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app.py
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</h1>
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<p style="margin-bottom: 10px; font-size: 94%">
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Paella is a novel text-to-image model that uses a compressed quantized latent space, based on a
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"""
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<div class="acknowledgments">
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<p><h4>Resources</h4>
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<a href="https://arxiv.org/abs/2211.07292" style="text-decoration: underline;">Paper</a>, <a href="https://github.com/dome272/Paella" style="text-decoration: underline;">official implementation</a>.
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</p>
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<p><h4>LICENSE</h4>
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<a href="https://github.com/dome272/Paella/blob/main/LICENSE" style="text-decoration: underline;">MIT</a>.
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</h1>
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</div>
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<p style="margin-bottom: 10px; font-size: 94%">
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Paella is a novel text-to-image model that uses a compressed quantized latent space, based on a VQGAN, and a masked training objective to achieve fast generation in ~10 inference steps.
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This version builds on top of our initial paper, bringing Paella to a similar level as other state-of-the-art models, while preserving the compactness and clarity of the previous implementations. Please, refer to the resources below for details.
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</p>
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"""
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</div>
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<div class="acknowledgments">
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<p><h4>Resources</h4>
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<a href="https://arxiv.org/abs/2211.07292" style="text-decoration: underline;">Paper</a>, <a href="https://github.com/dome272/Paella" style="text-decoration: underline;">official implementation</a>, <a href="https://huggingface.co/dome272/Paella" style="text-decoration: underline;">Model Card</a>.
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</p>
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<p><h4>LICENSE</h4>
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<a href="https://github.com/dome272/Paella/blob/main/LICENSE" style="text-decoration: underline;">MIT</a>.
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