Abstract

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.

Method

Results

Left: generated images on Parti-Prompts, in the order of SDv1.5, prompt engineering, DDPO, and TextCraftor. Right: examples from HPSv2, ordered as SDv1.5, prompt engineering, and TextCraftor.

Left: generated images on Parti-Prompts, in the order of SDv1.5, prompt engineering, DDPO, TextCraftor, and TextCraftor + UNet. Right: examples from HPSv2, ordered as SDv1.5, prompt engineering, TextCraftor, and TextCraftor + UNet.