Update app.py

app.py

@@ -6,116 +6,181 @@ import numpy as np
 import tempfile
 from typing import Optional, Tuple
 import time
-import subprocess
-import sys
 
 # ZeroGPU import
 try:
     import spaces
     SPACES_AVAILABLE = True
-    print("✅ Spaces library loaded successfully")
 except ImportError:
-    print("⚠️ Spaces library not available")
     SPACES_AVAILABLE = False
-    # Create dummy decorator
-    def spaces_gpu_decorator(duration=60):
-        def decorator(func):
-            return func
-        return decorator
-    spaces = type('spaces', (), {'GPU': spaces_gpu_decorator})()
+    class spaces:
+        @staticmethod
+        def GPU(duration=60):
+            def decorator(func):
+                return func
+            return decorator
 
-# Environment checks
 IS_ZERO_GPU = os.environ.get("SPACES_ZERO_GPU") == "true"
 IS_SPACES = os.environ.get("SPACE_ID") is not None
 
-print(f"Environment: ZeroGPU={IS_ZERO_GPU}, Spaces={IS_SPACES}")
-
-def check_and_install_requirements():
-    """Check and install missing requirements"""
+def load_ltx_model_manual():
+    """Manually load LTX-Video model using transformers"""
     try:
-        import diffusers
-        print(f"✅ Diffusers version: {diffusers.__version__}")
-        return True
-    except ImportError:
-        print("❌ Diffusers not found, attempting to install...")
+        print("🔄 Attempting to load LTX-Video with transformers...")
+        
+        from transformers import AutoModel, AutoTokenizer, AutoProcessor
+        
+        model_id = "Lightricks/LTX-Video"
+        
+        # Try loading with AutoModel
         try:
-            subprocess.check_call([sys.executable, "-m", "pip", "install", "diffusers[torch]>=0.30.0"])
-            subprocess.check_call([sys.executable, "-m", "pip", "install", "transformers>=4.35.0"])
-            subprocess.check_call([sys.executable, "-m", "pip", "install", "accelerate"])
-            import diffusers
-            print(f"✅ Diffusers installed successfully: {diffusers.__version__}")
-            return True
+            processor = AutoProcessor.from_pretrained(model_id)
+            model = AutoModel.from_pretrained(
+                model_id,
+                torch_dtype=torch.float16,
+                low_cpu_mem_usage=True,
+                trust_remote_code=True  # Important for new models
+            )
+            
+            if torch.cuda.is_available():
+                model = model.to("cuda")
+            
+            print("✅ Model loaded with transformers")
+            return model, processor, None
+            
         except Exception as e:
-            print(f"❌ Failed to install diffusers: {e}")
-            return False
+            print(f"AutoModel failed: {e}")
+            return None, None, str(e)
+            
+    except Exception as e:
+        return None, None, f"Manual loading failed: {e}"
 
-def load_model_safe():
-    """Safely load the LTX-Video model with comprehensive error handling"""
-    
-    # First, ensure requirements are installed
-    if not check_and_install_requirements():
-        return None, "Failed to install required packages"
-    
+def load_alternative_video_model():
+    """Load a working alternative video generation model"""
     try:
-        print("🔄 Attempting to load LTX-Video model...")
+        print("🔄 Loading alternative video model...")
         
-        # Import after installation
-        from diffusers import LTXVideoPipeline
-        import torch
+        from diffusers import DiffusionPipeline
         
-        model_id = "Lightricks/LTX-Video"
+        # Use Zeroscope or ModelScope as alternatives
+        alternatives = [
+            "cerspense/zeroscope_v2_576w",
+            "damo-vilab/text-to-video-ms-1.7b",
+            "ali-vilab/text-to-video-ms-1.7b"
+        ]
         
-        # Check available memory
-        if torch.cuda.is_available():
-            gpu_memory = torch.cuda.get_device_properties(0).total_memory / (1024**3)
-            print(f"📊 Available GPU memory: {gpu_memory:.1f} GB")
+        for model_id in alternatives:
+            try:
+                print(f"Trying {model_id}...")
+                pipe = DiffusionPipeline.from_pretrained(
+                    model_id,
+                    torch_dtype=torch.float16,
+                    use_safetensors=True,
+                    variant="fp16"
+                )
+                
+                if torch.cuda.is_available():
+                    pipe = pipe.to("cuda")
+                
+                # Enable optimizations
+                pipe.enable_sequential_cpu_offload()
+                pipe.enable_vae_slicing()
+                
+                print(f"✅ Successfully loaded {model_id}")
+                return pipe, model_id, None
+                
+            except Exception as e:
+                print(f"Failed to load {model_id}: {e}")
+                continue
         
-        # Load with conservative settings
-        print("📥 Loading pipeline...")
-        pipe = LTXVideoPipeline.from_pretrained(
-            model_id,
-            torch_dtype=torch.bfloat16,
-            use_safetensors=True,
-            variant="fp16"
-        )
+        return None, None, "All alternative models failed"
         
-        # Move to GPU if available
-        if torch.cuda.is_available():
-            pipe = pipe.to("cuda")
-            print("🚀 Model moved to GPU")
+    except Exception as e:
+        return None, None, f"Alternative loading failed: {e}"
+
+def create_mock_video(prompt, num_frames=16, width=512, height=512):
+    """Create a mock video for demonstration"""
+    try:
+        import cv2
+        from PIL import Image, ImageDraw, ImageFont
         
-        # Enable optimizations
-        try:
-            pipe.enable_vae_slicing()
-            pipe.enable_vae_tiling()
-            print("⚡ Memory optimizations enabled")
-        except Exception as e:
-            print(f"⚠️ Some optimizations failed: {e}")
+        # Create temporary video file
+        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp_file:
+            video_path = tmp_file.name
         
-        print("✅ Model loaded successfully!")
-        return pipe, None
+        # Video settings
+        fps = 8
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        out = cv2.VideoWriter(video_path, fourcc, fps, (width, height))
+        
+        # Color themes
+        colors = [(255, 100, 100), (100, 255, 100), (100, 100, 255), (255, 255, 100)]
+        
+        for i in range(num_frames):
+            # Create frame
+            img = Image.new('RGB', (width, height), color=colors[i % len(colors)])
+            draw = ImageDraw.Draw(img)
+            
+            try:
+                font = ImageFont.truetype("arial.ttf", 24)
+            except:
+                font = ImageFont.load_default()
+            
+            # Add text
+            draw.text((50, height//2 - 50), f"Frame {i+1}/{num_frames}", fill=(255, 255, 255), font=font)
+            draw.text((50, height//2), f"Prompt: {prompt[:30]}...", fill=(255, 255, 255), font=font)
+            draw.text((50, height//2 + 50), "DEMO MODE", fill=(0, 0, 0), font=font)
+            
+            # Convert to OpenCV format
+            frame = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
+            out.write(frame)
         
-    except ImportError as e:
-        error_msg = f"Import error: {e}. Please check if diffusers is properly installed."
-        print(f"❌ {error_msg}")
-        return None, error_msg
+        out.release()
+        return video_path
         
     except Exception as e:
-        error_msg = f"Model loading failed: {str(e)}"
-        print(f"❌ {error_msg}")
-        return None, error_msg
+        return None
 
-# Global model variable
+# Global variables
 MODEL = None
+PROCESSOR = None
+MODEL_TYPE = None
 MODEL_ERROR = None
 
 def initialize_model():
-    """Initialize model on first use"""
-    global MODEL, MODEL_ERROR
-    if MODEL is None and MODEL_ERROR is None:
-        print("🚀 Initializing model for first use...")
-        MODEL, MODEL_ERROR = load_model_safe()
-    return MODEL is not None
+    """Initialize model with fallback options"""
+    global MODEL, PROCESSOR, MODEL_TYPE, MODEL_ERROR
+    
+    if MODEL is not None:
+        return True
+    
+    if MODEL_ERROR is not None:
+        return False
+    
+    print("🚀 Initializing video model...")
+    
+    # Strategy 1: Try manual LTX-Video loading
+    print("Trying LTX-Video...")
+    MODEL, PROCESSOR, error = load_ltx_model_manual()
+    if MODEL is not None:
+        MODEL_TYPE = "LTX-Video"
+        return True
+    
+    print(f"LTX-Video failed: {error}")
+    
+    # Strategy 2: Try alternative models
+    print("Trying alternative models...")
+    MODEL, MODEL_TYPE, error = load_alternative_video_model()
+    if MODEL is not None:
+        PROCESSOR = None  # Diffusion pipeline doesn't need separate processor
+        return True
+    
+    print(f"Alternative models failed: {error}")
+    
+    # Strategy 3: Use mock generation
+    MODEL_TYPE = "mock"
+    MODEL_ERROR = "All models failed - using demo mode"
+    return False
 
 @spaces.GPU(duration=120) if SPACES_AVAILABLE else lambda x: x
 def generate_video(
@@ -128,45 +193,85 @@ def generate_video(
     guidance_scale: float = 7.5,
     seed: int = -1
 ) -> Tuple[Optional[str], str]:
-    """Generate video using LTX-Video with ZeroGPU"""
-    
-    global MODEL, MODEL_ERROR
+    """Generate video with fallback strategies"""
     
-    # Initialize model if needed
-    if not initialize_model():
-        error_msg = f"❌ Model initialization failed: {MODEL_ERROR or 'Unknown error'}"
-        return None, error_msg
+    # Initialize model
+    model_loaded = initialize_model()
     
     # Input validation
     if not prompt.strip():
         return None, "❌ Please enter a valid prompt."
     
-    if len(prompt) > 200:
-        return None, "❌ Prompt too long. Please keep it under 200 characters."
-    
-    # Limit parameters for stability
-    num_frames = min(max(num_frames, 8), 24)
-    num_inference_steps = min(max(num_inference_steps, 10), 25)
+    # Limit parameters
+    num_frames = min(max(num_frames, 8), 25)
+    num_inference_steps = min(max(num_inference_steps, 10), 30)
     height = min(max(height, 256), 768)
     width = min(max(width, 256), 768)
     
+    # Set seed
+    if seed == -1:
+        seed = np.random.randint(0, 2**32 - 1)
+    
     try:
         # Clear memory
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
         gc.collect()
         
-        # Set seed
-        if seed == -1:
-            seed = np.random.randint(0, 2**32 - 1)
+        start_time = time.time()
         
-        generator = torch.Generator(device="cuda" if torch.cuda.is_available() else "cpu").manual_seed(seed)
+        if MODEL_TYPE == "mock" or not model_loaded:
+            # Mock generation
+            print("🎭 Using mock generation")
+            video_path = create_mock_video(prompt, num_frames, width, height)
+            
+            if video_path:
+                end_time = time.time()
+                return video_path, f"""
+🎭 **Demo Video Generated**
+
+📝 Prompt: {prompt}
+⚠️ Note: This is a demo mode because video models couldn't be loaded.
+
+🎬 Frames: {num_frames}
+📐 Resolution: {width}x{height}
+⏱️ Time: {end_time - start_time:.1f}s
+🔧 Status: {MODEL_ERROR or 'Demo mode'}
+
+💡 **To enable real video generation:**
+- Check if LTX-Video is available in your region
+- Try upgrading diffusers: `pip install diffusers --upgrade`
+- Or wait for official LTX-Video support in diffusers
+                """
+            else:
+                return None, "❌ Even demo generation failed"
         
-        print(f"🎬 Generating: '{prompt[:50]}...'")
-        start_time = time.time()
+        elif MODEL_TYPE == "LTX-Video":
+            # Manual LTX-Video generation
+            print("🚀 Using LTX-Video")
+            
+            # This would need the actual implementation based on the model's API
+            # For now, return a message about manual implementation needed
+            return None, f"""
+⚠️ **Manual Implementation Required**
+
+LTX-Video model was loaded but requires custom generation code.
+The model API is not yet standardized in diffusers.
+
+📋 **Next Steps:**
+1. Check Lightricks/LTX-Video model documentation
+2. Implement custom inference pipeline
+3. Or wait for official diffusers support
+
+🔧 **Current Status:** Model loaded, awaiting implementation
+            """
         
-        # Generate video
-        with torch.autocast("cuda" if torch.cuda.is_available() else "cpu", dtype=torch.bfloat16):
+        else:
+            # Alternative model generation
+            print(f"🔄 Using {MODEL_TYPE}")
+            
+            generator = torch.Generator(device="cuda" if torch.cuda.is_available() else "cpu").manual_seed(seed)
+            
             result = MODEL(
                 prompt=prompt,
                 negative_prompt=negative_prompt if negative_prompt.strip() else None,
@@ -175,49 +280,32 @@ def generate_video(
                 width=width,
                 num_inference_steps=num_inference_steps,
                 guidance_scale=guidance_scale,
-                generator=generator,
+                generator=generator
             )
-        
-        end_time = time.time()
-        generation_time = end_time - start_time
-        
-        # Save video
-        video_frames = result.frames[0]
-        
-        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp_file:
-            try:
+            
+            # Export video
+            video_frames = result.frames[0]
+            
+            with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp_file:
                 from diffusers.utils import export_to_video
                 export_to_video(video_frames, tmp_file.name, fps=8)
                 video_path = tmp_file.name
-            except Exception as e:
-                # Fallback: save as individual frames if export fails
-                print(f"⚠️ Video export failed, trying alternative: {e}")
-                return None, f"❌ Video export failed: {str(e)}"
-        
-        # Clear memory
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        gc.collect()
-        
-        success_msg = f"""✅ Video generated successfully!
+            
+            end_time = time.time()
+            
+            return video_path, f"""
+✅ **Video Generated Successfully!**
 
-📝 **Prompt:** {prompt}
-🎬 **Frames:** {num_frames}
-📐 **Resolution:** {width}x{height}
-⚙️ **Inference Steps:** {num_inference_steps}
-🎯 **Guidance Scale:** {guidance_scale}
-🎲 **Seed:** {seed}
-⏱️ **Generation Time:** {generation_time:.1f}s
-🖥️ **Device:** {'CUDA' if torch.cuda.is_available() else 'CPU'}
-⚡ **ZeroGPU:** {'✅' if IS_ZERO_GPU else '❌'}"""
-        
-        return video_path, success_msg
-        
-    except torch.cuda.OutOfMemoryError:
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        gc.collect()
-        return None, "❌ GPU memory exceeded. Try reducing frames/resolution or try again in a moment."
+📝 Prompt: {prompt}
+🤖 Model: {MODEL_TYPE}
+🎬 Frames: {num_frames}
+📐 Resolution: {width}x{height}
+⚙️ Steps: {num_inference_steps}
+🎯 Guidance: {guidance_scale}
+🎲 Seed: {seed}
+⏱️ Time: {end_time - start_time:.1f}s
+🖥️ Device: {'CUDA' if torch.cuda.is_available() else 'CPU'}
+            """
     
     except Exception as e:
         if torch.cuda.is_available():
@@ -226,165 +314,115 @@ def generate_video(
         return None, f"❌ Generation failed: {str(e)}"
 
 def get_system_info():
-    """Get comprehensive system information"""
-    
-    # Check package versions
-    package_info = {}
-    try:
-        import diffusers
-        package_info['diffusers'] = diffusers.__version__
-    except ImportError:
-        package_info['diffusers'] = '❌ Not installed'
+    """Get system information"""
     
+    # Check what's available
     try:
-        import transformers
-        package_info['transformers'] = transformers.__version__
-    except ImportError:
-        package_info['transformers'] = '❌ Not installed'
-    
-    # GPU info
-    gpu_info = "❌ Not available"
-    gpu_memory = 0
-    if torch.cuda.is_available():
+        from diffusers import __version__ as diffusers_version
+        available_pipelines = []
         try:
-            gpu_info = torch.cuda.get_device_name(0)
-            gpu_memory = torch.cuda.get_device_properties(0).total_memory / (1024**3)
-        except:
-            gpu_info = "✅ Available (details unavailable)"
+            from diffusers import LTXVideoPipeline
+            available_pipelines.append("✅ LTXVideoPipeline")
+        except ImportError:
+            available_pipelines.append("❌ LTXVideoPipeline")
+        
+        try:
+            from diffusers import DiffusionPipeline
+            available_pipelines.append("✅ DiffusionPipeline")
+        except ImportError:
+            available_pipelines.append("❌ DiffusionPipeline")
+            
+    except ImportError:
+        diffusers_version = "❌ Not installed"
+        available_pipelines = ["❌ Diffusers not available"]
     
-    return f"""## 🖥️ System Information
+    return f"""
+## 🖥️ System Information
 
 **Environment:**
-- 🚀 ZeroGPU: {'✅ Active' if IS_ZERO_GPU else '❌ Not detected'}  
+- 🚀 ZeroGPU: {'✅ Active' if IS_ZERO_GPU else '❌ Not detected'}
 - 🏠 HF Spaces: {'✅' if IS_SPACES else '❌'}
 - 🔥 CUDA: {'✅' if torch.cuda.is_available() else '❌'}
-- 🖥️ GPU: {gpu_info} ({gpu_memory:.1f} GB)
 
 **Packages:**
 - PyTorch: {torch.__version__}
-- Diffusers: {package_info.get('diffusers', 'Unknown')}
-- Transformers: {package_info.get('transformers', 'Unknown')}
-- Spaces: {'✅' if SPACES_AVAILABLE else '❌'}
+- Diffusers: {diffusers_version}
+- Available Pipelines: {', '.join(available_pipelines)}
 
 **Model Status:**
-- LTX-Video: {'✅ Loaded' if MODEL is not None else '⏳ Will load on first use' if MODEL_ERROR is None else f'❌ Error: {MODEL_ERROR}'}
+- Current Model: {MODEL_TYPE or 'Not loaded'}
+- Status: {'✅ Ready' if MODEL is not None else '⚠️ ' + (MODEL_ERROR or 'Not initialized')}
 
-**Tips:**
-{'🎯 Ready to generate!' if MODEL is not None else '⚡ First generation will take longer due to model loading'}"""
-
-def test_dependencies():
-    """Test if all dependencies are working"""
-    results = []
-    
-    # Test torch
-    try:
-        import torch
-        results.append(f"✅ PyTorch {torch.__version__}")
-        if torch.cuda.is_available():
-            results.append(f"✅ CUDA {torch.version.cuda}")
-        else:
-            results.append("⚠️ CUDA not available")
-    except Exception as e:
-        results.append(f"❌ PyTorch: {e}")
-    
-    # Test diffusers
-    try:
-        import diffusers
-        results.append(f"✅ Diffusers {diffusers.__version__}")
-    except Exception as e:
-        results.append(f"❌ Diffusers: {e}")
-    
-    # Test transformers
-    try:
-        import transformers
-        results.append(f"✅ Transformers {transformers.__version__}")
-    except Exception as e:
-        results.append(f"❌ Transformers: {e}")
-    
-    return "\n".join(results)
+**Recommendation:**
+- LTX-Video is very new and may not be in stable diffusers yet
+- Using alternative models or demo mode
+- Check back later for official support
+    """
 
 # Create Gradio interface
-with gr.Blocks(title="LTX-Video ZeroGPU", theme=gr.themes.Soft()) as demo:
+with gr.Blocks(title="Video Generator with Fallbacks", theme=gr.themes.Soft()) as demo:
     
     gr.Markdown("""
-    # 🚀 LTX-Video Generator (ZeroGPU)
+    # 🎬 Advanced Video Generator
     
-    Generate high-quality videos from text using **Lightricks LTX-Video** model with **ZeroGPU**!
+    Attempts to use LTX-Video, falls back to alternative models, or provides demo mode.
     """)
     
-    # Status indicator
-    with gr.Row():
-        gr.Markdown(f"""
-        **Status:** {'🟢 ZeroGPU Active' if IS_ZERO_GPU else '🟡 CPU Mode'} | 
-        **Environment:** {'HF Spaces' if IS_SPACES else 'Local'}
-        """)
-    
     with gr.Tab("🎥 Generate Video"):
         with gr.Row():
             with gr.Column(scale=1):
                 prompt_input = gr.Textbox(
                     label="📝 Video Prompt",
-                    placeholder="A majestic eagle soaring through mountain peaks...",
-                    lines=3,
-                    max_lines=5
+                    placeholder="A serene mountain lake at sunrise...",
+                    lines=3
                 )
                 
                 negative_prompt_input = gr.Textbox(
-                    label="🚫 Negative Prompt (Optional)",
-                    placeholder="blurry, low quality, distorted...",
+                    label="🚫 Negative Prompt",
+                    placeholder="blurry, low quality...",
                     lines=2
                 )
                 
-                with gr.Accordion("⚙️ Settings", open=True):
-                    with gr.Row():
-                        num_frames = gr.Slider(8, 24, value=16, step=1, label="🎬 Frames")
-                        num_steps = gr.Slider(10, 25, value=20, step=1, label="🔄 Steps") 
-                    
-                    with gr.Row():
-                        width = gr.Dropdown([256, 512, 768], value=512, label="📐 Width")
-                        height = gr.Dropdown([256, 512, 768], value=512, label="📏 Height")
-                    
-                    with gr.Row():
-                        guidance_scale = gr.Slider(1.0, 12.0, value=7.5, step=0.5, label="🎯 Guidance")
-                        seed = gr.Number(value=-1, precision=0, label="🎲 Seed (-1=random)")
+                with gr.Row():
+                    num_frames = gr.Slider(8, 25, value=16, step=1, label="🎬 Frames")
+                    num_steps = gr.Slider(10, 30, value=20, step=1, label="🔄 Steps")
+                
+                with gr.Row():
+                    width = gr.Dropdown([256, 512, 768], value=512, label="📐 Width")
+                    height = gr.Dropdown([256, 512, 768], value=512, label="📏 Height")
+                
+                with gr.Row():
+                    guidance_scale = gr.Slider(1.0, 15.0, value=7.5, step=0.5, label="🎯 Guidance")
+                    seed = gr.Number(value=-1, precision=0, label="🎲 Seed")
                 
                 generate_btn = gr.Button("🚀 Generate Video", variant="primary", size="lg")
                 
             with gr.Column(scale=1):
                 video_output = gr.Video(label="🎥 Generated Video", height=400)
-                result_text = gr.Textbox(label="📋 Results", lines=6, show_copy_button=True)
+                result_text = gr.Textbox(label="📋 Results", lines=8, show_copy_button=True)
         
-        # Event handlers
         generate_btn.click(
             fn=generate_video,
             inputs=[prompt_input, negative_prompt_input, num_frames, height, width, num_steps, guidance_scale, seed],
             outputs=[video_output, result_text]
         )
         
-        # Examples
         gr.Examples(
             examples=[
-                ["A peaceful cat sleeping in a sunny garden", "", 16, 512, 512, 20, 7.5, 42],
-                ["Ocean waves at sunset, cinematic view", "blurry", 20, 512, 512, 20, 8.0, 123],
-                ["A hummingbird hovering near red flowers", "", 16, 512, 512, 15, 7.0, 456]
+                ["A peaceful cat in a sunny garden", "", 16, 512, 512, 20, 7.5, 42],
+                ["Ocean waves at golden hour", "blurry", 20, 512, 512, 20, 8.0, 123],
+                ["A butterfly on a flower", "", 16, 512, 512, 15, 7.0, 456]
             ],
             inputs=[prompt_input, negative_prompt_input, num_frames, height, width, num_steps, guidance_scale, seed]
         )
     
     with gr.Tab("ℹ️ System Info"):
-        info_btn = gr.Button("🔍 Check System", variant="secondary")
+        info_btn = gr.Button("🔍 Check System")
         system_output = gr.Markdown()
         
         info_btn.click(fn=get_system_info, outputs=system_output)
         demo.load(fn=get_system_info, outputs=system_output)
-    
-    with gr.Tab("🔧 Debug"):
-        test_btn = gr.Button("🧪 Test Dependencies")
-        test_output = gr.Textbox(label="Test Results", lines=10)
-        
-        test_btn.click(fn=test_dependencies, outputs=test_output)
 
-# Launch
 if __name__ == "__main__":
     demo.queue(max_size=5)
     demo.launch(