Fixed issue with new ldm folder requiring the personalization_config to be set even if empty.

Added shutup as a dependency to shutup python warnings for good.

requirements.txt

@@ -67,6 +67,7 @@ retry==0.9.2  # used by sd_utils
 python-slugify==6.1.2  # used by sd_utils
 piexif==1.1.3  # used by sd_utils
 pywebview==3.6.3 # used by streamlit_webview.py
+shutup==0.2.0 # remove all the annoying warnings
 
 accelerate==0.12.0
 albumentations==0.4.3

scripts/sd_utils/__init__.py

@@ -72,6 +72,7 @@ from io import BytesIO
 from packaging import version
 from pathlib import Path
 from huggingface_hub import hf_hub_download
+import shutup
 
 #import librosa
 from logger import logger, set_logger_verbosity, quiesce_logger
@@ -91,6 +92,9 @@ except ImportError as e:
 # end of imports
 #---------------------------------------------------------------------------------------------------------------
 
+# remove all the annoying python warnings.
+shutup.please()
+
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
     from transformers import logging
@@ -261,10 +265,13 @@ def set_page_title(title):
 
 
 def make_grid(n_items=5, n_cols=5):
+    # Compute number of rows
     n_rows = 1 + n_items // int(n_cols)
 
+    # Create rows
     rows = [st.container() for _ in range(n_rows)]
 
+    # Create columns in each row
     cols_per_row = [r.columns(n_cols) for r in rows]
     cols = [column for row in cols_per_row for column in row]
 
@@ -272,29 +279,29 @@ def make_grid(n_items=5, n_cols=5):
 
 
 def merge(file1, file2, out, weight):
-    alpha = (weight)/100
     if not(file1.endswith(".ckpt")):
         file1 += ".ckpt"
     if not(file2.endswith(".ckpt")):
         file2 += ".ckpt"
     if not(out.endswith(".ckpt")):
         out += ".ckpt"
-    #Load Models
-    model_0 = torch.load(file1)
-    model_1 = torch.load(file2)
-    theta_0 = model_0['state_dict']
-    theta_1 = model_1['state_dict']
-
-    for key in theta_0.keys():
-        if 'model' in key and key in theta_1:
-            theta_0[key] = (alpha) * theta_0[key] + (1-alpha) * theta_1[key]
-
-    logger.info("RUNNING...\n(STAGE 2)")
-
-    for key in theta_1.keys():
-        if 'model' in key and key not in theta_0:
-            theta_0[key] = theta_1[key]
-    torch.save(model_0, out)
+    try: 
+        #Load Models
+        model_0 = torch.load(file1)
+        model_1 = torch.load(file2)
+        theta_0 = model_0['state_dict']
+        theta_1 = model_1['state_dict']
+        alpha = (weight)/100
+        for key in theta_0.keys():
+            if 'model' in key and key in theta_1:
+                theta_0[key] = (alpha) * theta_0[key] + (1-alpha) * theta_1[key]
+        logger.info("RUNNING...\n(STAGE 2)")
+        for key in theta_1.keys():
+            if 'model' in key and key not in theta_0:
+                theta_0[key] = theta_1[key]
+        torch.save(model_0, out)
+    except:
+        logger.error("Error in merging") 
 
 
 def human_readable_size(size, decimal_places=3):
@@ -483,7 +490,7 @@ def load_model_from_config(config, ckpt, verbose=False):
         if "global_step" in pl_sd:
             logger.info(f"Global Step: {pl_sd['global_step']}")
         sd = pl_sd["state_dict"]
-        model = instantiate_from_config(config.model)
+        model = instantiate_from_config(config.model, personalization_config='')
         m, u = model.load_state_dict(sd, strict=False)
         if len(m) > 0 and verbose:
             logger.info("missing keys:")
@@ -2395,7 +2402,7 @@ def process_images(
             else: # just behave like usual
                 c = (server_state["model"] if not st.session_state['defaults'].general.optimized else server_state["modelCS"]).get_learned_conditioning(prompts)
 
-
+                
             shape = [opt_C, height // opt_f, width // opt_f]
 
             if st.session_state['defaults'].general.optimized: