Editing Diffusion Models (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Expert evaluation of diffusion models uses both automatic metrics and human studies:

'''FID (Fréchet Inception Distance)''': The standard metric for image generation quality and diversity. Lower is better. Computes the distance between the distribution of generated and real images in a feature space. FID captures both quality (are samples sharp and realistic?) and diversity (do they cover the real data distribution?).

'''CLIP score''': Measures text-image alignment using CLIP's joint embedding space. Higher means the generated image better matches the text prompt.

'''Human evaluation''': For production systems, human raters assess photorealism, prompt adherence, aesthetic quality, and safety. Tools like RLHF for diffusion models (using human feedback to steer the model toward preferred outputs) are emerging.

'''Perceptual studies''': Present raters with real vs generated images and measure discrimination accuracy. Models that fool humans reliably are considered high-quality.

Expert practitioners also evaluate '''controllability''' — does the model reliably respond to specific attributes in the prompt? Does it understand spatial relationships ("a cat to the left of a dog")? Composition benchmarks like T2I-CompBench evaluate these capabilities.
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