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The five-dollar model is a lightweight text-to-image generative architecturethat generates low dimensional images from an encoded text prompt. This modelcan successfully generate accurate and aesthetically pleasing content in lowdimensional domains, with limited amounts of training data. Despite the smallsize of both the model and datasets, the generated images are still able tomaintain the encoded semantic meaning of the textual prompt. We apply thismodel to three small datasets: pixel art video game maps, video game spriteimages, and down-scaled emoji images and apply novel augmentation strategies toimprove the performance of our model on these limited datasets. We evaluate ourmodels performance using cosine similarity score between text-image pairsgenerated by the CLIP VIT-B/32 model.

The Five-Dollar Model: Generating Game Maps and Sprites from Sentence Embeddings