---
title: yasminkwen
canonical_url: "https://www.modelscope.ai/models/aborinphoto/yasminkwen"
md_url: "https://www.modelscope.ai/models/aborinphoto/yasminkwen.md"
repository: aborinphoto/yasminkwen
last_updated: 2026-09-11
license: "Apache License 2.0"
pipeline_tag: text-to-image-synthesis
tasks:
  - text-to-image-synthesis
base_model:
  - Qwen/Qwen-Image-2512
base_model_relation: adapter
parameters: 1.4B
tensor_type:
  - BF16
library_name:
  - pytorch
  - lora
  - safetensors
supports_inference: txt2img
downloads: 68
stars: 0
tags:
  - LoRA
  - text-to-image
---

# yasminkwen

> yasminkwen - An open-source model by aborinphoto on ModelScope. This model is trained using the Civision/model training environment and computing resources provided by ModelScope.

aborinphoto/yasminkwen is a 1.4B-parameter text-to-image-synthesis model on ModelScope. licensed under Apache License 2.0. derived from Qwen/Qwen-Image-2512. and supports online inference (txt2img).

- **Repository**: aborinphoto/yasminkwen
- **License**: Apache License 2.0
- **Tasks**: text-to-image-synthesis
- **Parameters**: 1.4B
- **Base model**: Qwen/Qwen-Image-2512
- **Online inference**: txt2img
- **Tags**: LoRA, text-to-image
- **Downloads**: 68
- **Stars**: 0
- **Last updated**: 2026-09-11

Source: https://www.modelscope.ai/models/aborinphoto/yasminkwen

---

# 

## 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：55
* Total training steps：6000
* 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="aborinphoto/yasminkwen", origin_file_pattern="yasminkwen_c1-st6000.safetensors"))
prompt = "a cat"
image = pipe(prompt)
image.save("image.jpg")
```