Update

1 files changed, 28 insertions, 29 deletions

diff --git a/textual_inversion.py b/textual_inversion.py
index 4f2de9e..09871d4 100644
--- a/textual_inversion.py
+++ b/textual_inversion.py
@@ -4,6 +4,7 @@ import math
 import os
 import datetime
 import logging
+import json
 from pathlib import Path
 
 import numpy as np
@@ -22,8 +23,6 @@ from tqdm.auto import tqdm
 from transformers import CLIPTextModel, CLIPTokenizer
 from slugify import slugify
 from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
-import json
-import os
 
 from data.csv import CSVDataModule
 
@@ -70,7 +69,7 @@ def parse_args():
     parser.add_argument(
         "--num_class_images",
         type=int,
-        default=4,
+        default=200,
         help="How many class images to generate per training image."
     )
     parser.add_argument(
@@ -141,7 +140,7 @@ def parse_args():
     parser.add_argument(
         "--lr_scheduler",
         type=str,
-        default="constant",
+        default="linear",
         help=(
             'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
             ' "constant", "constant_with_warmup"]'
@@ -402,20 +401,20 @@ class Checkpointer:
             generator=generator,
         )
 
-        for pool, data, latents in [("stable", val_data, stable_latents), ("val", val_data, None), ("train", train_data, None)]:
-            all_samples = []
-            file_path = samples_path.joinpath(pool, f"step_{step}.png")
-            file_path.parent.mkdir(parents=True, exist_ok=True)
+        with torch.inference_mode():
+            for pool, data, latents in [("stable", val_data, stable_latents), ("val", val_data, None), ("train", train_data, None)]:
+                all_samples = []
+                file_path = samples_path.joinpath(pool, f"step_{step}.png")
+                file_path.parent.mkdir(parents=True, exist_ok=True)
 
-            data_enum = enumerate(data)
+                data_enum = enumerate(data)
 
-            for i in range(self.sample_batches):
-                batches = [batch for j, batch in data_enum if j * data.batch_size < self.sample_batch_size]
-                prompt = [prompt.format(self.placeholder_token)
-                          for batch in batches for prompt in batch["prompts"]][:self.sample_batch_size]
-                nprompt = [prompt for batch in batches for prompt in batch["nprompts"]][:self.sample_batch_size]
+                for i in range(self.sample_batches):
+                    batches = [batch for j, batch in data_enum if j * data.batch_size < self.sample_batch_size]
+                    prompt = [prompt.format(self.placeholder_token)
+                              for batch in batches for prompt in batch["prompts"]][:self.sample_batch_size]
+                    nprompt = [prompt for batch in batches for prompt in batch["nprompts"]][:self.sample_batch_size]
 
-                with self.accelerator.autocast():
                     samples = pipeline(
                         prompt=prompt,
                         negative_prompt=nprompt,
@@ -429,15 +428,15 @@ class Checkpointer:
                         output_type='pil'
                     )["sample"]
 
-                all_samples += samples
+                    all_samples += samples
 
-                del samples
+                    del samples
 
-            image_grid = make_grid(all_samples, self.sample_batches, self.sample_batch_size)
-            image_grid.save(file_path)
+                image_grid = make_grid(all_samples, self.sample_batches, self.sample_batch_size)
+                image_grid.save(file_path)
 
-            del all_samples
-            del image_grid
+                del all_samples
+                del image_grid
 
         del unwrapped
         del scheduler
@@ -623,7 +622,7 @@ def main():
     datamodule.setup()
 
     if args.num_class_images != 0:
-        missing_data = [item for item in datamodule.data if not item[1].exists()]
+        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]
@@ -643,20 +642,20 @@ def main():
             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]
-                prompt = [p[2].format(args.initializer_token) for p in batch]
-                nprompt = [p[3] for p in batch]
+            with torch.inference_mode():
+                for batch in batched_data:
+                    image_name = [p.class_image_path for p in batch]
+                    prompt = [p.prompt.format(args.initializer_token) for p in batch]
+                    nprompt = [p.nprompt for p in batch]
 
-                with accelerator.autocast():
                     images = pipeline(
                         prompt=prompt,
                         negative_prompt=nprompt,
                         num_inference_steps=args.sample_steps
                     ).images
 
-                for i, image in enumerate(images):
-                    image.save(image_name[i])
+                    for i, image in enumerate(images):
+                        image.save(image_name[i])
 
             del pipeline