2 files changed, 14 insertions, 6 deletions

diff --git a/data/csv.py b/data/csv.py
index a6cd065..d52d251 100644
--- a/data/csv.py
+++ b/data/csv.py
@@ -104,11 +104,10 @@ def collate_fn(dtype: torch.dtype, tokenizer: CLIPTokenizer, with_guidance: bool
     nprompt_ids = [example["nprompt_ids"] for example in examples]
 
     input_ids = [example["instance_prompt_ids"] for example in examples]
+    negative_input_ids = [example["negative_prompt_ids"] for example in examples]
     pixel_values = [example["instance_images"] for example in examples]
 
-    if with_guidance:
-        input_ids += [example["negative_prompt_ids"] for example in examples]
-    elif with_prior_preservation:
+    if with_prior_preservation:
         input_ids += [example["class_prompt_ids"] for example in examples]
         pixel_values += [example["class_images"] for example in examples]
 
@@ -118,13 +117,16 @@ def collate_fn(dtype: torch.dtype, tokenizer: CLIPTokenizer, with_guidance: bool
     prompts = unify_input_ids(tokenizer, prompt_ids)
     nprompts = unify_input_ids(tokenizer, nprompt_ids)
     inputs = unify_input_ids(tokenizer, input_ids)
+    negative_inputs = unify_input_ids(tokenizer, negative_input_ids)
 
     batch = {
         "prompt_ids": prompts.input_ids,
         "nprompt_ids": nprompts.input_ids,
         "input_ids": inputs.input_ids,
+        "negative_input_ids": negative_inputs.attention_mask,
         "pixel_values": pixel_values,
         "attention_mask": inputs.attention_mask,
+        "negative_attention_mask": negative_inputs.attention_mask,
     }
 
     return batch
diff --git a/training/functional.py b/training/functional.py
index d285366..109845b 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -344,9 +344,15 @@ def loss_step(
         raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
 
     if guidance_scale != 0:
-        # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
-        model_pred_uncond, model_pred_text = torch.chunk(model_pred, 2, dim=0)
-        model_pred = model_pred_uncond + guidance_scale * (model_pred_text - model_pred_uncond)
+        uncond_encoder_hidden_states = get_extended_embeddings(
+            text_encoder,
+            batch["negative_input_ids"],
+            batch["negative_attention_mask"]
+        )
+        uncond_encoder_hidden_states = uncond_encoder_hidden_states.to(dtype=unet.dtype)
+
+        model_pred_uncond = unet(noisy_latents, timesteps, uncond_encoder_hidden_states).sample
+        model_pred = model_pred_uncond + guidance_scale * (model_pred - model_pred_uncond)
 
         loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
     elif prior_loss_weight != 0: