Generatieve AI & Synthesis

// Multi-Modal Generative AI Pipeline
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForCausalLM
from diffusers import StableDiffusionPipeline, ControlNetModel
from typing import Dict, List, Optional, Union
import numpy as np

class MultiModalGenerativeEngine:
    def __init__(self, config: Dict):
        # Text Generation Models
        self.text_model = AutoModelForCausalLM.from_pretrained(
            config['text_model_path'],
            torch_dtype=torch.float16,
            device_map="auto"
        )
        self.tokenizer = AutoTokenizer.from_pretrained(config['text_model_path'])

        # Image Generation Pipeline
        self.image_pipeline = StableDiffusionPipeline.from_pretrained(
            config['image_model_path'],
            torch_dtype=torch.float16,
            safety_checker=None,
            requires_safety_checker=False
        ).to("cuda")

        # ControlNet voor precision control
        self.controlnet = ControlNetModel.from_pretrained(
            config['controlnet_path'],
            torch_dtype=torch.float16
        )

        # Custom fine-tuned models
        self.lora_adapters = self.load_lora_adapters(config['lora_paths'])
        self.custom_embeddings = self.load_custom_embeddings(config['embedding_paths'])

        # Quality control
        self.safety_filter = SafetyFilter()
        self.quality_scorer = ContentQualityScorer()

    def generate_text(self, prompt: str, max_length: int = 512,
                     temperature: float = 0.7) -> Dict:
        """Generate high-quality text met controllable parameters"""
        # Tokenize input
        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.text_model.device)

        # Generate with custom sampling
        with torch.no_grad():
            outputs = self.text_model.generate(
                inputs.input_ids,
                max_length=max_length,
                temperature=temperature,
                do_sample=True,
                top_p=0.9,
                top_k=50,
                repetition_penalty=1.1,
                pad_token_id=self.tokenizer.eos_token_id
            )

        # Decode generated text
        generated_text = self.tokenizer.decode(
            outputs[0][inputs.input_ids.shape[1]:],
            skip_special_tokens=True
        )

        # Quality scoring
        quality_score = self.quality_scorer.score_text(generated_text)

        return {
            'text': generated_text,
            'quality_score': quality_score,
            'perplexity': self.calculate_perplexity(generated_text),
            'tokens_generated': len(outputs[0]) - inputs.input_ids.shape[1]
        }

    def generate_image(self, prompt: str, negative_prompt: str = "",
                      guidance_scale: float = 7.5, num_inference_steps: int = 50,
                      control_image: Optional[np.ndarray] = None) -> Dict:
        """Generate high-quality images met precision control"""
        # Apply LoRA adapters als gespecificeerd
        if hasattr(self.image_pipeline.unet, 'set_adapters'):
            self.image_pipeline.unet.set_adapters(['style_adapter'])

        # ControlNet conditioning
        if control_image is not None:
            control_image = self.preprocess_control_image(control_image)

        # Generate image
        with torch.autocast("cuda"):
            result = self.image_pipeline(
                prompt=prompt,
                negative_prompt=negative_prompt,
                num_inference_steps=num_inference_steps,
                guidance_scale=guidance_scale,
                image=control_image if control_image is not None else None,
                controlnet_conditioning_scale=1.0,
                generator=torch.Generator("cuda").manual_seed(42)
            )

        image = result.images[0]

        # Quality assessment
        quality_metrics = self.assess_image_quality(image, prompt)

        # Safety filtering
        safety_result = self.safety_filter.check_image(image)

        return {
            'image': image,
            'quality_metrics': quality_metrics,
            'safety_passed': safety_result['safe'],
            'generation_time': result.latents.shape[0] * num_inference_steps
        }

# Advanced LoRA Fine-tuning System
class LoRAFineTuningSystem:
    def __init__(self, base_model: nn.Module, config: Dict):
        self.base_model = base_model
        self.lora_config = config
        self.lora_layers = {}

        # Initialize LoRA layers
        self.initialize_lora_layers()

    def initialize_lora_layers(self):
        """Initialize LoRA adaptation layers"""
        for name, module in self.base_model.named_modules():
            if isinstance(module, (nn.Linear, nn.Conv2d)):
                if any(target in name for target in self.lora_config['target_modules']):
                    lora_layer = LoRALayer(
                        in_features=module.in_features if hasattr(module, 'in_features') else module.in_channels,
                        out_features=module.out_features if hasattr(module, 'out_features') else module.out_channels,
                        rank=self.lora_config['rank'],
                        alpha=self.lora_config['alpha'],
                        dropout=self.lora_config['dropout']
                    )
                    self.lora_layers[name] = lora_layer

    def fine_tune(self, dataset: torch.utils.data.Dataset,
                 num_epochs: int = 3, learning_rate: float = 1e-4):
        """Fine-tune model met LoRA adapters"""
        # Setup optimizer alleen voor LoRA parameters
        lora_params = []
        for lora_layer in self.lora_layers.values():
            lora_params.extend(list(lora_layer.parameters()))

        optimizer = torch.optim.AdamW(lora_params, lr=learning_rate)
        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
            optimizer, T_max=num_epochs
        )

        dataloader = torch.utils.data.DataLoader(
            dataset, batch_size=self.lora_config['batch_size'], shuffle=True
        )

        # Training loop
        for epoch in range(num_epochs):
            total_loss = 0
            for batch_idx, batch in enumerate(dataloader):
                optimizer.zero_grad()

                # Forward pass met LoRA adaptation
                outputs = self.forward_with_lora(batch['input_ids'])
                loss = self.compute_loss(outputs, batch['labels'])

                loss.backward()
                torch.nn.utils.clip_grad_norm_(lora_params, max_norm=1.0)
                optimizer.step()

                total_loss += loss.item()

            avg_loss = total_loss / len(dataloader)
            print(f"Epoch {epoch+1}/{num_epochs}, Loss: {avg_loss:.4f}")

            scheduler.step()

        return {
            'final_loss': avg_loss,
            'lora_weights': {name: layer.state_dict()
                           for name, layer in self.lora_layers.items()}
        }

class LoRALayer(nn.Module):
    def __init__(self, in_features: int, out_features: int,
                 rank: int = 16, alpha: float = 16, dropout: float = 0.1):
        super().__init__()
        self.rank = rank
        self.alpha = alpha

        # LoRA matrices
        self.lora_A = nn.Linear(in_features, rank, bias=False)
        self.lora_B = nn.Linear(rank, out_features, bias=False)
        self.dropout = nn.Dropout(dropout)
        self.scaling = alpha / rank

        # Initialize weights
        nn.init.kaiming_uniform_(self.lora_A.weight, a=np.sqrt(5))
        nn.init.zeros_(self.lora_B.weight)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.lora_B(self.dropout(self.lora_A(x))) * self.scaling

# Content Quality Assessment System
class ContentQualityScorer:
    def __init__(self):
        # Pre-trained quality assessment models
        self.clip_model = self.load_clip_model()
        self.aesthetic_scorer = self.load_aesthetic_model()
        self.text_quality_model = self.load_text_quality_model()

    def score_image(self, image: np.ndarray, prompt: str) -> Dict:
        """Comprehensive image quality assessment"""
        # Technical quality metrics
        sharpness = self.calculate_sharpness(image)
        contrast = self.calculate_contrast(image)
        color_balance = self.calculate_color_balance(image)

        # Semantic quality (CLIP score)
        clip_score = self.calculate_clip_score(image, prompt)

        # Aesthetic quality
        aesthetic_score = self.aesthetic_scorer.predict(image)

        # Artifact detection
        artifacts = self.detect_artifacts(image)

        return {
            'technical_quality': {
                'sharpness': sharpness,
                'contrast': contrast,
                'color_balance': color_balance
            },
            'semantic_alignment': clip_score,
            'aesthetic_score': aesthetic_score,
            'artifacts_detected': artifacts,
            'overall_score': self.compute_overall_score(
                sharpness, contrast, clip_score, aesthetic_score, artifacts
            )
        }

    def score_text(self, text: str) -> Dict:
        """Comprehensive text quality assessment"""
        # Linguistic quality
        grammar_score = self.assess_grammar(text)
        coherence_score = self.assess_coherence(text)
        fluency_score = self.assess_fluency(text)

        # Content quality
        informativeness = self.assess_informativeness(text)
        creativity_score = self.assess_creativity(text)

        # Safety checks
        toxicity_score = self.assess_toxicity(text)
        bias_score = self.assess_bias(text)

        return {
            'linguistic_quality': {
                'grammar': grammar_score,
                'coherence': coherence_score,
                'fluency': fluency_score
            },
            'content_quality': {
                'informativeness': informativeness,
                'creativity': creativity_score
            },
            'safety_scores': {
                'toxicity': toxicity_score,
                'bias': bias_score
            },
            'overall_score': self.compute_text_overall_score(
                grammar_score, coherence_score, informativeness, toxicity_score
            )
        }