VIF-Bench: Evaluating Visual Instruction Following in Multi-Reference Image Generation

arXiv:2609.37709 · cs.CV, cs.AI · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/shim0114/VIF-Bench

Terminology

• Benchmark — 46× in this paper · explained in 2 episodes
• Consistency — 28× in this paper · explained in 3 episodes
• Instruction Following — 24× in this paper
• Diffusion Model — 18× in this paper · explained in 8 episodes
• Diffusion Models — 15× in this paper · explained in 6 episodes
• Conference on Computer Vision and Pattern Recognition — 6× in this paper
• Fidelity — 6× in this paper · explained in 2 episodes
• Image Quality — 5× in this paper
• Transformer — 5× in this paper · explained in 2 episodes
• APEX — 4× in this paper · explained in 1 episode
• Commentary — 4× in this paper · explained in 2 episodes
• Failure Modes — 4× in this paper · explained in 3 episodes
• Generative AI — 4× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Benchmarking — 3× in this paper · explained in 3 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• In European Conference on Computer Vision — 3× in this paper
• Abstraction — 2× in this paper · explained in 1 episode
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Source Fidelity — 2× in this paper
• Test-time Scaling — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Evaluation Dimensions — 1× in this paper · explained in 3 episodes
• Flow Matching — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• LAION Aesthetic Predictor — 1× in this paper
• Latent Space — 1× in this paper · explained in 7 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Test-Time Scaling (TTS) — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Qwen3-VL Technical Report
• EditVal: Benchmarking Diffusion Based Text-Guided Image Editing Methods
• Emerging Properties in Self-Supervised Vision Transformers
• MIBE: Multi-subject Interaction Benchmark and Evaluator for Personalized Image Generation
• Improving Diffusion Models for Authentic Virtual Try-on in the Wild
• Emerging Properties in Unified Multimodal Pretraining
• Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback
• Gemini: A Family of Highly Capable Multimodal Models
• SpotEdit: Evaluating Visually-Guided Image Editing Methods
• Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following
• GEMS: Agent-Native Multimodal Generation with Memory and Skills
• Scaling Multi-Reference Image Generation with Dynamic Reward Optimization
• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
• GLIGEN: Open-Set Grounded Text-to-Image Generation
• Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
• T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
• Boost Your Human Image Generation Model via Direct Preference Optimization
• Multi-Axis Max@K Reinforcement Learning for Representative Diversity in Text-to-Image Generation
• GPT-4 Technical Report
• Learning Transferable Visual Models From Natural Language Supervision

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