Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in
  Text-to-Image Generation
In this work, we share three insights for achieving state-of-the-art
aesthetic quality in text-to-image generative models. We focus on three
critical aspects for model improvement: enhancing color and contrast, improving
generation across multiple aspect ratios, and improving human-centric fine
details. First, we delve into the significance of the noise schedule in
training a diffusion model, demonstrating its profound impact on realism and
visual fidelity. Second, we address the challenge of accommodating various
aspect ratios in image generation, emphasizing the importance of preparing a
balanced bucketed dataset. Lastly, we investigate the crucial role of aligning
model outputs with human preferences, ensuring that generated images resonate
with human perceptual expectations. Through extensive analysis and experiments,
Playground v2.5 demonstrates state-of-the-art performance in terms of aesthetic
quality under various conditions and aspect ratios, outperforming both
widely-used open-source models like SDXL and Playground v2, and closed-source
commercial systems such as DALLE 3 and Midjourney v5.2. Our model is
open-source, and we hope the development of Playground v2.5 provides valuable
guidelines for researchers aiming to elevate the aesthetic quality of
diffusion-based image generation models.
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Daiqing Li
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2024-02-28T12:42:00.530Z
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Li
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Daiqing
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Aleks Kamko
admin_assigned
2024-02-28T12:42:11.828Z
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Aleks Kamko
user
aykamko
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Ehsan Akhgari
admin_assigned
2024-02-28T12:42:18.669Z
636c0c4eaae2da3c76b8a9a3
https://cdn-avatars.huggingface.co/v1/production/uploads/1675208663648-636c0c4eaae2da3c76b8a9a3.png
Ehsan Akhgari
user
ehsanakh
65deb1fa06a8c85c5e9bb731
Ali Sabet
admin_assigned
2024-02-28T12:42:59.815Z
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Ali Sabet
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asabet-pai
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Linmiao Xu
admin_assigned
2024-02-28T12:43:06.121Z
63653383a7a1324ccd56b370
/avatars/d623a5bf1ee8fb8ebaeef26b661101ff.svg
Linmiao Xu
user
linrock
65deb1fa06a8c85c5e9bb733
Suhail Doshi
Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in
  Text-to-Image Generation
In this work, we share three insights for achieving state-of-the-art
aesthetic quality in text-to-image generative models. We focus on three
critical aspects for model improvement: enhancing color and contrast, improving
generation across multiple aspect ratios, and improving human-centric fine
details. First, we delve into the significance of the noise schedule in
training a diffusion model, demonstrating its profound impact on realism and
visual fidelity. Second, we address the challenge of accommodating various
aspect ratios in image generation, emphasizing the importance of preparing a
balanced bucketed dataset. Lastly, we investigate the crucial role of aligning
model outputs with human preferences, ensuring that generated images resonate
with human perceptual expectations. Through extensive analysis and experiments,
Playground v2.5 demonstrates state-of-the-art performance in terms of aesthetic
quality under various conditions and aspect ratios, outperforming both
widely-used open-source models like SDXL and Playground v2, and closed-source
commercial systems such as DALLE 3 and Midjourney v5.2. Our model is
open-source, and we hope the development of Playground v2.5 provides valuable
guidelines for researchers aiming to elevate the aesthetic quality of
diffusion-based image generation models.
Enhancements to noise schedules, aspect ratio accommodation, and alignment with human perceptual preferences improve the aesthetic quality of text-to-image generative models, leading to state-of-the-art performance.
diffusion model noise schedule realism visual fidelity balanced bucketed dataset human preferences aesthetic quality state-of-the-art performance SDXL Midjourney v5.2