Analyze a Generative Diffusion Model

Evaluate pre-trained generative diffusion models through quantitative quality metrics, noise schedule inspection, cross-attention map analysis, and latent space probing to understand model behavior, diagnose failure modes, and guide fine-tuning decisions.

When to Use

• Evaluating a pre-trained generative diffusion model's output quality with standard metrics
• Computing FID, IS, CLIP score, or precision/recall for generated image sets
• Inspecting and comparing noise schedules (linear, cosine, learned) via SNR curves
• Extracting cross-attention maps to understand text-to-image token-region correspondences
• Interpolating between latent codes or discovering semantic directions in the latent space
• Detecting out-of-distribution inputs for a diffusion model pipeline

Inputs