Make center crop and random flip as args for unconditional image generation (#2259)

* Add center crop and horizontal flip to args

* Update command to use center crop and random flip

* Add center crop and horizontal flip to args

* Update command to use center crop and random flip

examples/research_projects/onnxruntime/unconditional_image_generation/README.md

@@ -36,7 +36,7 @@ The command to train a DDPM UNet model on the Oxford Flowers dataset with onnxru
 ```bash
 accelerate launch train_unconditional_ort.py \
   --dataset_name="huggan/flowers-102-categories" \
-  --resolution=64 \
+  --resolution=64 --center_crop --random_flip \
   --output_dir="ddpm-ema-flowers-64" \
   --use_ema \
   --train_batch_size=16 \
@@ -47,4 +47,4 @@ accelerate launch train_unconditional_ort.py \
   --mixed_precision=fp16
   ```
 
-Please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.
+Please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.

examples/research_projects/onnxruntime/unconditional_image_generation/train_unconditional.py

@@ -20,15 +20,7 @@ from diffusers.training_utils import EMAModel
 from diffusers.utils import check_min_version
 from huggingface_hub import HfFolder, Repository, create_repo, whoami
 from onnxruntime.training.ortmodule import ORTModule
-from torchvision.transforms import (
-    CenterCrop,
-    Compose,
-    InterpolationMode,
-    Normalize,
-    RandomHorizontalFlip,
-    Resize,
-    ToTensor,
-)
+from torchvision import transforms
 from tqdm.auto import tqdm
 
 
@@ -105,6 +97,21 @@ def parse_args():
             " resolution"
         ),
     )
+    parser.add_argument(
+        "--center_crop",
+        default=False,
+        action="store_true",
+        help=(
+            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
+            " cropped. The images will be resized to the resolution first before cropping."
+        ),
+    )
+    parser.add_argument(
+        "--random_flip",
+        default=False,
+        action="store_true",
+        help="whether to randomly flip images horizontally",
+    )
     parser.add_argument(
         "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
     )
@@ -369,13 +376,13 @@ def main(args):
         # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
 
     # Preprocessing the datasets and DataLoaders creation.
-    augmentations = Compose(
+    augmentations = transforms.Compose(
         [
-            Resize(args.resolution, interpolation=InterpolationMode.BILINEAR),
-            CenterCrop(args.resolution),
-            RandomHorizontalFlip(),
-            ToTensor(),
-            Normalize([0.5], [0.5]),
+            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
+            transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
+            transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
+            transforms.ToTensor(),
+            transforms.Normalize([0.5], [0.5]),
         ]
     )
 

examples/unconditional_image_generation/README.md

@@ -34,7 +34,7 @@ The command to train a DDPM UNet model on the Oxford Flowers dataset:
 ```bash
 accelerate launch train_unconditional.py \
   --dataset_name="huggan/flowers-102-categories" \
-  --resolution=64 \
+  --resolution=64 --center_crop --random_flip \
   --output_dir="ddpm-ema-flowers-64" \
   --train_batch_size=16 \
   --num_epochs=100 \
@@ -59,7 +59,7 @@ The command to train a DDPM UNet model on the Pokemon dataset:
 ```bash
 accelerate launch train_unconditional.py \
   --dataset_name="huggan/pokemon" \
-  --resolution=64 \
+  --resolution=64 --center_crop --random_flip \
   --output_dir="ddpm-ema-pokemon-64" \
   --train_batch_size=16 \
   --num_epochs=100 \
@@ -139,4 +139,4 @@ dataset.push_to_hub("name_of_your_dataset", private=True)
 
 and that's it! You can now train your model by simply setting the `--dataset_name` argument to the name of your dataset on the hub.
 
-More on this can also be found in [this blog post](https://huggingface.co/blog/image-search-datasets).
+More on this can also be found in [this blog post](https://huggingface.co/blog/image-search-datasets).

examples/unconditional_image_generation/train_unconditional.py

@@ -19,15 +19,7 @@ from diffusers.optimization import get_scheduler
 from diffusers.training_utils import EMAModel
 from diffusers.utils import check_min_version
 from huggingface_hub import HfFolder, Repository, create_repo, whoami
-from torchvision.transforms import (
-    CenterCrop,
-    Compose,
-    InterpolationMode,
-    Normalize,
-    RandomHorizontalFlip,
-    Resize,
-    ToTensor,
-)
+from torchvision import transforms
 from tqdm.auto import tqdm
 
 
@@ -105,6 +97,21 @@ def parse_args():
             " resolution"
         ),
     )
+    parser.add_argument(
+        "--center_crop",
+        default=False,
+        action="store_true",
+        help=(
+            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
+            " cropped. The images will be resized to the resolution first before cropping."
+        ),
+    )
+    parser.add_argument(
+        "--random_flip",
+        default=False,
+        action="store_true",
+        help="whether to randomly flip images horizontally",
+    )
     parser.add_argument(
         "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
     )
@@ -369,13 +376,13 @@ def main(args):
         # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
 
     # Preprocessing the datasets and DataLoaders creation.
-    augmentations = Compose(
+    augmentations = transforms.Compose(
         [
-            Resize(args.resolution, interpolation=InterpolationMode.BILINEAR),
-            CenterCrop(args.resolution),
-            RandomHorizontalFlip(),
-            ToTensor(),
-            Normalize([0.5], [0.5]),
+            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
+            transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
+            transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
+            transforms.ToTensor(),
+            transforms.Normalize([0.5], [0.5]),
         ]
     )