GLM-Image - Auto-regressive for dense-knowledge & high-fidelity images
GLM-Image combines a 9B Auto-regressive model with a 7B Diffusion decoder. This hybrid architecture excels at knowledge-dense generation, perfect for posters, diagrams, and precise text rendering. Open-source and ready for T2I & I2I tasks.
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Flowtica Scribe
Hi everyone!
The architecture here is what caught my eye immediately. We have been debating "Autoregressive vs. Diffusion" for a long time in the generative AI space, but GLM-Image just said: "Why not both?"
It uses a 9B AR model (basically a modified LLM) to handle the "logic", like layout, complex prompt understanding, and text planning, and then passes that to a 7B Diffusion decoder to handle the "art" and high-frequency details.
This approach seems to directly address the biggest weaknesses of current pure diffusion models: accurate text rendering and complex spatial relationships. The benchmarks on both English and Chinese text accuracy look incredible.
Haven't tried the local deployment yet. The hardware requirements look a bit heavy but definitely promising for the quality. And I am sure the quantized versions are on the way.
Wow this is huge, okay I'm adding this to ImageRouter right now! You guys are unstoppable!
How can I get higher API limits?