Image and Video Generation
How diffusion models and generative AI create visual content
What it is
Modern image generation primarily uses diffusion models, systems trained to gradually denoise random noise into coherent images conditioned on a text prompt. During training, images are progressively corrupted with noise; the model learns to reverse this process. At inference, the model starts from random noise and iteratively denoises it guided by the text prompt.
Models like DALL-E 3 and Stable Diffusion combine a text encoder (often CLIP or LLM-based) with a UNet or transformer diffusion model. Video generation extends this to temporal consistency, requiring coherent motion across frames, a significantly harder problem.
The integration trend is toward multimodal LLMs that natively generate and understand images rather than requiring separate model pipelines.