#
## Model Introduction
This model is trained using the [Civision/model training](https://modelscope.ai/civision/modelTraining) environment and computing resources provided by ModelScope.
* Model Type: LoRA
* Base Model: [Qwen/Qwen-Image-2512](https://modelscope.ai/models/Qwen/Qwen-Image-2512)
* Training Code: [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)
* Data amount：26
* Total training steps：2000
* License: Apache-2.0
## Inference Code
Install [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio):
```bash
pip install diffsynth
```
Inference:
```python
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
import torch
pipe = QwenImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="Qwen/Qwen-Image-2512", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
],
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
)
pipe.load_lora(pipe.dit, ModelConfig(model_id="imaty07/qwen_2512_beatrice_v2", origin_file_pattern="qwen_2512_beatrice_v2_c2-st2000.safetensors"))
prompt = "a cat"
image = pipe(prompt)
image.save("image.jpg")
```