The recent advancements in text-to-image generation and diffusion models have significantly enhanced the capabilities of design and artistic creation. A notable trend is the development of training-free frameworks that leverage diffusion features for tasks such as style attribution and identity generation. These frameworks address critical issues like data privacy, copyright infringement, and the need for fine-grained control in image generation. Innovations like LineArt focus on high-quality appearance transfer for design drawings, preserving structural accuracy and material precision without the need for extensive training or precise 3D modeling. Similarly, methods like LoRA and IntroStyle demonstrate the potential of parameter-efficient fine-tuning and introspective style attribution, respectively, to cluster and retrieve artistic styles accurately. MagicNaming introduces a novel concept of a 'Name Space' in diffusion models, enabling consistent identity generation through name embeddings, which preserves the original generation capabilities of text-to-image models. These developments collectively push the boundaries of what is possible in design, art, and image generation, offering new tools and insights for professionals in these fields.