  Text-to-image (T2I) diffusion models, notably the unCLIP models (e.g.,
DALL-E-2), achieve state-of-the-art (SOTA) performance on various compositional
T2I benchmarks, at the cost of significant computational resources. The unCLIP
stack comprises T2I prior and diffusion image decoder. The T2I prior model
alone adds a billion parameters compared to the Latent Diffusion Models, which
increases the computational and high-quality data requirements. We introduce
ECLIPSE, a novel contrastive learning method that is both parameter and
data-efficient. ECLIPSE leverages pre-trained vision-language models (e.g.,
CLIP) to distill the knowledge into the prior model. We demonstrate that the
ECLIPSE trained prior, with only 3.3% of the parameters and trained on a mere
2.8% of the data, surpasses the baseline T2I priors with an average of 71.6%
preference score under resource-limited setting. It also attains performance on
par with SOTA big models, achieving an average of 63.36% preference score in
terms of the ability to follow the text compositions. Extensive experiments on
two unCLIP diffusion image decoders, Karlo and Kandinsky, affirm that ECLIPSE
priors consistently deliver high performance while significantly reducing
resource dependency.
