4 files changed, 29 insertions, 15 deletions

diff --git a/dreambooth.py b/dreambooth.py
index 0044c1e..1ef5156 100644
--- a/dreambooth.py
+++ b/dreambooth.py
@@ -723,13 +723,14 @@ def main():
         pixel_values = torch.stack(pixel_values)
         pixel_values = pixel_values.to(dtype=weight_dtype, memory_format=torch.contiguous_format)
 
-        input_ids = prompt_processor.unify_input_ids(input_ids)
+        inputs = prompt_processor.unify_input_ids(input_ids)
 
         batch = {
             "prompts": prompts,
             "nprompts": nprompts,
-            "input_ids": input_ids,
+            "input_ids": inputs.input_ids,
             "pixel_values": pixel_values,
+            "attention_mask": inputs.attention_mask,
         }
         return batch
 
@@ -935,7 +936,7 @@ def main():
                     noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
 
                     # Get the text embedding for conditioning
-                    encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"])
+                    encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"])
 
                     # Predict the noise residual
                     model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
@@ -1047,7 +1048,7 @@ def main():
 
                     noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
 
-                    encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"])
+                    encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"])
 
                     model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
 
diff --git a/models/clip/prompt.py b/models/clip/prompt.py
index 6b6b7e9..9b427a0 100644
--- a/models/clip/prompt.py
+++ b/models/clip/prompt.py
@@ -22,11 +22,15 @@ class PromptProcessor():
             padding=True,
             pad_to_multiple_of=self.tokenizer.model_max_length,
             return_tensors="pt"
-        ).input_ids
+        )
 
-    def get_embeddings(self, input_ids: torch.IntTensor):
+    def get_embeddings(self, input_ids: torch.IntTensor, attention_mask=None):
         prompts = input_ids.shape[0]
+
         input_ids = input_ids.reshape((-1, self.tokenizer.model_max_length)).to(self.text_encoder.device)
-        text_embeddings = self.text_encoder(input_ids)[0]
+        if attention_mask is not None:
+            attention_mask = attention_mask.reshape((-1, self.tokenizer.model_max_length)).to(self.text_encoder.device)
+
+        text_embeddings = self.text_encoder(input_ids, attention_mask=attention_mask)[0]
         text_embeddings = text_embeddings.reshape((prompts, -1, text_embeddings.shape[2]))
         return text_embeddings
diff --git a/pipelines/stable_diffusion/vlpn_stable_diffusion.py b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
index f80e951..78a34d5 100644
--- a/pipelines/stable_diffusion/vlpn_stable_diffusion.py
+++ b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
@@ -194,7 +194,7 @@ class VlpnStableDiffusion(DiffusionPipeline):
 
         return prompt, negative_prompt
 
-    def encode_prompt(self, prompt, negative_prompt, num_images_per_prompt, do_classifier_free_guidance):
+    def encode_prompt(self, prompt, negative_prompt, num_images_per_prompt, do_classifier_free_guidance, device):
         text_input_ids = self.prompt_processor.get_input_ids(prompt)
         text_input_ids *= num_images_per_prompt
 
@@ -203,8 +203,15 @@ class VlpnStableDiffusion(DiffusionPipeline):
             unconditional_input_ids *= num_images_per_prompt
             text_input_ids = unconditional_input_ids + text_input_ids
 
-        text_input_ids = self.prompt_processor.unify_input_ids(text_input_ids)
-        text_embeddings = self.prompt_processor.get_embeddings(text_input_ids)
+        text_inputs = self.prompt_processor.unify_input_ids(text_input_ids)
+        text_input_ids = text_inputs.input_ids
+
+        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+            attention_mask = text_inputs.attention_mask.to(device)
+        else:
+            attention_mask = None
+
+        text_embeddings = self.prompt_processor.get_embeddings(text_input_ids, attention_mask)
 
         return text_embeddings
 
@@ -373,7 +380,8 @@ class VlpnStableDiffusion(DiffusionPipeline):
             prompt,
             negative_prompt,
             num_images_per_prompt,
-            do_classifier_free_guidance
+            do_classifier_free_guidance,
+            device
         )
 
         # 4. Prepare timesteps
diff --git a/textual_inversion.py b/textual_inversion.py
index 1a5a8d0..da7c747 100644
--- a/textual_inversion.py
+++ b/textual_inversion.py
@@ -641,13 +641,14 @@ def main():
         pixel_values = torch.stack(pixel_values)
         pixel_values = pixel_values.to(dtype=weight_dtype, memory_format=torch.contiguous_format)
 
-        input_ids = prompt_processor.unify_input_ids(input_ids)
+        inputs = prompt_processor.unify_input_ids(input_ids)
 
         batch = {
             "prompts": prompts,
             "nprompts": nprompts,
-            "input_ids": input_ids,
+            "input_ids": inputs.input_ids,
             "pixel_values": pixel_values,
+            "attention_mask": inputs.attention_mask,
         }
         return batch
 
@@ -849,7 +850,7 @@ def main():
                     noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
 
                     # Get the text embedding for conditioning
-                    encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"])
+                    encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"])
                     encoder_hidden_states = encoder_hidden_states.to(dtype=weight_dtype)
 
                     # Predict the noise residual
@@ -948,7 +949,7 @@ def main():
 
                     noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
 
-                    encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"])
+                    encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"])
                     encoder_hidden_states = encoder_hidden_states.to(dtype=weight_dtype)
 
                     model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample