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Trained on 196 images by Asura. I've only tested it on the Animagine checkpoint and i would recommend using it. Use a weight around 0.9 and a CFG Scale of 6 for best results.
This model is a fine-tuned version of Salesforce/blip-image-captioning-base, adapted for captioning UI elements from macOS application screenshots.
Originally posted to HuggingFace by sd-concepts-library hours style on Stable Diffusion This is the concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the Stable Conceptualizer...
The lovely Chastity exists only as bits and bytes, and any resemblance to anyone, whether living, dead, undead or other, is entirely coincidental.
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Works best at 0.75 weight.
Original synthetic characters, bundles and AI tools are in my shop:
This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set: Loss: 1.5381 Accuracy: 0.6667
Semantic segmentation model trained on Pascal VOC 2012. Architecture: MobileNetV3-Large + LR-ASPP (pretrained COCO backbone). Optimised with Optuna TPE-sampler HPO.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
