<a target="_blank" href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/evaluation.ipynb">
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Evaluation of generative models like [Stable Diffusion](https://huggingface.co/docs/diffusers/stable_diffusion) is subjective in nature. But as practitioners and researchers, we often have to make careful choices amongst many different possibilities. So, when working with different generative models (like GANs, Diffusion, etc.), how do we choose one over the other?  
Qualitative evaluation of such models can be error-prone and might incorrectly influence a decision.
However, quantitative metrics don't necessarily correspond to image quality. So, usually, a combination
of both qualitative and quantitative evaluations provides a stronger signal when choosing one model
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](https://huggingface.co/docs/diffusers/main/en/api/schedulers/overview) keeping the underlying generation model fixed.
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/conceptual/evaluation.md