How do you implement image generation and manipulation with diffusion models?

Diffusion models generate images by learning to reverse a noise-adding process. Training: start with real images, progressively add Gaussian noise over T steps until the image becomes pure noise, then train a neural network (U-Net or Transformer) to predict and remove the noise at each step. Generation: start from random noise, iteratively denoise using the trained model, producing a coherent image after T denoising steps. Key models: Stable Diffusion (open source, runs locally, supports inpainting, outpainting, and image-to-image), DALL-E 3 (OpenAI, excellent text rendering and instruction following), and Midjourney (highest aesthetic quality). Text-to-image: CLIP text encoder converts the prompt into an embedding that guides the denoising process, with classifier-free guidance scaling the influence of text conditioning. Fine-tuning: LoRA adapters train on 10-50 images to learn specific styles or subjects (DreamBooth trains on 5-20 images of a specific subject), and textual inversion learns new concept tokens from a few examples. ControlNet: adds spatial conditioning (pose, depth, edge maps) to guide generation while maintaining the quality of the base model. Practical implementation: use diffusers library (Hugging Face) for programmatic access, implement ComfyUI for visual workflow-based generation, and deploy with a REST API for application integration. Applications: product visualization, architectural rendering, game asset generation, marketing material creation, and data augmentation for training other ML models. Optimization: use quantized models for faster inference, implement batched generation, cache CLIP embeddings for repeated prompts, and use progressive resolution (generate at low resolution, upscale with a super-resolution model).