diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 88d68c76c..375178ede 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):
+    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):
         self.filename = filename
         self.filename_text = filename_text
         self.latent_dist = latent_dist
@@ -25,6 +25,7 @@ class DatasetEntry:
         self.cond = cond
         self.cond_text = cond_text
         self.pixel_values = pixel_values
+        self.img_shape = img_shape
 
 
 class PersonalizedBase(Dataset):
@@ -33,8 +34,6 @@ class PersonalizedBase(Dataset):
 
         self.placeholder_token = placeholder_token
 
-        self.width = width
-        self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
 
         self.dataset = []
@@ -59,7 +58,11 @@ class PersonalizedBase(Dataset):
             if shared.state.interrupted:
                 raise Exception("interrupted")
             try:
-                image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
+                image = Image.open(path).convert('RGB')
+                if width < 2000:
+                    image = image.resize((width, height), PIL.Image.BICUBIC)
+                else:
+                    assert batch_size == 1, 'variable img size must have batch size 1'
             except Exception:
                 continue
 
@@ -88,14 +91,14 @@ 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)
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, img_shape=image.size)
             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)
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, img_shape=image.size)
             elif latent_sampling_method == "random":
-                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist)
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, img_shape=image.size)
 
             if not (self.tag_drop_out != 0 or self.shuffle_tags):
                 entry.cond_text = self.create_text(filename_text)
@@ -151,6 +154,7 @@ 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 45882ed68..9f96d0fda 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -451,8 +451,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                     else:
                         p.prompt = batch.cond_text[0]
                         p.steps = 20
-                        p.width = training_width
-                        p.height = training_height
+                        p.width = batch.img_shape[0][0]
+                        p.height = batch.img_shape[0][1]
 
                     preview_text = p.prompt