326 citations · 423 across the 4 of their papers we have counts for
4 papers
Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis
Hila Chefer, Patrick Esser, Dominik Lorenz +5
Strong semantic representations improve the convergence and generation quality of diffusion and flow models. Existing approaches largely rely on external models, which require sepa…
FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
Black Forest Labs, Stephen Batifol, Andreas Blattmann +18
We present evaluation results for FLUX.1 Kontext, a generative flow matching model that unifies image generation and editing. The model generates novel output views by incorporatin…
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Patrick Esser, Sumith Kulal, Andreas Blattmann +14
Diffusion models create data from noise by inverting the forward paths of data towards noise and have emerged as a powerful generative modeling technique for high-dimensional, perc…
SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
Dustin Podell, Zion English, Kyle Lacey +5
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 inc…