update flax controlnet training script (#2951)

* load_from_disk + checkpointing_steps

* apply feedback

examples/controlnet/train_controlnet_flax.py

@@ -27,13 +27,13 @@ import optax
 import torch
 import torch.utils.checkpoint
 import transformers
-from datasets import load_dataset
+from datasets import load_dataset, load_from_disk
 from flax import jax_utils
 from flax.core.frozen_dict import unfreeze
 from flax.training import train_state
 from flax.training.common_utils import shard
 from huggingface_hub import create_repo, upload_folder
-from PIL import Image
+from PIL import Image, PngImagePlugin
 from torch.utils.data import IterableDataset
 from torchvision import transforms
 from tqdm.auto import tqdm
@@ -49,6 +49,11 @@ from diffusers import (
 from diffusers.utils import check_min_version, is_wandb_available
 
 
+# To prevent an error that occurs when there are abnormally large compressed data chunk in the png image
+# see more https://github.com/python-pillow/Pillow/issues/5610
+LARGE_ENOUGH_NUMBER = 100
+PngImagePlugin.MAX_TEXT_CHUNK = LARGE_ENOUGH_NUMBER * (1024**2)
+
 if is_wandb_available():
     import wandb
 
@@ -246,6 +251,12 @@ def parse_args():
         default=None,
         help="Total number of training steps to perform.",
     )
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=5000,
+        help=("Save a checkpoint of the training state every X updates."),
+    )
     parser.add_argument(
         "--learning_rate",
         type=float,
@@ -344,9 +355,17 @@ def parse_args():
         type=str,
         default=None,
         help=(
-            "A folder containing the training data. Folder contents must follow the structure described in"
-            " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
-            " must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
+            "A folder containing the training dataset. By default it will use `load_dataset` method to load a custom dataset from the folder."
+            "Folder must contain a dataset script as described here https://huggingface.co/docs/datasets/dataset_script) ."
+            "If `--load_from_disk` flag is passed, it will use `load_from_disk` method instead. Ignored if `dataset_name` is specified."
+        ),
+    )
+    parser.add_argument(
+        "--load_from_disk",
+        action="store_true",
+        help=(
+            "If True, will load a dataset that was previously saved using `save_to_disk` from `--train_data_dir`"
+            "See more https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.load_from_disk"
         ),
     )
     parser.add_argument(
@@ -478,6 +497,11 @@ def make_train_dataset(args, tokenizer, batch_size=None):
         )
     else:
         if args.train_data_dir is not None:
+            if args.load_from_disk:
+                dataset = load_from_disk(
+                    args.train_data_dir,
+                )
+            else:
                 dataset = load_dataset(
                     args.train_data_dir,
                     cache_dir=args.cache_dir,
@@ -545,6 +569,7 @@ def make_train_dataset(args, tokenizer, batch_size=None):
     image_transforms = transforms.Compose(
         [
             transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
+            transforms.CenterCrop(args.resolution),
             transforms.ToTensor(),
             transforms.Normalize([0.5], [0.5]),
         ]
@@ -553,6 +578,7 @@ def make_train_dataset(args, tokenizer, batch_size=None):
     conditioning_image_transforms = transforms.Compose(
         [
             transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
+            transforms.CenterCrop(args.resolution),
             transforms.ToTensor(),
         ]
     )
@@ -981,6 +1007,11 @@ def main():
                             "train/loss": jax_utils.unreplicate(train_metric)["loss"],
                         }
                     )
+            if global_step % args.checkpointing_steps == 0 and jax.process_index() == 0:
+                controlnet.save_pretrained(
+                    f"{args.output_dir}/{global_step}",
+                    params=get_params_to_save(state.params),
+                )
 
         train_metric = jax_utils.unreplicate(train_metric)
         train_step_progress_bar.close()