---
title: renxiang-duoduo-coser-Qwen-Image-2512-v1.0
canonical_url: "https://www.modelscope.cn/models/laonansheng/renxiang-duoduo-coser-Qwen-Image-2512-v1.0"
md_url: "https://www.modelscope.cn/models/laonansheng/renxiang-duoduo-coser-Qwen-Image-2512-v1.0.md"
repository: laonansheng/renxiang-duoduo-coser-Qwen-Image-2512-v1.0
chinese_name: "人像多多-COSER-千问2512-v1.0"
last_updated: 2026-09-23
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: 1.7B
tensor_type:
  - BF16
library_name:
  - pytorch
  - lora
  - safetensors
supports_inference: txt2img
downloads: 41
stars: 0
tags:
  - LoRA
  - text-to-image
---

# renxiang-duoduo-coser-Qwen-Image-2512-v1.0

> renxiang-duoduo-coser-Qwen-Image-2512-v1.0 - laonansheng 在 ModelScope 开源的模型。人像多多-COSER-千问2512-v1.0

laonansheng/renxiang-duoduo-coser-Qwen-Image-2512-v1.0 是 ModelScope 魔搭社区上的 1.7B 参数text-to-image-synthesis模型，采用 Apache License 2.0 许可，基于 Qwen/Qwen-Image-2512 构建，并支持在线推理（txt2img）。

- **Repository**: laonansheng/renxiang-duoduo-coser-Qwen-Image-2512-v1.0
- **License**: Apache License 2.0
- **Tasks**: text-to-image-synthesis
- **Parameters**: 1.7B
- **Base model**: Qwen/Qwen-Image-2512
- **Online inference**: txt2img
- **Tags**: LoRA, text-to-image
- **Downloads**: 41
- **Stars**: 0
- **Last updated**: 2026-09-23

Source: https://www.modelscope.cn/models/laonansheng/renxiang-duoduo-coser-Qwen-Image-2512-v1.0

---

# 人像多多-COSER-千问2512-v1.0

## 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-2512](https://modelscope.cn/models/Qwen/Qwen-Image-2512)
* 训练代码：[DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)
* 训练数据量：100
* 总训练步数：7000
* 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 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="laonansheng/renxiang-duoduo-coser-Qwen-Image-2512-v1.0", origin_file_pattern="renxiang-duoduo-coser-Qwen-Image-2512-v1.0_c1-st7000.safetensors"))
prompt = "a cat"
image = pipe(prompt)
image.save("image.jpg")
```