Update app.py

app.py

@@ -236,6 +236,7 @@ def do_inference_tts( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
 	parser.add_argument("--refine-on-stop", action="store_true")
 	parser.add_argument("--denoise-start", type=float, default=0.0)
 	parser.add_argument("--cfg-strength", type=float, default=kwargs['cfg-strength'])
+	parser.add_argument("--cfg-rescale", type=float, default=kwargs['cfg-rescale'])
 	args, unknown = parser.parse_known_args()
 
 	if is_windows:
@@ -284,6 +285,7 @@ def do_inference_tts( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
 		input_prompt_prefix=args.input_prompt_prefix,
 		input_prompt_length=args.input_prompt_length,
 		cfg_strength=args.cfg_strength,
+		cfg_rescale=args.cfg_rescale,
 	)
 
 	with timer("Inferenced in", callback=lambda msg: gr.Info( msg )) as t:
@@ -425,21 +427,23 @@ with ui:
 				with gr.Column(scale=7):
 					with gr.Tab("Basic Settings"):
 						with gr.Row():
-							layout["inference_tts"]["inputs"]["max-duration"] = gr.Slider(value=12, minimum=1, maximum=32, step=0.1, label="Maximum Seconds", info="Limits how many steps to perform in the AR pass.")
+							layout["inference_tts"]["inputs"]["max-duration"] = gr.Slider(value=12, minimum=1, maximum=32, step=0.1, label="Maximum Duration", info="Limits how many steps to perform in the AR pass.")
+							layout["inference_tts"]["inputs"]["max-steps"] = gr.Slider(value=50, minimum=1, maximum=200, step=1, label="Max Steps (NAR-len)", info="Limits how many steps to perform in the NAR-len (demask) pass.")
 							layout["inference_tts"]["inputs"]["input-prompt-length"] = gr.Slider(value=5.0, minimum=0.0, maximum=12.0, step=0.05, label="Input Prompt Repeat/Trim Length", info="Repeats and trims the input prompt down to X seconds. Set 0 to disable.")
 						with gr.Row():
-							layout["inference_tts"]["inputs"]["ar-temperature"] = gr.Slider(value=1.0, minimum=0.0, maximum=1.5, step=0.05, label="Temperature (AR)", info="Modifies the randomness from the samples in the AR. (0 to greedy* sample)")
+							layout["inference_tts"]["inputs"]["ar-temperature"] = gr.Slider(value=1.0, minimum=0.0, maximum=1.5, step=0.05, label="Temperature (AR/NAR-len)", info="Modifies the randomness from the samples in the AR/NAR-len. (0 to greedy* sample)")
 							layout["inference_tts"]["inputs"]["nar-temperature"] = gr.Slider(value=0.0, minimum=0.0, maximum=1.5, step=0.05, label="Temperature (NAR)", info="Modifies the randomness from the samples in the NAR. (0 to greedy sample)")
+							layout["inference_tts"]["inputs"]["modality"] = gr.Dropdown(value="Auto", choices=["Auto", "AR+NAR", "NAR-len"], label="Modality", info="Whether to inference with the AR+NAR or through the NAR-len.")
 						with gr.Row():
-							layout["inference_tts"]["inputs"]["cfg-strength"] = gr.Slider(value=1.0, minimum=0.0, maximum=14.0, step=0.05, label="CFG Strength", info="Classifier Free Guidance scale")
+							layout["inference_tts"]["inputs"]["cfg-strength"] = gr.Slider(value=1.0, minimum=0.0, maximum=14.0, step=0.05, label="CFG Strength", info="Classifier Free Guidance scale (AR needs 1, NAR-len needs 3).")
+							layout["inference_tts"]["inputs"]["cfg-rescale"] = gr.Slider(value=0.75, minimum=0.0, maximum=1.0, step=0.05, label="CFG Rescale (Phi)", info="Factor when rescaling for Classifier Free Guidance (0 to disable).")
 							layout["inference_tts"]["inputs"]["language"] = gr.Dropdown(choices=get_languages(), label="Language", value="en")
-							layout["inference_tts"]["inputs"]["modality"] = gr.Dropdown(value="Auto", choices=["Auto", "AR+NAR", "NAR-len"], label="Modality", info="Whether to inference with the AR+NAR or through the NAR-len.")
 					with gr.Tab("Sampler Settings"):
 						with gr.Row():
 							layout["inference_tts"]["inputs"]["top-p"] = gr.Slider(value=1.0, minimum=0.0, maximum=1.0, step=0.05, label="Top P", info=r"Limits the samples that are outside the top P% of probabilities.")
 							layout["inference_tts"]["inputs"]["top-k"] = gr.Slider(value=0, minimum=0, maximum=1024, step=1, label="Top K", info="Limits the samples to the top K of probabilities.")
 							layout["inference_tts"]["inputs"]["top-no"] = gr.Slider(value=0, minimum=0, maximum=2, step=0.05, label="Top-nσ", info="Performs top-nσ logits processing.")
-							layout["inference_tts"]["inputs"]["min-p"] = gr.Slider(value=0.0, minimum=0.0, maximum=1.0, step=0.05, label="Min P")
+							layout["inference_tts"]["inputs"]["min-p"] = gr.Slider(value=0.0, minimum=0.0, maximum=1.0, step=0.05, label="Min P", info="Filter out logits lower than this value.")
 						with gr.Row():
 							layout["inference_tts"]["inputs"]["repetition-penalty"] = gr.Slider(value=1.0, minimum=0.0, maximum=5.0, step=0.05, label="Repetition Penalty", info="Incurs a penalty to tokens based on how often they appear in a sequence.")
 							layout["inference_tts"]["inputs"]["repetition-penalty-decay"] = gr.Slider(value=0.0, minimum=-2.0, maximum=2.0, step=0.05, label="Repetition Penalty Length Decay", info="Modifies the reptition penalty based on how far back in time the token appeared in the sequence.")
@@ -453,19 +457,16 @@ with ui:
 							layout["inference_tts"]["inputs"]["dry-allowed-length"] = gr.Slider(value=2, minimum=0, maximum=75, step=1, label="Allowed Length", info="The maximimum length a token can be to perform DRY penalty with.")
 					with gr.Tab("Experimental Settings", visible=cfg.experimental):
 						with gr.Row():
-							layout["inference_tts"]["inputs"]["max-steps"] = gr.Slider(value=25, minimum=1, maximum=500, step=1, label="Max NAR Steps", info="Limits how many steps to perform in the NAR (demask) pass.")
 							layout["inference_tts"]["inputs"]["max-levels"] = gr.Slider(value=7, minimum=0, maximum=7, step=1, label="Max NAR Levels", info="Limits how many steps to perform in the NAR pass.")
-						with gr.Row():
-							layout["inference_tts"]["inputs"]["input-prompt-prefix"] = gr.Checkbox(label="Input Prompt as Prefix", info="Treats the input prompt clip as the prefix of the generated sequence.")
+							layout["inference_tts"]["inputs"]["beam-width"] = gr.Slider(value=0, minimum=0, maximum=32, step=1, label="Beam Width", info="Number of branches to search through for beam search sampling.")
 							layout["inference_tts"]["inputs"]["prefix-silence"] = gr.Slider(value=0.0, minimum=0.0, maximum=1.0, step=0.05, label="Silence Prefix Duration", info="Amount of silence to prefix to the output response before beginning inference.")
 						with gr.Row():
-							layout["inference_tts"]["inputs"]["beam-width"] = gr.Slider(value=0, minimum=0, maximum=32, step=1, label="Beam Width", info="Number of branches to search through for beam search sampling.")
+							layout["inference_tts"]["inputs"]["input-prompt-prefix"] = gr.Checkbox(label="Input Prompt as Prefix", info="Treats the input prompt clip as the prefix of the generated sequence.")
 							layout["inference_tts"]["inputs"]["dynamic-sampling"] = gr.Checkbox(label="Dynamic Temperature", info="Dynamically adjusts the temperature based on the highest confident predicted token per sampling step.")
 							layout["inference_tts"]["inputs"]["entropix-sampling"] = gr.Checkbox(label="Entropix Sampling", info="Dynamically samples based on entropy/varentropy values from the logits / attention scores.")
-						with gr.Row():
-							layout["inference_tts"]["inputs"]["layer-skip"] = gr.Checkbox(label="Layer Skip", info="Performs self-speculative early exit 'sampling'")
 							layout["inference_tts"]["inputs"]["refine-on-stop"] = gr.Checkbox(label="Refine on <stop>", info="Uses the last step's logits for the AR sequence instead.")
-						with gr.Row():
+						with gr.Row(visible=False):
+							layout["inference_tts"]["inputs"]["layer-skip"] = gr.Checkbox(label="Layer Skip", info="Performs self-speculative early exit 'sampling'")
 							layout["inference_tts"]["inputs"]["layer-skip-exit-layer"] = gr.Slider(value=11, minimum=0, maximum=11, step=1, label="Layer Skip Exit Layer", info="Maximum model layer to exit early from.")
 							layout["inference_tts"]["inputs"]["layer-skip-entropy-threshold"] = gr.Slider(value=0.1, minimum=0, maximum=1.0, step=0.01, label="Layer Skip Entropy Threshold", info="Entropy threshold for early-exit")
 							layout["inference_tts"]["inputs"]["layer-skip-varentropy-threshold"] = gr.Slider(value=0.1, minimum=0, maximum=1.0, step=0.01, label="Layer Skip Varentropy Threshold", info="Varentropy threshold for early-exit")