[Paper] ProEdit: Inversion-based Editing From Prompts Done Right

Source: arXiv - 2512.22118v1

Overview

ProEdit tackles a long‑standing pain point in diffusion‑based image and video editing: the tendency of inversion‑driven methods to cling too tightly to the original content, making it hard to apply bold changes such as altering a subject’s pose, color, or count. By redesigning how source information is blended during the diffusion sampling step, the authors deliver a plug‑and‑play upgrade that yields noticeably sharper, more faithful edits while preserving background consistency.

Key Contributions

• KV‑mix attention module – mixes the key/value pairs of the source and target latents only inside the user‑specified edit region, reducing unwanted “source bias” without breaking the overall scene coherence.
• Latents‑Shift perturbation – deliberately nudges the source latent in the edit region before sampling, preventing the inverted latent from dominating the generation process.
• Universal compatibility – the two components are architecture‑agnostic and can be dropped into existing inversion‑based pipelines (e.g., RF‑Solver, FireFlow, UniEdit) with no retraining.
• State‑of‑the‑art results on multiple image‑ and video‑editing benchmarks, outperforming prior methods on both quantitative metrics (e.g., CLIP‑Score, FID) and human preference studies.
• Extensive ablation studies that isolate the impact of KV‑mix and Latents‑Shift, confirming that each contributes independently to the overall gain.

Methodology

1. Inversion baseline – Start with any diffusion inversion technique that maps an input image/video to a latent representation (the “source latent”).
2. Region‑aware KV‑mix
   • During each denoising step, the attention mechanism normally uses the same key/value (KV) tensors for the whole canvas.
   • KV‑mix replaces the KV tensors inside the edit mask with a weighted blend of the source KV and the target KV (derived from the prompt).
   • This localized mixing lets the model treat the edit region as “new content” while still using the source KV for the untouched background.
3. Latents‑Shift
   • Before the diffusion loop, the source latent is perturbed in the masked region using a small random Gaussian shift plus a prompt‑conditioned bias.
   • The shift breaks the tight coupling between the inverted latent and the subsequent sampling, giving the model room to follow the new instruction.
4. Plug‑and‑play integration – Both KV‑mix and Latents‑Shift are inserted as thin wrappers around the existing diffusion scheduler, requiring only a few extra lines of code and no extra training data.

Results & Findings

• Qualitative: Users report that ProEdit can change a dog’s breed, rotate a car, or add/remove objects without ghosting artifacts, something earlier inversion methods struggled with.
• Ablation: Removing KV‑mix drops CLIP‑Score by ~0.03; removing Latents‑Shift drops it by ~0.04, confirming both are essential.
• Speed: The added operations cost < 5 ms per diffusion step on an RTX 3090, preserving real‑time‑ish editing pipelines.

Practical Implications

• Content creation tools – Integrate ProEdit into photo‑editing SaaS (e.g., Canva, Figma plugins) to let non‑experts rewrite image elements via natural language prompts without sacrificing background fidelity.
• Video post‑production – Apply ProEdit to frame‑level editing for quick visual effects (changing clothing colors, adding props) without re‑rendering the entire clip.
• Game asset pipelines – Designers can generate variant sprites or textures on‑the‑fly by prompting changes, accelerating iteration cycles.
• E‑commerce – Dynamically adapt product photos (e.g., swapping colors, adding accessories) based on user queries, reducing the need for multiple photoshoots.
• Open‑source adoption – Because ProEdit is a drop‑in module, existing diffusion‑based libraries (Diffusers, Stable Diffusion WebUI) can be upgraded with a single pip install, making it low‑friction for developers.

Limitations & Future Work

• Mask dependence – ProEdit still requires a reasonably accurate edit mask; automatic mask generation remains an open challenge.
• Extreme pose or geometry changes – Very large transformations (e.g., turning a cat into a horse) can still produce distortions, indicating that the latent shift magnitude may need adaptive scaling.
• Video temporal consistency – While results improve, occasional flicker appears when the edit region moves rapidly; future work could incorporate temporal attention or optical‑flow‑guided KV‑mix.
• Broader modality testing – The paper focuses on RGB images/videos; extending to depth maps, segmentation masks, or 3‑D assets would broaden applicability.

ProEdit demonstrates that a modest, well‑targeted tweak to the diffusion attention pipeline can unlock far more expressive, prompt‑driven editing—an insight that should inspire a wave of “plug‑and‑play” upgrades across the generative AI toolbox.

Authors

• Zhi Ouyang
• Dian Zheng
• Xiao-Ming Wu
• Jian-Jian Jiang
• Kun-Yu Lin
• Jingke Meng
• Wei-Shi Zheng

Paper Information

• arXiv ID: 2512.22118v1
• Categories: cs.CV
• Published: December 26, 2025
• PDF: Download PDF