1 citations · 2 across the 10 of their papers we have counts for
10 papers
Preventing Shortcuts in Adapter Training via Providing the Shortcuts
Anujraaj Argo Goyal, Guocheng Gordon Qian, Huseyin Coskun +8
Adapter-based training has emerged as a key mechanism for extending the capabilities of powerful foundation image generators, enabling personalized and stylized text-to-image synth…
ComposeMe: Attribute-Specific Image Prompts for Controllable Human Image Generation
Guocheng Gordon Qian, Daniil Ostashev, Egor Nemchinov +4
Generating high-fidelity images of humans with fine-grained control over attributes such as hairstyle and clothing remains a core challenge in personalized text-to-image synthesis.…
CanvasComposer: Personalized Group Photo Generation via a Multi-Reference Canvas
Gordon Guocheng Qian, Ruihang Zhang, Tsai-Shien Chen +11
Existing personalized image generators still struggle to preserve multiple reference identities in natural and coherent multi-human generations. To address these limitations, we pr…
Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing
Rishubh Parihar, Or Patashnik, Daniil Ostashev +3
Instruction-based image editing offers a powerful and intuitive way to manipulate images through natural language. Yet, relying solely on text instructions limits fine-grained cont…
Scaling Group Inference for Diverse and High-Quality Generation
Gaurav Parmar, Or Patashnik, Daniil Ostashev +4
Generative models typically sample outputs independently, and recent inference-time guidance and scaling algorithms focus on improving the quality of individual samples. However, i…
Object-level Visual Prompts for Compositional Image Generation
Gaurav Parmar, Or Patashnik, Kuan-Chieh Wang +5
We introduce a method for composing object-level visual prompts within a text-to-image diffusion model. Our approach addresses the task of generating semantically coherent composit…
Nested Attention: Semantic-aware Attention Values for Concept Personalization
Or Patashnik, Rinon Gal, Daniil Ostashev +3
Personalizing text-to-image models to generate images of specific subjects across diverse scenes and styles is a rapidly advancing field. Current approaches often face challenges i…
Omni-ID: Holistic Identity Representation Designed for Generative Tasks
Guocheng Qian, Kuan-Chieh Wang, Or Patashnik +5
We introduce Omni-ID, a novel facial representation designed specifically for generative tasks. Omni-ID encodes holistic information about an individual's appearance across diverse…
MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation
Kuan-Chieh Wang, Daniil Ostashev, Yuwei Fang +2
We introduce a new architecture for personalization of text-to-image diffusion models, coined Mixture-of-Attention (MoA). Inspired by the Mixture-of-Experts mechanism utilized in l…
Personalized Restoration via Dual-Pivot Tuning
Pradyumna Chari, Sizhuo Ma, Daniil Ostashev +4
Generative diffusion models can serve as a prior which ensures that solutions of image restoration systems adhere to the manifold of natural images. However, for restoring facial i…