analyze-generative-diffusion-model

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 dataset.

1. Set up the evaluation pipeline:

import torch
from diffusers import StableDiffusionPipeline
from torchmetrics.image.fid import FrechetInceptionDistance
from torchmetrics.image.inception import InceptionScore

device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = StableDiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
).to(device)

fid = FrechetInceptionDistance(feature=2048, normalize=True).to(device)
inception = InceptionScore(normalize=True).to(device)