Implemented extended prompt limit

1 files changed, 32 insertions, 12 deletions

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