from constants import css
import gradio as gr
block = gr.Blocks(css=css)
with block:
gr.HTML(
"""
Evaluate Schedulers with StableDiffusionPipeline 🧨
This Space allows you to quantitatively compare different noise schedulers with a `StableDiffusionPipeline`.
One of the applications of this Space could be to evaluate different schedulers for a certain Stable Diffusion checkpoint for a fixed number of inference steps.
"""
)
gr.HTML(
"""
How it works?
The evaluator first sets a seed and then generates the initial noise which is passed as the initial latent to start the image generation process. It is done to ensure fair comparison.
This initial latent is used every time the pipeline is run (with different schedulers).
To quantify the quality of the generated images we use:
Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the LAION-5B dataset, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. You can read more in the model card