arXiv Computer Vision
MT Forcing: Towards Stable and Consistent Multi-Turn Image Editing with Autoregressive Diffusion
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ACEdit introduces a training scheme that stabilizes multi-turn image editing by forcing the editor to operate on its own previous outputs, a concept called Self-Forcing. To overcome unreliable supervision from self-generated trajectories, the method employs a distillation objective using a teacher editor that remains anchored to the original image while receiving compound instructions. Experiments demonstrate reduced degradation over long editing sequences while preserving quality and instruction adherence.
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AnchorEdit: Maintaining Temporal Consistency in Multi-turn Image Editing via Causal Memory
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MT-EditFlow: Reinforcement Learning for Multi-Turn Image Editing with Flow Matching
Recent breakthroughs in instruction-based image editing have captured significant attention, as models are now capable of handling real-world editing demands with the practicality required by everyday users. However, editing models trained primarily for single-turn edits often break down in multi-turn editing--the natural interactive setting where a user iteratively refines an image based on the model's own previous outputs.
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DuET: Dual Expert Trajectories for Diffusion Image Editing
Recent diffusion editors perform diverse instruction-based edits while conditioning on the source image at every denoising step. Yet persistent source-image conditioning can limit how fully an edit is executed and how natural the result appears, especially when the target scene diverges substantially from the input.
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EditStream is a unified DiT‑based framework that supports a wide range of interactive video tasks—Text‑to‑Video, Image‑to‑Video, Video‑to‑Video, Editing Propagation, Reference‑guided Video Editing, and Camera Pose Change—within a single system. It achieves fast, few‑step autoregressive generation by applying a two‑stage distillation process that combines Velocity Moment Matching with autoregressive unrolling, thereby preserving motion quality and temporal stability. The approach aims to make high‑quality diffusion‑based video models practical for real‑time creative workflows.