diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index d31963d4..f4ce4552 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -19,9 +19,10 @@ 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, weight=None):
         self.filename = filename
         self.filename_text = filename_text
+        self.weight = weight
         self.latent_dist = latent_dist
         self.latent_sample = latent_sample
         self.cond = cond
@@ -56,10 +57,16 @@ class PersonalizedBase(Dataset):
 
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
+            alpha_channel = None
             if shared.state.interrupted:
                 raise Exception("interrupted")
             try:
-                image = Image.open(path).convert('RGB')
+                image = Image.open(path)
+                #Currently does not work for single color transparency
+                #We would need to read image.info['transparency'] for that
+                if 'A' in image.getbands():
+                    alpha_channel = image.getchannel('A')
+                image = image.convert('RGB')
                 if not varsize:
                     image = image.resize((width, height), PIL.Image.BICUBIC)
             except Exception:
@@ -87,17 +94,33 @@ class PersonalizedBase(Dataset):
             with devices.autocast():
                 latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
 
-            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)
-            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)
-            elif latent_sampling_method == "random":
-                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist)
+            #Perform latent sampling, even for random sampling.
+            #We need the sample dimensions for the weights
+            if latent_sampling_method == "deterministic":
+                if isinstance(latent_dist, DiagonalGaussianDistribution):
+                    # Works only for DiagonalGaussianDistribution
+                    latent_dist.std = 0
+                else:
+                    latent_sampling_method = "once"
+            latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
+
+            if alpha_channel is not None:
+                channels, *latent_size = latent_sample.shape
+                weight_img = alpha_channel.resize(latent_size)
+                npweight = np.array(weight_img).astype(np.float32)
+                #Repeat for every channel in the latent sample
+                weight = torch.tensor([npweight] * channels).reshape([channels] + latent_size)
+                #Normalize the weight to a minimum of 0 and a mean of 1, that way the loss will be comparable to default.
+                weight -= weight.min()
+                weight /= weight.mean()
+            else:
+                #If an image does not have a alpha channel, add a ones weight map anyway so we can stack it later
+                weight = torch.ones([channels] + latent_size)
+            
+            if latent_sampling_method == "random":
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight)
+            else:
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, weight=weight)
 
             if not (self.tag_drop_out != 0 or self.shuffle_tags):
                 entry.cond_text = self.create_text(filename_text)
@@ -110,6 +133,7 @@ class PersonalizedBase(Dataset):
             del torchdata
             del latent_dist
             del latent_sample
+            del weight
 
         self.length = len(self.dataset)
         self.groups = list(groups.values())
@@ -195,6 +219,7 @@ 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.weight = torch.stack([entry.weight for entry in data]).squeeze(1)
         #self.emb_index = [entry.emb_index for entry in data]
         #print(self.latent_sample.device)