diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4b3676886..76d8005c4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -238,6 +238,7 @@ As of this release, default backend is set to **diffusers** as its more feature
   - reduce system memory usage after compile  
   - fix cache loading with multiple models  
 - **fixes**  
+  - civitai model download: enable downloads of embeddings
   - ipadapter: allow changing of model/image on-the-fly  
   - ipadapter: fix fallback of cross-attention on unload  
   - rebasin iterations, thanks @AI-Casanova
diff --git a/modules/modelloader.py b/modules/modelloader.py
index 00c29ac85..bab65c100 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -80,11 +80,14 @@ def download_civit_model_thread(model_name, model_url, model_path, model_type, p
     if model_type == 'LoRA':
         model_file = os.path.join(shared.opts.lora_dir, model_path, model_name)
         temp_file = os.path.join(shared.opts.lora_dir, model_path, temp_file)
+    elif model_type == 'Embedding':
+        model_file = os.path.join(shared.opts.embeddings_dir, model_path, model_name)
+        temp_file = os.path.join(shared.opts.embeddings_dir, model_path, temp_file)
     else:
         model_file = os.path.join(shared.opts.ckpt_dir, model_path, model_name)
         temp_file = os.path.join(shared.opts.ckpt_dir, model_path, temp_file)
 
-    res = f'CivitAI download: name={model_name} url={model_url} path={model_path} temp={temp_file}'
+    res = f'CivitAI download: name="{model_name}" url="{model_url}" path="{model_path}" temp="{temp_file}"'
     if os.path.isfile(model_file):
         res += ' already exists'
         shared.log.warning(res)
@@ -101,7 +104,7 @@ def download_civit_model_thread(model_name, model_url, model_path, model_type, p
 
     r = shared.req(model_url, headers=headers, stream=True)
     total_size = int(r.headers.get('content-length', 0))
-    res += f' size={round((starting_pos + total_size)/1024/1024)}Mb'
+    res += f' size={round((starting_pos + total_size)/1024/1024, 2)}Mb'
     shared.log.info(res)
     shared.state.begin('civitai')
     block_size = 16384 # 16KB blocks
@@ -117,7 +120,7 @@ def download_civit_model_thread(model_name, model_url, model_path, model_type, p
                     written = written + len(data)
                     f.write(data)
                     download_pbar.update(task, description="Download", completed=written)
-            if written < 1024 * 1024: # min threshold
+            if written < 1024: # min threshold
                 os.remove(temp_file)
                 raise ValueError(f'removed invalid download: bytes={written}')
             if preview is not None:
diff --git a/modules/ui_models.py b/modules/ui_models.py
index a0135a6ca..4f09066ac 100644
--- a/modules/ui_models.py
+++ b/modules/ui_models.py
@@ -417,13 +417,19 @@ def create_ui():
 
                 def civit_search_model(name, tag, model_type):
                     # types = 'LORA' if model_type == 'LoRA' else 'Checkpoint'
-                    url = 'https://civitai.com/api/v1/models?limit=25&&Sort=Newest'
+                    url = 'https://civitai.com/api/v1/models?limit=25&Sort=Newest'
+                    if model_type == 'SD 1.5' or model_type == 'SD XL':
+                        url += '&types=Checkpoint'
+                    elif model_type == 'LoRA':
+                        url += '&types=LORA'
+                    elif model_type == 'Embedding':
+                        url += '&types=TextualInversion'
                     if name is not None and len(name) > 0:
                         url += f'&query={name}'
                     if tag is not None and len(tag) > 0:
                         url += f'&tag={tag}'
                     r = req(url)
-                    log.debug(f'CivitAI search: name="{name}" tag={tag or "none"} status={r.status_code}')
+                    log.debug(f'CivitAI search: name="{name}" tag={tag or "none"} url="{url}" status={r.status_code}')
                     if r.status_code != 200:
                         return [], [], []
                     body = r.json()
@@ -434,16 +440,21 @@ def create_ui():
                         found = 0
                         if model_type == 'LoRA' and model['type'] in ['LORA', 'LoCon']:
                             found += 1
-                        for variant in model['modelVersions']:
-                            if model_type == 'SD 1.5':
-                                if 'SD 1.' in variant['baseModel']:
-                                    found += 1
-                            if model_type == 'SD XL':
-                                if 'SDXL' in variant['baseModel']:
-                                    found += 1
-                            else:
-                                if 'SD 1.' not in variant['baseModel'] and 'SDXL' not in variant['baseModel']:
-                                    found += 1
+                        elif model_type == 'Embedding' and model['type'] == 'TextualInversion':
+                            found += 1
+                        elif model_type.startswith('SD') and model['type'] == 'Checkpoint':
+                            for variant in model['modelVersions']:
+                                if model_type == 'SD 1.5':
+                                    if 'SD 1.' in variant['baseModel']:
+                                        found += 1
+                                if model_type == 'SD XL':
+                                    if 'SDXL' in variant['baseModel']:
+                                        found += 1
+                                else:
+                                    if 'SD 1.' not in variant['baseModel'] and 'SDXL' not in variant['baseModel']:
+                                        found += 1
+                        elif model_type == 'Other':
+                            found += 1
                         if found > 0:
                             data1.append([
                                 model['id'],
@@ -581,7 +592,7 @@ def create_ui():
                     gr.HTML('<h2>Search for models</h2>')
                 with gr.Row():
                     with gr.Column(scale=1):
-                        civit_model_type = gr.Dropdown(label='Model type', choices=['SD 1.5', 'SD XL', 'LoRA', 'Other'], value='LoRA')
+                        civit_model_type = gr.Dropdown(label='Model type', choices=['SD 1.5', 'SD XL', 'LoRA', 'Embedding', 'Other'], value='LoRA')
                     with gr.Column(scale=15):
                         with gr.Row():
                             civit_search_text = gr.Textbox('', label='Search models', placeholder='keyword')