Understanding image and video generation with diffusion-based approaches.
Diffusion models are the dominant approach for AI image and video generation. They work by learning to reverse a noise-adding process: given an image gradually corrupted into random noise, the model learns to denoise it step by step. At generation time, the model starts with pure noise and iteratively refines it into a coherent image guided by a text prompt.
This approach has proven remarkably powerful. Models like Stable Diffusion, DALL-E 3, Midjourney, and Flux can generate photorealistic images, artistic illustrations, and even video from text descriptions. The ecosystem includes customization tools like LoRA adapters and ControlNet that allow fine-tuning generation for specific styles or structural constraints.
How Diffusion Works
Forward process gradually adds noise to an image until it becomes random static. The model learns to reverse this โ starting from noise and iteratively denoising into a coherent image.
Text Conditioning
CLIP or T5 text encoders translate your text prompt into a guidance signal. This signal steers the denoising process to produce images matching your description.
Key Models
Stable Diffusion 3 (open), DALL-E 3 (OpenAI), Midjourney v6 (subscription), Flux (Black Forest Labs), Ideogram (text rendering). Each has different strengths in style, quality, and control.
Latent Diffusion
Working in compressed latent space (64x smaller than raw pixels) makes generation fast and memory-efficient. A VAE encoder/decoder bridges between pixel and latent spaces.
ControlNet
Add structural guidance via edge maps, depth maps, pose estimation, or segmentation masks. Lets you control the composition while the diffusion model handles details and style.
LoRA Adapters
Lightweight fine-tuning (typically 10-100MB) that teaches the model new styles, characters, or concepts without full retraining. Community shares thousands of LoRAs on CivitAI and HuggingFace.
Video Generation
Sora (OpenAI), Runway Gen-3, Kling (Kuaishou), Pika extend diffusion to temporal sequences. Video gen adds motion consistency and temporal coherence challenges.
Inpainting & Outpainting
Edit specific regions of generated or real images. Inpainting replaces a masked area, outpainting extends the image beyond its borders. Both use the same diffusion process.
Diffusion vs GANs
GANs (Generative Adversarial Networks) are faster but harder to train and less diverse. Diffusion models produce higher quality and more varied outputs at the cost of slower generation.
Local Tools
ComfyUI (node-based, flexible) and Automatic1111 (web UI, user-friendly) are popular open-source interfaces for running Stable Diffusion locally on your own GPU.
DiffusionImage generation process that starts from random noise and iteratively refines it into a coherent output.
Latent SpaceCompressed mathematical representation of images that models work in for efficiency.
LoRALow-Rank Adaptation โ a lightweight method to customize models without full retraining.
ControlNetExtension that adds structural guidance (edges, poses, depth) to diffusion generation.