Update app.py

app.py

@@ -1,510 +1,35 @@
-import types
-import random
-import spaces
-import logging
 import os
-from pathlib import Path
-from datetime import datetime
-
-import torch
-import numpy as np
-import torchaudio
-from diffusers import AutoencoderKLWan, UniPCMultistepScheduler
-from diffusers.utils import export_to_video
-from diffusers import AutoModel
-import gradio as gr
-import tempfile
-from huggingface_hub import hf_hub_download
-
-from src.pipeline_wan_nag import NAGWanPipeline
-from src.transformer_wan_nag import NagWanTransformer3DModel
-
-# MMAudio imports
-try:
-    import mmaudio
-except ImportError:
-    os.system("pip install -e .")
-    import mmaudio
-
-from mmaudio.eval_utils import (ModelConfig, all_model_cfg, generate as mmaudio_generate, 
-                                load_video, make_video, setup_eval_logging)
-from mmaudio.model.flow_matching import FlowMatching
-from mmaudio.model.networks import MMAudio, get_my_mmaudio
-from mmaudio.model.sequence_config import SequenceConfig
-from mmaudio.model.utils.features_utils import FeaturesUtils
-
-# NAG Video Settings
-MOD_VALUE = 32
-DEFAULT_DURATION_SECONDS = 4
-DEFAULT_STEPS = 4
-DEFAULT_SEED = 2025
-DEFAULT_H_SLIDER_VALUE = 480
-DEFAULT_W_SLIDER_VALUE = 832
-NEW_FORMULA_MAX_AREA = 480.0 * 832.0
-
-SLIDER_MIN_H, SLIDER_MAX_H = 128, 896
-SLIDER_MIN_W, SLIDER_MAX_W = 128, 896
-MAX_SEED = np.iinfo(np.int32).max
-
-FIXED_FPS = 16
-MIN_FRAMES_MODEL = 8
-MAX_FRAMES_MODEL = 129
-
-DEFAULT_NAG_NEGATIVE_PROMPT = "Static, motionless, still, ugly, bad quality, worst quality, poorly drawn, low resolution, blurry, lack of details"
-DEFAULT_AUDIO_NEGATIVE_PROMPT = "music"
-
-# NAG Model Settings
-MODEL_ID = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
-SUB_MODEL_ID = "vrgamedevgirl84/Wan14BT2VFusioniX"
-SUB_MODEL_FILENAME = "Wan14BT2VFusioniX_fp16_.safetensors"
-LORA_REPO_ID = "Kijai/WanVideo_comfy"
-LORA_FILENAME = "Wan21_CausVid_14B_T2V_lora_rank32.safetensors"
-
-# MMAudio Settings
-torch.backends.cuda.matmul.allow_tf32 = True
-torch.backends.cudnn.allow_tf32 = True
-log = logging.getLogger()
-device = 'cuda'
-dtype = torch.bfloat16
-audio_model_config: ModelConfig = all_model_cfg['large_44k_v2']
-audio_model_config.download_if_needed()
-setup_eval_logging()
-
-# Initialize NAG Video Model
-vae = AutoencoderKLWan.from_pretrained(MODEL_ID, subfolder="vae", torch_dtype=torch.float32)
-wan_path = hf_hub_download(repo_id=SUB_MODEL_ID, filename=SUB_MODEL_FILENAME)
-transformer = NagWanTransformer3DModel.from_single_file(wan_path, torch_dtype=torch.bfloat16)
-pipe = NAGWanPipeline.from_pretrained(
-    MODEL_ID, vae=vae, transformer=transformer, torch_dtype=torch.bfloat16
-)
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=5.0)
-pipe.to("cuda")
-
-pipe.transformer.__class__.attn_processors = NagWanTransformer3DModel.attn_processors
-pipe.transformer.__class__.set_attn_processor = NagWanTransformer3DModel.set_attn_processor
-pipe.transformer.__class__.forward = NagWanTransformer3DModel.forward
-
-# Initialize MMAudio Model
-def get_mmaudio_model() -> tuple[MMAudio, FeaturesUtils, SequenceConfig]:
-    seq_cfg = audio_model_config.seq_cfg
-    
-    net: MMAudio = get_my_mmaudio(audio_model_config.model_name).to(device, dtype).eval()
-    net.load_weights(torch.load(audio_model_config.model_path, map_location=device, weights_only=True))
-    log.info(f'Loaded MMAudio weights from {audio_model_config.model_path}')
-    
-    feature_utils = FeaturesUtils(tod_vae_ckpt=audio_model_config.vae_path,
-                                  synchformer_ckpt=audio_model_config.synchformer_ckpt,
-                                  enable_conditions=True,
-                                  mode=audio_model_config.mode,
-                                  bigvgan_vocoder_ckpt=audio_model_config.bigvgan_16k_path,
-                                  need_vae_encoder=False)
-    feature_utils = feature_utils.to(device, dtype).eval()
-    
-    return net, feature_utils, seq_cfg
-
-audio_net, audio_feature_utils, audio_seq_cfg = get_mmaudio_model()
-
-# Audio generation function
-@torch.inference_mode()
-def add_audio_to_video(video_path, prompt, audio_negative_prompt, audio_steps, audio_cfg_strength, duration):
-    """Generate and add audio to video using MMAudio"""
-    rng = torch.Generator(device=device)
-    rng.seed()  # Random seed for audio
-    fm = FlowMatching(min_sigma=0, inference_mode='euler', num_steps=audio_steps)
-    
-    video_info = load_video(video_path, duration)
-    clip_frames = video_info.clip_frames
-    sync_frames = video_info.sync_frames
-    duration = video_info.duration_sec
-    clip_frames = clip_frames.unsqueeze(0)
-    sync_frames = sync_frames.unsqueeze(0)
-    audio_seq_cfg.duration = duration
-    audio_net.update_seq_lengths(audio_seq_cfg.latent_seq_len, audio_seq_cfg.clip_seq_len, audio_seq_cfg.sync_seq_len)
-    
-    audios = mmaudio_generate(clip_frames,
-                              sync_frames, [prompt],
-                              negative_text=[audio_negative_prompt],
-                              feature_utils=audio_feature_utils,
-                              net=audio_net,
-                              fm=fm,
-                              rng=rng,
-                              cfg_strength=audio_cfg_strength)
-    audio = audios.float().cpu()[0]
-    
-    # Create video with audio
-    video_with_audio_path = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4').name
-    make_video(video_info, video_with_audio_path, audio, sampling_rate=audio_seq_cfg.sampling_rate)
-    
-    return video_with_audio_path
-
-# Combined generation function
-def get_duration(prompt, nag_negative_prompt, nag_scale, height, width, duration_seconds, 
-                 steps, seed, randomize_seed, enable_audio, audio_negative_prompt, 
-                 audio_steps, audio_cfg_strength):
-    # Calculate total duration including audio processing if enabled
-    video_duration = int(duration_seconds) * int(steps) * 2.25 + 5
-    audio_duration = 30 if enable_audio else 0  # Additional time for audio processing
-    return video_duration + audio_duration
-
-@spaces.GPU(duration=get_duration)
-def generate_video_with_audio(
-        prompt,
-        nag_negative_prompt, nag_scale,
-        height=DEFAULT_H_SLIDER_VALUE, width=DEFAULT_W_SLIDER_VALUE, duration_seconds=DEFAULT_DURATION_SECONDS,
-        steps=DEFAULT_STEPS,
-        seed=DEFAULT_SEED, randomize_seed=False,
-        enable_audio=True, audio_negative_prompt=DEFAULT_AUDIO_NEGATIVE_PROMPT,
-        audio_steps=25, audio_cfg_strength=4.5,
-):
-    # Generate video first
-    target_h = max(MOD_VALUE, (int(height) // MOD_VALUE) * MOD_VALUE)
-    target_w = max(MOD_VALUE, (int(width) // MOD_VALUE) * MOD_VALUE)
-    
-    num_frames = np.clip(int(round(int(duration_seconds) * FIXED_FPS) + 1), MIN_FRAMES_MODEL, MAX_FRAMES_MODEL)
-    
-    current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
-    
-    with torch.inference_mode():
-        nag_output_frames_list = pipe(
-            prompt=prompt,
-            nag_negative_prompt=nag_negative_prompt,
-            nag_scale=nag_scale,
-            nag_tau=3.5,
-            nag_alpha=0.5,
-            height=target_h, width=target_w, num_frames=num_frames,
-            guidance_scale=0.,
-            num_inference_steps=int(steps),
-            generator=torch.Generator(device="cuda").manual_seed(current_seed)
-        ).frames[0]
-    
-    # Save initial video without audio
-    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
-        temp_video_path = tmpfile.name
-    export_to_video(nag_output_frames_list, temp_video_path, fps=FIXED_FPS)
-    
-    # Add audio if enabled
-    if enable_audio:
-        try:
-            final_video_path = add_audio_to_video(
-                temp_video_path, 
-                prompt,  # Use the same prompt for audio generation
-                audio_negative_prompt,
-                audio_steps,
-                audio_cfg_strength,
-                duration_seconds
-            )
-            # Clean up temp video
-            if os.path.exists(temp_video_path):
-                os.remove(temp_video_path)
-        except Exception as e:
-            log.error(f"Audio generation failed: {e}")
-            final_video_path = temp_video_path
-    else:
-        final_video_path = temp_video_path
-    
-    return final_video_path, current_seed
-
-# Example generation function
-def generate_with_example(prompt, nag_negative_prompt, nag_scale):
-    video_path, seed = generate_video_with_audio(
-        prompt=prompt,
-        nag_negative_prompt=nag_negative_prompt, nag_scale=nag_scale,
-        height=DEFAULT_H_SLIDER_VALUE, width=DEFAULT_W_SLIDER_VALUE, 
-        duration_seconds=DEFAULT_DURATION_SECONDS,
-        steps=DEFAULT_STEPS,
-        seed=DEFAULT_SEED, randomize_seed=False,
-        enable_audio=True, audio_negative_prompt=DEFAULT_AUDIO_NEGATIVE_PROMPT,
-        audio_steps=25, audio_cfg_strength=4.5,
-    )
-    return video_path, \
-        DEFAULT_H_SLIDER_VALUE, DEFAULT_W_SLIDER_VALUE, \
-        DEFAULT_DURATION_SECONDS, DEFAULT_STEPS, seed, \
-        True, DEFAULT_AUDIO_NEGATIVE_PROMPT, 25, 4.5
-
-# Examples with audio descriptions
-examples = [
-    ["Midnight highway outside a neon-lit city. A black 1973 Porsche 911 Carrera RS speeds at 120 km/h. Inside, a stylish singer-guitarist sings while driving, vintage sunburst guitar on the passenger seat. Sodium streetlights streak over the hood; RGB panels shift magenta to blue on the driver. Camera: drone dive, Russian-arm low wheel shot, interior gimbal, FPV barrel roll, overhead spiral. Neo-noir palette, rain-slick asphalt reflections, roaring flat-six engine blended with live guitar.", DEFAULT_NAG_NEGATIVE_PROMPT, 11],
-    ["Arena rock concert packed with 20 000 fans. A flamboyant lead guitarist in leather jacket and mirrored aviators shreds a cherry-red Flying V on a thrust stage. Pyro flames shoot up on every downbeat, CO₂ jets burst behind. Moving-head spotlights swirl teal and amber, follow-spots rim-light the guitarist’s hair. Steadicam 360-orbit, crane shot rising over crowd, ultra-slow-motion pick attack at 1 000 fps. Film-grain teal-orange grade, thunderous crowd roar mixes with screaming guitar solo.", DEFAULT_NAG_NEGATIVE_PROMPT, 11],
-    ["Golden-hour countryside road winding through rolling wheat fields. A man and woman ride a vintage café-racer motorcycle, hair and scarf fluttering in the warm breeze. Drone chase shot reveals endless patchwork farmland; low slider along rear wheel captures dust trail. Sun-flare back-lights the riders, lens blooms on highlights. Soft acoustic rock underscore; engine rumble mixed at –8 dB. Warm pastel color grade, gentle film-grain for nostalgic vibe.", DEFAULT_NAG_NEGATIVE_PROMPT, 11],
-]
-
-# CSS styling
-css = """
-.container {
-    max-width: 1400px;
-    margin: auto;
-    padding: 20px;
-}
-.main-title {
-    text-align: center;
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
-    font-size: 2.5em;
-    font-weight: bold;
-    margin-bottom: 10px;
-}
-.subtitle {
-    text-align: center;
-    color: #6b7280;
-    margin-bottom: 30px;
-}
-.prompt-container {
-    background: linear-gradient(135deg, #f3f4f6 0%, #e5e7eb 100%);
-    border-radius: 15px;
-    padding: 20px;
-    margin-bottom: 20px;
-    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-}
-.generate-btn {
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-    color: white;
-    font-size: 1.2em;
-    font-weight: bold;
-    padding: 15px 30px;
-    border-radius: 10px;
-    border: none;
-    cursor: pointer;
-    transition: all 0.3s ease;
-    width: 100%;
-    margin-top: 20px;
-}
-.generate-btn:hover {
-    transform: translateY(-2px);
-    box-shadow: 0 6px 20px rgba(102, 126, 234, 0.4);
-}
-.video-output {
-    border-radius: 15px;
-    overflow: hidden;
-    box-shadow: 0 10px 30px rgba(0, 0, 0, 0.2);
-    background: #1a1a1a;
-    padding: 10px;
-}
-.settings-panel {
-    background: #f9fafb;
-    border-radius: 15px;
-    padding: 20px;
-    box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05);
-}
-.slider-container {
-    background: white;
-    padding: 15px;
-    border-radius: 10px;
-    margin-bottom: 15px;
-    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
-}
-.info-box {
-    background: linear-gradient(135deg, #e0e7ff 0%, #c7d2fe 100%);
-    border-radius: 10px;
-    padding: 15px;
-    margin: 10px 0;
-    border-left: 4px solid #667eea;
-}
-.audio-settings {
-    background: linear-gradient(135deg, #fef3c7 0%, #fde68a 100%);
-    border-radius: 10px;
-    padding: 15px;
-    margin-top: 10px;
-    border-left: 4px solid #f59e0b;
-}
-"""
-
-# Gradio interface
-with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
-    with gr.Column(elem_classes="container"):
-        gr.HTML("""
-            <h1 class="main-title">🎬 VEO3 Free</h1>
-            <p class="subtitle">Wan2.1-T2V-14B + Fast 4-step with NAG + Automatic Audio Generation</p>
-        """)
+import sys
+import streamlit as st
+from tempfile import NamedTemporaryFile
+
+def main():
+    try:
+        # Get the code from secrets
+        code = os.environ.get("MAIN_CODE")
         
-        gr.HTML("""
-        <div class="badge-container"
-        style="display:flex; gap:8px; flex-wrap:wrap; justify-content:center; align-items:center;">
-
-            <a href="https://huggingface.co/spaces/ginigen/VEO3-Free" target="_blank">
-                <img src="https://img.shields.io/static/v1?label=Text%20to%20Video%2BAudio&message=VEO3%20free&color=%230000ff&labelColor=%23800080&logo=huggingface&logoColor=%23ffa500&style=for-the-badge" alt="badge">
-            </a>
-            <a href="https://huggingface.co/spaces/ginigen/VEO3-Free-mirror" target="_blank">
-                <img src="https://img.shields.io/static/v1?label=Text%20to%20Video%2BAudio&message=VEO3%20free%28mirror%29&color=%230000ff&labelColor=%23800080&logo=huggingface&logoColor=%23ffa500&style=for-the-badge" alt="badge">
-            </a>
-            <a href="https://huggingface.co/spaces/ginigen/VEO3-Directors" target="_blank">
-                <img src="https://img.shields.io/static/v1?label=DIRECTORS&message=VEO3&color=%23ffd700&labelColor=%23000080&logo=huggingface&logoColor=%23ffa500&style=for-the-badge" alt="badge">
-            </a>
-            <a href="https://discord.gg/openfreeai" target="_blank">
-                <img src="https://img.shields.io/static/v1?label=Discord&message=Openfree%20AI&color=%230000ff&labelColor=%23800080&logo=discord&logoColor=%23ffa500&style=for-the-badge" alt="badge">
-            </a>
-        </div>
-        """)
-        with gr.Row():
-            with gr.Column(scale=1):
-                with gr.Group(elem_classes="prompt-container"):
-                    prompt = gr.Textbox(
-                        label="✨ Video Prompt (also used for audio generation)",
-                        placeholder="Describe your video scene in detail...",
-                        lines=3,
-                        elem_classes="prompt-input"
-                    )
-                    
-                    with gr.Accordion("🎨 Advanced Video Settings", open=False):
-                        nag_negative_prompt = gr.Textbox(
-                            label="Video Negative Prompt",
-                            value=DEFAULT_NAG_NEGATIVE_PROMPT,
-                            lines=2,
-                        )
-                        nag_scale = gr.Slider(
-                            label="NAG Scale",
-                            minimum=1.0,
-                            maximum=20.0,
-                            step=0.25,
-                            value=11.0,
-                            info="Higher values = stronger guidance"
-                        )
-                
-                with gr.Group(elem_classes="settings-panel"):
-                    gr.Markdown("### ⚙️ Video Settings")
-                    
-                    with gr.Row():
-                        duration_seconds_input = gr.Slider(
-                            minimum=1,
-                            maximum=8,
-                            step=1,
-                            value=DEFAULT_DURATION_SECONDS,
-                            label="📱 Duration (seconds)",
-                            elem_classes="slider-container"
-                        )
-                        steps_slider = gr.Slider(
-                            minimum=1,
-                            maximum=8,
-                            step=1,
-                            value=DEFAULT_STEPS,
-                            label="🔄 Inference Steps",
-                            elem_classes="slider-container"
-                        )
-                    
-                    with gr.Row():
-                        height_input = gr.Slider(
-                            minimum=SLIDER_MIN_H,
-                            maximum=SLIDER_MAX_H,
-                            step=MOD_VALUE,
-                            value=DEFAULT_H_SLIDER_VALUE,
-                            label=f"📐 Height (×{MOD_VALUE})",
-                            elem_classes="slider-container"
-                        )
-                        width_input = gr.Slider(
-                            minimum=SLIDER_MIN_W,
-                            maximum=SLIDER_MAX_W,
-                            step=MOD_VALUE,
-                            value=DEFAULT_W_SLIDER_VALUE,
-                            label=f"📐 Width (×{MOD_VALUE})",
-                            elem_classes="slider-container"
-                        )
-                    
-                    with gr.Row():
-                        seed_input = gr.Slider(
-                            label="🌱 Seed",
-                            minimum=0,
-                            maximum=MAX_SEED,
-                            step=1,
-                            value=DEFAULT_SEED,
-                            interactive=True
-                        )
-                        randomize_seed_checkbox = gr.Checkbox(
-                            label="🎲 Random Seed",
-                            value=True,
-                            interactive=True
-                        )
-                
-                with gr.Group(elem_classes="audio-settings"):
-                    gr.Markdown("### 🎵 Audio Generation Settings")
-                    
-                    enable_audio = gr.Checkbox(
-                        label="🔊 Enable Automatic Audio Generation",
-                        value=True,
-                        interactive=True
-                    )
-                    
-                    with gr.Column(visible=True) as audio_settings_group:
-                        audio_negative_prompt = gr.Textbox(
-                            label="Audio Negative Prompt",
-                            value=DEFAULT_AUDIO_NEGATIVE_PROMPT,
-                            placeholder="Elements to avoid in audio (e.g., music, speech)",
-                        )
-                        
-                        with gr.Row():
-                            audio_steps = gr.Slider(
-                                minimum=10,
-                                maximum=50,
-                                step=5,
-                                value=25,
-                                label="🎚️ Audio Steps",
-                                info="More steps = better quality"
-                            )
-                            audio_cfg_strength = gr.Slider(
-                                minimum=1.0,
-                                maximum=10.0,
-                                step=0.5,
-                                value=4.5,
-                                label="🎛️ Audio Guidance",
-                                info="Strength of prompt guidance"
-                            )
-                    
-                    # Toggle audio settings visibility
-                    enable_audio.change(
-                        fn=lambda x: gr.update(visible=x),
-                        inputs=[enable_audio],
-                        outputs=[audio_settings_group]
-                    )
-                
-                generate_button = gr.Button(
-                    "🎬 Generate Video with Audio",
-                    variant="primary",
-                    elem_classes="generate-btn"
-                )
-            
-            with gr.Column(scale=1):
-                video_output = gr.Video(
-                    label="Generated Video with Audio",
-                    autoplay=True,
-                    interactive=False,
-                    elem_classes="video-output"
-                )
-                
-                gr.HTML("""
-                    <div style="text-align: center; margin-top: 20px; color: #6b7280;">
-                        <p>💡 Tip: The same prompt is used for both video and audio generation!</p>
-                        <p>🎧 Audio is automatically matched to the visual content</p>
-                    </div>
-                """)
+        if not code:
+            st.error("⚠️ The application code wasn't found in secrets. Please add the MAIN_CODE secret.")
+            return
+        
+        # Create a temporary Python file
+        with NamedTemporaryFile(suffix='.py', delete=False, mode='w') as tmp:
+            tmp.write(code)
+            tmp_path = tmp.name
         
-        gr.Markdown("### 🎯 Example Prompts")
-        gr.Examples(
-            examples=examples,
-            fn=generate_with_example,
-            inputs=[prompt, nag_negative_prompt, nag_scale],
-            outputs=[
-                video_output,
-                height_input, width_input, duration_seconds_input,
-                steps_slider, seed_input,
-                enable_audio, audio_negative_prompt, audio_steps, audio_cfg_strength
-            ],
-            cache_examples="lazy"
-        )
-    
-    # Connect UI elements
-    ui_inputs = [
-        prompt,
-        nag_negative_prompt, nag_scale,
-        height_input, width_input, duration_seconds_input,
-        steps_slider,
-        seed_input, randomize_seed_checkbox,
-        enable_audio, audio_negative_prompt, audio_steps, audio_cfg_strength,
-    ]
-    
-    generate_button.click(
-        fn=generate_video_with_audio,
-        inputs=ui_inputs,
-        outputs=[video_output, seed_input],
-    )
+        # Execute the code
+        exec(compile(code, tmp_path, 'exec'), globals())
+        
+        # Clean up the temporary file
+        try:
+            os.unlink(tmp_path)
+        except:
+            pass
+            
+    except Exception as e:
+        st.error(f"⚠️ Error loading or executing the application: {str(e)}")
+        import traceback
+        st.code(traceback.format_exc())
 
 if __name__ == "__main__":
-    demo.queue().launch()
+    main()