Yannic Kilcher Videos (Audio Only)
DeepFloyd IF - Pixel-Based Text-to-Image Diffusion (w/ Authors)
Aug 28, 2023
Guests Misha Konstantinov and Daria Bakshandaeva from DeepFloyd discuss their open-source model, IF, which follows Google's implementation of Imagen. They explain the working of the model, its performance in creating realistic images, experiments with text encoders, multilingual content generation, and plans for future releases and collaborations.
53:31
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Quick takeaways
- The IF model is an open-source replication of Google's Imagen model that operates directly on pixel space, resulting in high-quality generated images following specific prompts in various languages.
- The evaluation of text-to-image models, particularly concerning the FID score, lacks consistency and transparency, highlighting the need for a predefined evaluation set or mean score to evaluate all models consistently.
Deep dives
Model Overview
The podcast episode discusses the IF model, an open-source replication of the Imagine model from Google Research. Unlike stable diffusion, the IF model operates directly on pixel space, making the diffusion process happen on pixels. It uses up samplers to improve the quality of the generated images. The model demonstrates good text understanding and can generate images that follow specific prompts in various languages.
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