions'], example['loss_weights'], example['input_ids'])):
-      print(f'{ik}, size: {train_dataset.image_data[ik].image_size}, caption: "{cap}", loss weight: {lw}')
+      print(f'{ik}, size: {train_dataset.image_data[ik].image_size}, loss weight: {lw}, caption: "{cap}"')
       if show_input_ids:
         print(f"input ids: {iid}")
       if example['images'] is not None:
         im = example['images'][j]
+        print(f"image size: {im.size()}")
         im = ((im.numpy() + 1.0) * 127.5).astype(np.uint8)
         im = np.transpose(im, (1, 2, 0))                # c,H,W -> H,W,c
         im = im[:, :, ::-1]                             # RGB -> BGR (OpenCV)