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Loading...Introduction to Diffusion Models
Last quarter, our team discovered the power of diffusion models for image synthesis. We were working on a project that required generating high-quality images from text prompts, and after trying several approaches, we stumbled upon Stable Diffusion 2.1 and DreamFusion. Here's what we learned about these models and how we implemented them in our production environment.
What are Diffusion Models?
Diffusion models are a type of deep learning model that have shown great promise in image synthesis tasks. They work by iteratively refining a noise signal until it converges to a specific image. This process is called diffusion-based image synthesis.
Stable Diffusion 2.1
Stable Diffusion 2.1 is a state-of-the-art diffusion model that has achieved impressive results in image synthesis tasks.
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- Real-world examples and metrics
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