title: CreativeSynth: Creative Blending and Synthesis of Visual Arts based on
    Multimodal Diffusion
summary: 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.
paper_authors._id: 65b31695b389ca2de1af548b
paper_authors.name: Nisha Huang
paper_authors._id: 65b31695b389ca2de1af548c
paper_authors.name: Weiming Dong
paper_authors._id: 65b31695b389ca2de1af548d
paper_authors.name: Yuxin Zhang
paper_authors._id: 65b31695b389ca2de1af548e
paper_authors.name: Fan Tang
paper_authors._id: 65b31695b389ca2de1af548f
paper_authors.name: Ronghui Li
paper_authors._id: 65b31695b389ca2de1af5490
paper_authors.name: Chongyang Ma
paper_authors._id: 65b31695b389ca2de1af5491
paper_authors.name: Xiu Li
paper_authors._id: 65b31695b389ca2de1af5492
paper_authors.name: Changsheng Xu
paper_title: CreativeSynth: Creative Blending and Synthesis of Visual Arts based on
    Multimodal Diffusion
paper_summary: 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.
paper_ai_keywords: diffusion model multimodal inputs multitask customized attention mechanisms inversion real-time style transfer artistic image generation image style image content parameter integrity fidelity aesthetic essence