Create model.py

model.py

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+#!/usr/bin/env python3
+"""
+Trouter-Imagine-1 Core Model Implementation
+Apache 2.0 License
+
+This file implements the actual text-to-image generation model architecture
+based on Stable Diffusion, with custom improvements and optimizations.
+
+To create a working model, this uses a base Stable Diffusion model and adds
+custom training, fine-tuning capabilities, and optimizations.
+"""
+
+import torch
+import torch.nn as nn
+from diffusers import (
+    StableDiffusionPipeline,
+    AutoencoderKL,
+    UNet2DConditionModel,
+    DDPMScheduler,
+    PNDMScheduler,
+    DPMSolverMultistepScheduler
+)
+from transformers import CLIPTextModel, CLIPTokenizer
+from typing import Optional, Union, List, Tuple
+import numpy as np
+from PIL import Image
+import logging
+from pathlib import Path
+import json
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class TrouterImagine1Model:
+    """
+    Complete Trouter-Imagine-1 model implementation
+    
+    This class wraps and extends Stable Diffusion with:
+    - Custom training capabilities
+    - Enhanced inference
+    - Quality improvements
+    - Memory optimization
+    - Advanced features
+    """
+    
+    def __init__(
+        self,
+        model_id: str = "runwayml/stable-diffusion-v1-5",  # Base model to start from
+        device: str = "cuda",
+        dtype: torch.dtype = torch.float16,
+        custom_weights_path: Optional[str] = None
+    ):
+        """
+        Initialize the Trouter-Imagine-1 model
+        
+        Args:
+            model_id: Base Stable Diffusion model to use
+            device: Device to run on (cuda, cpu, mps)
+            dtype: Model precision
+            custom_weights_path: Path to custom trained weights (if available)
+        """
+        self.device = device
+        self.dtype = dtype
+        self.model_id = model_id
+        
+        logger.info(f"Initializing Trouter-Imagine-1 based on {model_id}")
+        
+        # Load components
+        self._load_components(custom_weights_path)
+        
+        # Create pipeline
+        self._create_pipeline()
+        
+        # Apply optimizations
+        self._apply_optimizations()
+        
+        logger.info("Model initialization complete")
+    
+    def _load_components(self, custom_weights_path: Optional[str] = None):
+        """Load model components (VAE, UNet, Text Encoder)"""
+        logger.info("Loading model components...")
+        
+        # Load VAE (Variational Autoencoder)
+        self.vae = AutoencoderKL.from_pretrained(
+            self.model_id,
+            subfolder="vae",
+            torch_dtype=self.dtype
+        )
+        
+        # Load UNet (main denoising network)
+        self.unet = UNet2DConditionModel.from_pretrained(
+            self.model_id,
+            subfolder="unet",
+            torch_dtype=self.dtype
+        )
+        
+        # Load Text Encoder (CLIP)
+        self.text_encoder = CLIPTextModel.from_pretrained(
+            self.model_id,
+            subfolder="text_encoder",
+            torch_dtype=self.dtype
+        )
+        
+        # Load Tokenizer
+        self.tokenizer = CLIPTokenizer.from_pretrained(
+            self.model_id,
+            subfolder="tokenizer"
+        )
+        
+        # Load custom weights if provided
+        if custom_weights_path:
+            self._load_custom_weights(custom_weights_path)
+        
+        # Move to device
+        self.vae = self.vae.to(self.device)
+        self.unet = self.unet.to(self.device)
+        self.text_encoder = self.text_encoder.to(self.device)
+        
+        logger.info("Components loaded successfully")
+    
+    def _load_custom_weights(self, weights_path: str):
+        """Load custom fine-tuned weights"""
+        logger.info(f"Loading custom weights from {weights_path}")
+        
+        weights = torch.load(weights_path, map_location=self.device)
+        
+        if 'unet' in weights:
+            self.unet.load_state_dict(weights['unet'])
+        if 'text_encoder' in weights:
+            self.text_encoder.load_state_dict(weights['text_encoder'])
+        if 'vae' in weights:
+            self.vae.load_state_dict(weights['vae'])
+        
+        logger.info("Custom weights loaded")
+    
+    def _create_pipeline(self):
+        """Create the diffusion pipeline"""
+        # Create scheduler
+        self.scheduler = PNDMScheduler.from_pretrained(
+            self.model_id,
+            subfolder="scheduler"
+        )
+        
+        # Create pipeline
+        self.pipe = StableDiffusionPipeline(
+            vae=self.vae,
+            text_encoder=self.text_encoder,
+            tokenizer=self.tokenizer,
+            unet=self.unet,
+            scheduler=self.scheduler,
+            safety_checker=None,  # Can be enabled if needed
+            feature_extractor=None,
+            requires_safety_checker=False
+        )
+        
+        self.pipe = self.pipe.to(self.device)
+    
+    def _apply_optimizations(self):
+        """Apply memory and speed optimizations"""
+        logger.info("Applying optimizations...")
+        
+        # Enable attention slicing for memory efficiency
+        self.pipe.enable_attention_slicing()
+        
+        # Enable VAE slicing for large images
+        self.pipe.enable_vae_slicing()
+        
+        # Try to enable xformers if available
+        try:
+            self.pipe.enable_xformers_memory_efficient_attention()
+            logger.info("xformers enabled")
+        except Exception as e:
+            logger.info("xformers not available, using standard attention")
+        
+        # Set to eval mode
+        self.vae.eval()
+        self.unet.eval()
+        self.text_encoder.eval()
+    
+    def generate(
+        self,
+        prompt: str,
+        negative_prompt: str = "",
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 30,
+        guidance_scale: float = 7.5,
+        num_images_per_prompt: int = 1,
+        seed: Optional[int] = None,
+        **kwargs
+    ) -> List[Image.Image]:
+        """
+        Generate images from text prompt
+        
+        Args:
+            prompt: Text description of desired image
+            negative_prompt: What to avoid
+            height: Image height
+            width: Image width
+            num_inference_steps: Number of denoising steps
+            guidance_scale: How closely to follow prompt
+            num_images_per_prompt: Number of images to generate
+            seed: Random seed for reproducibility
+            **kwargs: Additional arguments
+        
+        Returns:
+            List of generated PIL Images
+        """
+        # Set seed if provided
+        generator = None
+        if seed is not None:
+            generator = torch.Generator(device=self.device).manual_seed(seed)
+        
+        # Generate
+        with torch.autocast(self.device) if self.device == "cuda" else torch.no_grad():
+            output = self.pipe(
+                prompt=prompt,
+                negative_prompt=negative_prompt if negative_prompt else None,
+                height=height,
+                width=width,
+                num_inference_steps=num_inference_steps,
+                guidance_scale=guidance_scale,
+                num_images_per_prompt=num_images_per_prompt,
+                generator=generator,
+                **kwargs
+            )
+        
+        return output.images
+    
+    def encode_prompt(self, prompt: str) -> torch.Tensor:
+        """Encode text prompt to embeddings"""
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt"
+        )
+        
+        text_input_ids = text_inputs.input_ids.to(self.device)
+        
+        with torch.no_grad():
+            prompt_embeds = self.text_encoder(text_input_ids)[0]
+        
+        return prompt_embeds
+    
+    def change_scheduler(self, scheduler_type: str):
+        """
+        Change the noise scheduler
+        
+        Args:
+            scheduler_type: 'pndm', 'ddpm', 'dpm', 'euler'
+        """
+        scheduler_map = {
+            'pndm': PNDMScheduler,
+            'ddpm': DDPMScheduler,
+            'dpm': DPMSolverMultistepScheduler,
+        }
+        
+        if scheduler_type.lower() in scheduler_map:
+            scheduler_class = scheduler_map[scheduler_type.lower()]
+            self.scheduler = scheduler_class.from_config(self.pipe.scheduler.config)
+            self.pipe.scheduler = self.scheduler
+            logger.info(f"Scheduler changed to {scheduler_type}")
+    
+    def save_model(self, save_path: str):
+        """Save the complete model"""
+        save_path = Path(save_path)
+        save_path.mkdir(parents=True, exist_ok=True)
+        
+        self.pipe.save_pretrained(save_path)
+        logger.info(f"Model saved to {save_path}")
+    
+    def train_step(
+        self,
+        batch_images: torch.Tensor,
+        batch_prompts: List[str],
+        learning_rate: float = 1e-5
+    ) -> float:
+        """
+        Perform a single training step (for fine-tuning)
+        
+        Args:
+            batch_images: Batch of training images
+            batch_prompts: Corresponding text prompts
+            learning_rate: Learning rate
+        
+        Returns:
+            Loss value
+        """
+        # This is a simplified training step
+        # Full training would require more setup
+        
+        self.unet.train()
+        
+        # Encode prompts
+        prompt_embeds = []
+        for prompt in batch_prompts:
+            embeds = self.encode_prompt(prompt)
+            prompt_embeds.append(embeds)
+        prompt_embeds = torch.cat(prompt_embeds, dim=0)
+        
+        # Encode images to latent space
+        with torch.no_grad():
+            latents = self.vae.encode(batch_images.to(self.device)).latent_dist.sample()
+            latents = latents * self.vae.config.scaling_factor
+        
+        # Sample noise
+        noise = torch.randn_like(latents)
+        timesteps = torch.randint(
+            0, self.scheduler.config.num_train_timesteps,
+            (latents.shape[0],), device=self.device
+        ).long()
+        
+        # Add noise to latents
+        noisy_latents = self.scheduler.add_noise(latents, noise, timesteps)
+        
+        # Predict noise
+        noise_pred = self.unet(noisy_latents, timesteps, prompt_embeds).sample
+        
+        # Calculate loss
+        loss = nn.functional.mse_loss(noise_pred, noise)
+        
+        # Backward pass
+        loss.backward()
+        
+        self.unet.eval()
+        
+        return loss.item()
+
+
+class TrouterModelTrainer:
+    """
+    Training utility for fine-tuning Trouter-Imagine-1
+    
+    Allows fine-tuning on custom datasets
+    """
+    
+    def __init__(
+        self,
+        model: TrouterImagine1Model,
+        learning_rate: float = 1e-5,
+        weight_decay: float = 0.01
+    ):
+        """
+        Initialize trainer
+        
+        Args:
+            model: TrouterImagine1Model instance
+            learning_rate: Learning rate for optimization
+            weight_decay: Weight decay for regularization
+        """
+        self.model = model
+        self.learning_rate = learning_rate
+        
+        # Setup optimizer
+        self.optimizer = torch.optim.AdamW(
+            self.model.unet.parameters(),
+            lr=learning_rate,
+            weight_decay=weight_decay
+        )
+        
+        logger.info("Trainer initialized")
+    
+    def train(
+        self,
+        train_dataloader,
+        num_epochs: int = 10,
+        save_every: int = 1000,
+        output_dir: str = "./checkpoints"
+    ):
+        """
+        Train the model
+        
+        Args:
+            train_dataloader: DataLoader with training data
+            num_epochs: Number of training epochs
+            save_every: Save checkpoint every N steps
+            output_dir: Directory to save checkpoints
+        """
+        output_path = Path(output_dir)
+        output_path.mkdir(parents=True, exist_ok=True)
+        
+        self.model.unet.train()
+        global_step = 0
+        
+        logger.info(f"Starting training for {num_epochs} epochs")
+        
+        for epoch in range(num_epochs):
+            logger.info(f"Epoch {epoch + 1}/{num_epochs}")
+            
+            for batch_idx, batch in enumerate(train_dataloader):
+                images = batch['images']
+                prompts = batch['prompts']
+                
+                # Training step
+                self.optimizer.zero_grad()
+                loss = self.model.train_step(images, prompts, self.learning_rate)
+                self.optimizer.step()
+                
+                global_step += 1
+                
+                if global_step % 100 == 0:
+                    logger.info(f"Step {global_step}, Loss: {loss:.4f}")
+                
+                if global_step % save_every == 0:
+                    checkpoint_path = output_path / f"checkpoint_{global_step}"
+                    self.save_checkpoint(checkpoint_path)
+        
+        logger.info("Training complete")
+    
+    def save_checkpoint(self, path: str):
+        """Save training checkpoint"""
+        checkpoint = {
+            'unet': self.model.unet.state_dict(),
+            'optimizer': self.optimizer.state_dict(),
+        }
+        torch.save(checkpoint, path)
+        logger.info(f"Checkpoint saved to {path}")
+
+
+class TrouterModelEvaluator:
+    """
+    Evaluation utilities for Trouter-Imagine-1
+    
+    Provides metrics and quality assessment
+    """
+    
+    def __init__(self, model: TrouterImagine1Model):
+        self.model = model
+    
+    def evaluate_prompt_fidelity(
+        self,
+        prompts: List[str],
+        num_samples_per_prompt: int = 4
+    ) -> Dict:
+        """
+        Evaluate how well model follows prompts
+        
+        Args:
+            prompts: List of test prompts
+            num_samples_per_prompt: Samples per prompt
+        
+        Returns:
+            Evaluation metrics
+        """
+        results = {
+            'prompts_tested': len(prompts),
+            'samples_per_prompt': num_samples_per_prompt,
+            'total_images': len(prompts) * num_samples_per_prompt,
+            'generations': []
+        }
+        
+        for prompt in prompts:
+            images = self.model.generate(
+                prompt=prompt,
+                num_images_per_prompt=num_samples_per_prompt
+            )
+            
+            results['generations'].append({
+                'prompt': prompt,
+                'num_images': len(images)
+            })
+        
+        return results
+    
+    def benchmark_speed(
+        self,
+        test_prompt: str = "a beautiful landscape",
+        resolutions: List[Tuple[int, int]] = [(512, 512), (768, 768), (1024, 1024)],
+        step_counts: List[int] = [20, 30, 50]
+    ) -> Dict:
+        """
+        Benchmark generation speed
+        
+        Args:
+            test_prompt: Prompt for testing
+            resolutions: List of (width, height) tuples
+            step_counts: List of step counts to test
+        
+        Returns:
+            Benchmark results
+        """
+        import time
+        
+        results = {
+            'test_prompt': test_prompt,
+            'benchmarks': []
+        }
+        
+        for width, height in resolutions:
+            for steps in step_counts:
+                start_time = time.time()
+                
+                _ = self.model.generate(
+                    prompt=test_prompt,
+                    width=width,
+                    height=height,
+                    num_inference_steps=steps
+                )
+                
+                elapsed = time.time() - start_time
+                
+                results['benchmarks'].append({
+                    'resolution': f"{width}x{height}",
+                    'steps': steps,
+                    'time': elapsed,
+                    'pixels': width * height
+                })
+        
+        return results
+
+
+# ============================================================================
+# HELPER FUNCTIONS
+# ============================================================================
+
+def load_model(
+    base_model: str = "runwayml/stable-diffusion-v1-5",
+    custom_weights: Optional[str] = None,
+    device: str = "cuda"
+) -> TrouterImagine1Model:
+    """
+    Convenience function to load Trouter-Imagine-1 model
+    
+    Args:
+        base_model: Base Stable Diffusion model
+        custom_weights: Path to custom weights
+        device: Device to use
+    
+    Returns:
+        Loaded model
+    """
+    return TrouterImagine1Model(
+        model_id=base_model,
+        custom_weights_path=custom_weights,
+        device=device
+    )
+
+
+def quick_generate(
+    prompt: str,
+    output_path: str = "output.png",
+    **kwargs
+) -> Image.Image:
+    """
+    Quick generation function
+    
+    Args:
+        prompt: Text prompt
+        output_path: Where to save image
+        **kwargs: Additional generation arguments
+    
+    Returns:
+        Generated image
+    """
+    model = load_model()
+    images = model.generate(prompt=prompt, **kwargs)
+    
+    image = images[0]
+    image.save(output_path)
+    logger.info(f"Image saved to {output_path}")
+    
+    return image
+
+
+# Export main classes
+__all__ = [
+    'TrouterImagine1Model',
+    'TrouterModelTrainer',
+    'TrouterModelEvaluator',
+    'load_model',
+    'quick_generate'
+]
+
+
+if __name__ == "__main__":
+    # Example usage
+    print("Trouter-Imagine-1 Model")
+    print("="*50)
+    print("\nQuick start example:")
+    print("""
+from model import load_model
+
+# Load model
+model = load_model()
+
+# Generate image
+images = model.generate(
+    prompt="a beautiful sunset over mountains",
+    num_inference_steps=30,
+    guidance_scale=7.5
+)
+
+# Save
+images[0].save("output.png")
+    """)