DeepFloyd IF: The Future of Text-to-Image Synthesis and Upcoming Release

DeepFloyd IF, a state-of-the-art open-source text-to-image model, has been gaining attention due to its photorealism and language understanding capabilities. The model is a modular composition of a frozen text encoder and three cascaded pixel diffusion modules, generating images in 64x64 px, 256x256 px, and 1024x1024 px resolutions. It utilizes a T5 transformer-based frozen text encoder to extract text embeddings, which are then fed into a UNet architecture enhanced with cross-attention and attention pooling. DeepFloyd IF has achieved a zero-shot FID score of 6.66 on the COCO dataset, surpassing current state-of-the-art models. Find out more on their GitHub repository.

The actual model is set to release in a few days under a non-commercial, restrictive license. The license prohibits circumventing the "safety checker" feature, and users are not allowed to remove or disable any inference filters or filter mechanisms.

Questions have been raised about the availability of model weights and the potential for circumventing the safety filter. The weights were briefly available on Hugging Face before being taken down. Keep an eye out for further updates and potential reuploads on platforms like Hugging Face.

Tags: DeepFloyd IF, text-to-image model, T5 transformer, UNet architecture, COCO dataset, Hugging Face

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