over the other. In this document, we provide a non-exhaustive overview of qualitative and quantitative methods to evaluate Diffusion models. For quantitative methods, we specifically focus on how to implement them alongside diffusers. The methods shown in this document can also be used to evaluate different noise schedulers keeping the underlying generation model fixed.  Scenarios We cover Diffusion models with the following pipelines: Text-guided image generation (such as the StableDiffusionPipeline). Text-guided image generation, additionally conditioned on an input image (such as the StableDiffusionImg2ImgPipeline and StableDiffusionInstructPix2PixPipeline). Class-conditioned image generation models (such as the DiTPipeline).  Qualitative Evaluation Qualitative evaluation typically involves human assessment of generated images. Quality is measured across aspects such as compositionality, image-text alignment, and spatial relations. Common prompts provide a degree of uniformity for subjective metrics.