Understanding Diffusion Models for Generative AI | Generated by AI
A diffusion model is a type of generative AI model that creates new data (like images or videos) by gradually refining random noise into a structured output. Here’s how it works in simple terms:
Core Idea: Noise to Data
- Start with Noise: The process begins with a random noise image (or video frame).
- Step-by-Step Refinement: The model iteratively removes noise, transforming it into a coherent image or video that matches the desired output.
Key Steps in a Diffusion Model
- Forward Process (Adding Noise)
- A real image is gradually corrupted by adding Gaussian noise over many steps.
- This creates a sequence of increasingly noisy versions of the original image.
- Reverse Process (Generating Data)
- The model learns to reverse this process: starting from pure noise, it predicts and removes noise step-by-step.
- At each step, the model uses a neural network (often a U-Net or transformer) to estimate and remove the noise, slowly revealing the final image or video.
- Conditioning (Optional)
- The process can be guided by text prompts, class labels, or other inputs to ensure the output matches the user’s request.
Why Diffusion Models?
- High Quality: They produce sharp, detailed, and diverse outputs.
- Stability: The gradual refinement process makes training more stable compared to other generative models.
- Flexibility: They can be applied to images, videos, audio, and more.
Example: Image Generation
- You start with a blank canvas of noise.
- The model iteratively “denoises” it, guided by your prompt (e.g., “a cat sitting on a couch”).
- After many steps, the noise transforms into a realistic image of a cat on a couch.
Diffusion Models in Sora
In Sora, the diffusion model is paired with a transformer to handle both the high-level structure (transformer) and fine visual details (diffusion), enabling the generation of complex, high-quality videos.
Would you like a deeper explanation of any specific part, such as the math behind the noise scheduling or how conditioning works?