Update gradio_app.py
🚀 Update 0.3
This commit is contained in:
drbaph
106
gradio_app.py
106
gradio_app.py
@@ -9,27 +9,21 @@ import os
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from datetime import datetime
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import gradio as gr
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# Define the function to generate audio based on a prompt
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def generate_audio(prompt, steps, cfg_scale, sigma_min, sigma_max, generation_time, seed, sampler_type, model_half):
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# Download model
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# Define a function to set up the model and device
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def setup_model(model_half):
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model, model_config = get_pretrained_model("audo/stable-audio-open-1.0")
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sample_rate = model_config["sample_rate"]
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sample_size = model_config["sample_size"]
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = model.to(device)
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# Print model data type before conversion
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print("Model data type before conversion:", next(model.parameters()).dtype)
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# Convert model to float16 if model_half is True
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if model_half:
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model = model.to(torch.float16)
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print("Model data type:", next(model.parameters()).dtype)
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# Print model data type after conversion
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print("Model data type after conversion:", next(model.parameters()).dtype)
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return model, model_config, device
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# Define the function to generate audio based on a prompt
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def generate_audio(prompt, steps, cfg_scale, sigma_min, sigma_max, generation_time, seed, sampler_type, model_half, model, model_config, device):
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# Set up text and timing conditioning
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conditioning = [{
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"prompt": prompt,
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@@ -43,7 +37,7 @@ def generate_audio(prompt, steps, cfg_scale, sigma_min, sigma_max, generation_ti
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steps=steps,
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cfg_scale=cfg_scale,
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conditioning=conditioning,
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sample_size=sample_size,
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sample_size=model_config["sample_size"],
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sigma_min=sigma_min,
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sigma_max=sigma_max,
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sampler_type=sampler_type,
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@@ -51,9 +45,6 @@ def generate_audio(prompt, steps, cfg_scale, sigma_min, sigma_max, generation_ti
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seed=seed
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)
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# Print output data type
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print("Output data type:", output.dtype)
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# Rearrange audio batch to a single sequence
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output = rearrange(output, "b d n -> d (b n)")
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@@ -64,7 +55,7 @@ def generate_audio(prompt, steps, cfg_scale, sigma_min, sigma_max, generation_ti
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else:
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output = output.to(torch.float32).to(torch.int16).cpu()
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torchaudio.save("temp_output.wav", output, sample_rate)
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torchaudio.save("temp_output.wav", output, model_config["sample_rate"])
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# Convert to MP3 format using pydub
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audio = AudioSegment.from_wav("temp_output.wav")
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@@ -76,7 +67,7 @@ def generate_audio(prompt, steps, cfg_scale, sigma_min, sigma_max, generation_ti
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os.makedirs(save_path, exist_ok=True)
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# Generate a filename based on the prompt
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filename = re.sub(r'\W+', '_', prompt) + ".mp3" # Replace non-alphanumeric characters with underscores
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filename = re.sub(r'\W+', '_', prompt) + ".mp3"
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full_path = os.path.join(save_path, filename)
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# Ensure the filename is unique by appending a number if the file already exists
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@@ -105,43 +96,56 @@ def audio_generator(prompt, sampler_type, steps, cfg_scale, sigma_min, sigma_max
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print("Seed:", seed)
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print("Model Half Precision:", model_half)
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filename = generate_audio(prompt, steps, cfg_scale, sigma_min, sigma_max, generation_time, seed, sampler_type, model_half)
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# Set up the model and device
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model, model_config, device = setup_model(model_half)
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filename = generate_audio(prompt, steps, cfg_scale, sigma_min, sigma_max, generation_time, seed, sampler_type, model_half, model, model_config, device)
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return gr.Audio(filename), f"Generated: {filename}"
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except Exception as e:
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return str(e)
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# Create Gradio interface
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prompt_textbox = gr.Textbox(lines=5, label="Prompt")
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sampler_dropdown = gr.Dropdown(
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label="Sampler Type",
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choices=[
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"dpmpp-3m-sde",
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"dpmpp-2m-sde",
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"k-heun",
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"k-lms",
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"k-dpmpp-2s-ancestral",
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"k-dpm-2",
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"k-dpm-fast"
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],
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value="dpmpp-3m-sde"
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)
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steps_slider = gr.Slider(minimum=0, maximum=200, label="Steps", step=1, value=100)
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cfg_scale_slider = gr.Slider(minimum=0, maximum=15, label="CFG Scale", step=0.1, value=7)
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sigma_min_slider = gr.Slider(minimum=0, maximum=50, label="Sigma Min", step=0.1, value=0.3)
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sigma_max_slider = gr.Slider(minimum=0, maximum=1000, label="Sigma Max", step=0.1, value=500)
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generation_time_slider = gr.Slider(minimum=0, maximum=47, label="Generation Time (seconds)", step=1, value=47)
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seed_slider = gr.Slider(minimum=-1, maximum=999999, label="Seed", step=1, value=123456)
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model_half_checkbox = gr.Checkbox(label="Low VRAM (float16)", value=False)
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with gr.Blocks() as demo:
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gr.Markdown("<h1 style='text-align: center; font-size: 300%;'>💀🔊 StableAudioWebUI 💀🔊</h1>")
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gr.Markdown("<p style='text-align: center;'><a href='https://github.com/Saganaki22/StableAudioWebUI'>Github Repository</a></p>")
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output_textbox = gr.Textbox(label="Output")
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# Main input components
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prompt_textbox = gr.Textbox(lines=5, label="Prompt")
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sampler_dropdown = gr.Dropdown(
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label="Sampler Type",
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choices=[
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"dpmpp-3m-sde",
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"dpmpp-2m-sde",
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"k-heun",
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"k-lms",
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"k-dpmpp-2s-ancestral",
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"k-dpm-2",
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"k-dpm-fast"
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],
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value="dpmpp-3m-sde"
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)
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steps_slider = gr.Slider(minimum=0, maximum=200, label="Steps", step=1, value=100)
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generation_time_slider = gr.Slider(minimum=0, maximum=47, label="Generation Time (seconds)", step=1, value=47)
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seed_slider = gr.Slider(minimum=-1, maximum=999999, label="Seed", step=1, value=123456)
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title = "💀🔊 StableAudioWebUI 💀🔊"
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description = "[Github Repository](https://github.com/Saganaki22/StableAudioWebUI)"
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# Advanced parameters accordion
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with gr.Accordion("Advanced Parameters", open=False):
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cfg_scale_slider = gr.Slider(minimum=0, maximum=15, label="CFG Scale", step=0.1, value=7)
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sigma_min_slider = gr.Slider(minimum=0, maximum=50, label="Sigma Min", step=0.1, value=0.3)
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sigma_max_slider = gr.Slider(minimum=0, maximum=1000, label="Sigma Max", step=0.1, value=500)
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gr.Interface(
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audio_generator,
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[prompt_textbox, sampler_dropdown, steps_slider, cfg_scale_slider, sigma_min_slider, sigma_max_slider, generation_time_slider, seed_slider, model_half_checkbox],
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[gr.Audio(), output_textbox],
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title=title,
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description=description
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).launch()
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# Low VRAM checkbox and submit button
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model_half_checkbox = gr.Checkbox(label="Low VRAM (float16)", value=False)
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submit_button = gr.Button("Generate")
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# Define the output components
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audio_output = gr.Audio()
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output_textbox = gr.Textbox(label="Output")
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# Link the button and the function
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submit_button.click(audio_generator,
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inputs=[prompt_textbox, sampler_dropdown, steps_slider, cfg_scale_slider, sigma_min_slider, sigma_max_slider, generation_time_slider, seed_slider, model_half_checkbox],
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outputs=[audio_output, output_textbox])
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# Launch the Gradio demo
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demo.launch()
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