From 3353ffb64c280a938a0f2513d13b716c1fca8c02 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Sat, 7 Jan 2023 17:10:06 +0100
Subject: Cleanup

---
 train_ti.py | 60 ++++++++++++++++--------------------------------------------
 1 file changed, 16 insertions(+), 44 deletions(-)

(limited to 'train_ti.py')

diff --git a/train_ti.py b/train_ti.py
index 38c9755..b4b602b 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -22,7 +22,7 @@ from slugify import slugify
 from util import load_config, load_embeddings_from_dir
 from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
 from data.csv import VlpnDataModule, VlpnDataItem
-from training.common import run_model
+from training.common import run_model, generate_class_images
 from training.optimization import get_one_cycle_schedule
 from training.lr import LRFinder
 from training.util import AverageMeter, CheckpointerBase, EMAModel, save_args
@@ -219,7 +219,6 @@ def parse_args():
     parser.add_argument(
         "--scale_lr",
         action="store_true",
-        default=True,
         help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
     )
     parser.add_argument(
@@ -734,50 +733,23 @@ def main():
     )
     datamodule.setup()
 
-    if args.num_class_images != 0:
-        missing_data = [item for item in datamodule.data_train if not item.class_image_path.exists()]
-
-        if len(missing_data) != 0:
-            batched_data = [
-                missing_data[i:i+args.sample_batch_size]
-                for i in range(0, len(missing_data), args.sample_batch_size)
-            ]
-
-            pipeline = VlpnStableDiffusion(
-                text_encoder=text_encoder,
-                vae=vae,
-                unet=unet,
-                tokenizer=tokenizer,
-                scheduler=checkpoint_scheduler,
-            ).to(accelerator.device)
-            pipeline.set_progress_bar_config(dynamic_ncols=True)
-
-            with torch.inference_mode():
-                for batch in batched_data:
-                    image_name = [item.class_image_path for item in batch]
-                    prompt = [item.cprompt for item in batch]
-                    nprompt = [item.nprompt for item in batch]
-
-                    images = pipeline(
-                        prompt=prompt,
-                        negative_prompt=nprompt,
-                        height=args.sample_image_size,
-                        width=args.sample_image_size,
-                        num_inference_steps=args.sample_steps
-                    ).images
-
-                    for i, image in enumerate(images):
-                        image.save(image_name[i])
-
-            del pipeline
-
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
     train_dataloaders = datamodule.train_dataloaders
-    default_train_dataloader = train_dataloaders[0]
     val_dataloader = datamodule.val_dataloader
 
+    if args.num_class_images != 0:
+        generate_class_images(
+            accelerator,
+            text_encoder,
+            vae,
+            unet,
+            tokenizer,
+            checkpoint_scheduler,
+            datamodule.data_train,
+            args.sample_batch_size,
+            args.sample_image_size,
+            args.sample_steps
+        )
+
     # Scheduler and math around the number of training steps.
     overrode_max_train_steps = False
     num_update_steps_per_dataloader = sum(len(dataloader) for dataloader in train_dataloaders)
@@ -898,7 +870,7 @@ def main():
             accelerator,
             text_encoder,
             optimizer,
-            default_train_dataloader,
+            train_dataloaders[0],
             val_dataloader,
             loop,
             on_train=on_train,
-- 
cgit v1.2.3-54-g00ecf