model.py

#!/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")
    """)