Model overview
A decoder-only finetune of the Wan2.1 VAE, with 2x upscaling integrated directly into the decoder. The main purpose of this is to kill the dreaded wan speckles/polka dots/grain, but it's also convenient for highres fix workflows. The outputs of the 2x decoder are usually much better than what you would get by running the outputs of the original decoder through an image upscale model, and even better, it's effectively free, since the compute cost of decoding is virtually unchanged. If you don't want to use the extra resolution, a slight blur and downsample will give you an original resolution image with much higher quality than the original decoder can produce. In particular, this VAE improves skin details and hair very significantly. It is trained almost exclusively on real images, so it may struggle with anime/lineart and text. It would be possible to finetune on anime/lineart, but I'm not aware of a suitable dataset that's licensed correctly and not just full of scraped media with massive copyright violations. If you know of an appropriate datset for this that is sourced from cc-by materials or similar, let me know and I'd be happy to try training it. The first released version is trained on images only, and is compatible with both Wan and Qwen since they share the same latent space. A video version is planned, but video training is more complex than image training, so it will take some time.
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：apache-2.0。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。