CharGen: Fast and Fluent Portrait Modification
Interactive editing of character images with diffusion models remains challenging due to the inherent trade-off between fine-grained control, generation speed, and visual fidelity. We introduce CharGen, a character-focused editor that combines attribute-specific Concept Sliders, trained to isolate and manipulate attributes such as facial feature size, expression, and decoration with the StreamDiffusion sampling pipeline for more interactive performance. To counteract the loss of detail that often accompanies accelerated sampling, we propose a lightweight Repair Step that reinstates fine textures without compromising structural consistency. Throughout extensive ablation studies and in comparison to open-source InstructPix2Pix and closed-source Google Gemini, and a comprehensive user study, CharGen achieves two-to-four-fold faster edit turnaround with precise editing control and identity-consistent results. Project page: https://chargen.jdihlmann.com/
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
CharGen: High Accurate Character-Level Visual Text Generation Model with MultiModal Encoder
Recently, significant advancements have been made in diffusion-based visual text generation models. Although the effectiveness of these methods in visual text rendering is rapidly improving, they still encounter challeng…
Text GenerationSay Cheese! Detail-Preserving Portrait Collection Generation via Natural Language Edits
As social media platforms proliferate, users increasingly demand intuitive ways to create diverse, high-quality portrait collections. In this work, we introduce Portrait Collection Generation (PCG), a novel task that gen…
PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering
Generating portrait images by controlling the motions of existing faces is an important task of great consequence to social media industries. For easy use and intuitive control, semantically meaningful and fully disentan…
Image GenerationNeural RenderingCosAvatar: Consistent and Animatable Portrait Video Tuning with Text Prompt
Recently, text-guided digital portrait editing has attracted more and more attentions. However, existing methods still struggle to maintain consistency across time, expression, and view or require specific data prerequis…
NeRFPastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer
Recent studies on StyleGAN show high performance on artistic portrait generation by transfer learning with limited data. In this paper, we explore more challenging exemplar-based high-resolution portrait style transfer b…
Style TransferTransfer LearningVocal Bursts Intensity Prediction