5 files changed, 31 insertions, 63 deletions

diff --git a/data/csv.py b/data/csv.py
index d400757..e25dd3f 100644
--- a/data/csv.py
+++ b/data/csv.py
@@ -38,6 +38,7 @@ class CSVDataItem(NamedTuple):
     instance_image_path: Path
     class_image_path: Path
     prompt: list[str]
+    cprompt: str
     nprompt: str
 
 
@@ -47,8 +48,6 @@ class CSVDataModule():
             batch_size: int,
             data_file: str,
             prompt_processor: PromptProcessor,
-            instance_identifier: str,
-            class_identifier: Optional[str] = None,
             class_subdir: str = "cls",
             num_class_images: int = 100,
             size: int = 512,
@@ -77,8 +76,6 @@ class CSVDataModule():
         self.num_class_images = num_class_images
 
         self.prompt_processor = prompt_processor
-        self.instance_identifier = instance_identifier
-        self.class_identifier = class_identifier
         self.size = size
         self.repeats = repeats
         self.dropout = dropout
@@ -96,14 +93,18 @@ class CSVDataModule():
     def prepare_items(self, template, expansions, data) -> list[CSVDataItem]:
         image = template["image"] if "image" in template else "{}"
         prompt = template["prompt"] if "prompt" in template else "{content}"
+        cprompt = template["cprompt"] if "cprompt" in template else "{content}"
         nprompt = template["nprompt"] if "nprompt" in template else "{content}"
 
         return [
             CSVDataItem(
                 self.data_root.joinpath(image.format(item["image"])),
                 None,
-                prompt_to_keywords(prompt.format(
-                    **prepare_prompt(item["prompt"] if "prompt" in item else "")), expansions),
+                prompt_to_keywords(
+                    prompt.format(**prepare_prompt(item["prompt"] if "prompt" in item else "")),
+                    expansions
+                ),
+                cprompt.format(**prepare_prompt(item["cprompt"] if "cprompt" in item else "")),
                 nprompt.format(**prepare_prompt(item["nprompt"] if "nprompt" in item else "")),
             )
             for item in data
@@ -123,6 +124,7 @@ class CSVDataModule():
                 item.instance_image_path,
                 self.class_root.joinpath(f"{item.instance_image_path.stem}_{i}{item.instance_image_path.suffix}"),
                 item.prompt,
+                item.cprompt,
                 item.nprompt,
             )
             for item in items
@@ -160,12 +162,10 @@ class CSVDataModule():
 
     def setup(self, stage=None):
         train_dataset = CSVDataset(self.data_train, self.prompt_processor, batch_size=self.batch_size,
-                                   instance_identifier=self.instance_identifier, class_identifier=self.class_identifier,
                                    num_class_images=self.num_class_images,
                                    size=self.size, interpolation=self.interpolation,
                                    center_crop=self.center_crop, repeats=self.repeats, dropout=self.dropout)
         val_dataset = CSVDataset(self.data_val, self.prompt_processor, batch_size=self.batch_size,
-                                 instance_identifier=self.instance_identifier,
                                  size=self.size, interpolation=self.interpolation,
                                  center_crop=self.center_crop)
         self.train_dataloader_ = DataLoader(train_dataset, batch_size=self.batch_size,
@@ -187,9 +187,7 @@ class CSVDataset(Dataset):
         self,
         data: List[CSVDataItem],
         prompt_processor: PromptProcessor,
-        instance_identifier: str,
         batch_size: int = 1,
-        class_identifier: Optional[str] = None,
         num_class_images: int = 0,
         size: int = 512,
         repeats: int = 1,
@@ -201,8 +199,6 @@ class CSVDataset(Dataset):
         self.data = data
         self.prompt_processor = prompt_processor
         self.batch_size = batch_size
-        self.instance_identifier = instance_identifier
-        self.class_identifier = class_identifier
         self.num_class_images = num_class_images
         self.dropout = dropout
         self.image_cache = {}
@@ -239,14 +235,12 @@ class CSVDataset(Dataset):
 
         return image
 
-    def get_input_ids(self, prompt, identifier):
-        return self.prompt_processor.get_input_ids(prompt.format(identifier))
-
     def get_example(self, i):
         item = self.data[i % self.num_instance_images]
 
         example = {}
         example["prompts"] = item.prompt
+        example["cprompts"] = item.cprompt
         example["nprompts"] = item.nprompt
         example["instance_images"] = self.get_image(item.instance_image_path)
         if self.num_class_images != 0:
@@ -260,13 +254,14 @@ class CSVDataset(Dataset):
         example = {}
 
         example["prompts"] = keywords_to_prompt(unprocessed_example["prompts"], self.dropout)
+        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.get_input_ids(example["prompts"], self.instance_identifier)
+        example["instance_prompt_ids"] = self.prompt_processor.get_input_ids(example["prompts"])
 
         if self.num_class_images != 0:
             example["class_images"] = self.image_transforms(unprocessed_example["class_images"])
-            example["class_prompt_ids"] = self.get_input_ids(example["prompts"], self.class_identifier)
+            example["class_prompt_ids"] = self.prompt_processor.get_input_ids(example["cprompts"])
 
         return example
diff --git a/environment.yaml b/environment.yaml
index e598f72..179fa38 100644
--- a/environment.yaml
+++ b/environment.yaml
@@ -21,4 +21,4 @@ dependencies:
       - test-tube>=0.7.5
       - transformers==4.25.1
       - triton==2.0.0.dev20221202
-      - xformers==0.0.16rc391
+      - xformers==0.0.16rc396
diff --git a/train_dreambooth.py b/train_dreambooth.py
index 9749c62..ff67d12 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -62,16 +62,10 @@ def parse_args():
         default="template",
     )
     parser.add_argument(
-        "--instance_identifier",
+        "--project",
         type=str,
         default=None,
-        help="A token to use as a placeholder for the concept.",
-    )
-    parser.add_argument(
-        "--class_identifier",
-        type=str,
-        default=None,
-        help="A token to use as a placeholder for the concept.",
+        help="The name of the current project.",
     )
     parser.add_argument(
         "--placeholder_token",
@@ -364,8 +358,8 @@ def parse_args():
     if args.pretrained_model_name_or_path is None:
         raise ValueError("You must specify --pretrained_model_name_or_path")
 
-    if args.instance_identifier is None:
-        raise ValueError("You must specify --instance_identifier")
+    if args.project is None:
+        raise ValueError("You must specify --project")
 
     if isinstance(args.initializer_token, str):
         args.initializer_token = [args.initializer_token]
@@ -397,7 +391,6 @@ class Checkpointer(CheckpointerBase):
         text_encoder,
         scheduler,
         output_dir: Path,
-        instance_identifier,
         placeholder_token,
         placeholder_token_id,
         sample_image_size,
@@ -408,7 +401,6 @@ class Checkpointer(CheckpointerBase):
         super().__init__(
             datamodule=datamodule,
             output_dir=output_dir,
-            instance_identifier=instance_identifier,
             placeholder_token=placeholder_token,
             placeholder_token_id=placeholder_token_id,
             sample_image_size=sample_image_size,
@@ -481,13 +473,8 @@ def main():
             "Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
         )
 
-    instance_identifier = args.instance_identifier
-
-    if len(args.placeholder_token) != 0:
-        instance_identifier = instance_identifier.format(args.placeholder_token[0])
-
     now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
-    basepath = Path(args.output_dir).joinpath(slugify(instance_identifier), now)
+    basepath = Path(args.output_dir).joinpath(slugify(args.project), now)
     basepath.mkdir(parents=True, exist_ok=True)
 
     accelerator = Accelerator(
@@ -637,6 +624,7 @@ def main():
 
     def collate_fn(examples):
         prompts = [example["prompts"] for example in examples]
+        cprompts = [example["cprompts"] for example in examples]
         nprompts = [example["nprompts"] for example in examples]
         input_ids = [example["instance_prompt_ids"] for example in examples]
         pixel_values = [example["instance_images"] for example in examples]
@@ -653,6 +641,7 @@ def main():
 
         batch = {
             "prompts": prompts,
+            "cprompts": cprompts,
             "nprompts": nprompts,
             "input_ids": inputs.input_ids,
             "pixel_values": pixel_values,
@@ -664,8 +653,6 @@ def main():
         data_file=args.train_data_file,
         batch_size=args.train_batch_size,
         prompt_processor=prompt_processor,
-        instance_identifier=instance_identifier,
-        class_identifier=args.class_identifier,
         class_subdir="cls",
         num_class_images=args.num_class_images,
         size=args.resolution,
@@ -703,7 +690,7 @@ def main():
             with torch.autocast("cuda"), torch.inference_mode():
                 for batch in batched_data:
                     image_name = [item.class_image_path for item in batch]
-                    prompt = [item.prompt.format(identifier=args.class_identifier) for item in batch]
+                    prompt = [item.cprompt for item in batch]
                     nprompt = [item.nprompt for item in batch]
 
                     images = pipeline(
@@ -812,7 +799,6 @@ def main():
         text_encoder=text_encoder,
         scheduler=checkpoint_scheduler,
         output_dir=basepath,
-        instance_identifier=instance_identifier,
         placeholder_token=args.placeholder_token,
         placeholder_token_id=placeholder_token_id,
         sample_image_size=args.sample_image_size,
diff --git a/train_ti.py b/train_ti.py
index 9d06c50..55daa35 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -63,16 +63,10 @@ def parse_args():
         default="template",
     )
     parser.add_argument(
-        "--instance_identifier",
+        "--project",
         type=str,
         default=None,
-        help="A token to use as a placeholder for the concept.",
-    )
-    parser.add_argument(
-        "--class_identifier",
-        type=str,
-        default=None,
-        help="A token to use as a placeholder for the concept.",
+        help="The name of the current project.",
     )
     parser.add_argument(
         "--placeholder_token",
@@ -334,6 +328,9 @@ def parse_args():
     if args.pretrained_model_name_or_path is None:
         raise ValueError("You must specify --pretrained_model_name_or_path")
 
+    if args.project is None:
+        raise ValueError("You must specify --project")
+
     if isinstance(args.initializer_token, str):
         args.initializer_token = [args.initializer_token]
 
@@ -366,7 +363,6 @@ class Checkpointer(CheckpointerBase):
         text_encoder,
         scheduler,
         text_embeddings,
-        instance_identifier,
         placeholder_token,
         placeholder_token_id,
         output_dir: Path,
@@ -378,7 +374,6 @@ class Checkpointer(CheckpointerBase):
         super().__init__(
             datamodule=datamodule,
             output_dir=output_dir,
-            instance_identifier=instance_identifier,
             placeholder_token=placeholder_token,
             placeholder_token_id=placeholder_token_id,
             sample_image_size=sample_image_size,
@@ -441,14 +436,9 @@ class Checkpointer(CheckpointerBase):
 def main():
     args = parse_args()
 
-    instance_identifier = args.instance_identifier
-
-    if len(args.placeholder_token) != 0:
-        instance_identifier = instance_identifier.format(args.placeholder_token[0])
-
     global_step_offset = args.global_step
     now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
-    basepath = Path(args.output_dir).joinpath(slugify(instance_identifier), now)
+    basepath = Path(args.output_dir).joinpath(slugify(args.project), now)
     basepath.mkdir(parents=True, exist_ok=True)
 
     accelerator = Accelerator(
@@ -567,6 +557,7 @@ def main():
 
     def collate_fn(examples):
         prompts = [example["prompts"] for example in examples]
+        cprompts = [example["cprompts"] for example in examples]
         nprompts = [example["nprompts"] for example in examples]
         input_ids = [example["instance_prompt_ids"] for example in examples]
         pixel_values = [example["instance_images"] for example in examples]
@@ -583,6 +574,7 @@ def main():
 
         batch = {
             "prompts": prompts,
+            "cprompts": cprompts,
             "nprompts": nprompts,
             "input_ids": inputs.input_ids,
             "pixel_values": pixel_values,
@@ -594,8 +586,6 @@ def main():
         data_file=args.train_data_file,
         batch_size=args.train_batch_size,
         prompt_processor=prompt_processor,
-        instance_identifier=args.instance_identifier,
-        class_identifier=args.class_identifier,
         class_subdir="cls",
         num_class_images=args.num_class_images,
         size=args.resolution,
@@ -634,7 +624,7 @@ def main():
             with torch.autocast("cuda"), torch.inference_mode():
                 for batch in batched_data:
                     image_name = [item.class_image_path for item in batch]
-                    prompt = [item.prompt.format(identifier=args.class_identifier) for item in batch]
+                    prompt = [item.cprompt for item in batch]
                     nprompt = [item.nprompt for item in batch]
 
                     images = pipeline(
@@ -744,7 +734,6 @@ def main():
         text_encoder=text_encoder,
         scheduler=checkpoint_scheduler,
         text_embeddings=text_embeddings,
-        instance_identifier=args.instance_identifier,
         placeholder_token=args.placeholder_token,
         placeholder_token_id=placeholder_token_id,
         output_dir=basepath,
diff --git a/training/util.py b/training/util.py
index e8d22ae..000173d 100644
--- a/training/util.py
+++ b/training/util.py
@@ -44,7 +44,6 @@ class CheckpointerBase:
         self,
         datamodule,
         output_dir: Path,
-        instance_identifier,
         placeholder_token,
         placeholder_token_id,
         sample_image_size,
@@ -54,7 +53,6 @@ class CheckpointerBase:
     ):
         self.datamodule = datamodule
         self.output_dir = output_dir
-        self.instance_identifier = instance_identifier
         self.placeholder_token = placeholder_token
         self.placeholder_token_id = placeholder_token_id
         self.sample_image_size = sample_image_size
@@ -90,7 +88,7 @@ class CheckpointerBase:
                     if j * data.batch_size < self.sample_batch_size * self.sample_batches
                 ]
                 prompts = [
-                    prompt.format(identifier=self.instance_identifier)
+                    prompt
                     for batch in batches
                     for prompt in batch["prompts"]
                 ]