Image and Video Generation

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.