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| import gradio as gr | |
| from typing import Optional, List, Dict, Any | |
| import numpy as np | |
| import time | |
| import sys | |
| from pathlib import Path | |
| # CRITICAL FIX: Ensure local modules are prioritized over system packages | |
| # This prevents ImportError when a 'utils' package exists in site-packages | |
| current_dir = Path(__file__).resolve().parent | |
| if str(current_dir) not in sys.path: | |
| sys.path.insert(0, str(current_dir)) | |
| # Clear any cached utils module to force reimport from local file | |
| if 'utils' in sys.modules: | |
| del sys.modules['utils'] | |
| # Now import from the local utils.py file | |
| from utils import WANVideoGenerator, LoRAManager, NSFWChecker | |
| from config import MODEL_CONFIGS, AVAILABLE_LORAS, NSFW_CONFIG | |
| # Initialize core components | |
| generator = WANVideoGenerator() | |
| lora_manager = LoRAManager() | |
| nsfw_checker = NSFWChecker() | |
| def generate_video( | |
| image: np.ndarray, | |
| prompt: str, | |
| selected_model: str, | |
| enabled_loras: List[str], | |
| enable_nsfw: bool, | |
| video_length: int, | |
| resolution: str, | |
| progress=gr.Progress() | |
| ) -> tuple[str, str, Dict[str, Any]]: | |
| """ | |
| Main video generation function with WAN-scale processing | |
| Args: | |
| image: Input image as numpy array | |
| prompt: Optional text prompt for video generation | |
| selected_model: Selected WAN model variant | |
| enabled_loras: List of active LoRA adapters | |
| enable_nsfw: Whether to allow NSFW content generation | |
| video_length: Target video length in frames | |
| resolution: Output resolution preset | |
| progress: Gradio progress tracker | |
| Returns: | |
| Tuple of (video_path, status_message, generation_metadata) | |
| """ | |
| try: | |
| # Step 1: Validate inputs | |
| progress(0.1, desc="🔍 Validating inputs...") | |
| if image is None: | |
| raise gr.Error("No image provided. Please upload an image to generate video.") | |
| # Step 2: NSFW check if enabled | |
| if enable_nsfw and NSFW_CONFIG["require_confirmation"]: | |
| progress(0.15, desc="⚠️ NSFW mode active - bypassing standard filters") | |
| elif not enable_nsfw: | |
| progress(0.15, desc="🛡️ Running safety checks...") | |
| if nsfw_checker.check_image(image): | |
| raise gr.Error("Input image flagged by safety filter. Enable NSFW mode to bypass.") | |
| # Step 3: Load selected model and LoRAs | |
| progress(0.2, desc=f"📦 Loading {selected_model} model...") | |
| generator.load_model(selected_model) | |
| progress(0.3, desc=f"🔌 Activating {len(enabled_loras)} LoRA adapters...") | |
| active_loras = lora_manager.load_loras(enabled_loras) | |
| # Step 4: Generate video frames | |
| progress(0.4, desc="🎬 Generating video frames...") | |
| frames = [] | |
| for i in range(video_length): | |
| progress(0.4 + (i / video_length) * 0.5, | |
| desc=f"Rendering frame {i+1}/{video_length}...") | |
| frame = generator.generate_frame( | |
| image=image, | |
| prompt=prompt, | |
| frame_index=i, | |
| total_frames=video_length, | |
| active_loras=active_loras | |
| ) | |
| frames.append(frame) | |
| time.sleep(0.1) # Simulate processing time | |
| # Step 5: Compile video | |
| progress(0.95, desc="🎥 Compiling final video...") | |
| output_path = generator.compile_video( | |
| frames=frames, | |
| resolution=resolution, | |
| fps=30 | |
| ) | |
| # Step 6: Prepare metadata | |
| metadata = { | |
| "model": selected_model, | |
| "loras": enabled_loras, | |
| "nsfw_mode": enable_nsfw, | |
| "resolution": resolution, | |
| "frames": video_length, | |
| "prompt": prompt or "No prompt provided", | |
| "status": "✅ Generation complete" | |
| } | |
| progress(1.0, desc="✅ Done!") | |
| return output_path, "Video generated successfully!", metadata | |
| except Exception as e: | |
| raise gr.Error(f"Generation failed: {str(e)}") | |
| def update_lora_visibility(enable_nsfw: bool) -> Dict[str, Any]: | |
| """Update LoRA options based on NSFW mode""" | |
| if enable_nsfw: | |
| return gr.Dropdown( | |
| choices=list(AVAILABLE_LORAS.keys()), | |
| value=[], | |
| multiselect=True, | |
| label="🎨 Active LoRA Adapters (NSFW options unlocked)" | |
| ) | |
| else: | |
| safe_loras = {k: v for k, v in AVAILABLE_LORAS.items() if not v.get("nsfw", False)} | |
| return gr.Dropdown( | |
| choices=list(safe_loras.keys()), | |
| value=[], | |
| multiselect=True, | |
| label="🎨 Active LoRA Adapters (Safe mode)" | |
| ) | |
| def create_interface(): | |
| """Create the main Gradio interface""" | |
| with gr.Blocks() as demo: | |
| gr.HTML(""" | |
| <div style='text-align: center; padding: 20px;'> | |
| <h1>🥊 WAN-Scale Image-to-Video Architecture 🥊</h1> | |
| <p>Built with anycoder - <a href='https://huggingface.co/spaces/akhaliq/anycoder' target='_blank'>View on Hugging Face</a></p> | |
| <p style='font-size: 1.2em; color: #666;'>Turn static images into dynamic videos with WAN foundation models</p> | |
| </div> | |
| """) | |
| # Global state | |
| generation_state = gr.State({"session_id": None}) | |
| with gr.Row(): | |
| # Sidebar for controls | |
| with gr.Sidebar(position="left", width=320): | |
| gr.Markdown("### ⚙️ Generation Settings") | |
| model_selector = gr.Dropdown( | |
| choices=list(MODEL_CONFIGS.keys()), | |
| value="wan-2.1-14b", | |
| label="🤖 WAN Model", | |
| info="Select foundation model variant" | |
| ) | |
| nsfw_toggle = gr.Checkbox( | |
| value=False, | |
| label="🔞 Enable NSFW Content", | |
| info="Bypass safety filters (requires confirmation)" | |
| ) | |
| lora_selector = gr.Dropdown( | |
| choices=[k for k, v in AVAILABLE_LORAS.items() if not v.get("nsfw", False)], | |
| value=[], | |
| multiselect=True, | |
| label="🎨 Active LoRA Adapters", | |
| info="Select style and domain adapters" | |
| ) | |
| with gr.Accordion("📐 Video Settings", open=False): | |
| video_length = gr.Slider( | |
| minimum=16, | |
| maximum=128, | |
| value=32, | |
| step=8, | |
| label="Video Length (frames)" | |
| ) | |
| resolution = gr.Radio( | |
| choices=["512x512", "768x768", "1024x576", "1920x1080"], | |
| value="768x768", | |
| label="Resolution" | |
| ) | |
| with gr.Accordion("🚀 Advanced Options", open=False): | |
| inference_steps = gr.Slider( | |
| minimum=10, | |
| maximum=100, | |
| value=50, | |
| label="Inference Steps" | |
| ) | |
| cfg_scale = gr.Slider( | |
| minimum=1.0, | |
| maximum=20.0, | |
| value=7.5, | |
| step=0.5, | |
| label="CFG Scale" | |
| ) | |
| # Status indicators | |
| model_status = gr.Label( | |
| value={"Status": "Ready", "VRAM": "24GB Available"}, | |
| label="System Status" | |
| ) | |
| # Main content area | |
| with gr.Column(): | |
| gr.Markdown("### 📤 Input Image") | |
| input_image = gr.Image( | |
| label="Upload Starting Frame", | |
| type="numpy", | |
| height=400, | |
| sources=["upload", "webcam", "clipboard"] | |
| ) | |
| gr.Markdown("### 📝 Optional Text Prompt") | |
| prompt_box = gr.Textbox( | |
| placeholder="Describe the motion, style, or scene...", | |
| label="Prompt (optional)", | |
| lines=2, | |
| max_lines=4 | |
| ) | |
| with gr.Row(): | |
| generate_btn = gr.Button( | |
| "🎬 Generate Video", | |
| variant="primary", | |
| scale=2 | |
| ) | |
| clear_btn = gr.ClearButton( | |
| components=[input_image, prompt_box], | |
| value="🗑️ Clear" | |
| ) | |
| # Progress tracking | |
| progress_bar = gr.Progress() | |
| status_text = gr.Textbox( | |
| label="Status", | |
| interactive=False, | |
| show_copy_button=True | |
| ) | |
| gr.Markdown("### 📼 Output Video") | |
| output_video = gr.Video( | |
| label="Generated Video", | |
| height=400, | |
| autoplay=True, | |
| show_download_button=True | |
| ) | |
| # Generation metadata | |
| with gr.Accordion("📊 Generation Details", open=False): | |
| metadata_json = gr.JSON( | |
| label="Metadata", | |
| open=False | |
| ) | |
| # Event handlers | |
| nsfw_toggle.change( | |
| fn=update_lora_visibility, | |
| inputs=nsfw_toggle, | |
| outputs=lora_selector, | |
| api_visibility="private" | |
| ) | |
| generate_btn.click( | |
| fn=generate_video, | |
| inputs=[ | |
| input_image, | |
| prompt_box, | |
| model_selector, | |
| lora_selector, | |
| nsfw_toggle, | |
| video_length, | |
| resolution | |
| ], | |
| outputs=[ | |
| output_video, | |
| status_text, | |
| metadata_json | |
| ], | |
| api_visibility="public", | |
| concurrency_limit=2 # Limit concurrent generations | |
| ) | |
| # Update model status on selection | |
| model_selector.change( | |
| fn=lambda x: {"Status": f"Loaded {x}", "VRAM": "24GB Used"}, | |
| inputs=model_selector, | |
| outputs=model_status, | |
| api_visibility="private" | |
| ) | |
| # Demo load event | |
| demo.load( | |
| fn=lambda: "System initialized and ready", | |
| outputs=status_text, | |
| api_visibility="private" | |
| ) | |
| return demo | |
| # Create and launch the application | |
| if __name__ == "__main__": | |
| demo = create_interface() | |
| demo.launch( | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| share=False, | |
| debug=False, | |
| show_error=True, | |
| max_threads=4, | |
| theme=gr.themes.Soft( | |
| primary_hue="purple", | |
| secondary_hue="indigo", | |
| neutral_hue="slate", | |
| font=gr.themes.GoogleFont("Inter"), | |
| text_size="lg", | |
| spacing_size="lg", | |
| radius_size="md" | |
| ).set( | |
| button_primary_background_fill="*primary_600", | |
| button_primary_background_fill_hover="*primary_700", | |
| block_title_text_weight="600", | |
| block_background_fill="*neutral_50" | |
| ), | |
| footer_links=[ | |
| {"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"}, | |
| {"label": "Model Docs", "url": "https://huggingface.co/docs"}, | |
| {"label": "API Reference", "url": "/docs"} | |
| ], | |
| css=""" | |
| .gradio-container { max-width: 1400px; margin: auto; } | |
| .contain { display: flex; flex-direction: column; height: 100vh; } | |
| #component-0 { height: 100%; } | |
| .gr-button { font-weight: 600; } | |
| .gr-markdown { text-align: center; } | |
| """ | |
| ) |