5 files changed, 1 insertions, 42 deletions

diff --git a/data/csv.py b/data/csv.py
index d726033..43bf14c 100644
--- a/data/csv.py
+++ b/data/csv.py
@@ -108,7 +108,6 @@ def collate_fn(
     dtype: torch.dtype,
     tokenizer: CLIPTokenizer,
     max_token_id_length: Optional[int],
-    with_guidance: bool,
     with_prior_preservation: bool,
     examples,
 ):
@@ -195,7 +194,6 @@ class VlpnDataModule:
         tokenizer: CLIPTokenizer,
         constant_prompt_length: bool = False,
         class_subdir: str = "cls",
-        with_guidance: bool = False,
         num_class_images: int = 1,
         size: int = 768,
         num_buckets: int = 0,
@@ -228,7 +226,6 @@ class VlpnDataModule:
         self.class_root.mkdir(parents=True, exist_ok=True)
         self.placeholder_tokens = placeholder_tokens
         self.num_class_images = num_class_images
-        self.with_guidance = with_guidance
 
         self.constant_prompt_length = constant_prompt_length
         self.max_token_id_length = None
@@ -356,7 +353,6 @@ class VlpnDataModule:
             self.dtype,
             self.tokenizer,
             self.max_token_id_length,
-            self.with_guidance,
             self.num_class_images != 0,
         )
 
diff --git a/train_dreambooth.py b/train_dreambooth.py
index 0543a35..939a8f3 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -194,11 +194,6 @@ def parse_args():
         help="Shuffle tags.",
     )
     parser.add_argument(
-        "--guidance_scale",
-        type=float,
-        default=0,
-    )
-    parser.add_argument(
         "--num_class_images",
         type=int,
         default=0,
@@ -874,7 +869,6 @@ def main():
         dtype=weight_dtype,
         seed=args.seed,
         compile_unet=args.compile_unet,
-        guidance_scale=args.guidance_scale,
         prior_loss_weight=args.prior_loss_weight if args.num_class_images != 0 else 0,
         sample_scheduler=sample_scheduler,
         sample_batch_size=args.sample_batch_size,
@@ -893,7 +887,6 @@ def main():
         tokenizer=tokenizer,
         constant_prompt_length=args.compile_unet,
         class_subdir=args.class_image_dir,
-        with_guidance=args.guidance_scale != 0,
         num_class_images=args.num_class_images,
         size=args.resolution,
         num_buckets=args.num_buckets,
diff --git a/train_lora.py b/train_lora.py
index b7ee2d6..51dc827 100644
--- a/train_lora.py
+++ b/train_lora.py
@@ -207,11 +207,6 @@ def parse_args():
         help="Shuffle tags.",
     )
     parser.add_argument(
-        "--guidance_scale",
-        type=float,
-        default=0,
-    )
-    parser.add_argument(
         "--num_class_images",
         type=int,
         default=0,
@@ -998,7 +993,6 @@ def main():
         dtype=weight_dtype,
         seed=args.seed,
         compile_unet=args.compile_unet,
-        guidance_scale=args.guidance_scale,
         prior_loss_weight=args.prior_loss_weight if args.num_class_images != 0 else 0,
         sample_scheduler=sample_scheduler,
         sample_batch_size=args.sample_batch_size,
@@ -1022,7 +1016,6 @@ def main():
         tokenizer=tokenizer,
         constant_prompt_length=args.compile_unet,
         class_subdir=args.class_image_dir,
-        with_guidance=args.guidance_scale != 0,
         num_class_images=args.num_class_images,
         size=args.resolution,
         num_buckets=args.num_buckets,
diff --git a/train_ti.py b/train_ti.py
index 7d1ef19..7f93960 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -125,11 +125,6 @@ def parse_args():
         action="store_true",
     )
     parser.add_argument(
-        "--guidance_scale",
-        type=float,
-        default=0,
-    )
-    parser.add_argument(
         "--num_class_images",
         type=int,
         default=0,
@@ -852,7 +847,6 @@ def main():
         dtype=weight_dtype,
         seed=args.seed,
         compile_unet=args.compile_unet,
-        guidance_scale=args.guidance_scale,
         prior_loss_weight=args.prior_loss_weight if args.num_class_images != 0 else 0,
         no_val=args.valid_set_size == 0,
         strategy=textual_inversion_strategy,
@@ -923,7 +917,6 @@ def main():
             batch_size=args.train_batch_size,
             tokenizer=tokenizer,
             class_subdir=args.class_image_dir,
-            with_guidance=args.guidance_scale != 0,
             num_class_images=args.num_class_images,
             size=args.resolution,
             num_buckets=args.num_buckets,
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,