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**Github Repo**
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The detailed work description and code can be found in https://github.com/foxintohumanbeing/DDA4210_Group_project.
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The creation of high-quality image content from text descriptions is a challenging yet highly desirable task in the field
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of artificial intelligence. We focus on the Simpsons, a popular animated series. Based on pretrained SOTA model, we
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investigate in obtaining high-quality dataset and efficient fine-tuning methods. We explore the options of manually
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creating the dataset and using different fine-tuning techniques such as simple baseline, LoRA, and Dreambooth. Our
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approach involves combining the advantages of each option to achieve better results.
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We propose dataset collection method and fine-tuning model(Simspon Artistic Memory). Moreover, to better
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illustrating our results, we create two APPs, one for generating images and one for annotating the images (you can find them in github link provided). By improving
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data collection and fine-tuning techniques on Simpsons, we hope to push the boundaries of what is achievable in the
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text-to-image synthesis domain and inspire further research in this area.
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**Prompts Format**
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"The Simpsons. a [closeup?] of a [emotional expression] [race] [X year old] [man / woman / etc.], with [hair and makeup style], wearing [clothing style] while [doing] near [nearby objects],[outside / inside] with [objects / color ] in the background,in [time period]."
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