[Paper] VINO: A Unified Visual Generator with Interleaved OmniModal Context

Source: arXiv - 2601.02358v1

Overview

The paper introduces VINO, a single diffusion‑based model that can generate and edit both images and videos using the same architecture. By treating text, image, and video inputs as interchangeable “conditioning tokens,” VINO eliminates the need for separate, task‑specific networks and opens the door to more flexible, instruction‑driven visual creation.

Key Contributions

• Unified visual generator: One backbone handles image synthesis, video synthesis, and editing across modalities.
• Interleaved omni‑modal conditioning: Text, image, and video cues are encoded as a single stream of tokens, enabling seamless multi‑reference grounding.
• Multimodal Diffusion Transformer (MMDiT): Extends the popular DiT architecture to accept heterogeneous conditioning without modality‑specific layers.
• Multi‑stage training pipeline: Starts from a video‑generation model and progressively adds image‑generation and editing capabilities, preserving learned knowledge while expanding functionality.
• Strong empirical performance: Improves identity preservation, attribute consistency, and instruction compliance on a suite of image/video generation and editing benchmarks.

Methodology

1. Vision‑Language Backbone – A pretrained vision‑language model (VLM) extracts embeddings from any combination of text, static images, or video frames.
2. Interleaved Conditioning Tokens – The embeddings are flattened into a single token sequence (e.g., [TXT] … [IMG] … [VID] …) and injected into the diffusion transformer at every layer. This “in‑context” format lets the model reason over mixed modalities the same way large language models reason over mixed text.
3. Multimodal Diffusion Transformer (MMDiT) – Built on the DiT (Diffusion Transformer) architecture, MMDiT processes the noisy latent representation of the target visual output while attending to the interleaved conditioning tokens. No separate encoders/decoders are needed for images vs. videos.
4. Training Stages
   • Stage 1: Train a video‑generation diffusion model on raw video data.
   • Stage 2: Freeze the video backbone and add image‑generation data, teaching the model to map single‑frame conditioning to the same latent space.
   • Stage 3: Introduce editing tasks (in‑painting, style transfer, identity preservation) with mixed‑modal prompts, fine‑tuning the whole system end‑to‑end.
5. Losses – Standard diffusion denoising loss plus auxiliary alignment losses that encourage the model to keep referenced identities consistent across frames or between source and edited outputs.

Results & Findings

Key observations

• Cross‑modal grounding works out‑of‑the‑box—e.g., a user can give a textual description plus a reference video clip, and VINO will generate a new video that respects both.
• Identity preservation across frames is markedly better than models that treat each frame independently, thanks to the shared conditioning stream.
• Control granularity improves: developers can swap out just the conditioning tokens (e.g., replace the image token while keeping the text) to achieve targeted edits without retraining.

Practical Implications

• One‑stop visual creation API – Companies can expose a single endpoint for image generation, video synthesis, and editing, simplifying product architecture and reducing maintenance overhead.
• Dynamic content pipelines – Marketing platforms can generate short video ads from a single textual brief and a brand logo image, with the model automatically preserving the logo’s identity across frames.
• Rapid prototyping for AR/VR – Designers can sketch a static concept, provide a short reference clip, and instantly obtain a coherent animated prototype, accelerating iteration cycles.
• Cost‑effective scaling – Training a unified model avoids the duplicated compute cost of maintaining separate image and video diffusion models, which is attractive for startups and cloud providers.
• Foundation for multimodal assistants – VINO’s interleaved token approach aligns with emerging “in‑context” multimodal LLMs, paving the way for chat‑based visual assistants that can edit videos on the fly.

Limitations & Future Work

• Resolution ceiling – The current implementation tops out at 512 × 512 for images and 64 × 64 per frame for videos; higher‑resolution scaling will need additional up‑sampling tricks.
• Training data bias – Because the model inherits biases from its video pre‑training corpus, certain demographic or cultural representations may be under‑ or over‑represented.
• Latency for long videos – Generating many frames sequentially still incurs noticeable latency; future work could explore frame‑parallel diffusion or caching strategies.
• Fine‑grained control – While multi‑reference grounding works well, precise spatial control (e.g., “move the object to the left in frame 10”) remains limited; integrating explicit layout tokens is a promising direction.

VINO demonstrates that a single diffusion backbone, when fed interleaved omni‑modal context, can rival specialized models across a spectrum of visual tasks. For developers, this translates into simpler APIs, lower infrastructure costs, and new creative workflows that blend text, images, and video in a unified, instruction‑driven interface.

Authors

• Junyi Chen
• Tong He
• Zhoujie Fu
• Pengfei Wan
• Kun Gai
• Weicai Ye

Paper Information

• arXiv ID: 2601.02358v1
• Categories: cs.CV
• Published: January 5, 2026
• PDF: Download PDF