4 files changed, 108 insertions, 58 deletions

diff --git a/data/csv.py b/data/csv.py
index aad970c..316c099 100644
--- a/data/csv.py
+++ b/data/csv.py
@@ -72,8 +72,8 @@ class CSVDataModule(pl.LightningDataModule):
         ]
 
     def prepare_data(self):
-        metadata = pd.read_csv(self.data_file)
-        metadata = [item for item in metadata.itertuples() if "skip" not in item or item.skip != "x"]
+        metadata = pd.read_json(self.data_file)
+        metadata = [item for item in metadata.itertuples() if "skip" not in item or item.skip != True]
         num_images = len(metadata)
 
         valid_set_size = int(num_images * 0.2)
@@ -163,6 +163,12 @@ class CSVDataset(Dataset):
 
         example = {}
 
+        if isinstance(item.prompt, str):
+            item.prompt = [item.prompt]
+
+        if isinstance(item.nprompt, str):
+            item.nprompt = [item.nprompt]
+
         example["prompts"] = item.prompt
         example["nprompts"] = item.nprompt
 
@@ -177,7 +183,7 @@ class CSVDataset(Dataset):
         example["instance_images"] = instance_image
         example["instance_prompt_ids"] = self.tokenizer(
             item.prompt.format(self.instance_identifier),
-            padding="do_not_pad",
+            padding="max_length",
             truncation=True,
             max_length=self.tokenizer.model_max_length,
         ).input_ids
@@ -190,7 +196,7 @@ class CSVDataset(Dataset):
             example["class_images"] = class_image
             example["class_prompt_ids"] = self.tokenizer(
                 item.prompt.format(self.class_identifier),
-                padding="do_not_pad",
+                padding="max_length",
                 truncation=True,
                 max_length=self.tokenizer.model_max_length,
             ).input_ids
diff --git a/dreambooth_plus.py b/dreambooth_plus.py
index a98417f..ae31377 100644
--- a/dreambooth_plus.py
+++ b/dreambooth_plus.py
@@ -124,7 +124,7 @@ def parse_args():
     parser.add_argument(
         "--max_train_steps",
         type=int,
-        default=1500,
+        default=1400,
         help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
     )
     parser.add_argument(
@@ -147,7 +147,7 @@ def parse_args():
     parser.add_argument(
         "--learning_rate_text",
         type=float,
-        default=5e-6,
+        default=1e-6,
         help="Initial learning rate (after the potential warmup period) to use.",
     )
     parser.add_argument(
@@ -469,9 +469,16 @@ class Checkpointer:
 
                 for i in range(self.sample_batches):
                     batches = [batch for j, batch in data_enum if j * data.batch_size < self.sample_batch_size]
-                    prompt = [prompt.format(self.instance_identifier)
-                              for batch in batches for prompt in batch["prompts"]][:self.sample_batch_size]
-                    nprompt = [prompt for batch in batches for prompt in batch["nprompts"]][:self.sample_batch_size]
+                    prompt = [
+                        [p.format(self.instance_identifier) for p in prompt]
+                        for batch in batches
+                        for prompt in batch["prompts"]
+                    ][:self.sample_batch_size]
+                    nprompt = [
+                        prompt
+                        for batch in batches
+                        for prompt in batch["nprompts"]
+                    ][:self.sample_batch_size]
 
                     samples = pipeline(
                         prompt=prompt,
@@ -666,6 +673,17 @@ def main():
         }
         return batch
 
+    def encode_input_ids(input_ids):
+        text_embeddings = []
+
+        for ids in input_ids:
+            embeddings = text_encoder(ids)[0]
+            embeddings = embeddings.reshape((1, -1, 768))
+            text_embeddings.append(embeddings)
+
+        text_embeddings = torch.cat(text_embeddings)
+        return text_embeddings
+
     datamodule = CSVDataModule(
         data_file=args.train_data_file,
         batch_size=args.train_batch_size,
@@ -688,8 +706,10 @@ def main():
         missing_data = [item for item in datamodule.data_train if not item.class_image_path.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)]
+            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"
@@ -706,9 +726,9 @@ def main():
 
             with torch.inference_mode():
                 for batch in batched_data:
-                    image_name = [p.class_image_path for p in batch]
-                    prompt = [p.prompt.format(args.class_identifier) for p in batch]
-                    nprompt = [p.nprompt for p in batch]
+                    image_name = [item.class_image_path for item in batch]
+                    prompt = [[p.format(args.class_identifier) for p in item.prompt] for item in batch]
+                    nprompt = [item.nprompt for item in batch]
 
                     images = pipeline(
                         prompt=prompt,
@@ -855,7 +875,7 @@ def main():
                     noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
 
                     # Get the text embedding for conditioning
-                    encoder_hidden_states = text_encoder(batch["input_ids"])[0]
+                    encoder_hidden_states = encode_input_ids(batch["input_ids"])
 
                     # Predict the noise residual
                     noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
@@ -954,7 +974,7 @@ def main():
 
                     noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
 
-                    encoder_hidden_states = text_encoder(batch["input_ids"])[0]
+                    encoder_hidden_states = encode_input_ids(batch["input_ids"])
 
                     noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
 
diff --git a/infer.py b/infer.py
index 1a0baf5..d744768 100644
--- a/infer.py
+++ b/infer.py
@@ -19,6 +19,9 @@ from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
 torch.backends.cuda.matmul.allow_tf32 = True
 
 
+line_sep = " <OR> "
+
+
 default_args = {
     "model": None,
     "scheduler": "euler_a",
@@ -95,10 +98,12 @@ def create_cmd_parser():
     parser.add_argument(
         "--prompt",
         type=str,
+        nargs="+",
     )
     parser.add_argument(
         "--negative_prompt",
         type=str,
+        nargs="*",
     )
     parser.add_argument(
         "--image",
@@ -271,9 +276,14 @@ def generate(output_dir, pipeline, args):
                 dynamic_ncols=True
             )
 
+            if isinstance(args.prompt, str):
+                args.prompt = [args.prompt]
+
+            prompt = [p.split(line_sep) for p in args.prompt] * args.batch_size
+
             generator = torch.Generator(device="cuda").manual_seed(seed + i)
             images = pipeline(
-                prompt=[args.prompt] * args.batch_size,
+                prompt=prompt,
                 height=args.height,
                 width=args.width,
                 negative_prompt=args.negative_prompt,
diff --git a/pipelines/stable_diffusion/vlpn_stable_diffusion.py b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
index 8b08a6f..b68b028 100644
--- a/pipelines/stable_diffusion/vlpn_stable_diffusion.py
+++ b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
@@ -24,6 +24,22 @@ def preprocess(image, w, h):
     return 2.0 * image - 1.0
 
 
+def normalize_prompt(prompt: Union[str, List[str], List[List[str]]], batch_size: int = 1, prompt_size: int = None):
+    if isinstance(prompt, str):
+        prompt = [prompt] * batch_size
+
+    if isinstance(prompt, list) and isinstance(prompt[0], str):
+        prompt = [[p] for p in prompt]
+
+    if isinstance(prompt, list) and isinstance(prompt[0], list):
+        prompt_size = prompt_size or max([len(p) for p in prompt])
+        prompt: List[List[str]] = [subprompt + [""] * (prompt_size - len(subprompt)) for subprompt in prompt]
+    else:
+        raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+    return prompt_size, prompt
+
+
 class VlpnStableDiffusion(DiffusionPipeline):
     def __init__(
         self,
@@ -85,11 +101,39 @@ class VlpnStableDiffusion(DiffusionPipeline):
         # set slice_size = `None` to disable `attention slicing`
         self.enable_attention_slicing(None)
 
+    def embeddings_for_prompt(self, prompt: List[List[str]]):
+        text_embeddings = []
+
+        for p in prompt:
+            inputs = self.tokenizer(
+                p,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                return_tensors="pt",
+            )
+            input_ids = inputs.input_ids
+
+            if input_ids.shape[-1] > self.tokenizer.model_max_length:
+                removed_text = self.tokenizer.batch_decode(input_ids[:, self.tokenizer.model_max_length:])
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+                print(f"Too many tokens: {removed_text}")
+                input_ids = input_ids[:, : self.tokenizer.model_max_length]
+
+            embeddings = self.text_encoder(input_ids.to(self.device))[0]
+            embeddings = embeddings.reshape((1, -1, 768))
+            text_embeddings.append(embeddings)
+
+        text_embeddings = torch.cat(text_embeddings)
+        return text_embeddings
+
     @torch.no_grad()
     def __call__(
         self,
-        prompt: Union[str, List[str]],
-        negative_prompt: Optional[Union[str, List[str]]] = None,
+        prompt: Union[str, List[str], List[List[str]]],
+        negative_prompt: Optional[Union[str, List[str], List[List[str]]]] = None,
         strength: float = 0.8,
         height: Optional[int] = 512,
         width: Optional[int] = 512,
@@ -151,23 +195,13 @@ class VlpnStableDiffusion(DiffusionPipeline):
             (nsfw) content, according to the `safety_checker`.
         """
 
-        if isinstance(prompt, str):
-            batch_size = 1
-        elif isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if negative_prompt is None:
-            negative_prompt = [""] * batch_size
-        elif isinstance(negative_prompt, str):
-            negative_prompt = [negative_prompt] * batch_size
-        elif isinstance(negative_prompt, list):
-            if len(negative_prompt) != batch_size:
-                raise ValueError(
-                    f"`prompt` and `negative_prompt` have to be the same length, but are {len(prompt)} and {len(negative_prompt)}")
-        else:
-            raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
+        prompt_size, prompt = normalize_prompt(prompt)
+        batch_size = len(prompt)
+        _, negative_prompt = normalize_prompt(negative_prompt or "", batch_size, prompt_size)
+
+        if len(negative_prompt) != batch_size:
+            raise ValueError(
+                f"`prompt` and `negative_prompt` have to be the same length, but are {batch_size} and {len(negative_prompt)}")
 
         if height % 8 != 0 or width % 8 != 0:
             raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
@@ -179,23 +213,7 @@ class VlpnStableDiffusion(DiffusionPipeline):
         self.scheduler.set_timesteps(num_inference_steps)
 
         # get prompt text embeddings
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-
-        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
-            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length:])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-            )
-            print(f"Too many tokens: {removed_text}")
-            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
-        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
+        text_embeddings = self.embeddings_for_prompt(prompt)
 
         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
@@ -203,11 +221,7 @@ class VlpnStableDiffusion(DiffusionPipeline):
         do_classifier_free_guidance = guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
-            max_length = text_input_ids.shape[-1]
-            uncond_input = self.tokenizer(
-                negative_prompt, padding="max_length", max_length=max_length, return_tensors="pt"
-            )
-            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+            uncond_embeddings = self.embeddings_for_prompt(negative_prompt)
 
             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch