Text-to-Image System Design

class ProductionTextToImageSystem:
    def __init__(self, config):
        # Core models
        self.clip_model = CLIPTextEncoder.from_pretrained(config.clip_model)
        self.unet = UNet2DConditionModel.from_pretrained(config.unet_model)
        self.vae = AutoencoderKL.from_pretrained(config.vae_model)
        self.scheduler = DDIMScheduler.from_pretrained(config.scheduler_config)
        
        # Safety and moderation
        self.safety_checker = SafetyChecker()
        self.content_filter = ContentFilter()
        
        # Performance optimization
        self.memory_efficient = config.memory_efficient
        self.attention_slicing = config.attention_slicing
        
        if config.compile_models:
            self.unet = torch.compile(self.unet)
            self.vae = torch.compile(self.vae)
    
    async def generate_image(self, 
                           prompt: str,
                           negative_prompt: str = None,
                           width: int = 1024,
                           height: int = 1024,
                           num_inference_steps: int = 50,
                           guidance_scale: float = 7.5,
                           safety_check: bool = True) -> GenerationResult:
        
        # Step 1: Input validation and safety
        if safety_check:
            safety_result = await self.safety_checker.check_prompt(prompt)
            if not safety_result.is_safe:
                raise SafetyViolationError(safety_result.reason)
        
        # Step 2: Text encoding
        with torch.inference_mode():
            text_embeddings = self.encode_prompt(prompt, negative_prompt)
        
        # Step 3: Image generation
        generation_start = time.time()
        
        # Latent initialization
        latents = self.initialize_latents(width, height)
        
        # Denoising loop
        self.scheduler.set_timesteps(num_inference_steps)
        for i, timestep in enumerate(self.scheduler.timesteps):
            # Predict noise
            with torch.inference_mode():
                noise_pred = self.unet(
                    latents,
                    timestep,
                    encoder_hidden_states=text_embeddings
                ).sample
            
            # Apply guidance
            if guidance_scale > 1.0:
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                noise_pred = noise_pred_uncond + guidance_scale * (
                    noise_pred_text - noise_pred_uncond
                )
            
            # Scheduler step
            latents = self.scheduler.step(noise_pred, timestep, latents).prev_sample
            
            # Memory optimization
            if self.memory_efficient and i % 5 == 0:
                torch.cuda.empty_cache()
        
        # Step 4: Decode to image
        with torch.inference_mode():
            image = self.vae.decode(latents / 0.18215).sample
            image = self.postprocess_image(image)
        
        generation_time = time.time() - generation_start
        
        # Step 5: Final safety check
        if safety_check:
            final_safety = await self.content_filter.check_image(image)
            if not final_safety.is_safe:
                return GenerationResult(
                    image=None,
                    error="Generated content blocked by safety filter",
                    generation_time=generation_time
                )
        
        return GenerationResult(
            image=image,
            prompt=prompt,
            generation_time=generation_time,
            parameters={
                "steps": num_inference_steps,
                "guidance": guidance_scale,
                "size": f"{width}x{height}"
            }
        )
    
    def encode_prompt(self, prompt: str, negative_prompt: str = None):
        """Encode text prompts to embeddings"""
        # Tokenize and encode positive prompt
        text_input = self.clip_model.tokenizer(
            prompt,
            padding="max_length",
            max_length=77,
            truncation=True,
            return_tensors="pt"
        )
        
        text_embeddings = self.clip_model.text_model(text_input.input_ids)[0]
        
        # Handle negative prompt
        if negative_prompt:
            negative_input = self.clip_model.tokenizer(
                negative_prompt,
                padding="max_length", 
                max_length=77,
                truncation=True,
                return_tensors="pt"
            )
            negative_embeddings = self.clip_model.text_model(negative_input.input_ids)[0]
        else:
            negative_embeddings = torch.zeros_like(text_embeddings)
        
        # Concatenate for classifier-free guidance
        return torch.cat([negative_embeddings, text_embeddings])
    
    def initialize_latents(self, width: int, height: int):
        """Initialize random latents for generation"""
        latent_height = height // self.vae.config.scaling_factor
        latent_width = width // self.vae.config.scaling_factor
        
        latents = torch.randn(
            (1, self.unet.config.in_channels, latent_height, latent_width),
            device=self.device,
            dtype=self.unet.dtype
        )
        
        return latents * self.scheduler.init_noise_sigma