  We present Corgi, a novel method for text-to-image generation. Corgi is based
on our proposed shifted diffusion model, which achieves better image embedding
generation from input text. Unlike the baseline diffusion model used in DALL-E
2, our method seamlessly encodes prior knowledge of the pre-trained CLIP model
in its diffusion process by designing a new initialization distribution and a
new transition step of the diffusion. Compared to the strong DALL-E 2 baseline,
our method performs better in generating image embedding from the text in terms
of both efficiency and effectiveness, resulting in better text-to-image
generation. Extensive large-scale experiments are conducted and evaluated in
terms of both quantitative measures and human evaluation, indicating a stronger
generation ability of our method compared to existing ones. Furthermore, our
model enables semi-supervised and language-free training for text-to-image
generation, where only part or none of the images in the training dataset have
an associated caption. Trained with only 1.7% of the images being captioned,
our semi-supervised model obtains FID results comparable to DALL-E 2 on
zero-shot text-to-image generation evaluated on MS-COCO. Corgi also achieves
new state-of-the-art results across different datasets on downstream
language-free text-to-image generation tasks, outperforming the previous
method, Lafite, by a large margin.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning