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| license: mit |
| pipeline_tag: text-to-image |
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| # Qanvas (PAUSED TO FOCUS ON QED-BASE-V3) |
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| ## Purpose of Research |
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| Development of a text-to-image model for mobile devices that provides good quality of the resulting image while reducing memory consumption and computing power required. |
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| The aim is to design the solution that can comprehend the meaning of prompts and generate images using knowledge-based representations instead of memorizing templates. |
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| # Current Status |
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| - QED-Base-V1 Tokenizer (REUSED): Completed |
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| - QLIP: Completed |
| - Lightweight CLIP-style text encoder trained from scratch |
| - The purpose of the model is to provide semantic alignment between texts and images in an resource-efficient way |
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| - Q-VAE: Completed |
| - Custom VAE trained from scratch to enable image latents compression/reconstruction |
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| - Qanvas Generator: Failed-Planning something new. |
| - Latent image generator conditioned on QLIP embeddings |
