FlowSteer: Conditioning Flow Field for Consistent Image Restoration

arXiv:2512.08125 · eess.IV, cs.CV · Submitted 2025-12-09 · Read on arXiv

eess.IV, cs.CV

Submitted: 2025-12-09

Updated: 2026-05-24

Code: https://github.com/openai/guided-diffusion

Terminology

• Fidelity — 77× in this paper · explained in 2 episodes
• Image Restoration — 33× in this paper
• Diffusion Model — 17× in this paper · explained in 8 episodes
• Diffusion Models — 13× in this paper · explained in 6 episodes
• Rectified Flow — 12× in this paper · explained in 3 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 6× in this paper
• Consistency — 6× in this paper · explained in 3 episodes
• Flow Matching — 5× in this paper · explained in 8 episodes
• Hallucination — 5× in this paper · explained in 3 episodes
• Inverse Problem — 5× in this paper · explained in 2 episodes
• alpha — 5× in this paper · explained in 1 episode
• Embeddings — 4× in this paper · explained in 2 episodes
• Latent Space — 4× in this paper · explained in 7 episodes
• CVF International Conference on Computer Vision — 3× in this paper
• Eleventh International Conference on Learning Representations — 3× in this paper
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Steering — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Core — 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
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptive Scheduler — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching Model — 1× in this paper · explained in 2 episodes
• For Rectified Flows — 1× in this paper
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Scaffolding — 1× in this paper · explained in 3 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing
• TokenFlow: Consistent Diffusion Features for Consistent Video Editing
• UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models
• Progressive Growing of GANs for Improved Quality, Stability, and Variation
• FlowAlign: Trajectory-Regularized, Inversion-Free Flow-based Image Editing
• Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
• D-Flow: Differentiating through Flows for Controlled Generation
• Diffusion Posterior Sampling for General Noisy Inverse Problems
• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
• Steering Rectified Flow Models in the Vector Field for Controlled Image Generation
• Flow Matching for Generative Modeling
• Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
• Text-to-Image Rectified Flow as Plug-and-Play Priors
• Denoising Diffusion Implicit Models
• Score-Based Generative Modeling through Stochastic Differential Equations
• Wan: Open and Advanced Large-Scale Video Generative Models
• Taming Rectified Flow for Inversion and Editing

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