# caijinjian_qwen2.1
## 模型介绍
本模型依托魔搭社区（ModelScope）AIGC专区[模型训练](https://modelscope.cn/aigc/modelTraining)环境与算力完成训练。
* 模型类型：LoRA
* 基础模型：[Qwen/Qwen-Image-2.1](https://modelscope.cn/models/Qwen/Qwen-Image-2.1)
* 训练代码：[DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)
* 训练数据量：15
* 总训练步数：6000
* 开源协议：Apache-2.0
## 推理代码
安装 [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)：
```bash
pip install diffsynth
```
开始推理：
```python
from diffsynth.pipelines.qwen_image_21 import QwenImage21Pipeline, ModelConfig
import torch
pipe = QwenImage21Pipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="Qwen/Qwen-Image-2.1", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image-2.1", origin_file_pattern="text_encoder/model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image-2.1", origin_file_pattern="vae/diffusion_pytorch_model*.safetensors"),
],
processor_config=ModelConfig(model_id="Qwen/Qwen-Image-2.1", origin_file_pattern="processor/"),
)
pipe.load_lora(pipe.dit, ModelConfig(model_id="a273587698/caijinjian_qwen2.1", origin_file_pattern="caijinjian_qwen2.1_c1-st6000.safetensors"))
prompt = "a cat"
image = pipe(prompt, seed=0, num_inference_steps=50)
image.save("image.png")
```