added mixing models (shamelessly inspired from voldy's web ui)

src/utils.py

@@ -2861,4 +2861,50 @@ def unload_whisper():
 		whisper_model = None
 		print("Unloaded Whisper")
 
-	do_gc()	
+	do_gc()	
+
+# shamelessly borrowed from Voldy's Web UI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/master/modules/extras.py#L74
+def merge_models( primary_model_name, secondary_model_name, alpha, progress=gr.Progress() ):
+	key_blacklist = []
+
+	def weighted_sum(theta0, theta1, alpha):
+		return ((1 - alpha) * theta0) + (alpha * theta1)
+
+	def read_model( filename ):
+		print(f"Loading {filename}")
+		return torch.load(filename)
+
+	theta_func = weighted_sum
+
+	theta_0 = read_model(primary_model_name)
+	theta_1 = read_model(secondary_model_name)
+
+	for key in enumerate_progress(theta_0.keys(), desc="Merging...", progress=progress):
+		if key in key_blacklist:
+			print("Skipping ignored key:", key)
+			continue
+		
+		a = theta_0[key]
+		b = theta_1[key]
+
+		if a.dtype != torch.float32 and a.dtype != torch.float16:
+			print("Skipping key:", key, a.dtype)
+			continue
+
+		if b.dtype != torch.float32 and b.dtype != torch.float16:
+			print("Skipping key:", key, b.dtype)
+			continue
+
+		theta_0[key] = theta_func(a, b, alpha)
+
+	del theta_1
+
+	primary_basename = os.path.splitext(os.path.basename(primary_model_name))[0]
+	secondary_basename = os.path.splitext(os.path.basename(secondary_model_name))[0]
+	suffix = "{:.3f}".format(alpha)
+	output_path = f'./models/finetunes/{primary_basename}_{secondary_basename}_{suffix}_merge.pth'
+
+	torch.save(theta_0, output_path)
+	message = f"Saved to {output_path}"
+	print(message)
+	return message

src/webui.py

@@ -27,6 +27,7 @@ GENERATE_SETTINGS = {}
 TRANSCRIBE_SETTINGS = {}
 EXEC_SETTINGS = {}
 TRAINING_SETTINGS = {}
+MERGER_SETTINGS = {}
 GENERATE_SETTINGS_ARGS = []
 
 PRESETS = {
@@ -359,7 +360,7 @@ def setup_gradio():
 					GENERATE_SETTINGS["candidates"] = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates")
 					GENERATE_SETTINGS["seed"] = gr.Number(value=0, precision=0, label="Seed")
 
-					preset = gr.Radio( ["Ultra Fast", "Fast", "Standard", "High Quality"], label="Preset", type="value" )
+					preset = gr.Radio( ["Ultra Fast", "Fast", "Standard", "High Quality"], label="Preset", type="value", value="Ultra Fast" )
 
 					GENERATE_SETTINGS["num_autoregressive_samples"] = gr.Slider(value=16, minimum=2, maximum=512, step=1, label="Samples")
 					GENERATE_SETTINGS["diffusion_iterations"] = gr.Slider(value=30, minimum=0, maximum=512, step=1, label="Iterations")
@@ -435,6 +436,17 @@ def setup_gradio():
 
 				with gr.Row():
 					text_tokenizier_button = gr.Button(value="Tokenize Text")
+			with gr.Tab("Model Merger"):
+				with gr.Column():
+					with gr.Row():
+						MERGER_SETTINGS["model_a"] = gr.Dropdown( choices=autoregressive_models, label="Model A", type="value", value=autoregressive_models[0] )
+						MERGER_SETTINGS["model_b"] = gr.Dropdown( choices=autoregressive_models, label="Model B", type="value", value=autoregressive_models[0] )
+					with gr.Row():
+						MERGER_SETTINGS["weight_slider"] = gr.Slider(label="Weight (from A to B)", value=0.5, minimum=0, maximum=1)
+					with gr.Row():
+						merger_button = gr.Button(value="Run Merger")
+				with gr.Column():
+					merger_output = gr.TextArea(label="Console Output", max_lines=8)
 		with gr.Tab("Training"):
 			with gr.Tab("Prepare Dataset"):
 				with gr.Row():
@@ -777,6 +789,11 @@ def setup_gradio():
 			outputs=text_tokenizier_output
 		)
 
+		merger_button.click(merge_models,
+			inputs=list(MERGER_SETTINGS.values()),
+			outputs=merger_output
+		)
+
 		refresh_configs.click(
 			lambda: gr.update(choices=get_training_list()),
 			inputs=None,