---
title: SD3.5-LoRA-Chinese-Line-Art
canonical_url: "https://www.modelscope.cn/models/LiblibAI/SD3.5-LoRA-Chinese-Line-Art"
md_url: "https://www.modelscope.cn/models/LiblibAI/SD3.5-LoRA-Chinese-Line-Art.md"
repository: LiblibAI/SD3.5-LoRA-Chinese-Line-Art
last_updated: 2024-11-21
license: other
pipeline_tag: text-to-image-synthesis
tasks:
  - text-to-image-synthesis
base_model:
  - stabilityai/stable-diffusion-3.5-large
base_model_relation: adapter
parameters: 11.8M
tensor_type:
  - BF16
library_name:
  - pytorch
  - lora
  - diffusers
  - safetensors
frameworks:
  - pytorch
downloads: 2099
stars: 0
tags:
  - text-to-image
  - lora
  - diffusers
  - "template:diffusion-lora"
---

# SD3.5-LoRA-Chinese-Line-Art

> SD3.5-LoRA-Chinese-Line-Art - LiblibAI 在 ModelScope 开源的模型。SD3.5-LoRA-Chinese-Line-Art

LiblibAI/SD3.5-LoRA-Chinese-Line-Art 是 ModelScope 魔搭社区上的 11.8M 参数text-to-image-synthesis模型，采用 other 许可，基于 stabilityai/stable-diffusion-3.5-large 构建。

- **Repository**: LiblibAI/SD3.5-LoRA-Chinese-Line-Art
- **License**: other
- **Tasks**: text-to-image-synthesis
- **Parameters**: 11.8M
- **Base model**: stabilityai/stable-diffusion-3.5-large
- **Tags**: text-to-image, lora, diffusers, template:diffusion-lora
- **Downloads**: 2099
- **Stars**: 0
- **Last updated**: 2024-11-21

Source: https://www.modelscope.cn/models/LiblibAI/SD3.5-LoRA-Chinese-Line-Art

---

# SD3.5-LoRA-Chinese-Line-Art

<Gallery />


## Trigger words

You should use `Chinese line art` to trigger the image generation.


## Inference

```python
import torch
from diffusers import StableDiffusion3Pipeline # pip install diffusers>=0.31.0

pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large", torch_dtype=torch.bfloat16)
pipe.load_lora_weights("Shakker-Labs/SD3.5-LoRA-Chinese-Line-Art", weight_name="SD35-lora-Chinese-Line-Art.safetensors")
pipe.fuse_lora(lora_scale=1.0)
pipe.to("cuda")

prompt = "a boat on the river, mountain in the distance, Chinese line art"
negative_prompt = "(lowres, low quality, worst quality)"

image = pipe(prompt=prompt,
             negative_prompt=negative_prompt
             num_inference_steps=24, 
             guidance_scale=4.0,
             width=960, height=1280,
            ).images[0]
image.save(f"toy_example.jpg")
```