Remove training guidance_scale

1 files changed, 1 insertions, 17 deletions

diff --git a/training/functional.py b/training/functional.py
index a3d1f08..43b03ac 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -342,7 +342,6 @@ def loss_step(
     schedule_sampler: ScheduleSampler,
     unet: UNet2DConditionModel,
     text_encoder: CLIPTextModel,
-    guidance_scale: float,
     prior_loss_weight: float,
     seed: int,
     input_pertubation: float,
@@ -400,19 +399,6 @@ def loss_step(
         noisy_latents, timesteps, encoder_hidden_states, return_dict=False
     )[0]
 
-    if guidance_scale != 0:
-        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, return_dict=False
-        )[0]
-        model_pred = model_pred_uncond + guidance_scale * (
-            model_pred - model_pred_uncond
-        )
-
     # Get the target for loss depending on the prediction type
     if noise_scheduler.config.prediction_type == "epsilon":
         target = noise
@@ -425,7 +411,7 @@ def loss_step(
 
     acc = (model_pred == target).float().mean()
 
-    if guidance_scale == 0 and prior_loss_weight != 0:
+    if prior_loss_weight != 0:
         # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
         model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
         target, target_prior = torch.chunk(target, 2, dim=0)
@@ -727,7 +713,6 @@ def train(
     milestone_checkpoints: bool = True,
     cycle: int = 1,
     global_step_offset: int = 0,
-    guidance_scale: float = 0.0,
     prior_loss_weight: float = 1.0,
     input_pertubation: float = 0.1,
     schedule_sampler: Optional[ScheduleSampler] = None,
@@ -787,7 +772,6 @@ def train(
         schedule_sampler,
         unet,
         text_encoder,
-        guidance_scale,
         prior_loss_weight,
         seed,
         input_pertubation,