  Diffusion-based text-to-image generative models, e.g., Stable Diffusion, have
revolutionized the field of content generation, enabling significant
advancements in areas like image editing and video synthesis. Despite their
formidable capabilities, these models are not without their limitations. It is
still challenging to synthesize an image that aligns well with the input text,
and multiple runs with carefully crafted prompts are required to achieve
satisfactory results. To mitigate these limitations, numerous studies have
endeavored to fine-tune the pre-trained diffusion models, i.e., UNet, utilizing
various technologies. Yet, amidst these efforts, a pivotal question of
text-to-image diffusion model training has remained largely unexplored: Is it
possible and feasible to fine-tune the text encoder to improve the performance
of text-to-image diffusion models? Our findings reveal that, instead of
replacing the CLIP text encoder used in Stable Diffusion with other large
language models, we can enhance it through our proposed fine-tuning approach,
TextCraftor, leading to substantial improvements in quantitative benchmarks and
human assessments. Interestingly, our technique also empowers controllable
image generation through the interpolation of different text encoders
fine-tuned with various rewards. We also demonstrate that TextCraftor is
orthogonal to UNet finetuning, and can be combined to further improve
generative quality.
