Vidu Q2: Precision Image-to-Image Generator with 4K Output on playground and API | RunComfy

Model overview

• Provider: Vidu
• Task: image-to-image
• Architecture: Diffusion-based image-to-image pipeline with cross-attention over reference images
• Resolution/Specs: Up to 4K still images
• Key strengths:

- Consistent subject and style transfer from multiple reference images

- High-fidelity 4K output with sharp detail retention

- Strong prompt adherence for fine-grained edits

- Fast, production-ready inference on managed GPUs

- Deterministic reproduction via seeding

Vidu Q2 is an image-to-image model that turns reference images and prompts into high-quality 4K visuals with precise, controllable edits. It is optimized for production pipelines that need speed, consistency, and repeatability on RunComfy.

How Vidu Q2 runs on RunComfy

RunComfy provides a managed environment for Vidu Q2 so you can prototype in minutes and scale to production without wrangling infrastructure. You get consistent performance, simple versioning, and reproducible runs.

• Playground UI: Experience the model directly in your browser without installation.
• Playground API: Developers can integrate Vidu Q2 via a scalable HTTP API. Endpoint: https://runcomfy.com/models/vidu/q2/reference-to-image/api

Input parameters

Below are the supported inputs for Vidu Q2.

Core prompts

Dimensions and settings

Required parameter: prompt

Recommended settings

These tips help get the best results from Vidu Q2 for image-to-image workflows:

• Use 1-3 high-resolution reference images of the same subject with consistent lighting and pose for the strongest identity transfer.
• Choose an aspect_ratio that matches your framing from the start to avoid unwanted cropping or stretching.
• Keep prompts concise but specific: include subject, scene, style descriptors (e.g., cinematic, editorial, painterly), lighting, and any constraints (e.g., no text, no watermark).
• Start with seed = 0 while exploring variations; lock a specific seed to finalize and reproduce a chosen look.
• Avoid conflicting style terms in the prompt (e.g., photorealistic and watercolor) unless you are intentionally blending styles.

Output quality and performance

• Output: A single high-resolution image (PNG/JPEG) generated from your references and prompt. Vidu Q2 targets up to 4K stills.
• Performance: On RunComfy you can expect responsive, seconds-level latency for most runs, with consistent p95 thanks to no cold starts and autoscaling. Concurrency is handled transparently for production workloads.

Recommended use cases

• Film, TV, and previsualization: maintain character/prop consistency while exploring new looks.
• Advertising and brand design: rapid creative variations and style-locked campaigns.
• Product and industrial design: material/finish iterations with consistent lighting and perspective.
• E-commerce and catalog imaging: restyle or upscale product shots while preserving identity.