Implement a Diffusion Network

Build a denoising diffusion probabilistic model (DDPM) or score-based generative model from scratch, including the forward noising process, U-Net denoiser, training objective, reverse sampling procedure, and accelerated inference via DDIM or DPM-Solver.

When to Use

• Building a generative model for image, audio, or molecular synthesis
• Implementing DDPM or score-based diffusion from a research paper
• Adding a custom noise schedule or conditioning mechanism to a diffusion pipeline
• Replacing a GAN-based generator with a diffusion-based alternative
• Prototyping a diffusion model before scaling to production with frameworks like diffusers

Inputs

• Required: Training dataset (images, spectrograms, point clouds, or other continuous data)
• Required: Target resolution and number of channels
• Required: Compute budget (GPU type and count, training time limit)
• Optional: Noise schedule type (default: cosine)
• Optional: Number of diffusion timesteps T (default: 1000)