Temporal Identity Anchoring
Maintain 98% subject consistency across shots without external training or complex prompting.
Temporal Identity Anchoring
Maintain 98% subject consistency across shots without external training or complex prompting.
viduq2-pro-fast / start-end-frame
vidu q2 pro video is a flagship multimodal model for near-instant 1080p generation. It offers cinematic physics and 128k context for visual reasoning, outperforming competitors in temporal consistency and subject identity across shots.
$ 0.024
$ 0.03
image
video
Playground
JSON
API
Input
Your request will cost$0per run, for$100you can run this model approximately0times
vidu q2 pro video Key Features
Explore the technical advantages that make vidu a leader in the video generation space.
Direct Camera Control
Control dolly zooms, pans, and focus shifts with 92% accuracy using native API parameters.
Direct Camera Control
Control dolly zooms, pans, and focus shifts with 92% accuracy using native API parameters.
Unified Visual Reasoning
Analyze up to 2 minutes of video to extract metadata or create logical narrative continuations.
Unified Visual Reasoning
Analyze up to 2 minutes of video to extract metadata or create logical narrative continuations.
Sub-Minute 1080p Clips
Generate high-definition cinematic video in under 35 seconds with the optimized pro-fast architecture.
Sub-Minute 1080p Clips
Generate high-definition cinematic video in under 35 seconds with the optimized pro-fast architecture.
How to Get a viduq2-pro-fast API Key
Getting a viduq2-pro-fast API key takes four steps and a few minutes. Create a free GPTProto account, add credits, generate your key, and make your first call — at $0.024 it's a cheaper viduq2-pro-fast API key than going direct, and one key works across every model on the platform. Full viduq2-pro-fast Documentation is in the docs.
Sign up
Create your free GPT Proto account to begin. You can set up an organization for your team at any time.
Top up
Your balance can be used across all models on the platform, including viduq2-pro-fast, giving you the flexibility to experiment and scale as needed.
Generate your API key
In your dashboard, create an API key — you'll need it to authenticate when making requests to viduq2-pro-fast.
Make your first API call
Use your API key with our sample code to send a request to viduq2-pro-fast via GPT Proto and see instant AI-powered results.
vidu q2 pro video: Frequently Asked Questions
Common questions about using the vidu q2 pro video model for professional high-fidelity generation.
How fast is vidu q2 pro video generation?
The vidu q2 pro video fast architecture is optimized for throughput. It can generate a 5-second cinematic 1080p clip in approximately 35 seconds. This makes vidu nearly three times faster than standard versions while maintaining professional-grade fidelity.
Does vidu maintain character consistency?
Yes, vidu is designed with advanced character anchoring. It maintains 98% visual identity consistency for human subjects across different shots. This allows for complex storytelling without the need for external LoRA training or specialized fine-tuning.
Can I use vidu q2 pro video for long films?
vidu supports generations up to 30 seconds per request. For longer narrative content, we recommend using the model's temporal consistency features to generate sequential clips and then stitching them together using traditional post-production tools.
What are the costs for vidu q2 pro video API?
Generation is priced at $0.15 per second of 1080p video. Reasoning tasks cost $2.50 per 1M input tokens and $10.00 per 1M output tokens. GPTProto users enjoy a 10% discount compared to direct retail pricing for enterprise-tier API keys.
Is my data safe with vidu q2 pro video?
We enforce a strict zero-data-retention policy for all vidu calls. Your input images, videos, and prompts are used solely for inference and are never used to train or fine-tune the base models, ensuring enterprise-grade privacy and security.
How does vidu handle complex physics?
vidu natively understands fluid dynamics and object collisions. It scores 0.84 on the VBench Physical Logic set, meaning it produces realistic motion for things like splashing water or falling objects with 92% instruction-following accuracy.
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