Merge branch 'master' of https://github.com/yfszzx/stable-diffusion-webui-plus

modules/extras.py

@@ -175,11 +175,14 @@ def run_pnginfo(image):
 
 
 def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
-    def weighted_sum(theta0, theta1, theta2, alpha):
+    def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
-    def add_difference(theta0, theta1, theta2, alpha):
-        return theta0 + (theta1 - theta2) * alpha
+    def get_difference(theta1, theta2):
+        return theta1 - theta2
+
+    def add_difference(theta0, theta1_2_diff, alpha):
+        return theta0 + (alpha * theta1_2_diff)
 
     primary_model_info = sd_models.checkpoints_list[primary_model_name]
     secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
@@ -198,23 +201,28 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
         teritary_model = torch.load(teritary_model_info.filename, map_location='cpu')
         theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model)
     else:
+        teritary_model = None
         theta_2 = None
 
     theta_funcs = {
-        "Weighted sum": weighted_sum,
-        "Add difference": add_difference,
+        "Weighted sum": (None, weighted_sum),
+        "Add difference": (get_difference, add_difference),
     }
-    theta_func = theta_funcs[interp_method]
+    theta_func1, theta_func2 = theta_funcs[interp_method]
 
     print(f"Merging...")
 
+    if theta_func1:
+        for key in tqdm.tqdm(theta_1.keys()):
+            if 'model' in key:
+                t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
+                theta_1[key] = theta_func1(theta_1[key], t2)
+    del theta_2, teritary_model
+
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
-            t2 = (theta_2 or {}).get(key)
-            if t2 is None:
-                t2 = torch.zeros_like(theta_0[key])
 
-            theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, multiplier)
+            theta_0[key] = theta_func2(theta_0[key], theta_1[key], multiplier)
 
             if save_as_half:
                 theta_0[key] = theta_0[key].half()

modules/interrogate.py

@@ -123,7 +123,7 @@ class InterrogateModels:
 
         return caption[0]
 
-    def interrogate(self, pil_image, include_ranks=False):
+    def interrogate(self, pil_image):
         res = None
 
         try:
@@ -156,10 +156,10 @@ class InterrogateModels:
                 for name, topn, items in self.categories:
                     matches = self.rank(image_features, items, top_count=topn)
                     for match, score in matches:
-                        if include_ranks:
-                            res += ", " + match
+                        if shared.opts.interrogate_return_ranks:
+                            res += f", ({match}:{score/100:.3f})"
                         else:
-                            res += f", ({match}:{score})"
+                            res += ", " + match
 
         except Exception:
             print(f"Error interrogating", file=sys.stderr)

modules/scripts.py

@@ -58,6 +58,9 @@ def load_scripts(basedir):
     for filename in sorted(os.listdir(basedir)):
         path = os.path.join(basedir, filename)
 
+        if os.path.splitext(path)[1].lower() != '.py':
+            continue
+
         if not os.path.isfile(path):
             continue
 

modules/shared.py

@@ -77,6 +77,16 @@ parser.add_argument("--disable-safe-unpickle", action='store_true', help="disabl
 
 
 cmd_opts = parser.parse_args()
+restricted_opts = [
+    "samples_filename_pattern",
+    "outdir_samples",
+    "outdir_txt2img_samples",
+    "outdir_img2img_samples",
+    "outdir_extras_samples",
+    "outdir_grids",
+    "outdir_txt2img_grids",
+    "outdir_save",
+]
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'])

modules/textual_inversion/textual_inversion.py

@@ -137,6 +137,7 @@ class EmbeddingDatabase:
                 continue
 
         print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
+        print("Embeddings:", ', '.join(self.word_embeddings.keys()))
 
     def find_embedding_at_position(self, tokens, offset):
         token = tokens[offset]

modules/ui.py

@@ -25,7 +25,7 @@ import gradio.routes
 
 from modules import sd_hijack, sd_models
 from modules.paths import script_path
-from modules.shared import opts, cmd_opts
+from modules.shared import opts, cmd_opts, restricted_opts
 if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
 import modules.shared as shared
@@ -1430,6 +1430,9 @@ Requested path was: {f}
             if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
                 continue
 
+            if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+                continue
+
             oldval = opts.data.get(key, None)
             opts.data[key] = value
 
@@ -1447,6 +1450,9 @@ Requested path was: {f}
         if not opts.same_type(value, opts.data_labels[key].default):
             return gr.update(visible=True), opts.dumpjson()
 
+        if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+            return gr.update(value=oldval), opts.dumpjson()
+
         oldval = opts.data.get(key, None)
         opts.data[key] = value