What is Diffusion Model?
Diffusion Model is a class of generative deep learning models that learn to generate data by gradually denoising a normally distributed variable, reversing a forward diffusion process that progressively adds Gaussian noise to training data until it becomes pure noise.
Quick Facts
| Full Name | Diffusion Probabilistic Model |
|---|---|
| Created | 2015 (initial concept), 2020 (DDPM by Ho et al.), 2022 (Stable Diffusion public release) |
| Specification | Official Specification |
How It Works
Diffusion models work through two processes: a forward diffusion process that gradually adds noise to data over multiple timesteps until it becomes indistinguishable from random noise, and a reverse denoising process where a neural network learns to predict and remove the noise step by step. This approach, formalized in Denoising Diffusion Probabilistic Models (DDPM), has become the foundation for state-of-the-art image generation systems. Notable implementations include Stable Diffusion, DALL-E 2/3, Midjourney, and Imagen. Latent diffusion models operate in a compressed latent space rather than pixel space, dramatically reducing computational requirements while maintaining high-quality outputs. These models have revolutionized AI-generated art and are expanding into video, audio, and 3D content generation. The latest generation of diffusion models includes Stable Diffusion 3 (improved text rendering and composition), FLUX (by Black Forest Labs, former Stability AI team), and DALL-E 3 (native integration with ChatGPT). These models demonstrate improved prompt following, text generation within images, and compositional understanding.
Key Characteristics
- Iterative denoising process that gradually transforms noise into coherent data
- Based on Markov chain theory with mathematically tractable training objectives
- Latent space diffusion enables efficient high-resolution image generation
- Supports conditional generation through text prompts, images, or other modalities
- Produces highly diverse outputs with excellent mode coverage
- Controllable generation through guidance scales and negative prompts
Common Use Cases
- Text-to-image generation: creating images from natural language descriptions (Stable Diffusion, DALL-E, Midjourney)
- Image editing and inpainting: modifying specific regions while preserving context
- Image-to-image translation: style transfer, super-resolution, and colorization
- Video generation: creating short video clips from text or image prompts (Sora, Runway Gen-2)
- 3D asset generation: generating 3D models and textures for games and design
Example
Loading code...Frequently Asked Questions
What is the difference between diffusion models and GANs?
Diffusion models generate images through iterative denoising steps, while GANs use a generator-discriminator adversarial setup. Diffusion models typically produce higher quality and more diverse outputs with better training stability, but are slower at inference. GANs are faster but can suffer from mode collapse and training instability. Diffusion models have largely replaced GANs for high-quality image generation.
What does 'guidance scale' mean in diffusion models?
Guidance scale (classifier-free guidance) controls how closely the generated image follows the text prompt. Higher values (7-15) produce images that more strictly match the prompt but may lose diversity and naturalness. Lower values (1-5) allow more creative freedom but may deviate from the prompt. A value of 7.5 is commonly used as a balanced default.
What are negative prompts and how do they work?
Negative prompts tell the model what to avoid in the generated image (e.g., 'blurry, low quality, distorted'). During generation, the model actively steers away from concepts in the negative prompt. They help improve image quality and exclude unwanted elements. Common negative prompts include quality issues (blur, noise) and unwanted content (extra limbs, watermarks).
What is latent diffusion and why is it important?
Latent diffusion operates in a compressed latent space (encoded by a VAE) rather than pixel space. This dramatically reduces computational requirements (8x or more) while maintaining high-quality outputs. Stable Diffusion uses this approach, enabling it to run on consumer GPUs. The latent space captures semantic information efficiently, making generation faster and more memory-efficient.
How many inference steps should I use for image generation?
More steps generally produce higher quality images but take longer. Common ranges: 20-30 steps for quick drafts, 50 steps for good quality (default for many models), 100+ steps for maximum quality with diminishing returns. Modern schedulers (DPM++, Euler) can achieve good results with fewer steps (20-30) compared to older methods (DDPM) that required 1000+ steps.