  Large-scale text-to-image generative models have made impressive strides,
showcasing their ability to synthesize a vast array of high-quality images.
However, adapting these models for artistic image editing presents two
significant challenges. Firstly, users struggle to craft textual prompts that
meticulously detail visual elements of the input image. Secondly, prevalent
models, when effecting modifications in specific zones, frequently disrupt the
overall artistic style, complicating the attainment of cohesive and
aesthetically unified artworks. To surmount these obstacles, we build the
innovative unified framework CreativeSynth, which is based on a diffusion model
with the ability to coordinate multimodal inputs and multitask in the field of
artistic image generation. By integrating multimodal features with customized
attention mechanisms, CreativeSynth facilitates the importation of real-world
semantic content into the domain of art through inversion and real-time style
transfer. This allows for the precise manipulation of image style and content
while maintaining the integrity of the original model parameters. Rigorous
qualitative and quantitative evaluations underscore that CreativeSynth excels
in enhancing artistic images' fidelity and preserves their innate aesthetic
essence. By bridging the gap between generative models and artistic finesse,
CreativeSynth becomes a custom digital palette.
