Model overview
This repository contains optimized community **GGUF quantizations** of Viggle/Qwen-Image-2.1-viggle-turbo, an official 4-step distilled student model of Qwen-Image-2.1 trained by Viggle using **Distribution Matching Distillation (DMD)**. These files were converted directly from the official full transformer weights (`transformer/diffusion_pytorch_model.safetensors`), allowing you to run near-instant 4-step image generation and image editing natively inside **ComfyUI** without needing to stack external LoRAs. Because this model is distilled with DMD, **it requires specific inference parameters**. Using standard diffusion parameters will degrade image quality or cause visual burn artifacts:
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。