update lists of models after merging them in checkpoints tab

support saving as half

modules/extras.py

@@ -13,6 +13,7 @@ from modules.ui import plaintext_to_html
 import modules.codeformer_model
 import piexif
 import piexif.helper
+import gradio as gr
 
 
 cached_images = {}
@@ -140,7 +141,7 @@ def run_pnginfo(image):
     return '', geninfo, info
 
 
-def run_modelmerger(primary_model_name, secondary_model_name, interp_method, interp_amount):
+def run_modelmerger(primary_model_name, secondary_model_name, interp_method, interp_amount, save_as_half):
     # Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
     def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
@@ -156,14 +157,14 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
         alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
         return theta0 + ((theta1 - theta0) * alpha)
 
-    primary_model_filename = sd_models.checkpoints_list[primary_model_name].filename
-    secondary_model_filename = sd_models.checkpoints_list[secondary_model_name].filename
+    primary_model_info = sd_models.checkpoints_list[primary_model_name]
+    secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
 
-    print(f"Loading {primary_model_filename}...")
-    primary_model = torch.load(primary_model_filename, map_location='cpu')
+    print(f"Loading {primary_model_info.filename}...")
+    primary_model = torch.load(primary_model_info.filename, map_location='cpu')
 
-    print(f"Loading {secondary_model_filename}...")
-    secondary_model = torch.load(secondary_model_filename, map_location='cpu')
+    print(f"Loading {secondary_model_info.filename}...")
+    secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
    
     theta_0 = primary_model['state_dict']
     theta_1 = secondary_model['state_dict']
@@ -179,16 +180,22 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
             theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
+            if save_as_half:
+                theta_0[key] = theta_0[key].half()
     
     for key in theta_1.keys():
         if 'model' in key and key not in theta_0:
             theta_0[key] = theta_1[key]
+            if save_as_half:
+                theta_0[key] = theta_0[key].half()
 
-    filename = primary_model_name + '_' + str(round(interp_amount,2)) + '-' + secondary_model_name + '_' + str(round((float(1.0) - interp_amount),2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
+    filename = primary_model_info.model_name + '_' + str(round(interp_amount, 2)) + '-' + secondary_model_info.model_name + '_' + str(round((float(1.0) - interp_amount), 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
     output_modelname = os.path.join(shared.cmd_opts.ckpt_dir, filename)
 
     print(f"Saving to {output_modelname}...")
     torch.save(primary_model, output_modelname)
 
+    sd_models.list_models()
+
     print(f"Checkpoint saved.")
-    return "Checkpoint saved to " + output_modelname
+    return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(3)]

modules/sd_models.py

@@ -23,6 +23,11 @@ except Exception:
     pass
 
 
+def checkpoint_tiles():
+    print(sorted([x.title for x in checkpoints_list.values()]))
+    return sorted([x.title for x in checkpoints_list.values()])
+
+
 def list_models():
     checkpoints_list.clear()
 
@@ -39,13 +44,14 @@ def list_models():
         if name.startswith("\\") or name.startswith("/"):
             name = name[1:]
 
-        return f'{name} [{h}]'
+        shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
+
+        return f'{name} [{h}]', shortname
 
     cmd_ckpt = shared.cmd_opts.ckpt
     if os.path.exists(cmd_ckpt):
         h = model_hash(cmd_ckpt)
-        title = modeltitle(cmd_ckpt, h)
-        model_name = title.rsplit(".",1)[0] # remove extension if present
+        title, model_name = modeltitle(cmd_ckpt, h)
         checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, model_name)
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found: {cmd_ckpt}", file=sys.stderr)
@@ -53,8 +59,7 @@ def list_models():
     if os.path.exists(model_dir):
         for filename in glob.glob(model_dir + '/**/*.ckpt', recursive=True):
             h = model_hash(filename)
-            title = modeltitle(filename, h)
-            model_name = title.rsplit(".",1)[0] # remove extension if present
+            title, model_name = modeltitle(filename, h)
             checkpoints_list[title] = CheckpointInfo(filename, title, h, model_name)
 
 

modules/shared.py

@@ -190,7 +190,7 @@ options_templates.update(options_section(('system', "System"), {
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": [x.title for x in modules.sd_models.checkpoints_list.values()]}),
+    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),    
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),

modules/ui.py

@@ -872,29 +872,16 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>/models</b> directory.</p>")
                 
                 with gr.Row():
-                    ckpt_name_list = sorted([x.title for x in modules.sd_models.checkpoints_list.values()])
-                    primary_model_name = gr.Dropdown(ckpt_name_list, elem_id="modelmerger_primary_model_name", label="Primary Model Name")
-                    secondary_model_name = gr.Dropdown(ckpt_name_list, elem_id="modelmerger_secondary_model_name", label="Secondary Model Name")
+                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary Model Name")
+                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary Model Name")
                 interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3)
                 interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method")
-                submit = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
+                save_as_half = gr.Checkbox(value=False, label="Safe as float16")
+                modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
             
             with gr.Column(variant='panel'):
                 submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
 
-            submit.click(
-                fn=run_modelmerger,
-                inputs=[
-                    primary_model_name,
-                    secondary_model_name,
-                    interp_method,
-                    interp_amount
-                ],
-                outputs=[
-                    submit_result,
-                ]
-            )
-
     def create_setting_component(key):
         def fun():
             return opts.data[key] if key in opts.data else opts.data_labels[key].default
@@ -918,6 +905,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         return comp(label=info.label, value=fun, **(args or {}))
 
     components = []
+    component_dict = {}
 
     def run_settings(*args):
         changed = 0
@@ -973,7 +961,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 
                     gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
 
-                components.append(create_setting_component(k))
+                component = create_setting_component(k)
+                component_dict[k] = component
+                components.append(component)
                 items_displayed += 1
 
         request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
@@ -1024,6 +1014,22 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             outputs=[result, text_settings],
         )
 
+        modelmerger_merge.click(
+            fn=run_modelmerger,
+            inputs=[
+                primary_model_name,
+                secondary_model_name,
+                interp_method,
+                interp_amount,
+                save_as_half,
+            ],
+            outputs=[
+                submit_result,
+                primary_model_name,
+                secondary_model_name,
+                component_dict['sd_model_checkpoint'],
+            ]
+        )
         paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Seed', 'Size-1', 'Size-2']
         txt2img_fields = [field for field,name in txt2img_paste_fields if name in paste_field_names]
         img2img_fields = [field for field,name in img2img_paste_fields if name in paste_field_names]