add keyword identifier for loras

modules/async_worker.py

@@ -112,7 +112,7 @@ def process(gen_data):
         outputs.append(["preview", (-1, f"Loading LoRA models ...", None)])
         lora_keywords = pipeline.load_loras(loras)
         if lora_keywords is None:
-            lora_keywords = " "
+            lora_keywords = ""
 
         if gen_data["performance_selection"] == NEWPERF:
             steps = gen_data["custom_steps"]
@@ -212,7 +212,7 @@ def callback(step, x0, x, total_steps, y):
             p_txt, n_txt = process_prompt(
                 gen_data["style_selection"], pos_stripped, neg_stripped, gen_data
             )
-            p_txt += lora_keywords
+            p_txt = lora_keywords + p_txt
             start_step = 0
             denoise = None
             with TimeIt("Pipeline process"):

modules/path.py

@@ -69,7 +69,7 @@ def get_abspath(path):
 extensions = [".pth", ".ckpt", ".bin", ".safetensors"]
 
 
-def get_model_filenames(folder_path):
+def get_model_filenames(folder_path, isLora=False):
     if not os.path.isdir(folder_path):
         raise ValueError("Folder path is not a valid directory.")
 
@@ -83,6 +83,11 @@ def get_model_filenames(folder_path):
             _, ext = os.path.splitext(filename)
             if ext.lower() in [".pth", ".ckpt", ".bin", ".safetensors"]:
                 path = os.path.join(relative_path, filename)
+                if isLora:
+                    txtcheck = path.replace(".safetensors", ".txt")
+                    if os.path.isfile(f"{folder_path}{txtcheck}"):
+                        path = path + " 🗒️"
+
                 filenames.append(path)
 
     return sorted(
@@ -94,7 +99,7 @@ def get_model_filenames(folder_path):
 def update_all_model_names():
     global model_filenames, lora_filenames, upscaler_filenames
     model_filenames = get_model_filenames(modelfile_path)
-    lora_filenames = get_model_filenames(lorafile_path)
+    lora_filenames = get_model_filenames(lorafile_path, True)
     upscaler_filenames = get_model_filenames(upscaler_path)
     return
 

modules/sdxl_pipeline.py

@@ -164,13 +164,14 @@ def load_all_keywords(self, loras):
         for name, weight in loras:
             if name == "None" or weight == 0:
                 continue
-
+            name = name.strip(" 🗒️")
             filename = os.path.join(modules.path.lorafile_path, name)
             lora_prompt_addition = (
-                f"{lora_prompt_addition}, {self.load_keywords(filename)}"
+                f"{lora_prompt_addition} {self.load_keywords(filename)}, "
             )
         return lora_prompt_addition
 
+    #   name = name.strip(" 🗒️")
     def load_loras(self, loras):
         lora_prompt_addition = self.load_all_keywords(loras)
         if self.xl_base_patched_hash == str(loras):
@@ -182,7 +183,7 @@ def load_loras(self, loras):
         for name, weight in loras:
             if name == "None" or weight == 0:
                 continue
-
+            name = name.strip(" 🗒️")
             filename = os.path.join(modules.path.lorafile_path, name)
             print(f"Loading LoRAs: {name}")
             with suppress_stdout():
@@ -232,7 +233,6 @@ def clean_prompt_cond_caches(self):
         self.conditions["-"]["cache"] = None
         self.conditions["switch"]["text"] = None
         self.conditions["switch"]["cache"] = None
-
 
     def textencode(self, id, text):
         update = False
@@ -244,39 +244,42 @@ def textencode(self, id, text):
             self.conditions[id]["text"] = text
             update = True
         return update
-    
+
     def set_timestep_range(self, conditioning, start, end):
         c = []
         for t in conditioning:
-            if 'pooled_output' in t:
-                t['start_percent'] = start
-                t['end_percent'] = end
-        
+            if "pooled_output" in t:
+                t["start_percent"] = start
+                t["end_percent"] = end
+
         return conditioning
-
-
+
     def prompt_switch_per_step(self, prompt, steps):
         # Find all occurrences of [option1|option2|...] in the input string
-        options_pattern = r'\[([^|\]]+(?:\|[^|\]]+)*)\]'
+        options_pattern = r"\[([^|\]]+(?:\|[^|\]]+)*)\]"
         matches = re.finditer(options_pattern, prompt)
         options_list = []
         exact_matches = []
 
         for match in matches:
-            options = match.group(1).split('|') if '|' in match.group(1) else [match.group(1)]
+            options = (
+                match.group(1).split("|") if "|" in match.group(1) else [match.group(1)]
+            )
             options_list.append(options)
             exact_matches.append(match.group(0))
 
         prompt_per_step = []
-        for i in range(0,steps):
+        for i in range(0, steps):
             prompt_to_append = prompt
-            
+
             for options, exact_match in zip(options_list, exact_matches):
-                    replacement = options[i % len(options)]  # Use modulo to cycle through options
-                    prompt_to_append = prompt_to_append.replace(exact_match, replacement, 1)
+                replacement = options[
+                    i % len(options)
+                ]  # Use modulo to cycle through options
+                prompt_to_append = prompt_to_append.replace(exact_match, replacement, 1)
 
             prompt_per_step.append(prompt_to_append)
-            
+
         return prompt_per_step
 
     @torch.inference_mode()
@@ -307,35 +310,33 @@ def process(
         if self.conditions is None:
             self.clean_prompt_cond_caches()
 
-
-
         if self.textencode("+", positive_prompt):
             updated_conditions = True
         if self.textencode("-", negative_prompt):
             updated_conditions = True
 
         prompt_switch_mode = False
-        if("|" in positive_prompt):
+        if "|" in positive_prompt:
             prompt_switch_mode = True
             prompt_per_step = self.prompt_switch_per_step(positive_prompt, steps)
-            
-            perc_per_step = round(100/steps,2)
+
+            perc_per_step = round(100 / steps, 2)
             positive_complete = []
             for i in range(len(prompt_per_step)):
                 if self.textencode("switch", prompt_per_step[i]):
                     updated_conditions = True
                 positive_switch = convert_cond(self.conditions["switch"]["cache"])
-                start_perc = round((perc_per_step*i)/100,2)
-                end_perc = round((perc_per_step*(i+1))/100,2)
+                start_perc = round((perc_per_step * i) / 100, 2)
+                end_perc = round((perc_per_step * (i + 1)) / 100, 2)
                 if end_perc >= 0.99:
                     end_perc = 1
-                positive_switch = self.set_timestep_range(positive_switch,start_perc, end_perc)
-
+                positive_switch = self.set_timestep_range(
+                    positive_switch, start_perc, end_perc
+                )
+
                 positive_complete += positive_switch
-
-
-            positive_switch = convert_cond(self.conditions["switch"]["cache"])
 
+            positive_switch = convert_cond(self.conditions["switch"]["cache"])
 
         if controlnet is not None and input_image is not None:
             worker.outputs.append(["preview", (-1, f"Powering up ...", None)])
@@ -347,7 +348,7 @@ def process(
             )[0]
             self.refresh_controlnet(name=controlnet["type"])
             if self.xl_controlnet:
-                if(prompt_switch_mode):
+                if prompt_switch_mode:
                     match controlnet["type"].lower():
                         case "canny":
                             input_image = Canny().detect_edge(

update_log.md

@@ -1,3 +1,6 @@
+### 1.25.1
+* Now displays 🗒️ at the end of the loraname if it has a keyword .txt file
+
 ### 1.25.0
 * Prompt Bracketing Now works ie `[cat|dog]`
 * Updated comfy version

version.py

@@ -1 +1 @@
-version = "1.25.0"
+version = "1.25.1"