Update app.py

app.py

@@ -29,40 +29,7 @@ current_model_name = None
 MODELS_DIR = os.path.join(os.path.dirname(__file__), "models")
 GENERATIONS_DIR = os.path.join(os.path.dirname(__file__), "generations")
 
-def prepare(t5, clip, img, prompt):
-    bs, c, h, w = img.shape
-    if bs == 1 and not isinstance(prompt, str):
-        bs = len(prompt)
-
-    img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
-    if img.shape[0] == 1 and bs > 1:
-        img = repeat(img, "1 ... -> bs ...", bs=bs)
-
-    img_ids = torch.zeros(h // 2, w // 2, 3)
-    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
-    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :]
-    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
-
-    if isinstance(prompt, str):
-        prompt = [prompt]
-    
-    # Generate text embeddings
-    txt = t5(prompt)
-    
-    if txt.shape[0] == 1 and bs > 1:
-        txt = repeat(txt, "1 ... -> bs ...", bs=bs)
-    txt_ids = torch.zeros(bs, txt.shape[1], 3)
-
-    vec = clip(prompt)
-    if vec.shape[0] == 1 and bs > 1:
-        vec = repeat(vec, "1 ... -> bs ...", bs=bs)
-
-    return img, {
-        "img_ids": img_ids.to(img.device),
-        "txt": txt.to(img.device),
-        "txt_ids": txt_ids.to(img.device),
-        "y": vec.to(img.device),
-    }
+# ... (keep other functions unchanged)
 
 def unload_current_model():
     global global_model, current_model_name
@@ -90,6 +57,9 @@ def load_model(model_name, device, model_url=None):
     else:
         model_path = os.path.join(MODELS_DIR, model_name)
     
+    if not os.path.exists(model_path):
+        return f"Model file not found: {model_path}"
+    
     # Determine model size from filename
     if 'musicflow_b' in model_name:
         model_size = "base"
@@ -104,9 +74,8 @@ def load_model(model_name, device, model_url=None):
     
     print(f"Loading {model_size} model: {model_name}")
     
-    global_model = build_model(model_size).to(device)
-    
     try:
+        global_model = build_model(model_size).to(device)
         state_dict = torch.load(model_path, map_location=device, weights_only=True)
         global_model.load_state_dict(state_dict['ema'], strict=False)
         global_model.eval()
@@ -115,178 +84,16 @@ def load_model(model_name, device, model_url=None):
         current_model_name = model_name
         return f"Successfully loaded model: {model_name}"
     except Exception as e:
+        global_model = None
+        current_model_name = None
         print(f"Error loading model {model_name}: {str(e)}")
         return f"Failed to load model: {model_name}. Error: {str(e)}"
 
-def load_resources(device):
-    global global_t5, global_clap, global_vae, global_vocoder, global_diffusion
-    
-    print("Loading T5 and CLAP models...")
-    global_t5 = load_t5(device, max_length=256)
-    global_clap = load_clap(device, max_length=256)
-    
-    print("Loading VAE and vocoder...")
-    global_vae = AutoencoderKL.from_pretrained('cvssp/audioldm2', subfolder="vae").to(device)
-    global_vocoder = SpeechT5HifiGan.from_pretrained('cvssp/audioldm2', subfolder="vocoder").to(device)
-    
-    print("Initializing diffusion...")
-    global_diffusion = RF()
-    
-    print("Base resources loaded successfully!")
-
-def generate_music(prompt, seed, cfg_scale, steps, duration, device, batch_size=4, progress=gr.Progress()):
-    global global_model, global_t5, global_clap, global_vae, global_vocoder, global_diffusion
-    
-    if global_model is None:
-        return "Please select and load a model first.", None
-    
-    if seed == 0:
-        seed = random.randint(1, 1000000)
-    print(f"Using seed: {seed}")
-    
-    torch.manual_seed(seed)
-    torch.set_grad_enabled(False)
-
-    # Calculate the number of segments needed for the desired duration
-    segment_duration = 10  # Each segment is 10 seconds
-    num_segments = int(np.ceil(duration / segment_duration))
-
-    all_waveforms = []
-
-    for i in range(num_segments):
-        progress(i / num_segments, desc=f"Generating segment {i+1}/{num_segments}")
-
-        # Use the same seed for all segments
-        torch.manual_seed(seed + i)  # Add i to slightly vary each segment while maintaining consistency
-
-        latent_size = (256, 16)
-        conds_txt = [prompt]
-        unconds_txt = ["low quality, gentle"]
-        L = len(conds_txt)
-
-        init_noise = torch.randn(L, 8, latent_size[0], latent_size[1]).to(device)
-
-        img, conds = prepare(global_t5, global_clap, init_noise, conds_txt)
-        _, unconds = prepare(global_t5, global_clap, init_noise, unconds_txt)
-
-        # Implement batching for CPU inference
-        images = []
-        for batch_start in range(0, img.shape[0], batch_size):
-            batch_end = min(batch_start + batch_size, img.shape[0])
-            batch_img = img[batch_start:batch_end]
-            batch_conds = {k: v[batch_start:batch_end] for k, v in conds.items()}
-            batch_unconds = {k: v[batch_start:batch_end] for k, v in unconds.items()}
-            
-            with torch.no_grad():
-                batch_images = global_diffusion.sample_with_xps(
-                    global_model, batch_img, conds=batch_conds, null_cond=batch_unconds, 
-                    sample_steps=steps, cfg=cfg_scale
-                )
-            images.append(batch_images[-1])
-        
-        images = torch.cat(images, dim=0)
-
-        images = rearrange(
-            images,
-            "b (h w) (c ph pw) -> b c (h ph) (w pw)",
-            h=128,
-            w=8,
-            ph=2,
-            pw=2,)
-
-        latents = 1 / global_vae.config.scaling_factor * images
-        mel_spectrogram = global_vae.decode(latents).sample
-
-        x_i = mel_spectrogram[0]
-        if x_i.dim() == 4:
-            x_i = x_i.squeeze(1)
-        waveform = global_vocoder(x_i)
-        waveform = waveform[0].cpu().float().detach().numpy()
-
-        all_waveforms.append(waveform)
-
-    # Concatenate all waveforms
-    final_waveform = np.concatenate(all_waveforms)
-
-    # Trim to exact duration
-    sample_rate = 16000
-    final_waveform = final_waveform[:int(duration * sample_rate)]
-
-    progress(0.9, desc="Saving audio file")
-    
-    # Create 'generations' folder
-    os.makedirs(GENERATIONS_DIR, exist_ok=True)
-
-    # Generate filename
-    prompt_part = re.sub(r'[^\w\s-]', '', prompt)[:10].strip().replace(' ', '_')
-    model_name = os.path.splitext(os.path.basename(global_model.model_path))[0]
-    model_suffix = '_mf_b' if model_name == 'musicflow_b' else f'_{model_name}'
-    base_filename = f"{prompt_part}_{seed}{model_suffix}"
-    output_path = os.path.join(GENERATIONS_DIR, f"{base_filename}.wav")
-    
-    # Check if file exists and add numerical suffix if needed
-    counter = 1
-    while os.path.exists(output_path):
-        output_path = os.path.join(GENERATIONS_DIR, f"{base_filename}_{counter}.wav")
-        counter += 1
-
-    wavfile.write(output_path, sample_rate, final_waveform)
-
-    progress(1.0, desc="Audio generation complete")
-    return f"Generated with seed: {seed}", output_path
-
-# Get list of .pt files in the models directory
-model_files = glob.glob(os.path.join(MODELS_DIR, "*.pt"))
-model_choices = [os.path.basename(f) for f in model_files]
-
-# Ensure we have at least one model
-if not model_choices:
-    print(f"No models found in the models directory: {MODELS_DIR}")
-    print("Available files in the directory:")
-    print(os.listdir(MODELS_DIR))
-    model_choices = ["No models available"]
-
-# Set default model
-default_model = 'musicflow_b.pt' if 'musicflow_b.pt' in model_choices else model_choices[0]
-
-# Set up dark grey theme
-theme = gr.themes.Monochrome(
-    primary_hue="gray",
-    secondary_hue="gray",
-    neutral_hue="gray",
-    radius_size=gr.themes.sizes.radius_sm,
-)
+# ... (keep the rest of the file unchanged)
 
 # Gradio Interface
 with gr.Blocks(theme=theme) as iface:
-    gr.Markdown(
-        """
-        <div style="text-align: center;">
-            <h1>FluxMusic Generator</h1>
-            <p>Generate music based on text prompts using FluxMusic model.</p>
-            <p>Feel free to clone this space and run on GPU locally or on Hugging Face.</p>
-        </div>
-        """)
-    
-    with gr.Row():
-        model_dropdown = gr.Dropdown(choices=model_choices, label="Select Model", value=default_model)
-        model_url = gr.Textbox(label="Or enter model URL")
-        device_choice = gr.Radio(["cpu", "cuda"], label="Device", value="cpu")
-        load_model_button = gr.Button("Load Model")
-        model_status = gr.Textbox(label="Model Status", value="No model loaded")
-    
-    with gr.Row():
-        prompt = gr.Textbox(label="Prompt")
-        seed = gr.Number(label="Seed", value=0)
-    
-    with gr.Row():
-        cfg_scale = gr.Slider(minimum=1, maximum=40, step=0.1, label="CFG Scale", value=20)
-        steps = gr.Slider(minimum=10, maximum=200, step=1, label="Steps", value=100)
-        duration = gr.Number(label="Duration (seconds)", value=10, minimum=10, maximum=300, step=1)
-    
-    generate_button = gr.Button("Generate Music")
-    output_status = gr.Textbox(label="Generation Status")
-    output_audio = gr.Audio(type="filepath")
+    # ... (keep the interface definition unchanged)
 
     def on_load_model_click(model_name, device, url):
         if url: