analyze-generative-diffusion-modellisted

# 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 - **Required**: Pre-trained model identifier or checkpoint path (e.g., `stabilityai/stable-diffusion-2-1`) - **Required**: Analysis mode — one or more of: `metrics`, `schedule`, `attention`, `latent` - **Required**: Reference dataset for metric computation (real images or dataset name) - **Optional**: Text prompts for attention analysis (default: model-appropriate test prompts) - **Optional**: Number of generated samples for metric computation (default: 10000) - **Optional**: Device configuration (default: `cuda` if available, else `cpu`) ## Procedure ### Step 1: Quantitative Evaluation Compute standard generative quality metrics against a reference datase