Training: Create multiple class images per training image

1 files changed, 35 insertions, 13 deletions

diff --git a/textual_inversion.py b/textual_inversion.py
index 11c324d..86fcdfe 100644
--- a/textual_inversion.py
+++ b/textual_inversion.py
@@ -68,16 +68,17 @@ def parse_args():
         help="A token to use as initializer word."
     )
     parser.add_argument(
-        "--use_class_images",
-        action="store_true",
-        default=True,
-        help="Include class images in the loss calculation a la Dreambooth.",
+        "--num_class_images",
+        type=int,
+        default=2,
+        help="How many class images to generate per training image."
     )
     parser.add_argument(
         "--repeats",
         type=int,
         default=100,
-        help="How many times to repeat the training data.")
+        help="How many times to repeat the training data."
+    )
     parser.add_argument(
         "--output_dir",
         type=str,
@@ -204,6 +205,12 @@ def parse_args():
         help="How often to save a checkpoint and sample image",
     )
     parser.add_argument(
+        "--sample_frequency",
+        type=int,
+        default=100,
+        help="How often to save a checkpoint and sample image",
+    )
+    parser.add_argument(
         "--sample_image_size",
         type=int,
         default=512,
@@ -381,6 +388,7 @@ class Checkpointer:
             scheduler=scheduler,
         ).to(self.accelerator.device)
         pipeline.enable_attention_slicing()
+        pipeline.set_progress_bar_config(dynamic_ncols=True)
 
         train_data = self.datamodule.train_dataloader()
         val_data = self.datamodule.val_dataloader()
@@ -577,7 +585,7 @@ def main():
         pixel_values = [example["instance_images"] for example in examples]
 
         # concat class and instance examples for prior preservation
-        if args.use_class_images and "class_prompt_ids" in examples[0]:
+        if args.num_class_images != 0 and "class_prompt_ids" in examples[0]:
             input_ids += [example["class_prompt_ids"] for example in examples]
             pixel_values += [example["class_images"] for example in examples]
 
@@ -599,8 +607,9 @@ def main():
         batch_size=args.train_batch_size,
         tokenizer=tokenizer,
         instance_identifier=args.placeholder_token,
-        class_identifier=args.initializer_token if args.use_class_images else None,
+        class_identifier=args.initializer_token,
         class_subdir="ti_cls",
+        num_class_images=args.num_class_images,
         size=args.resolution,
         repeats=args.repeats,
         center_crop=args.center_crop,
@@ -611,7 +620,7 @@ def main():
     datamodule.prepare_data()
     datamodule.setup()
 
-    if args.use_class_images:
+    if args.num_class_images != 0:
         missing_data = [item for item in datamodule.data if not item[1].exists()]
 
         if len(missing_data) != 0:
@@ -630,6 +639,7 @@ def main():
                 scheduler=scheduler,
             ).to(accelerator.device)
             pipeline.enable_attention_slicing()
+            pipeline.set_progress_bar_config(dynamic_ncols=True)
 
             for batch in batched_data:
                 image_name = [p[1] for p in batch]
@@ -729,11 +739,18 @@ def main():
             text_encoder,
             args.resolution, args.resolution, 7.5, 0.0, args.sample_steps)
 
-    local_progress_bar = tqdm(range(num_update_steps_per_epoch + num_val_steps_per_epoch),
-                              disable=not accelerator.is_local_main_process)
+    local_progress_bar = tqdm(
+        range(num_update_steps_per_epoch + num_val_steps_per_epoch),
+        disable=not accelerator.is_local_main_process,
+        dynamic_ncols=True
+    )
     local_progress_bar.set_description("Batch X out of Y")
 
-    global_progress_bar = tqdm(range(args.max_train_steps + val_steps), disable=not accelerator.is_local_main_process)
+    global_progress_bar = tqdm(
+        range(args.max_train_steps + val_steps),
+        disable=not accelerator.is_local_main_process,
+        dynamic_ncols=True
+    )
     global_progress_bar.set_description("Total progress")
 
     try:
@@ -744,6 +761,8 @@ def main():
             text_encoder.train()
             train_loss = 0.0
 
+            sample_checkpoint = False
+
             for step, batch in enumerate(train_dataloader):
                 with accelerator.accumulate(text_encoder):
                     # Convert images to latent space
@@ -769,7 +788,7 @@ def main():
                     # Predict the noise residual
                     noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
 
-                    if args.use_class_images:
+                    if args.num_class_images != 0:
                         # Chunk the noise and noise_pred into two parts and compute the loss on each part separately.
                         noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0)
                         noise, noise_prior = torch.chunk(noise, 2, dim=0)
@@ -812,6 +831,9 @@ def main():
 
                     global_step += 1
 
+                    if global_step % args.sample_frequency == 0:
+                        sample_checkpoint = True
+
                     if global_step % args.checkpoint_frequency == 0 and global_step > 0 and accelerator.is_main_process:
                         local_progress_bar.clear()
                         global_progress_bar.clear()
@@ -878,7 +900,7 @@ def main():
                 checkpointer.checkpoint(global_step + global_step_offset, "milestone", text_encoder)
                 min_val_loss = val_loss
 
-            if accelerator.is_main_process:
+            if sample_checkpoint and accelerator.is_main_process:
                 checkpointer.save_samples(
                     global_step + global_step_offset,
                     text_encoder,