wan-2.1/text-to-image is an advanced text-to-image model capable of generating high-quality images from textual descriptions with photographic authenticity and exceptional detail fidelity.
Key Features
- Ultra-realistic generation: Photographic quality output with exceptional detail fidelity.
- Advanced AI Technology: Revolutionary AI technology delivering ultra-realistic images.
- High resolution: Support for resolutions up to 1536x1536 with various aspect ratios.
- Versatile usage: Suitable for professional, creative, and commercial applications.
- Quality Control: Advanced safety checker and content filtering.
Technical Specifications
- Input formats: Text prompts with optional image input for img2img mode
- Output formats: JPEG, PNG, WebP with base64 encoding option
- Resolution range: 512x512 to 1536x1536 pixels
- Safety features: Built-in content filtering and safety checker
Out-of-Scope Use
The model and its derivatives may not be used in any way that violates applicable national, federal, state, local, or international law or regulation, including but not limited to:
- Exploiting, harming, or attempting to exploit or harm minors, including solicitation, creation, acquisition, or dissemination of child exploitative content.
- Generating or disseminating verifiably false information with the intent to harm others.
- Creating or distributing personal identifiable information that could be used to harm an individual.
- Harassing, abusing, threatening, stalking, or bullying individuals or groups.
- Producing non-consensual nudity or illegal pornographic content.
- Making fully automated decisions that adversely affect an individual's legal rights or create binding obligations.
- Facilitating large-scale disinformation campaigns.
Accelerated Inference
Our accelerated inference approach leverages advanced optimization technology from WaveSpeedAI. This innovative fusion technique significantly reduces computational overhead and latency, enabling rapid image generation without compromising quality. The entire system is designed to efficiently handle large-scale inference tasks while ensuring that real-time applications achieve an optimal balance between speed and accuracy.