support sdxl in prepare scipt

finetune/prepare_buckets_latents.py

@@ -34,12 +34,18 @@ def collate_fn_remove_corrupted(batch):
     return batch
 
 
-def get_latents(vae, images, weight_dtype):
+def get_latents(vae, key_and_images, weight_dtype):
-    img_tensors = [IMAGE_TRANSFORMS(image) for image in images]
+    img_tensors = [IMAGE_TRANSFORMS(image) for _, image in key_and_images]
     img_tensors = torch.stack(img_tensors)
     img_tensors = img_tensors.to(DEVICE, weight_dtype)
     with torch.no_grad():
-        latents = vae.encode(img_tensors).latent_dist.sample().float().to("cpu").numpy()
+        latents = vae.encode(img_tensors).latent_dist.sample()
+        
+    # check NaN
+    for (key, _), latents1 in zip(key_and_images, latents):
+        if torch.isnan(latents1).any():
+            raise ValueError(f"NaN detected in latents of {key}")
+
     return latents
 
 
@@ -107,24 +113,26 @@ def main(args):
     def process_batch(is_last):
         for bucket in bucket_manager.buckets:
             if (is_last and len(bucket) > 0) or len(bucket) >= args.batch_size:
-                latents = get_latents(vae, [img for _, img in bucket], weight_dtype)
+                latents = get_latents(vae, [(key, img) for key, img, _, _ in bucket], weight_dtype)
                 assert (
                     latents.shape[2] == bucket[0][1].shape[0] // 8 and latents.shape[3] == bucket[0][1].shape[1] // 8
                 ), f"latent shape {latents.shape}, {bucket[0][1].shape}"
 
-                for (image_key, _), latent in zip(bucket, latents):
+                for (image_key, _, original_size, crop_left_top), latent in zip(bucket, latents):
                     npz_file_name = get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, False, args.recursive)
-                    np.savez(npz_file_name, latent)
+                    train_util.save_latents_to_disk(npz_file_name, latent, original_size, crop_left_top)
 
                 # flip
                 if args.flip_aug:
-                    latents = get_latents(vae, [img[:, ::-1].copy() for _, img in bucket], weight_dtype)  # copyがないとTensor変換できない
+                    latents = get_latents(
+                        vae, [(key, img[:, ::-1].copy()) for key, img, _, _ in bucket], weight_dtype
+                    )  # copyがないとTensor変換できない
 
-                    for (image_key, _), latent in zip(bucket, latents):
+                    for (image_key, _, original_size, crop_left_top), latent in zip(bucket, latents):
                         npz_file_name = get_npz_filename_wo_ext(
                             args.train_data_dir, image_key, args.full_path, True, args.recursive
                         )
-                        np.savez(npz_file_name, latent)
+                        train_util.save_latents_to_disk(npz_file_name, latent, original_size, crop_left_top)
                 else:
                     # remove existing flipped npz
                     for image_key, _ in bucket:
@@ -194,7 +202,7 @@ def main(args):
             resized_size[0] >= reso[0] and resized_size[1] >= reso[1]
         ), f"internal error resized size is small: {resized_size}, {reso}"
 
-        # 既に存在するファイルがあればshapeを確認して同じならskipする
+        # 既に存在するファイルがあればshape等を確認して同じならskipする
         if args.skip_existing:
             npz_files = [get_npz_filename_wo_ext(args.train_data_dir, image_key, args.full_path, False, args.recursive) + ".npz"]
             if args.flip_aug:
@@ -208,8 +216,12 @@ def main(args):
                     found = False
                     break
 
-                dat = np.load(npz_file)["arr_0"]
+                latents, _, _ = train_util.load_latents_from_disk(npz_file)
-                if dat.shape[1] != reso[1] // 8 or dat.shape[2] != reso[0] // 8:  # latentsのshapeを確認
+                if latents is None:  # old version
+                    found = False
+                    break
+
+                if latents.shape[1] != reso[1] // 8 or latents.shape[2] != reso[0] // 8:  # latentsのshapeを確認
                     found = False
                     break
             if found:
@@ -221,13 +233,21 @@ def main(args):
         if resized_size[0] != image.shape[1] or resized_size[1] != image.shape[0]:  # リサイズ処理が必要？
             image = cv2.resize(image, resized_size, interpolation=cv2.INTER_AREA)
 
+        trim_left = 0
         if resized_size[0] > reso[0]:
             trim_size = resized_size[0] - reso[0]
             image = image[:, trim_size // 2 : trim_size // 2 + reso[0]]
+            trim_left = trim_size // 2
 
+        trim_top = 0
         if resized_size[1] > reso[1]:
             trim_size = resized_size[1] - reso[1]
             image = image[trim_size // 2 : trim_size // 2 + reso[1]]
+            trim_top = trim_size // 2
+
+        original_size_wh = (resized_size[0], resized_size[1])
+        # target_size_wh = (reso[0], reso[1])
+        crop_left_top = (trim_left, trim_top)
 
         assert (
             image.shape[0] == reso[1] and image.shape[1] == reso[0]
@@ -237,7 +257,7 @@ def main(args):
         # cv2.imwrite(f"r:\\test\\img_{len(img_ar_errors)}.jpg", image[:, :, ::-1])
 
         # バッチへ追加
-        bucket_manager.add_image(reso, (image_key, image))
+        bucket_manager.add_image(reso, (image_key, image, original_size_wh, crop_left_top))
 
         # バッチを推論するか判定して推論する
         process_batch(False)

library/train_util.py

@@ -124,11 +124,11 @@ class BucketManager:
 
         self.resos = []
         self.reso_to_id = {}
-        self.buckets = []  # 前処理時は (image_key, image)、学習時は image_key
+        self.buckets = []  # 前処理時は (image_key, image, original size, crop left/top)、学習時は image_key
 
-    def add_image(self, reso, image):
+    def add_image(self, reso, image_or_info):
         bucket_id = self.reso_to_id[reso]
-        self.buckets[bucket_id].append(image)
+        self.buckets[bucket_id].append(image_or_info)
 
     def shuffle(self):
         for bucket in self.buckets:
@@ -767,7 +767,10 @@ class BaseDataset(torch.utils.data.Dataset):
         img = np.array(image, np.uint8)
         return img
 
-    def trim_and_resize_if_required(self, subset: BaseSubset, image, reso, resized_size):
+    # 画像を読み込む。戻り値はnumpy.ndarray,(original width, original height),(crop left, crop top)
+    def trim_and_resize_if_required(
+        self, subset: BaseSubset, image, reso, resized_size
+    ) -> Tuple[np.ndarray, Tuple[int, int], Tuple[int, int]]:
         image_height, image_width = image.shape[0:2]
 
         if image_width != resized_size[0] or image_height != resized_size[1]:
@@ -907,19 +910,13 @@ class BaseDataset(torch.utils.data.Dataset):
 
             latents = vae.encode(img_tensors).latent_dist.sample().to("cpu")
 
+            for info, latent in zip(batch, latents):
                 # check NaN
-            for info, latents1 in zip(batch, latents):
+                if torch.isnan(latents).any():
-                if torch.isnan(latents1).any():
                     raise RuntimeError(f"NaN detected in latents: {info.absolute_path}")
 
-            for info, latent in zip(batch, latents):
                 if cache_to_disk:
-                    np.savez(
+                    save_latents_to_disk(info.latents_npz, latent, info.latents_original_size, info.latents_crop_left_top)
-                        info.latents_npz,
-                        latents=latent.float().numpy(),
-                        original_size=np.array(info.latents_original_size),
-                        crop_left_top=np.array(info.latents_crop_left_top),
-                    )
                 else:
                     info.latents = latent
 
@@ -927,12 +924,14 @@ class BaseDataset(torch.utils.data.Dataset):
                 img_tensors = torch.flip(img_tensors, dims=[3])
                 latents = vae.encode(img_tensors).latent_dist.sample().to("cpu")
                 for info, latent in zip(batch, latents):
+                    # check NaN
+                    if torch.isnan(latents).any():
+                        raise RuntimeError(f"NaN detected in latents: {info.absolute_path}")
+
                     if cache_to_disk:
-                        np.savez(
+                        # crop_left_top is reversed when making batch
-                            info.latents_npz_flipped,
+                        save_latents_to_disk(
-                            latents=latent.float().numpy(),
+                            info.latents_npz_flipped, latent, info.latents_original_size, info.latents_crop_left_top
-                            original_size=np.array(info.latents_original_size),
-                            crop_left_top=np.array(info.latents_crop_left_top),  # reverse horizontally when use flipped latents
                         )
                     else:
                         info.latents_flipped = latent
@@ -1005,18 +1004,7 @@ class BaseDataset(torch.utils.data.Dataset):
 
     def load_latents_from_npz(self, image_info: ImageInfo, flipped):
         npz_file = image_info.latents_npz_flipped if flipped else image_info.latents_npz
-        if npz_file is None:
+        return load_latents_from_disk(npz_file)
-            return None, None, None
-
-        npz = np.load(npz_file)
-        if "latents" not in npz:
-            print(f"error: npz is old format. please re-generate {npz_file}")
-            return None, None, None
-
-        latents = npz["latents"]
-        original_size = npz["original_size"].tolist()
-        crop_left_top = npz["crop_left_top"].tolist()
-        return latents, original_size, crop_left_top
 
     def __len__(self):
         return self._length
@@ -1762,6 +1750,31 @@ class DatasetGroup(torch.utils.data.ConcatDataset):
             dataset.disable_token_padding()
 
 
+# 戻り値は、latents_tensor, (original_size width, original_size height), (crop left, crop top)
+def load_latents_from_disk(npz_path) -> Tuple[Optional[torch.Tensor], Optional[List[int]], Optional[List[int]]]:
+    if npz_path is None:  # flipped doesn't exist
+        return None, None, None
+
+    npz = np.load(npz_path)
+    if "latents" not in npz:
+        print(f"error: npz is old format. please re-generate {npz_path}")
+        return None, None, None
+
+    latents = npz["latents"]
+    original_size = npz["original_size"].tolist()
+    crop_left_top = npz["crop_left_top"].tolist()
+    return latents, original_size, crop_left_top
+
+
+def save_latents_to_disk(npz_path, latents_tensor, original_size, crop_left_top):
+    np.savez(
+        npz_path,
+        latents=latents_tensor.float().cpu().numpy(),
+        original_size=np.array(original_size),
+        crop_left_top=np.array(crop_left_top),
+    )
+
+
 def debug_dataset(train_dataset, show_input_ids=False):
     print(f"Total dataset length (steps) / データセットの長さ（ステップ数）: {len(train_dataset)}")
     print("`S` for next step, `E` for next epoch no. , Escape for exit. / Sキーで次のステップ、Eキーで次のエポック、Escキーで中断、終了します")