From 43bb5190fc9e7ae479a5dc6640be202c9a71e464 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 9 Jan 2023 22:52:23 +0300
Subject: [PATCH] remove/simplify some changes from #6481

---
 modules/textual_inversion/dataset.py           | 14 +++++---------
 modules/textual_inversion/textual_inversion.py |  4 ++--
 modules/ui.py                                  |  2 +-
 3 files changed, 8 insertions(+), 12 deletions(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index bcad6848..fa48708e 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -17,7 +17,7 @@ re_numbers_at_start = re.compile(r"^[-\d]+\s*")
 
 
 class DatasetEntry:
-    def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None, img_shape=None):
+    def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None):
         self.filename = filename
         self.filename_text = filename_text
         self.latent_dist = latent_dist
@@ -25,7 +25,6 @@ class DatasetEntry:
         self.cond = cond
         self.cond_text = cond_text
         self.pixel_values = pixel_values
-        self.img_shape = img_shape
 
 
 class PersonalizedBase(Dataset):
@@ -46,12 +45,10 @@ class PersonalizedBase(Dataset):
         assert data_root, 'dataset directory not specified'
         assert os.path.isdir(data_root), "Dataset directory doesn't exist"
         assert os.listdir(data_root), "Dataset directory is empty"
-        if varsize:
-            assert batch_size == 1, 'variable img size must have batch size 1'
+        assert batch_size == 1 or not varsize, 'variable img size must have batch size 1'
 
         self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
 
-
         self.shuffle_tags = shuffle_tags
         self.tag_drop_out = tag_drop_out
 
@@ -91,14 +88,14 @@ class PersonalizedBase(Dataset):
             if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)):
                 latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
                 latent_sampling_method = "once"
-                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, img_shape=image.size)
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
             elif latent_sampling_method == "deterministic":
                 # Works only for DiagonalGaussianDistribution
                 latent_dist.std = 0
                 latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
-                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, img_shape=image.size)
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
             elif latent_sampling_method == "random":
-                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, img_shape=image.size)
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist)
 
             if not (self.tag_drop_out != 0 or self.shuffle_tags):
                 entry.cond_text = self.create_text(filename_text)
@@ -154,7 +151,6 @@ class BatchLoader:
         self.cond_text = [entry.cond_text for entry in data]
         self.cond = [entry.cond for entry in data]
         self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
-        self.img_shape = [entry.img_shape for entry in data]
         #self.emb_index = [entry.emb_index for entry in data]
         #print(self.latent_sample.device)
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index ad76297e..14be2c96 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -492,8 +492,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                     else:
                         p.prompt = batch.cond_text[0]
                         p.steps = 20
-                        p.width = batch.img_shape[0][0]
-                        p.height = batch.img_shape[0][1]
+                        p.width = training_width
+                        p.height = training_height
 
                     preview_text = p.prompt
 
diff --git a/modules/ui.py b/modules/ui.py
index 9d6b141e..ddfe1b1a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1348,7 +1348,7 @@ def create_ui():
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"), elem_id="train_template_file")
                     training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
                     training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
-                    varsize = gr.Checkbox(label="Ignore dimension settings and do not resize images", value=False, elem_id="train_varsize")
+                    varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
                     steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
 
                     with FormRow():