Update

3 files changed, 26 insertions, 10 deletions

diff --git a/data/csv.py b/data/csv.py
index b45ac77..0810c2c 100644
--- a/data/csv.py
+++ b/data/csv.py
@@ -39,8 +39,8 @@ class CSVDataItem(NamedTuple):
     instance_image_path: Path
     class_image_path: Path
     prompt: list[str]
-    cprompt: list[str]
-    nprompt: list[str]
+    cprompt: str
+    nprompt: str
 
 
 class CSVDataModule():
@@ -105,14 +105,14 @@ class CSVDataModule():
                     prompt.format(**prepare_prompt(item["prompt"] if "prompt" in item else "")),
                     expansions
                 ),
-                prompt_to_keywords(
+                keywords_to_prompt(prompt_to_keywords(
                     cprompt.format(**prepare_prompt(item["prompt"] if "prompt" in item else "")),
                     expansions
-                ),
-                prompt_to_keywords(
+                )),
+                keywords_to_prompt(prompt_to_keywords(
                     nprompt.format(**prepare_prompt(item["nprompt"] if "nprompt" in item else "")),
                     expansions
-                ),
+                )),
             )
             for item in data
         ]
@@ -261,8 +261,8 @@ class CSVDataset(Dataset):
         example = {}
 
         example["prompts"] = keywords_to_prompt(unprocessed_example["prompts"], self.dropout, True)
-        example["cprompts"] = keywords_to_prompt(unprocessed_example["cprompts"])
-        example["nprompts"] = keywords_to_prompt(unprocessed_example["nprompts"])
+        example["cprompts"] = unprocessed_example["cprompts"]
+        example["nprompts"] = unprocessed_example["nprompts"]
 
         example["instance_images"] = self.image_transforms(unprocessed_example["instance_images"])
         example["instance_prompt_ids"] = self.prompt_processor.get_input_ids(example["prompts"])
diff --git a/train_dreambooth.py b/train_dreambooth.py
index 1a79b2b..c7899a0 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -105,6 +105,12 @@ def parse_args():
         help="How many class images to generate."
     )
     parser.add_argument(
+        "--class_image_dir",
+        type=str,
+        default="cls",
+        help="The directory where class images will be saved.",
+    )
+    parser.add_argument(
         "--repeats",
         type=int,
         default=1,
@@ -653,7 +659,7 @@ def main():
         data_file=args.train_data_file,
         batch_size=args.train_batch_size,
         prompt_processor=prompt_processor,
-        class_subdir="cls",
+        class_subdir=args.class_image_dir,
         num_class_images=args.num_class_images,
         size=args.resolution,
         repeats=args.repeats,
@@ -696,6 +702,8 @@ def main():
                     images = pipeline(
                         prompt=prompt,
                         negative_prompt=nprompt,
+                        height=args.sample_image_size,
+                        width=args.sample_image_size,
                         num_inference_steps=args.sample_steps
                     ).images
 
diff --git a/train_ti.py b/train_ti.py
index cc208f0..52bd675 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -87,6 +87,12 @@ def parse_args():
         help="How many class images to generate."
     )
     parser.add_argument(
+        "--class_image_dir",
+        type=str,
+        default="cls",
+        help="The directory where class images will be saved.",
+    )
+    parser.add_argument(
         "--repeats",
         type=int,
         default=1,
@@ -586,7 +592,7 @@ def main():
         data_file=args.train_data_file,
         batch_size=args.train_batch_size,
         prompt_processor=prompt_processor,
-        class_subdir="cls",
+        class_subdir=args.class_image_dir,
         num_class_images=args.num_class_images,
         size=args.resolution,
         repeats=args.repeats,
@@ -630,6 +636,8 @@ def main():
                     images = pipeline(
                         prompt=prompt,
                         negative_prompt=nprompt,
+                        height=args.sample_image_size,
+                        width=args.sample_image_size,
                         num_inference_steps=args.sample_steps
                     ).images