Model overview
This repository contains an FP8 quantized version of the Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0 model. **This is NOT a fine-tuned model** but a direct quantization of the original BFloat16 model to FP8 format for optimized inference performance. We provide an online demo. This model has been quantized from the original BFloat16 format to FP8 format using PyTorch's native FP8 support. Here are the specifics: The benefits of FP8 quantization include: - **Reduced Memory Usage**: Approximately 50% smaller model size compared to BFloat16/FP16 - **Faster Inference**: Potential speed improvements, especially on hardware with FP8 support - **Minimal Quality Loss**: Carefully calibrated quantization process to preserve output quality
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.
Are these real-time billing prices?
These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.
Sources & traceability
许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。