Update

8 files changed, 80 insertions, 67 deletions

diff --git a/models/convnext/discriminator.py b/models/convnext/discriminator.py
index 7dbbe3a..571b915 100644
--- a/models/convnext/discriminator.py
+++ b/models/convnext/discriminator.py
@@ -15,13 +15,7 @@ class ConvNeXtDiscriminator():
         self.img_std = torch.tensor(IMAGENET_DEFAULT_STD).view(1, -1, 1, 1)
 
     def get_score(self, img):
-        img_mean = self.img_mean.to(device=img.device, dtype=img.dtype)
-        img_std = self.img_std.to(device=img.device, dtype=img.dtype)
-
-        img = ((img+1.)/2.).sub(img_mean).div(img_std)
-
-        img = F.interpolate(img, size=(self.input_size, self.input_size), mode='bicubic', align_corners=True)
-        pred = self.net(img)
+        pred = self.get_all(img)
         return torch.softmax(pred, dim=-1)[:, 1]
 
     def get_all(self, img):
diff --git a/models/sparse.py b/models/sparse.py
index bcb2897..07b3413 100644
--- a/models/sparse.py
+++ b/models/sparse.py
@@ -15,7 +15,7 @@ class PseudoSparseEmbedding(nn.Module):
         if dropout_p > 0.0:
             self.dropout = nn.Dropout(p=dropout_p)
         else:
-            self.dropout = lambda x: x
+            self.dropout = nn.Identity()
 
         self.register_buffer('mapping', torch.zeros(0, device=device, dtype=torch.long))
 
diff --git a/train_lora.py b/train_lora.py
index 29e40b2..073e939 100644
--- a/train_lora.py
+++ b/train_lora.py
@@ -87,6 +87,12 @@ def parse_args():
         help="How many cycles to run automatically."
     )
     parser.add_argument(
+        "--cycle_decay",
+        type=float,
+        default=1.0,
+        help="Learning rate decay per cycle."
+    )
+    parser.add_argument(
         "--placeholder_tokens",
         type=str,
         nargs='*',
@@ -924,39 +930,15 @@ def main():
     if args.sample_num is not None:
         lora_sample_frequency = math.ceil(num_train_epochs / args.sample_num)
 
-    params_to_optimize = []
     group_labels = []
     if len(args.placeholder_tokens) != 0:
-        params_to_optimize.append({
-            "params": text_encoder.text_model.embeddings.token_override_embedding.parameters(),
-            "lr": args.learning_rate_emb,
-            "weight_decay": 0,
-        })
         group_labels.append("emb")
-    params_to_optimize += [
-        {
-            "params": (
-                param
-                for param in unet.parameters()
-                if param.requires_grad
-            ),
-            "lr": args.learning_rate_unet,
-        },
-        {
-            "params": (
-                param
-                for param in itertools.chain(
-                    text_encoder.text_model.encoder.parameters(),
-                    text_encoder.text_model.final_layer_norm.parameters(),
-                )
-                if param.requires_grad
-            ),
-            "lr": args.learning_rate_text,
-        },
-    ]
     group_labels += ["unet", "text"]
 
     training_iter = 0
+    learning_rate_emb = args.learning_rate_emb
+    learning_rate_unet = args.learning_rate_unet
+    learning_rate_text = args.learning_rate_text
 
     lora_project = "lora"
 
@@ -973,6 +955,37 @@ def main():
         print(f"============ LoRA cycle {training_iter + 1} ============")
         print("")
 
+        params_to_optimize = []
+
+        if len(args.placeholder_tokens) != 0:
+            params_to_optimize.append({
+                "params": text_encoder.text_model.embeddings.token_override_embedding.parameters(),
+                "lr": learning_rate_emb,
+                "weight_decay": 0,
+            })
+            group_labels.append("emb")
+        params_to_optimize += [
+            {
+                "params": (
+                    param
+                    for param in unet.parameters()
+                    if param.requires_grad
+                ),
+                "lr": learning_rate_unet,
+            },
+            {
+                "params": (
+                    param
+                    for param in itertools.chain(
+                        text_encoder.text_model.encoder.parameters(),
+                        text_encoder.text_model.final_layer_norm.parameters(),
+                    )
+                    if param.requires_grad
+                ),
+                "lr": learning_rate_text,
+            },
+        ]
+
         lora_optimizer = create_optimizer(params_to_optimize)
 
         lora_lr_scheduler = create_lr_scheduler(
@@ -1002,6 +1015,12 @@ def main():
         )
 
         training_iter += 1
+        if args.learning_rate_emb is not None:
+            learning_rate_emb *= args.cycle_decay
+        if args.learning_rate_unet is not None:
+            learning_rate_unet *= args.cycle_decay
+        if args.learning_rate_text is not None:
+            learning_rate_text *= args.cycle_decay
 
     accelerator.end_training()
 
diff --git a/train_ti.py b/train_ti.py
index 082e9b7..94ddbb6 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -72,6 +72,12 @@ def parse_args():
         help="How many cycles to run automatically."
     )
     parser.add_argument(
+        "--cycle_decay",
+        type=float,
+        default=1.0,
+        help="Learning rate decay per cycle."
+    )
+    parser.add_argument(
         "--placeholder_tokens",
         type=str,
         nargs='*',
@@ -672,7 +678,6 @@ def main():
     convnext.to(accelerator.device, dtype=weight_dtype)
     convnext.requires_grad_(False)
     convnext.eval()
-    disc = ConvNeXtDiscriminator(convnext, input_size=384)
 
     if len(args.alias_tokens) != 0:
         alias_placeholder_tokens = args.alias_tokens[::2]
@@ -815,7 +820,6 @@ def main():
         milestone_checkpoints=not args.no_milestone_checkpoints,
         global_step_offset=global_step_offset,
         offset_noise_strength=args.offset_noise_strength,
-        disc=disc,
         # --
         use_emb_decay=args.use_emb_decay,
         emb_decay_target=args.emb_decay_target,
@@ -890,6 +894,7 @@ def main():
             sample_frequency = math.ceil(num_train_epochs / args.sample_num)
 
         training_iter = 0
+        learning_rate = args.learning_rate
 
         project = placeholder_tokens[0] if len(placeholder_tokens) == 1 else "ti"
 
@@ -908,7 +913,7 @@ def main():
 
             optimizer = create_optimizer(
                 text_encoder.text_model.embeddings.token_override_embedding.parameters(),
-                lr=args.learning_rate,
+                lr=learning_rate,
             )
 
             lr_scheduler = get_scheduler(
@@ -948,6 +953,8 @@ def main():
             )
 
             training_iter += 1
+            if args.learning_rate is not None:
+                learning_rate *= args.cycle_decay
 
         accelerator.end_training()
 
diff --git a/training/functional.py b/training/functional.py
index be39776..ed8ae3a 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -168,8 +168,7 @@ def save_samples(
             image_grid = pipeline.numpy_to_pil(image_grid.unsqueeze(0).permute(0, 2, 3, 1).numpy())[0]
             image_grid.save(file_path, quality=85)
 
-    del generator
-    del pipeline
+    del generator, pipeline
 
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
@@ -398,31 +397,32 @@ def loss_step(
     else:
         raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
 
-    if disc is None:
-        if guidance_scale == 0 and 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)
+    acc = (model_pred == target).float().mean()
 
-            # Compute instance loss
-            loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
+    if guidance_scale == 0 and 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)
 
-            # Compute prior loss
-            prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="none")
+        # Compute instance loss
+        loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
 
-            # Add the prior loss to the instance loss.
-            loss = loss + prior_loss_weight * prior_loss
-        else:
-            loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
+        # Compute prior loss
+        prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="none")
 
-        loss = loss.mean([1, 2, 3])
+        # Add the prior loss to the instance loss.
+        loss = loss + prior_loss_weight * prior_loss
     else:
+        loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
+
+    loss = loss.mean([1, 2, 3])
+
+    if disc is not None:
         rec_latent = get_original(noise_scheduler, model_pred, noisy_latents, timesteps)
         rec_latent /= vae.config.scaling_factor
         rec_latent = rec_latent.to(dtype=vae.dtype)
         rec = vae.decode(rec_latent).sample
         loss = 1 - disc.get_score(rec)
-        del rec_latent, rec
 
     if min_snr_gamma != 0:
         snr = compute_snr(timesteps, noise_scheduler)
@@ -432,7 +432,6 @@ def loss_step(
         loss *= mse_loss_weights
 
     loss = loss.mean()
-    acc = (model_pred == target).float().mean()
 
     return loss, acc, bsz
 
diff --git a/training/strategy/dreambooth.py b/training/strategy/dreambooth.py
index fa51bc7..4ae28b7 100644
--- a/training/strategy/dreambooth.py
+++ b/training/strategy/dreambooth.py
@@ -142,9 +142,7 @@ def dreambooth_strategy_callbacks(
             )
             pipeline.save_pretrained(checkpoint_output_dir)
 
-        del unet_
-        del text_encoder_
-        del pipeline
+        del unet_, text_encoder_, pipeline
 
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
@@ -165,8 +163,7 @@ def dreambooth_strategy_callbacks(
         unet_.to(dtype=orig_unet_dtype)
         text_encoder_.to(dtype=orig_text_encoder_dtype)
 
-        del unet_
-        del text_encoder_
+        del unet_, text_encoder_
 
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
diff --git a/training/strategy/lora.py b/training/strategy/lora.py
index 73ec8f2..1517ee8 100644
--- a/training/strategy/lora.py
+++ b/training/strategy/lora.py
@@ -140,8 +140,7 @@ def lora_strategy_callbacks(
             with open(checkpoint_output_dir / "lora_config.json", "w") as f:
                 json.dump(lora_config, f)
 
-        del unet_
-        del text_encoder_
+        del unet_, text_encoder_
 
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
@@ -153,8 +152,7 @@ def lora_strategy_callbacks(
 
         save_samples_(step=step, unet=unet_, text_encoder=text_encoder_)
 
-        del unet_
-        del text_encoder_
+        del unet_, text_encoder_
 
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
diff --git a/training/strategy/ti.py b/training/strategy/ti.py
index 08af89d..ca7cc3d 100644
--- a/training/strategy/ti.py
+++ b/training/strategy/ti.py
@@ -158,8 +158,7 @@ def textual_inversion_strategy_callbacks(
         unet_.to(dtype=orig_unet_dtype)
         text_encoder_.to(dtype=orig_text_encoder_dtype)
 
-        del unet_
-        del text_encoder_
+        del unet_, text_encoder_
 
         if torch.cuda.is_available():
             torch.cuda.empty_cache()