DiffusionToolkit VS improved-aesthetic-predictor

Diffusion Toolkit (https://github.com/RupertAvery/DiffusionToolkit) is an image metadata-indexer and viewer for AI-generated images. It aims to help you organize, search and sort your ever-growing collection, using the data that's right there on the images.

Awesome thanks, I wouldn't have thought to use 'works" I always do JCW or just (Mini Cooper). FYI you can pull the prompt info from the image of you are using Automatic1111 or vladdiffusion, under PNGinfo tab. If not using those repos, something like this works well: https://github.com/RupertAvery/DiffusionToolkit

Perhaps you could use an "aesthetic-evaluator": https://github.com/christophschuhmann/improved-aesthetic-predictor (probably not SoTA but it works)

"Calculates aesthetic score for generated images using CLIP+MLP Aesthetic Score Predictor based on Chad Scorer "

stable-diffusion-v1-2: Resumed from stable-diffusion-v1-1. 515,000 steps at resolution 512x512 on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size >= 512x512, estimated aesthetics score > 5.0, and an estimated watermark probability < 0.5. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an improved aesthetics estimator).

The data used for fine-tuning has come from a random sample of 56k Danbooru images, which were filtered based on CLIP Aesthetic Scoring where only images with an aesthetic score greater than 6.0 were used.

All they've said is they randomly picked 56,000 images that had an aesthetic score greater than 6.0. The score is created by this model. https://github.com/christophschuhmann/improved-aesthetic-predictor

Does anyone know how you could combine a model like Aesthetic Score Predictor with stable diffusion? They used this model to filter training images by score. Iit seems like a lot of people just tune their prompt to make images more aesthetic, by adding certain words.