Katherine Crowson
Also known as RiversHaveWings
As of October 2026, Katherine Crowson is an AI researcher whose work includes generative image models and model optimization. In 2022, she coauthored VQGAN-CLIP, a text-guided image-generation method, and LAION-5B, an open dataset of 5.85 billion image-text pairs. NeurIPS named the LAION-5B paper an Outstanding Paper in 2022. At Stability AI, she worked with Robin Rombach on next-generation media models and coauthored the Hourglass Diffusion Transformer in 2024. Her 2026 research includes LoRA-Muon, an optimizer method for low-rank fine-tuning.
- Now at
- Stability AI
- Works
- 5
- Citations
- 1,590
- h-index
- 3
Publication numbers from OpenAlex, as of 15 October 2026.
Timeline
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2022
VQGAN-CLIP image generation
She uses the RiversHaveWings handle on Hugging Face. In 2022, she and her coauthors present VQGAN-CLIP, which uses CLIP to guide VQGAN for open-domain image generation and editing.
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2022
The LAION-5B dataset
She coauthors LAION-5B, an open dataset of 5.85 billion CLIP-filtered image-text pairs designed to support research on large multimodal models.
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2022
A NeurIPS paper award
The LAION-5B paper receives a NeurIPS 2022 Outstanding Paper award.
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2022
Next-generation media models
At Stability AI, she works with Robin Rombach to create the next generation of media models.
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2024
Hourglass Diffusion Transformer
At Stability AI, she coauthors the 2024 Hourglass Diffusion Transformer (HDiT), which trains at high resolution directly in pixel space.
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As of October 2026
The LoRA-Muon optimizer
She is an AI researcher and coauthors LoRA-Muon, a June 2026 paper that applies Muon's spectral steepest-descent rule to low-rank fine-tuning.
Sources and credits
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- RiversHaveWings (Katherine Crowson)
- VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance
- LAION-5B: An open large-scale dataset for training next generation image-text models
- NeurIPS 2022 Awards
- Stable Diffusion 2.0 Release
- Hourglass Diffusion Transformers
- LoRA-Muon: Spectral Steepest Descent on the Low-Rank Manifold