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README.md
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**Limitations & Bias:**
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The Segmind-Vega Model faces challenges in achieving absolute photorealism, especially in human depictions. While it may encounter difficulties in incorporating clear text and maintaining the fidelity of complex compositions due to its autoencoding approach, these challenges present opportunities for future enhancements. Importantly, the model's exposure to a diverse dataset, though not a cure-all for ingrained societal and digital biases, represents a foundational step toward more equitable technology. Users are encouraged to interact with this pioneering tool with an understanding of its current limitations, fostering an environment of conscious engagement and anticipation for its continued evolution.
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**Limitations & Bias:**
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The Segmind-Vega Model faces challenges in achieving absolute photorealism, especially in human depictions. While it may encounter difficulties in incorporating clear text and maintaining the fidelity of complex compositions due to its autoencoding approach, these challenges present opportunities for future enhancements. Importantly, the model's exposure to a diverse dataset, though not a cure-all for ingrained societal and digital biases, represents a foundational step toward more equitable technology. Users are encouraged to interact with this pioneering tool with an understanding of its current limitations, fostering an environment of conscious engagement and anticipation for its continued evolution.
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## Citation
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```
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@misc{gupta2024progressive,
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title={Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer Level Loss},
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author={Yatharth Gupta and Vishnu V. Jaddipal and Harish Prabhala and Sayak Paul and Patrick Von Platen},
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year={2024},
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eprint={2401.02677},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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