5 files changed, 129 insertions, 164 deletions

diff --git a/data/dreambooth/csv.py b/data/dreambooth/csv.py
index c0b0067..4ebdc13 100644
--- a/data/dreambooth/csv.py
+++ b/data/dreambooth/csv.py
@@ -13,13 +13,11 @@ class CSVDataModule(pl.LightningDataModule):
                  batch_size,
                  data_file,
                  tokenizer,
-                 instance_prompt,
-                 class_data_root=None,
-                 class_prompt=None,
+                 instance_identifier,
+                 class_identifier=None,
                  size=512,
                  repeats=100,
                  interpolation="bicubic",
-                 identifier="*",
                  center_crop=False,
                  valid_set_size=None,
                  generator=None,
@@ -32,13 +30,14 @@ class CSVDataModule(pl.LightningDataModule):
             raise ValueError("data_file must be a file")
 
         self.data_root = self.data_file.parent
+        self.class_root = self.data_root.joinpath("db_cls")
+        self.class_root.mkdir(parents=True, exist_ok=True)
+
         self.tokenizer = tokenizer
-        self.instance_prompt = instance_prompt
-        self.class_data_root = class_data_root
-        self.class_prompt = class_prompt
+        self.instance_identifier = instance_identifier
+        self.class_identifier = class_identifier
         self.size = size
         self.repeats = repeats
-        self.identifier = identifier
         self.center_crop = center_crop
         self.interpolation = interpolation
         self.valid_set_size = valid_set_size
@@ -48,30 +47,36 @@ class CSVDataModule(pl.LightningDataModule):
 
     def prepare_data(self):
         metadata = pd.read_csv(self.data_file)
-        image_paths = [os.path.join(self.data_root, f_path) for f_path in metadata['image'].values]
+        instance_image_paths = [self.data_root.joinpath(f) for f in metadata['image'].values]
+        class_image_paths = [self.class_root.joinpath(Path(f).name) for f in metadata['image'].values]
         prompts = metadata['prompt'].values
-        nprompts = metadata['nprompt'].values if 'nprompt' in metadata else [""] * len(image_paths)
-        skips = metadata['skip'].values if 'skip' in metadata else [""] * len(image_paths)
-        self.data_full = [(i, p, n) for i, p, n, s in zip(image_paths, prompts, nprompts, skips) if s != "x"]
+        nprompts = metadata['nprompt'].values if 'nprompt' in metadata else [""] * len(instance_image_paths)
+        skips = metadata['skip'].values if 'skip' in metadata else [""] * len(instance_image_paths)
+        self.data = [(i, c, p, n)
+                     for i, c, p, n, s
+                     in zip(instance_image_paths, class_image_paths, prompts, nprompts, skips)
+                     if s != "x"]
 
     def setup(self, stage=None):
-        valid_set_size = int(len(self.data_full) * 0.2)
+        valid_set_size = int(len(self.data) * 0.2)
         if self.valid_set_size:
             valid_set_size = min(valid_set_size, self.valid_set_size)
-        train_set_size = len(self.data_full) - valid_set_size
-
-        self.data_train, self.data_val = random_split(self.data_full, [train_set_size, valid_set_size], self.generator)
-
-        train_dataset = CSVDataset(self.data_train, self.tokenizer, instance_prompt=self.instance_prompt,
-                                   class_data_root=self.class_data_root, class_prompt=self.class_prompt,
-                                   size=self.size, interpolation=self.interpolation, identifier=self.identifier,
-                                   center_crop=self.center_crop, repeats=self.repeats, batch_size=self.batch_size)
-        val_dataset = CSVDataset(self.data_val, self.tokenizer, instance_prompt=self.instance_prompt,
-                                 size=self.size, interpolation=self.interpolation, identifier=self.identifier,
-                                 center_crop=self.center_crop, batch_size=self.batch_size)
-        self.train_dataloader_ = DataLoader(train_dataset, batch_size=self.batch_size,
+        valid_set_size = max(valid_set_size, 1)
+        train_set_size = len(self.data) - valid_set_size
+
+        self.data_train, self.data_val = random_split(self.data, [train_set_size, valid_set_size], self.generator)
+
+        train_dataset = CSVDataset(self.data_train, self.tokenizer,
+                                   instance_identifier=self.instance_identifier, class_identifier=self.class_identifier,
+                                   size=self.size, interpolation=self.interpolation,
+                                   center_crop=self.center_crop, repeats=self.repeats)
+        val_dataset = CSVDataset(self.data_val, self.tokenizer,
+                                 instance_identifier=self.instance_identifier,
+                                 size=self.size, interpolation=self.interpolation,
+                                 center_crop=self.center_crop, repeats=self.repeats)
+        self.train_dataloader_ = DataLoader(train_dataset, batch_size=self.batch_size, drop_last=True,
                                             shuffle=True, pin_memory=True, collate_fn=self.collate_fn)
-        self.val_dataloader_ = DataLoader(val_dataset, batch_size=self.batch_size,
+        self.val_dataloader_ = DataLoader(val_dataset, batch_size=self.batch_size, drop_last=True,
                                           pin_memory=True, collate_fn=self.collate_fn)
 
     def train_dataloader(self):
@@ -85,39 +90,23 @@ class CSVDataset(Dataset):
     def __init__(self,
                  data,
                  tokenizer,
-                 instance_prompt,
-                 class_data_root=None,
-                 class_prompt=None,
+                 instance_identifier,
+                 class_identifier=None,
                  size=512,
                  repeats=1,
                  interpolation="bicubic",
-                 identifier="*",
                  center_crop=False,
-                 batch_size=1,
                  ):
 
         self.data = data
         self.tokenizer = tokenizer
-        self.instance_prompt = instance_prompt
-        self.identifier = identifier
-        self.batch_size = batch_size
+        self.instance_identifier = instance_identifier
+        self.class_identifier = class_identifier
         self.cache = {}
 
         self.num_instance_images = len(self.data)
         self._length = self.num_instance_images * repeats
 
-        if class_data_root is not None:
-            self.class_data_root = Path(class_data_root)
-            self.class_data_root.mkdir(parents=True, exist_ok=True)
-
-            self.class_images = list(self.class_data_root.iterdir())
-            self.num_class_images = len(self.class_images)
-            self._length = max(self.num_class_images, self.num_instance_images)
-
-            self.class_prompt = class_prompt
-        else:
-            self.class_data_root = None
-
         self.interpolation = {"linear": transforms.InterpolationMode.NEAREST,
                               "bilinear": transforms.InterpolationMode.BILINEAR,
                               "bicubic": transforms.InterpolationMode.BICUBIC,
@@ -134,46 +123,49 @@ class CSVDataset(Dataset):
         )
 
     def __len__(self):
-        return math.ceil(self._length / self.batch_size) * self.batch_size
+        return self._length
 
     def get_example(self, i):
-        image_path, prompt, nprompt = self.data[i % self.num_instance_images]
+        instance_image_path, class_image_path, prompt, nprompt = self.data[i % self.num_instance_images]
 
-        if image_path in self.cache:
-            return self.cache[image_path]
+        if instance_image_path in self.cache:
+            return self.cache[instance_image_path]
 
         example = {}
 
-        instance_image = Image.open(image_path)
+        example["prompts"] = prompt
+        example["nprompts"] = nprompt
+
+        instance_image = Image.open(instance_image_path)
         if not instance_image.mode == "RGB":
             instance_image = instance_image.convert("RGB")
 
-        prompt = prompt.format(self.identifier)
+        instance_prompt = prompt.format(self.instance_identifier)
 
-        example["prompts"] = prompt
-        example["nprompts"] = nprompt
         example["instance_images"] = instance_image
         example["instance_prompt_ids"] = self.tokenizer(
-            self.instance_prompt,
+            instance_prompt,
             padding="do_not_pad",
             truncation=True,
             max_length=self.tokenizer.model_max_length,
         ).input_ids
 
-        if self.class_data_root:
-            class_image = Image.open(self.class_images[i % self.num_class_images])
+        if self.class_identifier:
+            class_image = Image.open(class_image_path)
             if not class_image.mode == "RGB":
                 class_image = class_image.convert("RGB")
 
+            class_prompt = prompt.format(self.class_identifier)
+
             example["class_images"] = class_image
             example["class_prompt_ids"] = self.tokenizer(
-                self.class_prompt,
+                class_prompt,
                 padding="do_not_pad",
                 truncation=True,
                 max_length=self.tokenizer.model_max_length,
             ).input_ids
 
-        self.cache[image_path] = example
+        self.cache[instance_image_path] = example
         return example
 
     def __getitem__(self, i):
@@ -185,7 +177,7 @@ class CSVDataset(Dataset):
         example["instance_images"] = self.image_transforms(unprocessed_example["instance_images"])
         example["instance_prompt_ids"] = unprocessed_example["instance_prompt_ids"]
 
-        if self.class_data_root:
+        if self.class_identifier:
             example["class_images"] = self.image_transforms(unprocessed_example["class_images"])
             example["class_prompt_ids"] = unprocessed_example["class_prompt_ids"]
 
diff --git a/data/dreambooth/prompt.py b/data/dreambooth/prompt.py
index 34f510d..b3a83ce 100644
--- a/data/dreambooth/prompt.py
+++ b/data/dreambooth/prompt.py
@@ -2,8 +2,9 @@ from torch.utils.data import Dataset
 
 
 class PromptDataset(Dataset):
-    def __init__(self, prompt, num_samples):
+    def __init__(self, prompt, nprompt, num_samples):
         self.prompt = prompt
+        self.nprompt = nprompt
         self.num_samples = num_samples
 
     def __len__(self):
@@ -12,5 +13,6 @@ class PromptDataset(Dataset):
     def __getitem__(self, index):
         example = {}
         example["prompt"] = self.prompt
+        example["nprompt"] = self.nprompt
         example["index"] = index
         return example
diff --git a/data/textual_inversion/csv.py b/data/textual_inversion/csv.py
index 852b1cb..4c5e27e 100644
--- a/data/textual_inversion/csv.py
+++ b/data/textual_inversion/csv.py
@@ -52,13 +52,14 @@ class CSVDataModule(pl.LightningDataModule):
         valid_set_size = int(len(self.data_full) * 0.2)
         if self.valid_set_size:
             valid_set_size = min(valid_set_size, self.valid_set_size)
+        valid_set_size = max(valid_set_size, 1)
         train_set_size = len(self.data_full) - valid_set_size
 
         self.data_train, self.data_val = random_split(self.data_full, [train_set_size, valid_set_size], self.generator)
 
         train_dataset = CSVDataset(self.data_train, self.tokenizer, size=self.size, repeats=self.repeats, interpolation=self.interpolation,
                                    placeholder_token=self.placeholder_token, center_crop=self.center_crop)
-        val_dataset = CSVDataset(self.data_val, self.tokenizer, size=self.size, interpolation=self.interpolation,
+        val_dataset = CSVDataset(self.data_val, self.tokenizer, size=self.size, repeats=self.repeats, interpolation=self.interpolation,
                                  placeholder_token=self.placeholder_token, center_crop=self.center_crop)
         self.train_dataloader_ = DataLoader(train_dataset, batch_size=self.batch_size, pin_memory=True, shuffle=True)
         self.val_dataloader_ = DataLoader(val_dataset, batch_size=self.batch_size, pin_memory=True)
diff --git a/dreambooth.py b/dreambooth.py
index 9d6b8d6..2fe89ec 100644
--- a/dreambooth.py
+++ b/dreambooth.py
@@ -13,13 +13,12 @@ import torch.utils.checkpoint
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import LoggerType, set_seed
-from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel
+from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, UNet2DConditionModel
 from schedulers.scheduling_euler_a import EulerAScheduler
 from diffusers.optimization import get_scheduler
-from pipelines.stable_diffusion.no_check import NoCheck
 from PIL import Image
 from tqdm.auto import tqdm
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPTextModel, CLIPTokenizer
 from slugify import slugify
 from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
 import json
@@ -56,7 +55,13 @@ def parse_args():
         help="A folder containing the training data."
     )
     parser.add_argument(
-        "--identifier",
+        "--instance_identifier",
+        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.",
@@ -218,12 +223,6 @@ def parse_args():
         help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.",
     )
     parser.add_argument(
-        "--instance_prompt",
-        type=str,
-        default=None,
-        help="The prompt with identifier specifing the instance",
-    )
-    parser.add_argument(
         "--class_data_dir",
         type=str,
         default=None,
@@ -231,12 +230,6 @@ def parse_args():
         help="A folder containing the training data of class images.",
     )
     parser.add_argument(
-        "--class_prompt",
-        type=str,
-        default=None,
-        help="The prompt to specify images in the same class as provided intance images.",
-    )
-    parser.add_argument(
         "--prior_loss_weight",
         type=float,
         default=1.0,
@@ -255,15 +248,6 @@ def parse_args():
         help="Max gradient norm."
     )
     parser.add_argument(
-        "--num_class_images",
-        type=int,
-        default=100,
-        help=(
-            "Minimal class images for prior perversation loss. If not have enough images, additional images will be"
-            " sampled with class_prompt."
-        ),
-    )
-    parser.add_argument(
         "--config",
         type=str,
         default=None,
@@ -286,21 +270,12 @@ 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_prompt is None:
-        raise ValueError("You must specify --instance_prompt")
-
-    if args.identifier is None:
-        raise ValueError("You must specify --identifier")
+    if args.instance_identifier is None:
+        raise ValueError("You must specify --instance_identifier")
 
     if args.output_dir is None:
         raise ValueError("You must specify --output_dir")
 
-    if args.with_prior_preservation:
-        if args.class_data_dir is None:
-            raise ValueError("You must specify --class_data_dir")
-        if args.class_prompt is None:
-            raise ValueError("You must specify --class_prompt")
-
     return args
 
 
@@ -443,7 +418,7 @@ def main():
     args = parse_args()
 
     now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
-    basepath = Path(args.output_dir).joinpath(slugify(args.identifier), now)
+    basepath = Path(args.output_dir).joinpath(slugify(args.instance_identifier), now)
     basepath.mkdir(parents=True, exist_ok=True)
 
     accelerator = Accelerator(
@@ -488,47 +463,6 @@ def main():
     freeze_params(vae.parameters())
     freeze_params(text_encoder.parameters())
 
-    # Generate class images, if necessary
-    if args.with_prior_preservation:
-        class_images_dir = Path(args.class_data_dir)
-        class_images_dir.mkdir(parents=True, exist_ok=True)
-        cur_class_images = len(list(class_images_dir.iterdir()))
-
-        if cur_class_images < args.num_class_images:
-            scheduler = EulerAScheduler(
-                beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
-            )
-
-            pipeline = VlpnStableDiffusion(
-                text_encoder=text_encoder,
-                vae=vae,
-                unet=unet,
-                tokenizer=tokenizer,
-                scheduler=scheduler,
-            ).to(accelerator.device)
-            pipeline.enable_attention_slicing()
-            pipeline.set_progress_bar_config(disable=True)
-
-            num_new_images = args.num_class_images - cur_class_images
-            logger.info(f"Number of class images to sample: {num_new_images}.")
-
-            sample_dataset = PromptDataset(args.class_prompt, num_new_images)
-            sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
-
-            sample_dataloader = accelerator.prepare(sample_dataloader)
-
-            for example in tqdm(sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process):
-                with accelerator.autocast():
-                    images = pipeline(example["prompt"]).images
-
-                for i, image in enumerate(images):
-                    image.save(class_images_dir / f"{example['index'][i] + cur_class_images}.jpg")
-
-            del pipeline
-
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
     if args.scale_lr:
         args.learning_rate = (
             args.learning_rate * args.gradient_accumulation_steps *
@@ -564,6 +498,7 @@ def main():
 
     def collate_fn(examples):
         prompts = [example["prompts"] 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]
 
@@ -579,6 +514,7 @@ def main():
 
         batch = {
             "prompts": prompts,
+            "nprompts": nprompts,
             "input_ids": input_ids,
             "pixel_values": pixel_values,
         }
@@ -588,11 +524,9 @@ def main():
         data_file=args.train_data_file,
         batch_size=args.train_batch_size,
         tokenizer=tokenizer,
-        instance_prompt=args.instance_prompt,
-        class_data_root=args.class_data_dir if args.with_prior_preservation else None,
-        class_prompt=args.class_prompt,
+        instance_identifier=args.instance_identifier,
+        class_identifier=args.class_identifier,
         size=args.resolution,
-        identifier=args.identifier,
         repeats=args.repeats,
         center_crop=args.center_crop,
         valid_set_size=args.sample_batch_size*args.sample_batches,
@@ -601,6 +535,46 @@ def main():
     datamodule.prepare_data()
     datamodule.setup()
 
+    if args.class_identifier:
+        missing_data = [item for item in datamodule.data if not item[1].exists()]
+
+        if len(missing_data) != 0:
+            batched_data = [missing_data[i:i+args.sample_batch_size]
+                            for i in range(0, len(missing_data), args.sample_batch_size)]
+
+            scheduler = EulerAScheduler(
+                beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
+            )
+
+            pipeline = VlpnStableDiffusion(
+                text_encoder=text_encoder,
+                vae=vae,
+                unet=unet,
+                tokenizer=tokenizer,
+                scheduler=scheduler,
+            ).to(accelerator.device)
+            pipeline.enable_attention_slicing()
+
+            for batch in batched_data:
+                image_name = [p[1] for p in batch]
+                prompt = [p[2] for p in batch]
+                nprompt = [p[3] for p in batch]
+
+                with accelerator.autocast():
+                    images = pipeline(
+                        prompt=prompt,
+                        negative_prompt=nprompt,
+                        num_inference_steps=args.sample_steps
+                    ).images
+
+                for i, image in enumerate(images):
+                    image.save(image_name[i])
+
+            del pipeline
+
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
     train_dataloader = datamodule.train_dataloader()
     val_dataloader = datamodule.val_dataloader()
 
@@ -718,23 +692,22 @@ def main():
                     # Predict the noise residual
                     noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
 
-                    with accelerator.autocast():
-                        if args.with_prior_preservation:
-                            # Chunk the noise and noise_pred into two parts and compute the loss on each part separately.
-                            noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0)
-                            noise, noise_prior = torch.chunk(noise, 2, dim=0)
+                    if args.with_prior_preservation:
+                        # Chunk the noise and noise_pred into two parts and compute the loss on each part separately.
+                        noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0)
+                        noise, noise_prior = torch.chunk(noise, 2, dim=0)
 
-                            # Compute instance loss
-                            loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
+                        # Compute instance loss
+                        loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
 
-                            # Compute prior loss
-                            prior_loss = F.mse_loss(noise_pred_prior, noise_prior,
-                                                    reduction="none").mean([1, 2, 3]).mean()
+                        # Compute prior loss
+                        prior_loss = F.mse_loss(noise_pred_prior, noise_prior,
+                                                reduction="none").mean([1, 2, 3]).mean()
 
-                            # Add the prior loss to the instance loss.
-                            loss = loss + args.prior_loss_weight * prior_loss
-                        else:
-                            loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
+                        # Add the prior loss to the instance loss.
+                        loss = loss + args.prior_loss_weight * prior_loss
+                    else:
+                        loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
 
                     accelerator.backward(loss)
                     if accelerator.sync_gradients:
@@ -786,8 +759,7 @@ def main():
 
                     noise_pred, noise = accelerator.gather_for_metrics((noise_pred, noise))
 
-                    with accelerator.autocast():
-                        loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
+                    loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
 
                     loss = loss.detach().item()
                     val_loss += loss
diff --git a/textual_inversion.py b/textual_inversion.py
index 5fc2338..4c4da29 100644
--- a/textual_inversion.py
+++ b/textual_inversion.py
@@ -694,8 +694,7 @@ def main():
                     # Predict the noise residual
                     noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
 
-                    with accelerator.autocast():
-                        loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
+                    loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
 
                     accelerator.backward(loss)
 
@@ -766,8 +765,7 @@ def main():
 
                     noise_pred, noise = accelerator.gather_for_metrics((noise_pred, noise))
 
-                    with accelerator.autocast():
-                        loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
+                    loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
 
                     loss = loss.detach().item()
                     val_loss += loss