Fan benefits: Click on the avatar in the upper right corner, enter the invitation code rh v0990, and you can receive 1000 RH coins; log in daily to receive an additional 100 RH coins.
If it does not automatically credit your account, please bind/activate the invitation code in the upper right corner and enter rh v0990 to claim it.

The domestic site no longer supports local decoding. If local decoding is required, please visit the overseas site:

Registration link: Overseas version RH (decoding test workflow, requires VPN):
https://www.runninghub.ai/user center/1936823199386537986/webapp?inviteCode=rh v0990

This model uses the Tutu Annotator for material labeling, combined with Banana 2 / Banana Pro for assisted labeling, and cleans tags through Tutu's intelligent subject filtering feature. Training is completed using the Tutu Trainer and Tutu Training Workflow.

The Tutu Annotator is an AI-driven image and video annotation tool optimized for model training, capable of batch-generating high-quality training datasets and quickly reverse-engineering prompts for images and videos.

The Tutu Trainer is a free LoRA training tool that supports model training, dataset management, model resource management, parameter presets, training checks, and pre-release organization. Its goal is to enable creators to train high-quality LoRA models more quickly and stably without repeatedly tweaking environments and parameters.

Tutu's HiSilk Fishnet Stockings Z Image Workflow is pre-bound with the "Tutu's HiSilk Fishnet Stockings Z Image Turbo" LoRA, which can be opened to test model effects online.

Official website: https://zhaotutu.xyz/
Tutu Trainer: https://zhaotutu.xyz/downloads/tututrainer/
Tutu Annotator: https://zhaotutu.xyz/downloads/tutuannotator/
Bilibili: https://space.bilibili.com/431046154
YouTube: https://www.youtube.com/@zhaotutu/videos
X: https://x.com/bigpox12
Telegram: https://t.me/zhaotutu
TikTok: https://www.tiktok.com/@zhaotututu
GitHub: https://github.com/zhaotututu
Email: bigpox12@gmail.com
QQ: 331506796


Recommended usage

It is recommended to start testing LoRA weights at around 0.7.
If the effect is too weak, you can appropriately increase the weight; if the texture is too strong or affects other parts of the image, prioritize lowering the LoRA weight rather than stacking many negative prompts at the beginning.