---
title: yasmin2
canonical_url: "https://www.modelscope.ai/models/aborinphoto/yasmin2"
md_url: "https://www.modelscope.ai/models/aborinphoto/yasmin2.md"
repository: aborinphoto/yasmin2
last_updated: 2026-09-12
license: "Apache License 2.0"
pipeline_tag: text-to-image-synthesis
tasks:
  - text-to-image-synthesis
base_model:
  - Tongyi-MAI/Z-Image-Turbo
base_model_relation: adapter
parameters: 476.3M
tensor_type:
  - BF16
library_name:
  - pytorch
  - lora
  - safetensors
supports_inference: txt2img
downloads: 70
stars: 0
tags:
  - LoRA
  - text-to-image
---

# yasmin2

> yasmin2 - 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/yasmin2 is a 476.3M-parameter text-to-image-synthesis model on ModelScope. licensed under Apache License 2.0. derived from Tongyi-MAI/Z-Image-Turbo. and supports online inference (txt2img).

- **Repository**: aborinphoto/yasmin2
- **License**: Apache License 2.0
- **Tasks**: text-to-image-synthesis
- **Parameters**: 476.3M
- **Base model**: Tongyi-MAI/Z-Image-Turbo
- **Online inference**: txt2img
- **Tags**: LoRA, text-to-image
- **Downloads**: 70
- **Stars**: 0
- **Last updated**: 2026-09-12

Source: https://www.modelscope.ai/models/aborinphoto/yasmin2

---

# 

## 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: [Tongyi-MAI/Z-Image-Turbo](https://modelscope.ai/models/Tongyi-MAI/Z-Image-Turbo)
* Training Code: [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)
* Data amount：39
* 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.z_image import ZImagePipeline, ModelConfig
import torch

pipe = ZImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="transformer/*.safetensors"),
        ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="text_encoder/*.safetensors"),
        ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
    ],
    tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
)
pipe.load_lora(pipe.dit, ModelConfig(model_id="aborinphoto/yasmin2", origin_file_pattern="yasmin2_c1-st6000.safetensors"))
prompt = "a cat"
image = pipe(prompt=prompt, num_inference_steps=8, cfg_scale=1)
image.save("image.jpg")
```