πŸ“„
SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis paper by Dustin Podell, Zion English, Kyle Lacey, A. Blattmann, Tim Dockhorn, Jonas Muller, Joe Penna, Robin Rombach
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Paper

SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

by Dustin Podell, Zion English, Kyle Lacey, A. Blattmann, Tim Dockhorn, Jonas Muller, Joe Penna, Robin Rombach 2307.01952
Free2AITools Nexus Index
64.3
S: Semantic 50

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A: Authority 95
P: Popularity 77
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

We present SDXL, a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. We design multiple novel conditioning schemes and train SDXL on multiple aspect ratios. We also introduce a refinement model which is used to improve the visual fidelity of sample...

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Semantic Scholar 4.0K Citations
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Registry ID 2307.01952
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{arxiv_2307_01952,
  author = {Dustin Podell, Zion English, Kyle Lacey, A. Blattmann, Tim Dockhorn, Jonas Muller, Joe Penna, Robin Rombach},
  title = {SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2307.01952}},
  note = {Accessed via Free2AITools.}
}
APA Style
Dustin Podell, Zion English, Kyle Lacey, A. Blattmann, Tim Dockhorn, Jonas Muller, Joe Penna, Robin Rombach. (2026). SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis [Paper]. Free2AITools. https://arxiv.org/abs/2307.01952

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βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 95
Popularity (P) 77
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis: Authority (A:95), Popularity (P:77), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"We present SDXL, a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. We design multiple novel conditioning schemes and train SDXL on multiple aspect ratios. We also introduce a refinement model which is used to improve the visual fidelity of sample..."

❝ Cite Node 

@article{Podell2026SDXL:,
  title={SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis},
  author={Dustin Podell and Zion English and Kyle Lacey and A. Blattmann and Tim Dockhorn and Jonas Muller and Joe Penna and Robin Rombach},
  journal={arXiv preprint arXiv:2307.01952},
  year={2026}
}

πŸ‘₯ Collaborating Minds

Dustin Podell Zion English Kyle Lacey A. Blattmann Tim Dockhorn Jonas Muller Joe Penna Robin Rombach

πŸ”— Full Paper

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πŸ“Š Research Signals

πŸ“ˆ3,958CitationsSemantic Scholar
πŸ›οΈ95AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈvision multimediaField

🏷️ Research Topics

image generationrag retrievalattention mechanism
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πŸ†” Identity & Source

id
2307.01952
slug
2307.01952
source
semantic_scholar
author
Dustin Podell, Zion English, Kyle Lacey, A. Blattmann, Tim Dockhorn, Jonas Muller, Joe Penna, Robin Rombach
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

πŸ“Š Engagement & Metrics

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citations
3,958

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