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This model was designed as a general purpose fine tuning of SD1.5 on high quality photographs and traditional artworks upscaled and denoised before training to get the sharpest cleanest results possible.
It's trained at 576 resolution using the offset noise fix so generations are sharp and detailed with vibrant colors, deep blacks, and a well balanced contrast.

This model was not trained with A.I. generated images or merged with any other models which means your images won't have that green/aqua color cast seen in so many models today.

The largest portion of the dataset consists of photographs of women, men, gorgeous landscapes, and luxurious home/cabin/hotel interiors, some abandoned buildings and cityscapes, followed by a few unique art styles that can most easily be discovered by prompting with the commonly used words below.

Some tips for realistic images:
Don't use the word "realistic"
part of the dataset contains "realistic porcelain dolls" and it will likely reference those dolls to a degree when prompted. I tend to avoid that word anyways because "realistic" is usually used to describe high quality paintings, cgi, or other classically non realistic artforms and when I want realism I'm trying to reference photographs but do whatever works for you, there's plenty of latent space to explore and I'm no expert. It shouldn't be an issue but If your faces look a bit smoother than you like try throwing the word "doll" in the negative prompt.
This model is great at both high detailed realism and stylized images so keep these two words in mind when generating.

A few common words found in the training dataset include:
Papercut
Liquid Splash
Realistic Porcelain Doll
Interior
Landscape
...and a few others for you to discover.

If your so inclined to leave a tip I'm happy to accept monero at this address:
82s3fk8bQB2DHJ3r9idZUsST1Dvf5cKKC6Fu87rYgV9dAFbCbAcXMPXaP59yDwWzRXfYfTBszHZno6xGwDb17xUzEkCsAah

Thanks for checking out my work and enjoy!

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model_index.json

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scheduler/scheduler_config.json

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text_encoder/config.json

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tokenizer/special_tokens_map.json

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tokenizer/tokenizer_config.json

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