For a long time, physicists assumed the effect was instantaneous.Įinstein was eventually awarded the 1921 Nobel Prize in Physics “for his services to Theoretical Physics, and especially for his discovery of the law of the photoelectric effect.” Parenthetically, when he gave his Nobel Prize lecture, it was not in Stockholm in December 1922 (Einstein was in Japan at that time), but in the middle of the summer of 1923, in Gothenburg – a unique event in the history of the Nobel Prize. In 1905, Albert Einstein published the first explanation of the photoelectric effect, but at the time, it was impossible to resolve the timescales that were relevant for this effect. ITI-GEN: Inclusive Text-to-Image Generation ITI-GEN requires no model fine-tuning, making it computationally efficient to augment existing text-to-image models.Ĭheck out our oral presentation Wednesday 4th at 1:30 pm at ICCV-2023. Moreover, the prompt-inclusive embeddings can be combined linearly and are general enough to apply to other network architectures (e.g., pix2pix, control-net) to modify inclusive attributes within the images. The key idea is learning a set of prompt-inclusive embeddings to generate images that can effectively represent all desired attribute categories. Our solution, ITI-GEN, leverages readily available reference images for inclusive Text-to-Image Generation. : We embrace the adage that "a picture is worth a thousand words,", and that images can represent concepts more expressively than text. Unfortunately, directly expressing the desired attributes in the prompt often leads to sub-optimal results due to linguistic ambiguity or model misrepresentation. Text-to-image generative models often reflect the biases of the training data, leading to unequal representations of underrepresented groups. : How can we generate text-to-image generative models that are uniformly balanced across attributes of interest?
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