README.md

---
tags:
- text-to-image
- lora
- diffusers
- template:sd-lora
- ai-toolkit
widget:
-   text: "\u5DE2\u6E56\u3001\u6E14\u8239\u3001\u6E14\u6C11\u6492\u7F51"
    output:
        url: samples/1777822294363__000001000_0.jpg
-   text: "\u5DE2\u6E56\u3001\u6E14\u8239\u3001\u6E14\u6C11\u6492\u7F51\u3001\u570B\
        \u756B\u98A8"
    output:
        url: samples/1777822395149__000001000_1.jpg
-   text: "\u5DE2\u6E56\u3001\u6E14\u8239\u3001\u6E14\u6C11\u6492\u7F51\u3001\u4E24\
        \u5CB8\u9752\u5C71\u3001\u88D5\u6EAA\u6CB3\u7530\u56ED\u5C71\u666F"
    output:
        url: samples/1777822495797__000001000_2.jpg
base_model: Qwen/Qwen-Image
instance_prompt: 巢湖
license: creativeml-openrail-m


---

# chao-hu
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
<Gallery />

## Trigger words

You should use `巢湖` to trigger the image generation.

## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.

Weights for this model are available in Safetensors format.

[Download](/PakNin/chao-hu/tree/main) them in the Files & versions tab.

## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)

```py
from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained('Qwen/Qwen-Image', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('PakNin/chao-hu', weight_name='chao-hu.safetensors')
image = pipeline('巢湖、渔船、渔民撒网').images[0]
image.save("my_image.png")
```

For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)