Update app.py

app.py

@@ -12,7 +12,143 @@ import re
 import requests
 import time
 
-# ... (keep the imports and global variables)
+# Import necessary functions and classes
+from utils import load_t5, load_clap
+from train import RF
+from constants import build_model
+
+# Global variables to store loaded models and resources
+global_model = None
+global_t5 = None
+global_clap = None
+global_vae = None
+global_vocoder = None
+global_diffusion = None
+current_model_name = None
+
+# Set the models directory
+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),
+    }
+
+def unload_current_model():
+    global global_model, current_model_name
+    if global_model is not None:
+        del global_model
+        torch.cuda.empty_cache()
+        global_model = None
+    current_model_name = None
+
+def load_model(model_name, device, model_url=None):
+    global global_model, current_model_name
+    
+    unload_current_model()
+    
+    if model_url:
+        print(f"Downloading model from URL: {model_url}")
+        response = requests.get(model_url)
+        if response.status_code == 200:
+            model_path = os.path.join(MODELS_DIR, "downloaded_model.pt")
+            with open(model_path, 'wb') as f:
+                f.write(response.content)
+            model_name = "downloaded_model.pt"
+        else:
+            return f"Failed to download model from URL: {model_url}"
+    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"
+    elif 'musicflow_g' in model_name:
+        model_size = "giant"
+    elif 'musicflow_l' in model_name:
+        model_size = "large"
+    elif 'musicflow_s' in model_name:
+        model_size = "small"
+    else:
+        model_size = "base"  # Default to base if unrecognized
+    
+    print(f"Loading {model_size} model: {model_name}")
+    
+    try:
+        start_time = time.time()
+        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()
+        
+        global_model.model_path = model_path
+        current_model_name = model_name
+        end_time = time.time()
+        load_time = end_time - start_time
+        return f"Successfully loaded model: {model_name} in {load_time:.2f} seconds"
+    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
+    
+    try:
+        start_time = time.time()
+        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()
+        
+        end_time = time.time()
+        load_time = end_time - start_time
+        print(f"Base resources loaded successfully in {load_time:.2f} seconds!")
+        return f"Resources loaded successfully in {load_time:.2f} seconds!"
+    except Exception as e:
+        print(f"Error loading resources: {str(e)}")
+        return f"Failed to load resources. Error: {str(e)}"
 
 def generate_music(prompt, seed, cfg_scale, steps, duration, device, batch_size=1, progress=gr.Progress()):
     global global_model, global_t5, global_clap, global_vae, global_vocoder, global_diffusion
@@ -93,13 +229,88 @@ def generate_music(prompt, seed, cfg_scale, steps, duration, device, batch_size=
 
         all_waveforms.append(waveform)
 
-    # ... (keep the rest of the function unchanged)
+    # 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
 
-# ... (keep the rest of the file unchanged)
+    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,
+)
 
 # Gradio Interface
 with gr.Blocks(theme=theme) as iface:
-    # ... (keep the interface definition unchanged)
+    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")
 
     def on_load_model_click(model_name, device, url):
         # Ensure we're using CPU if CUDA is not available