通过改进扩散模型，让文字生成图像更稳定、多样且高质量。
Development and Enhancement of Text-to-Image Diffusion Models
- 引入无分类器引导和指数移动平均技术提升生成效果
- 在主流模型上实现图像质量、多样性与训练稳定性的显著提升
- 适合想了解生成模型优化思路的研究者和开发者
本研究聚焦于文本到图像去噪扩散模型的开发与优化，针对样本多样性不足和训练不稳定等关键问题，结合无分类器引导（Classifier-Free Guidance）与指数移动平均（Exponential Moving Average）技术，显著提升图像质量、多样性与训练稳定性。基于Hugging Face的先进文本到图像生成模型，所提方法建立新的性能基准。研究深入探讨扩散模型原理，实施先进策略以克服现有局限，并全面评估改进效果。结果表明，该方法在从文本描述生成稳定、多样且高质量图像方面取得显著进展，推动生成式人工智能发展，为未来应用奠定新基础。
原文摘要 · Abstract (English)
This research focuses on the development and enhancement of text-to-image denoising diffusion models, addressing key challenges such as limited sample diversity and training instability. By incorporating Classifier-Free Guidance (CFG) and Exponential Moving Average (EMA) techniques, this study significantly improves image quality, diversity, and stability. Utilizing Hugging Face's state-of-the-art text-to-image generation model, the proposed enhancements establish new benchmarks in generative AI. This work explores the underlying principles of diffusion models, implements advanced strategies to overcome existing limitations, and presents a comprehensive evaluation of the improvements achieved. Results demonstrate substantial progress in generating stable, diverse, and high-quality images from textual descriptions, advancing the field of generative artificial intelligence and providing new foundations for future applications. Keywords: Text-to-image, Diffusion model, Classifier-free guidance, Exponential moving average, Image generation.
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