Diffusion models generate data by reversing a gradual noising process. They learn to denoise samples through a Markov chain defined by variance schedules.
Key Uses:
High-fidelity image synthesis (Stable Diffusion), molecular structure generation in drug discovery, and audio synthesis.
Hacks:
Use Classifier-Free Guidance to improve conditional generation without a separate classifier. Apply Latent Diffusion Models (LDMs) to operate in compressed latent space for efficiency. Fine-tune with DreamBooth or LoRA for subject-specific generation with few images. ControlNet for adding structural conditions (edges, depth maps) to generation.
References:
Ho, J., et al. (2020). Denoising Diffusion Probabilistic Models. NeurIPS.
Rombach, R., et al. (2022). High-Resolution Image Synthesis with Latent Diffusion Models