cache latents to disk in dreambooth method

fine_tune.py

@@ -142,12 +142,14 @@ def train(args):
         vae.requires_grad_(False)
         vae.eval()
         with torch.no_grad():
-            train_dataset_group.cache_latents(vae, args.vae_batch_size)
+            train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)
         vae.to("cpu")
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
         gc.collect()
 
+        accelerator.wait_for_everyone()
+
     # 学習を準備する：モデルを適切な状態にする
     training_models = []
     if args.gradient_checkpointing:

library/train_util.py

@@ -722,7 +722,7 @@ class BaseDataset(torch.utils.data.Dataset):
     def is_latent_cacheable(self):
         return all([not subset.color_aug and not subset.random_crop for subset in self.subsets])
 
-    def cache_latents(self, vae, vae_batch_size=1):
+    def cache_latents(self, vae, vae_batch_size=1, cache_to_disk=False, is_main_process=True):
         # ちょっと速くした
         print("caching latents.")
 
@@ -740,11 +740,38 @@ class BaseDataset(torch.utils.data.Dataset):
             if info.latents_npz is not None:
                 info.latents = self.load_latents_from_npz(info, False)
                 info.latents = torch.FloatTensor(info.latents)
-                info.latents_flipped = self.load_latents_from_npz(info, True)  # might be None
+
+                # might be None, but that's ok because check is done in dataset
+                info.latents_flipped = self.load_latents_from_npz(info, True)
                 if info.latents_flipped is not None:
                     info.latents_flipped = torch.FloatTensor(info.latents_flipped)
                 continue
 
+            # check disk cache exists and size of latents
+            if cache_to_disk:
+                # TODO: refactor to unify with FineTuningDataset
+                info.latents_npz = os.path.splitext(info.absolute_path)[0] + ".npz"
+                info.latents_npz_flipped = os.path.splitext(info.absolute_path)[0] + "_flip.npz"
+                if not is_main_process:
+                    continue
+
+                cache_available = False
+                expected_latents_size = (info.bucket_reso[1] // 8, info.bucket_reso[0] // 8)  # bucket_resoはWxHなので注意
+                if os.path.exists(info.latents_npz):
+                    cached_latents = np.load(info.latents_npz)
+                    if cached_latents["latents"].shape[1:3] == expected_latents_size:
+                        cache_available = True
+
+                        if subset.flip_aug:
+                            cache_available = False
+                            if os.path.exists(info.latents_npz_flipped):
+                                cached_latents_flipped = np.load(info.latents_npz_flipped)
+                                if cached_latents_flipped["latents"].shape[1:3] == expected_latents_size:
+                                    cache_available = True
+
+                if cache_available:
+                    continue
+
             # if last member of batch has different resolution, flush the batch
             if len(batch) > 0 and batch[-1].bucket_reso != info.bucket_reso:
                 batches.append(batch)
@@ -760,6 +787,9 @@ class BaseDataset(torch.utils.data.Dataset):
         if len(batch) > 0:
             batches.append(batch)
 
+        if cache_to_disk and not is_main_process:  # don't cache latents in non-main process, set to info only
+            return
+
         # iterate batches
         for batch in tqdm(batches, smoothing=1, total=len(batches)):
             images = []
@@ -773,14 +803,21 @@ class BaseDataset(torch.utils.data.Dataset):
             img_tensors = img_tensors.to(device=vae.device, dtype=vae.dtype)
 
             latents = vae.encode(img_tensors).latent_dist.sample().to("cpu")
+
             for info, latent in zip(batch, latents):
-                info.latents = latent
+                if cache_to_disk:
+                    np.savez(info.latents_npz, latent.float().numpy())
+                else:
+                    info.latents = latent
 
             if subset.flip_aug:
                 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):
-                    info.latents_flipped = latent
+                    if cache_to_disk:
+                        np.savez(info.latents_npz_flipped, latent.float().numpy())
+                    else:
+                        info.latents_flipped = latent
 
     def get_image_size(self, image_path):
         image = Image.open(image_path)
@@ -873,10 +910,10 @@ class BaseDataset(torch.utils.data.Dataset):
             loss_weights.append(self.prior_loss_weight if image_info.is_reg else 1.0)
 
             # image/latentsを処理する
-            if image_info.latents is not None:
+            if image_info.latents is not None:  # cache_latents=Trueの場合
                 latents = image_info.latents if not subset.flip_aug or random.random() < 0.5 else image_info.latents_flipped
                 image = None
-            elif image_info.latents_npz is not None:
+            elif image_info.latents_npz is not None:  # FineTuningDatasetまたはcache_latents_to_disk=Trueの場合
                 latents = self.load_latents_from_npz(image_info, subset.flip_aug and random.random() >= 0.5)
                 latents = torch.FloatTensor(latents)
                 image = None
@@ -1340,10 +1377,10 @@ class DatasetGroup(torch.utils.data.ConcatDataset):
         for dataset in self.datasets:
             dataset.enable_XTI(*args, **kwargs)
 
-    def cache_latents(self, vae, vae_batch_size=1):
+    def cache_latents(self, vae, vae_batch_size=1, cache_to_disk=False, is_main_process=True):
         for i, dataset in enumerate(self.datasets):
             print(f"[Dataset {i}]")
-            dataset.cache_latents(vae, vae_batch_size)
+            dataset.cache_latents(vae, vae_batch_size, cache_to_disk, is_main_process)
 
     def is_latent_cacheable(self) -> bool:
         return all([dataset.is_latent_cacheable() for dataset in self.datasets])
@@ -2144,9 +2181,14 @@ def add_dataset_arguments(
     parser.add_argument(
         "--cache_latents",
         action="store_true",
-        help="cache latents to reduce memory (augmentations must be disabled) / メモリ削減のためにlatentをcacheする（augmentationは使用不可）",
+        help="cache latents to main memory to reduce VRAM usage (augmentations must be disabled) / VRAM削減のためにlatentをメインメモリにcacheする（augmentationは使用不可） ",
     )
     parser.add_argument("--vae_batch_size", type=int, default=1, help="batch size for caching latents / latentのcache時のバッチサイズ")
+    parser.add_argument(
+        "--cache_latents_to_disk",
+        action="store_true",
+        help="cache latents to disk to reduce VRAM usage (augmentations must be disabled) / VRAM削減のためにlatentをディスクにcacheする（augmentationは使用不可）",
+    )
     parser.add_argument(
         "--enable_bucket", action="store_true", help="enable buckets for multi aspect ratio training / 複数解像度学習のためのbucketを有効にする"
     )
@@ -3203,4 +3245,4 @@ class collater_class:
         # set epoch and step
         dataset.set_current_epoch(self.current_epoch.value)
         dataset.set_current_step(self.current_step.value)
-        return examples[0]
+        return examples[0]

train_db.py

@@ -117,12 +117,14 @@ def train(args):
         vae.requires_grad_(False)
         vae.eval()
         with torch.no_grad():
-            train_dataset_group.cache_latents(vae, args.vae_batch_size)
+            train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)
         vae.to("cpu")
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
         gc.collect()
 
+        accelerator.wait_for_everyone()
+
     # 学習を準備する：モデルを適切な状態にする
     train_text_encoder = args.stop_text_encoder_training is None or args.stop_text_encoder_training >= 0
     unet.requires_grad_(True)  # 念のため追加

train_network.py

@@ -172,12 +172,14 @@ def train(args):
         vae.requires_grad_(False)
         vae.eval()
         with torch.no_grad():
-            train_dataset_group.cache_latents(vae, args.vae_batch_size)
+            train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)
         vae.to("cpu")
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
         gc.collect()
 
+        accelerator.wait_for_everyone()
+
     # prepare network
     import sys
 

train_textual_inversion.py

@@ -233,12 +233,14 @@ def train(args):
         vae.requires_grad_(False)
         vae.eval()
         with torch.no_grad():
-            train_dataset_group.cache_latents(vae, args.vae_batch_size)
+            train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)
         vae.to("cpu")
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
         gc.collect()
 
+        accelerator.wait_for_everyone()
+
     if args.gradient_checkpointing:
         unet.enable_gradient_checkpointing()
         text_encoder.gradient_checkpointing_enable()

train_textual_inversion_XTI.py

@@ -267,12 +267,14 @@ def train(args):
         vae.requires_grad_(False)
         vae.eval()
         with torch.no_grad():
-            train_dataset_group.cache_latents(vae, args.vae_batch_size)
+            train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)
         vae.to("cpu")
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
         gc.collect()
 
+        accelerator.wait_for_everyone()
+
     if args.gradient_checkpointing:
         unet.enable_gradient_checkpointing()
         text_encoder.gradient_checkpointing_enable()