Update app.py

app.py

@@ -7,427 +7,697 @@ import tempfile
 from typing import Optional, Tuple
 import time
 
-# ZeroGPU import
+# ZeroGPU with H200 support
 try:
     import spaces
     SPACES_AVAILABLE = True
+    print("✅ Spaces library loaded - H200 detected!")
 except ImportError:
     SPACES_AVAILABLE = False
     class spaces:
         @staticmethod
-        def GPU(duration=60):
-            def decorator(func):
-                return func
+        def GPU(duration=300):
+            def decorator(func): return func
             return decorator
 
+# Environment
 IS_ZERO_GPU = os.environ.get("SPACES_ZERO_GPU") == "true"
 IS_SPACES = os.environ.get("SPACE_ID") is not None
+HAS_CUDA = torch.cuda.is_available()
 
-def load_ltx_model_manual():
-    """Manually load LTX-Video model using transformers"""
-    try:
-        print("🔄 Attempting to load LTX-Video with transformers...")
-        
-        from transformers import AutoModel, AutoTokenizer, AutoProcessor
-        
-        model_id = "Lightricks/LTX-Video"
-        
-        # Try loading with AutoModel
+print(f"🚀 H200 Environment: ZeroGPU={IS_ZERO_GPU}, Spaces={IS_SPACES}, CUDA={HAS_CUDA}")
+
+# Premium models optimized for H200's massive memory
+PREMIUM_MODELS = {
+    "ltx": {
+        "id": "Lightricks/LTX-Video",
+        "name": "LTX-Video",
+        "pipeline_class": "LTXVideoPipeline",
+        "resolution_options": [(512, 512), (768, 768), (1024, 1024), (1280, 720), (1920, 1080)],
+        "max_frames": 161,  # H200 can handle more frames
+        "dtype": torch.bfloat16,
+        "priority": 1,
+        "description": "Lightricks' flagship model - professional quality"
+    },
+    "hunyuan": {
+        "id": "tencent/HunyuanVideo", 
+        "name": "HunyuanVideo",
+        "pipeline_class": "HunyuanVideoPipeline",
+        "resolution_options": [(512, 512), (768, 768), (1024, 1024), (1280, 720)],
+        "max_frames": 129,  # Extended for H200
+        "dtype": torch.bfloat16,
+        "priority": 2,
+        "description": "Tencent's advanced video model with superior motion"
+    },
+    "wan": {
+        "id": "wangfuyun/AnimateLCM",
+        "name": "AnimateLCM", 
+        "pipeline_class": "DiffusionPipeline",
+        "resolution_options": [(512, 512), (768, 768), (1024, 1024)],
+        "max_frames": 64,
+        "dtype": torch.float16,
+        "priority": 3,
+        "description": "Fast, high-quality animation model"
+    },
+    "cogvideo": {
+        "id": "THUDM/CogVideoX-5b",
+        "name": "CogVideoX-5B",
+        "pipeline_class": "CogVideoXPipeline", 
+        "resolution_options": [(720, 480), (1280, 720)],
+        "max_frames": 49,
+        "dtype": torch.bfloat16,
+        "priority": 4,
+        "description": "CogVideo's 5B parameter model"
+    }
+}
+
+# Global variables
+MODEL = None
+MODEL_INFO = None
+LOADING_ERROR = None
+
+def get_gpu_memory():
+    """Get H200 GPU memory info"""
+    if HAS_CUDA:
         try:
-            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
+            total_memory = torch.cuda.get_device_properties(0).total_memory / (1024**3)
+            allocated = torch.cuda.memory_allocated(0) / (1024**3)
+            cached = torch.cuda.memory_reserved(0) / (1024**3)
+            return total_memory, allocated, cached
+        except:
+            return 0, 0, 0
+    return 0, 0, 0
+
+def load_premium_model():
+    """Load first available premium model with H200 optimizations"""
+    global MODEL, MODEL_INFO, LOADING_ERROR
+    
+    if MODEL is not None:
+        return True
+    
+    # Sort models by priority
+    sorted_models = sorted(PREMIUM_MODELS.items(), key=lambda x: x[1]["priority"])
+    
+    for key, info in sorted_models:
+        try:
+            print(f"🔄 Loading {info['name']} on H200...")
+            total_mem, allocated, cached = get_gpu_memory()
+            print(f"💾 GPU Memory: {total_mem:.1f}GB total, {allocated:.1f}GB allocated")
             
-        except Exception as e:
-            print(f"AutoModel failed: {e}")
-            return None, None, str(e)
+            from diffusers import DiffusionPipeline
             
-    except Exception as e:
-        return None, None, f"Manual loading failed: {e}"
-
-def load_alternative_video_model():
-    """Load a working alternative video generation model"""
-    try:
-        print("🔄 Loading alternative video model...")
-        
-        from diffusers import DiffusionPipeline
-        
-        # 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"
-        ]
-        
-        for model_id in alternatives:
+            # Try specific pipeline class first
             try:
-                print(f"Trying {model_id}...")
+                if info["pipeline_class"] == "LTXVideoPipeline":
+                    from diffusers import LTXVideoPipeline
+                    pipe = LTXVideoPipeline.from_pretrained(
+                        info["id"],
+                        torch_dtype=info["dtype"],
+                        use_safetensors=True,
+                        variant="fp16"
+                    )
+                elif info["pipeline_class"] == "HunyuanVideoPipeline":
+                    from diffusers import HunyuanVideoPipeline  
+                    pipe = HunyuanVideoPipeline.from_pretrained(
+                        info["id"],
+                        torch_dtype=info["dtype"],
+                        use_safetensors=True,
+                        variant="fp16"
+                    )
+                elif info["pipeline_class"] == "CogVideoXPipeline":
+                    from diffusers import CogVideoXPipeline
+                    pipe = CogVideoXPipeline.from_pretrained(
+                        info["id"],
+                        torch_dtype=info["dtype"],
+                        use_safetensors=True
+                    )
+                else:
+                    # Generic DiffusionPipeline
+                    pipe = DiffusionPipeline.from_pretrained(
+                        info["id"],
+                        torch_dtype=info["dtype"],
+                        use_safetensors=True,
+                        variant="fp16",
+                        trust_remote_code=True
+                    )
+            except ImportError as e:
+                print(f"⚠️ Specific pipeline not available: {e}")
+                print("Trying generic DiffusionPipeline...")
                 pipe = DiffusionPipeline.from_pretrained(
-                    model_id,
-                    torch_dtype=torch.float16,
+                    info["id"],
+                    torch_dtype=info["dtype"],
                     use_safetensors=True,
-                    variant="fp16"
+                    variant="fp16",
+                    trust_remote_code=True
                 )
+            
+            # H200 optimizations - we have plenty of memory!
+            if HAS_CUDA:
+                pipe = pipe.to("cuda")
+                print(f"📱 Moved {info['name']} to H200 CUDA")
                 
-                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
+                # Enable all optimizations but keep model in VRAM
+                if hasattr(pipe, 'enable_vae_slicing'):
+                    pipe.enable_vae_slicing()
+                if hasattr(pipe, 'enable_vae_tiling'):
+                    pipe.enable_vae_tiling()
+                if hasattr(pipe, 'enable_memory_efficient_attention'):
+                    pipe.enable_memory_efficient_attention()
+                # Don't use CPU offload on H200 - keep everything in GPU memory
                 
-            except Exception as e:
-                print(f"Failed to load {model_id}: {e}")
-                continue
-        
-        return None, None, "All alternative models failed"
-        
-    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
-        
-        # Create temporary video file
-        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp_file:
-            video_path = tmp_file.name
-        
-        # 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)
+                # Enable xformers if available for extra speed
+                try:
+                    pipe.enable_xformers_memory_efficient_attention()
+                    print("🚀 XFormers acceleration enabled")
+                except:
+                    print("⚠️ XFormers not available")
             
-            try:
-                font = ImageFont.truetype("arial.ttf", 24)
-            except:
-                font = ImageFont.load_default()
+            MODEL = pipe
+            MODEL_INFO = info
             
-            # 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)
+            final_mem = torch.cuda.memory_allocated(0) / (1024**3)
+            print(f"✅ {info['name']} loaded successfully! Memory used: {final_mem:.1f}GB")
+            return True
             
-            # Convert to OpenCV format
-            frame = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
-            out.write(frame)
-        
-        out.release()
-        return video_path
-        
-    except Exception as e:
-        return None
-
-# Global variables
-MODEL = None
-PROCESSOR = None
-MODEL_TYPE = None
-MODEL_ERROR = None
-
-def initialize_model():
-    """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}")
+        except Exception as e:
+            print(f"❌ Failed to load {info['name']}: {e}")
+            # Clear memory before trying next model
+            if HAS_CUDA:
+                torch.cuda.empty_cache()
+            gc.collect()
+            continue
     
-    # Strategy 3: Use mock generation
-    MODEL_TYPE = "mock"
-    MODEL_ERROR = "All models failed - using demo mode"
+    LOADING_ERROR = "All premium models failed to load"
     return False
 
-@spaces.GPU(duration=120) if SPACES_AVAILABLE else lambda x: x
+@spaces.GPU(duration=300) if SPACES_AVAILABLE else lambda x: x  # 5 minutes for H200
 def generate_video(
     prompt: str,
     negative_prompt: str = "",
-    num_frames: int = 16,
-    height: int = 512,
-    width: int = 512,
-    num_inference_steps: int = 20,
+    num_frames: int = 49,
+    resolution: str = "1024x1024",
+    num_inference_steps: int = 30,
     guidance_scale: float = 7.5,
-    seed: int = -1
+    seed: int = -1,
+    fps: int = 8
 ) -> Tuple[Optional[str], str]:
-    """Generate video with fallback strategies"""
+    """Generate premium video with H200 power"""
     
-    # Initialize model
-    model_loaded = initialize_model()
+    global MODEL, MODEL_INFO, LOADING_ERROR
+    
+    # Load model if needed
+    if not load_premium_model():
+        return None, f"❌ No premium models available: {LOADING_ERROR}"
     
     # Input validation
     if not prompt.strip():
         return None, "❌ Please enter a valid prompt."
     
-    # 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)
+    if len(prompt) > 1000:  # H200 can handle longer prompts
+        return None, "❌ Prompt too long. Please keep it under 1000 characters."
+    
+    # Parse resolution
+    try:
+        width, height = map(int, resolution.split('x'))
+    except:
+        width, height = 1024, 1024
+    
+    # Validate parameters against model capabilities  
+    max_frames = MODEL_INFO["max_frames"]
+    num_frames = min(max(num_frames, 8), max_frames)
     
-    # Set seed
-    if seed == -1:
-        seed = np.random.randint(0, 2**32 - 1)
+    # Check if resolution is supported
+    if (width, height) not in MODEL_INFO["resolution_options"]:
+        # Use best supported resolution
+        best_res = MODEL_INFO["resolution_options"][-1]  # Highest resolution
+        width, height = best_res
+        print(f"⚠️ Adjusted resolution to {width}x{height}")
     
     try:
-        # Clear memory
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        gc.collect()
+        # H200 memory management - we have tons of memory!
+        start_memory = torch.cuda.memory_allocated(0) / (1024**3) if HAS_CUDA else 0
         
-        start_time = time.time()
+        # Set seed
+        if seed == -1:
+            seed = np.random.randint(0, 2**32 - 1)
         
-        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"
-        
-        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
-            """
+        device = "cuda" if HAS_CUDA else "cpu"
+        generator = torch.Generator(device=device).manual_seed(seed)
         
-        else:
-            # Alternative model generation
-            print(f"🔄 Using {MODEL_TYPE}")
+        print(f"🎬 H200 Generation: {MODEL_INFO['name']} - '{prompt[:70]}...'")
+        print(f"📐 {width}x{height}, {num_frames} frames, {num_inference_steps} steps")
+        start_time = time.time()
+        
+        # Generate with H200's full power
+        with torch.autocast(device, dtype=MODEL_INFO["dtype"]):
+            generation_kwargs = {
+                "prompt": prompt,
+                "num_frames": num_frames,
+                "height": height,
+                "width": width,
+                "num_inference_steps": num_inference_steps,
+                "guidance_scale": guidance_scale,
+                "generator": generator
+            }
             
-            generator = torch.Generator(device="cuda" if torch.cuda.is_available() else "cpu").manual_seed(seed)
+            # Add negative prompt if provided
+            if negative_prompt.strip():
+                generation_kwargs["negative_prompt"] = negative_prompt
             
-            result = MODEL(
-                prompt=prompt,
-                negative_prompt=negative_prompt if negative_prompt.strip() else None,
-                num_frames=num_frames,
-                height=height,
-                width=width,
-                num_inference_steps=num_inference_steps,
-                guidance_scale=guidance_scale,
-                generator=generator
-            )
+            # Model-specific parameters
+            if MODEL_INFO["name"] == "CogVideoX-5B":
+                generation_kwargs["num_videos_per_prompt"] = 1
             
-            # Export video
+            # Generate with progress tracking
+            print("🚀 Starting generation on H200...")
+            result = MODEL(**generation_kwargs)
+        
+        end_time = time.time()
+        generation_time = end_time - start_time
+        
+        # Extract video frames
+        if hasattr(result, 'frames'):
             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
-            
-            end_time = time.time()
-            
-            return video_path, f"""
-✅ **Video Generated Successfully!**
+        elif hasattr(result, 'videos'):
+            video_frames = result.videos[0]
+        else:
+            return None, "❌ Could not extract video frames from model output"
+        
+        # Export with custom FPS
+        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=fps)
+            video_path = tmp_file.name
+        
+        # Memory stats
+        end_memory = torch.cuda.memory_allocated(0) / (1024**3) if HAS_CUDA else 0
+        memory_used = end_memory - start_memory
+        
+        success_msg = f"""✅ **H200 Premium Video Generated!**
 
-📝 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'}
-            """
+🚀 **Model:** {MODEL_INFO['name']} 
+📝 **Prompt:** {prompt}
+🎬 **Frames:** {num_frames} @ {fps} FPS
+📐 **Resolution:** {width}x{height}
+⚙️ **Inference Steps:** {num_inference_steps}
+🎯 **Guidance Scale:** {guidance_scale}
+🎲 **Seed:** {seed}
+⏱️ **Generation Time:** {generation_time:.1f}s
+🖥️ **Device:** H200 CUDA
+💾 **Memory Used:** {memory_used:.1f}GB
+🎥 **Video Length:** {num_frames/fps:.1f}s"""
+        
+        return video_path, success_msg
+        
+    except torch.cuda.OutOfMemoryError:
+        # Should be rare on H200!
+        torch.cuda.empty_cache()
+        gc.collect()
+        return None, "❌ GPU memory exceeded (rare on H200!). Try reducing parameters."
     
     except Exception as e:
-        if torch.cuda.is_available():
+        if HAS_CUDA:
             torch.cuda.empty_cache()
         gc.collect()
         return None, f"❌ Generation failed: {str(e)}"
 
-def get_system_info():
-    """Get system information"""
+def get_h200_status():
+    """Get H200 specific status"""
+    if not HAS_CUDA:
+        return "❌ CUDA not available"
     
-    # Check what's available
     try:
-        from diffusers import __version__ as diffusers_version
-        available_pipelines = []
-        try:
-            from diffusers import LTXVideoPipeline
-            available_pipelines.append("✅ LTXVideoPipeline")
-        except ImportError:
-            available_pipelines.append("❌ LTXVideoPipeline")
+        total_mem, allocated, cached = get_gpu_memory()
+        gpu_name = torch.cuda.get_device_name(0)
         
-        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"]
+        model_status = "⏳ Model will load on first use"
+        if MODEL is not None:
+            model_status = f"✅ {MODEL_INFO['name']} loaded and ready"
+        elif LOADING_ERROR:
+            model_status = f"❌ {LOADING_ERROR}"
+        
+        return f"""## 🚀 H200 Status
+
+**🖥️ Hardware:**
+- GPU: {gpu_name}
+- Total Memory: {total_mem:.1f} GB
+- Allocated: {allocated:.1f} GB  
+- Cached: {cached:.1f} GB
+- Free: {total_mem - allocated:.1f} GB
+
+**🤖 Model Status:**
+{model_status}
+
+**⚡ H200 Advantages:**
+- 141GB HBM3 memory (3.5x more than A100)
+- 4.8TB/s memory bandwidth
+- Can handle larger models & longer videos
+- Multiple high-res generations without swapping"""
+        
+    except Exception as e:
+        return f"❌ Error getting H200 status: {e}"
+
+def suggest_h200_settings():
+    """Suggest optimal settings for H200"""
+    if MODEL is None:
+        return "Load a model first to get personalized recommendations"
+    
+    model_name = MODEL_INFO['name']
+    max_frames = MODEL_INFO['max_frames']
+    max_res = MODEL_INFO['resolution_options'][-1]
     
-    return f"""
-## 🖥️ System Information
+    return f"""## 🎯 H200 Optimized Settings for {model_name}
 
-**Environment:**
-- 🚀 ZeroGPU: {'✅ Active' if IS_ZERO_GPU else '❌ Not detected'}
-- 🏠 HF Spaces: {'✅' if IS_SPACES else '❌'}
-- 🔥 CUDA: {'✅' if torch.cuda.is_available() else '❌'}
+**🚀 Maximum Quality (Recommended):**
+- Resolution: {max_res[0]}x{max_res[1]}
+- Frames: {max_frames}
+- Inference Steps: 50
+- Expected Time: 3-5 minutes
 
-**Packages:**
-- PyTorch: {torch.__version__}
-- Diffusers: {diffusers_version}
-- Available Pipelines: {', '.join(available_pipelines)}
+**⚖️ Balanced (Fast & Good):**
+- Resolution: 1024x1024
+- Frames: {max_frames//2}
+- Inference Steps: 30
+- Expected Time: 1-2 minutes
 
-**Model Status:**
-- Current Model: {MODEL_TYPE or 'Not loaded'}
-- Status: {'✅ Ready' if MODEL is not None else '⚠️ ' + (MODEL_ERROR or 'Not initialized')}
+**⚡ Speed Test:**
+- Resolution: 512x512  
+- Frames: 25
+- Inference Steps: 20
+- Expected Time: 30-60 seconds
 
-**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
-    """
+**💡 H200 Tips:**
+- Use longer prompts - H200 can handle complexity
+- Try higher inference steps (30-50) for maximum quality
+- Experiment with longer videos (40+ frames)
+- Multiple generations won't cause memory issues"""
 
-# Create Gradio interface
-with gr.Blocks(title="Video Generator with Fallbacks", theme=gr.themes.Soft()) as demo:
+# Create H200-optimized interface
+with gr.Blocks(title="H200 Premium Video Generator", theme=gr.themes.Glass()) as demo:
     
     gr.Markdown("""
-    # 🎬 Advanced Video Generator
+    # 🚀 H200 Premium Video Generator
     
-    Attempts to use LTX-Video, falls back to alternative models, or provides demo mode.
+    **Powered by NVIDIA H200** • **141GB Memory** • **Premium Models Only**
+    
+    *LTX-Video • HunyuanVideo • CogVideoX-5B • AnimateLCM*
     """)
     
-    with gr.Tab("🎥 Generate Video"):
+    # H200 status bar
+    with gr.Row():
+        gr.Markdown("""
+        <div style="text-align: center; padding: 10px; background: linear-gradient(45deg, #FF6B6B, #4ECDC4); border-radius: 10px; color: white; font-weight: bold;">
+        🔥 H200 ACTIVE - MAXIMUM PERFORMANCE MODE 🔥
+        </div>
+        """)
+    
+    with gr.Tab("🎥 H200 Video Generation"):
         with gr.Row():
             with gr.Column(scale=1):
                 prompt_input = gr.Textbox(
-                    label="📝 Video Prompt",
-                    placeholder="A serene mountain lake at sunrise...",
-                    lines=3
+                    label="📝 Detailed Video Prompt (H200 can handle complexity!)",
+                    placeholder="A breathtaking aerial view of a majestic golden eagle soaring through dramatic mountain peaks during a spectacular sunrise, with volumetric lighting piercing through morning mist, cinematic composition with dynamic camera movement following the eagle's graceful flight path, professional cinematography with shallow depth of field and warm golden color grading, 8K quality with film grain texture...",
+                    lines=5,
+                    max_lines=8
                 )
                 
                 negative_prompt_input = gr.Textbox(
                     label="🚫 Negative Prompt",
-                    placeholder="blurry, low quality...",
+                    placeholder="blurry, low quality, distorted, pixelated, watermark, text, signature, amateur, static, boring, unnatural motion...",
                     lines=2
                 )
                 
-                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.Accordion("🚀 H200 Advanced Settings", open=True):
+                    with gr.Row():
+                        num_frames = gr.Slider(
+                            minimum=8,
+                            maximum=161,  # H200 can handle more
+                            value=49,
+                            step=1,
+                            label="🎬 Frames (H200 can handle long videos!)"
+                        )
+                        
+                        fps = gr.Slider(
+                            minimum=4,
+                            maximum=30,
+                            value=8,
+                            step=1,
+                            label="🎞️ FPS (frames per second)"
+                        )
+                    
+                    with gr.Row():
+                        resolution = gr.Dropdown(
+                            choices=["512x512", "768x768", "1024x1024", "1280x720", "1920x1080"],
+                            value="1024x1024",
+                            label="📐 Resolution (H200 loves high-res!)"
+                        )
+                        
+                        num_steps = gr.Slider(
+                            minimum=15,
+                            maximum=100,  # H200 can handle more steps
+                            value=30,
+                            step=1,
+                            label="⚙️ Inference Steps (more = better quality)"
+                        )
+                    
+                    with gr.Row():
+                        guidance_scale = gr.Slider(
+                            minimum=1.0,
+                            maximum=20.0,
+                            value=7.5,
+                            step=0.5,
+                            label="🎯 Guidance Scale"
+                        )
+                        
+                        seed = gr.Number(
+                            label="🎲 Seed (-1 for random)",
+                            value=-1,
+                            precision=0
+                        )
                 
-                with gr.Row():
-                    width = gr.Dropdown([256, 512, 768], value=512, label="📐 Width")
-                    height = gr.Dropdown([256, 512, 768], value=512, label="📏 Height")
+                generate_btn = gr.Button(
+                    "🚀 Generate on H200", 
+                    variant="primary", 
+                    size="lg"
+                )
                 
-                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")
+                gr.Markdown("""
+                **⏱️ H200 Generation:** 1-5 minutes depending on settings
                 
-                generate_btn = gr.Button("🚀 Generate Video", variant="primary", size="lg")
+                **🔥 H200 Power:**
+                - 141GB memory = No limits!
+                - Generate 1080p videos
+                - 100+ frames possible  
+                - 50+ inference steps for max quality
+                """)
                 
             with gr.Column(scale=1):
-                video_output = gr.Video(label="🎥 Generated Video", height=400)
-                result_text = gr.Textbox(label="📋 Results", lines=8, show_copy_button=True)
+                video_output = gr.Video(
+                    label="🎥 H200 Generated Premium Video",
+                    height=400
+                )
+                
+                result_text = gr.Textbox(
+                    label="📋 H200 Generation Report",
+                    lines=12,
+                    show_copy_button=True
+                )
         
+        # Event handler
         generate_btn.click(
             fn=generate_video,
-            inputs=[prompt_input, negative_prompt_input, num_frames, height, width, num_steps, guidance_scale, seed],
+            inputs=[
+                prompt_input, negative_prompt_input, num_frames,
+                resolution, num_steps, guidance_scale, seed, fps
+            ],
             outputs=[video_output, result_text]
         )
         
+        # H200-optimized examples
         gr.Examples(
             examples=[
-                ["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]
+                [
+                    "A majestic golden eagle soaring through misty mountain peaks at sunrise, cinematic aerial cinematography with dramatic volumetric lighting, professional color grading with warm golden tones, shallow depth of field, dynamic camera movement tracking the eagle's flight, 8K quality with film grain",
+                    "blurry, low quality, pixelated, static, amateur, watermark, text",
+                    49, "1024x1024", 35, 7.5, 42, 8
+                ],
+                [
+                    "Powerful ocean waves crashing against dramatic coastal cliffs during a storm, slow motion macro cinematography capturing water droplets and spray, dynamic lighting with storm clouds, professional cinematography with high contrast and desaturated colors",
+                    "calm, peaceful, low quality, distorted, pixelated, watermark",
+                    65, "1280x720", 40, 8.0, 123, 12
+                ],
+                [
+                    "A steaming artisanal coffee cup on rustic wooden table by rain-streaked window, cozy cafe atmosphere with warm ambient lighting, shallow depth of field bokeh background, steam rising elegantly, cinematic close-up with perfect exposure",
+                    "cold, harsh lighting, plastic, fake, low quality, blurry, text",
+                    33, "1024x1024", 30, 7.0, 456, 8
+                ],
+                [
+                    "Cherry blossom petals falling like snow in traditional Japanese garden with wooden bridge over koi pond, peaceful zen atmosphere with soft natural lighting, time-lapse effect showing seasonal transition, cinematic wide shot with perfect composition",
+                    "modern, urban, chaotic, low quality, distorted, artificial, watermark",
+                    81, "1280x720", 45, 7.5, 789, 10
+                ]
             ],
-            inputs=[prompt_input, negative_prompt_input, num_frames, height, width, num_steps, guidance_scale, seed]
+            inputs=[prompt_input, negative_prompt_input, num_frames, resolution, num_steps, guidance_scale, seed, fps]
         )
     
-    with gr.Tab("ℹ️ System Info"):
-        info_btn = gr.Button("🔍 Check System")
-        system_output = gr.Markdown()
+    with gr.Tab("💾 H200 Status"):
+        with gr.Row():
+            status_btn = gr.Button("🔍 Check H200 Status", variant="secondary")
+            settings_btn = gr.Button("🎯 Get Optimal Settings", variant="secondary")
+            
+        status_output = gr.Markdown()
+        settings_output = gr.Markdown()
+        
+        status_btn.click(fn=get_h200_status, outputs=status_output)
+        settings_btn.click(fn=suggest_h200_settings, outputs=settings_output)
+        
+        # Auto-load status
+        demo.load(fn=get_h200_status, outputs=status_output)
+    
+    with gr.Tab("🎬 H200 Master Guide"):
+        gr.Markdown("""
+        ## 🚀 H200 Video Generation Mastery
+        
+        ### 💎 Why H200 is Game-Changing:
+        
+        **🔥 Raw Power:**
+        - **141GB HBM3 Memory** (vs 80GB A100)
+        - **4.8TB/s Bandwidth** (vs 3.35TB/s A100)  
+        - **67% More Memory** for bigger models & longer videos
+        - **No Memory Swapping** = Consistent performance
+        
+        ### 🎯 H200-Optimized Strategies:
+        
+        **🎬 Long-Form Content (H200 Specialty):**
+        - Frames: 80-161 (2-20 second videos)
+        - Resolution: 1280x720 or 1024x1024
+        - Steps: 40-50 for cinematic quality
+        - Perfect for: Storytelling, commercials, art pieces
+        
+        **🖼️ Ultra High-Res (H200 Advantage):**
+        - Resolution: 1920x1080 (if model supports)
+        - Frames: 25-49 (manageable length)
+        - Steps: 30-40
+        - Perfect for: Wallpapers, presentations, demos
+        
+        **⚡ Rapid Prototyping:**
+        - Multiple quick generations to test ideas
+        - 512x512, 25 frames, 20 steps
+        - Iterate quickly, then scale up
+        
+        ### ✍️ Advanced Prompt Engineering for H200:
+        
+        **Complex Scene Composition:**
+        ```
+        [Main Subject] + [Detailed Action] + [Environment Description] + 
+        [Camera Work] + [Lighting] + [Color Grading] + [Technical Quality]
+        ```
+        
+        **Example Structure:**
+        - **Subject:** "A majestic red dragon"
+        - **Action:** "gracefully flying through ancient mountain peaks"  
+        - **Environment:** "shrouded in mystical fog with ancient ruins visible below"
+        - **Camera:** "cinematic aerial tracking shot with dynamic movement"
+        - **Lighting:** "golden hour lighting with volumetric rays piercing the mist"
+        - **Grading:** "warm color palette with high contrast and film grain"
+        - **Quality:** "8K cinematography with shallow depth of field"
+        
+        ### 🎨 Style Modifiers for Premium Results:
+        
+        **Cinematic Styles:**
+        - "Christopher Nolan cinematography"
+        - "Blade Runner 2049 aesthetic"  
+        - "Studio Ghibli animation style"
+        - "BBC Planet Earth documentary style"
+        - "Marvel movie action sequence"
+        
+        **Technical Quality:**
+        - "8K RED camera footage"
+        - "IMAX quality cinematography" 
+        - "Zeiss lens bokeh"
+        - "Professional color grading"
+        - "Film grain texture overlay"
+        
+        ### 🔧 H200 Performance Optimization:
+        
+        **Memory Management:**
+        - H200's 141GB means you rarely hit limits
+        - Can run multiple models simultaneously
+        - No need for CPU offloading
+        - Keep all components in GPU memory
+        
+        **Speed Optimization:**
+        - Use bfloat16 for modern models (LTX, HunyuanVideo)
+        - Enable XFormers attention for 20-30% speedup
+        - Batch operations when possible
+        - H200's bandwidth handles large tensors efficiently
+        
+        **Quality Maximization:**
+        - Push inference steps to 40-50
+        - Use guidance scales 7-12 for detailed control
+        - Experiment with longer sequences (80+ frames)
+        - Try ultra-high resolutions (1080p+)
+        
+        ### 🎪 Advanced Techniques:
+        
+        **Multi-Shot Sequences:**
+        1. Generate wide establishing shot
+        2. Generate medium character shot
+        3. Generate close-up detail shot
+        4. Combine in post-production
+        
+        **Style Consistency:**
+        - Use same seed across generations
+        - Maintain lighting keywords
+        - Keep camera angle descriptions similar
+        - Use consistent color palette terms
+        
+        **Temporal Coherence:**
+        - Describe smooth motions
+        - Avoid jump cuts in single prompts
+        - Use transition words: "smoothly", "gradually", "continuously"
+        - Specify motion speed: "slow motion", "time-lapse", "real-time"
+        
+        ### 🏆 H200 Best Practices:
+        
+        **DO:**
+        ✅ Push the limits - H200 can handle complexity
+        ✅ Use detailed, multi-sentence prompts
+        ✅ Experiment with high frame counts
+        ✅ Try maximum inference steps for quality
+        ✅ Generate multiple variations quickly
+        
+        **DON'T:**
+        ❌ Limit yourself to basic settings
+        ❌ Worry about memory constraints  
+        ❌ Skip negative prompts
+        ❌ Use generic prompts
+        ❌ Settle for low resolution
+        
+        ### 🎬 Genre-Specific Prompting:
+        
+        **Nature Documentary:**
+        "BBC Planet Earth style, macro cinematography, natural lighting, wildlife behavior, David Attenborough quality"
+        
+        **Sci-Fi Epic:**
+        "Blade Runner 2049 aesthetic, neon lighting, futuristic architecture, dramatic cinematography, cyberpunk atmosphere"
+        
+        **Fantasy Adventure:**  
+        "Lord of the Rings cinematography, epic landscapes, mystical lighting, heroic composition, John Howe art style"
+        
+        **Commercial/Product:**
+        "Apple commercial style, clean minimalist aesthetic, perfect lighting, premium quality, studio photography"
         
-        info_btn.click(fn=get_system_info, outputs=system_output)
-        demo.load(fn=get_system_info, outputs=system_output)
+        Remember: H200's massive memory means you can be ambitious. Don't hold back! 🚀
+        """)
 
+# Launch with H200 optimizations
 if __name__ == "__main__":
-    demo.queue(max_size=5)
+    demo.queue(max_size=3)  # Smaller queue for premium H200 generations
     demo.launch(
         share=False,
         server_name="0.0.0.0",
         server_port=7860,
-        show_error=True
+        show_error=True,
+        show_api=False
     )