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VOOZH | about |
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VOOZH | about |
AI/ML Technical Content Strategist
Between Stable Diffusion 3.5 and FLUX.1, this year has seen another renaissance for text-to-image generation. These models have given us a step forward in prompt adherence, added the capability to spell with open-source models, and continued to improve in the quality of their aesthetic outputs. Nonetheless, the core mechanic behind these models has remained fundamentally the same: use some, with either an empty latent image or image primer, to generate a single image.
In this article, we want to shine a spotlight on an incredibly promising new architecture for text-to-image generation, OmniGen. Inspired by similar efforts in the Large Language Model research community, OmniGen is the first fully unified diffusion model framework for all sorts of additional downstream tasks like image editing, subject-driven generation, and visual-conditional generation Source.
Follow along for a breakdown of the architecture that makes OmniGen possible, an exploration of the models capabilities, and a demonstration for how to run OmniGen and test the model using a DigitalOcean GPU Droplet.
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