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Daniil Ostashev

4 accepted papers

2026

Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing

Rishubh Parihar, Or Patashnik, Daniil Ostashev, Venkatesh Babu Radhakrishnan, Daniel Cohen-Or, Kuan-Chieh Jackson Wang
CVPR 2026

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 control over the extent of edits. We introduce Kontinuous Kontext, an instruction-driven editing model that provides a new d

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2026

Scaling Group Inference for Diverse and High-Quality Generation

Gaurav Parmar, Or Patashnik, Daniil Ostashev, Kuan-Chieh Jackson Wang, Kfir Aberman, Srinivasa Narasimhan +1
ICLR 2026poster

Generative models typically sample outputs independently, and recent inference-time guidance and scaling algorithms focus on improving the quality of individual samples. However, in real-world applications, users are often presented with a set of multiple images (e.g., 4-8) for each prompt, where in…

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2025

Omni-ID: Holistic Identity Representation Designed for Generative Tasks

Guocheng Qian, Kuan-Chieh Wang, Or Patashnik, Negin Heravi, Daniil Ostashev, Sergey Tulyakov +2
CVPR 2025poster

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 expressions and poses within a fixed-size representation. It consolidates information from a varied number of unstructu…

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2025

Preventing Shortcuts in Adapter Training via Providing the Shortcuts

Anujraaj Goyal, Guocheng Qian, Huseyin Coskun, Aarush Gupta, Himmy Tam, Daniil Ostashev +5
NeurIPS 2025poster

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 synthesis. These adapters are typically trained to capture a specific target attribute, such as subject identity, using singl…

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