Challenge reflecting the difficulty (such as “Basic”, “Complex”, “Writing & Symbols”, etc.).

Category of the prompt (such as “Abstract”, “World Knowledge”, etc.).

P2 can be used to measure model capabilities across various categories and challenge aspects. PartiPrompts (P2) is a rich set of over 1600 prompts in English that we release as part of this work. DrawBench and PartiPrompts were introduced by Imagen and Parti respectively. Common prompts provide a degree of uniformity for subjective metrics.ĭrawBench and PartiPrompts are prompt datasets used for qualitative benchmarking.

Quality is measured across aspects such as compositionality, image-text alignment, and spatial relations. Qualitative evaluation typically involves human assessment of generated images.

Class-conditioned image generation models (such as the DiTPipeline).

Text-guided image generation, additionally conditioned on an input image (such as the StableDiffusionImg2ImgPipeline, and StableDiffusionInstructPix2PixPipeline).

Text-guided image generation (such as the StableDiffusionPipeline).

We cover Diffusion models with the following pipelines: The methods shown in this document can also be used to evaluate different noise schedulers keeping the underlying generation model fixed. For quantitative methods, we specifically focus on how to implement them alongside diffusers. In this document, we provide a non-exhaustive overview of qualitative and quantitative methods to evaluate Diffusion models. Of both qualitative and quantitative evaluations provides a stronger signal when choosing one model However, quantitative metrics don’t necessarily correspond to image quality. Qualitative evaluation of such models can be error-prone and might incorrectly influence a decision. So, when working with different generative models (like GANs, Diffusion, etc.), how do we choose one over the other? But as practitioners and researchers, we often have to make careful choices amongst many different possibilities. Evaluation of generative models like Stable Diffusion is subjective in nature.

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