A beginner's guide to the Flash-Eval model by Andreasjansson on Replicate

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This is a simplified guide to an AI model called Flash-Eval maintained by Andreasjansson. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.

Model overview

flash-eval is a suite of models developed by Andreas Jansson to evaluate the image quality of text-to-image models with respect to their input prompts. It is designed to provide fast and accurate evaluation of text-to-image diffusion generative models. The flash-eval suite includes similar models like sdxl-lightning-4step, stable-diffusion-animation, blip-2, clip-features, and if-v1.0 that also focus on image generation and evaluation.

Model inputs and outputs

The flash-eval suite takes in a set of text prompts and their corresponding generated images, and outputs evaluation scores for various metrics like CLIP, Aesthetic, ImageReward, and HPS. These metrics measure different aspects of the text-to-image generation quality, such as text-image alignment, image aesthetics, and human preference.

Inputs

Prompts and Images: A newline-separated list of prompt/image URL pairs. Each pair is formatted as <prompt>:<image1>[,<image2>[,<image3>[,...]]], where the prompt is followed by a colon and one or more comma-separated image URLs.
Models: A comma-separated list of models to use for evaluation. Valid models are ImageReward, Aesthetic, CLIP, BLIP, and PickScore.
Image Separator: A string to use as the separator between image URLs.
Prompt Images Separator: A string to use as the separator between the prompt and the list of images.

Outputs

Evaluation Scores: The model outputs an array of objects, where each object represents the evaluation scores for a given prompt and its corresponding images.