---
title: zit-hashimotokanna-char-r1
canonical_url: "https://www.modelscope.cn/models/begoshensaid/zit-hashimotokanna-char-r1"
md_url: "https://www.modelscope.cn/models/begoshensaid/zit-hashimotokanna-char-r1.md"
repository: begoshensaid/zit-hashimotokanna-char-r1
chinese_name: "zit-桥本环奈-char-r1"
last_updated: 2026-06-05
license: "Apache License 2.0"
pipeline_tag: text-to-image-synthesis
tasks:
  - text-to-image-synthesis
base_model:
  - Tongyi-MAI/Z-Image
base_model_relation: adapter
parameters: 1.6B
tensor_type:
  - BF16
library_name:
  - pytorch
  - lora
  - safetensors
frameworks:
  - pytorch
supports_inference: txt2img
downloads: 229
stars: 2
tags:
  - LoRA
---

# zit-hashimotokanna-char-r1

> zit-hashimotokanna-char-r1 - begoshensaid 在 ModelScope 开源的模型。zit-桥本环奈-char-r1 / zib-桥本环奈-char-r1

begoshensaid/zit-hashimotokanna-char-r1 是 ModelScope 魔搭社区上的 1.6B 参数text-to-image-synthesis模型，采用 Apache License 2.0 许可，基于 Tongyi-MAI/Z-Image 构建，并支持在线推理（txt2img）。

- **Repository**: begoshensaid/zit-hashimotokanna-char-r1
- **License**: Apache License 2.0
- **Tasks**: text-to-image-synthesis
- **Parameters**: 1.6B
- **Base model**: Tongyi-MAI/Z-Image
- **Online inference**: txt2img
- **Tags**: LoRA
- **Downloads**: 229
- **Stars**: 2
- **Last updated**: 2026-06-05

Source: https://www.modelscope.cn/models/begoshensaid/zit-hashimotokanna-char-r1

---

# zit-桥本环奈-char-r1 / zib-桥本环奈-char-r1 

> 将在下载数量超过 100后，公开训练数据

## 模型介绍

起名起错了。 这是个 Z Image Base 的Lora模型

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：[Tongyi-MAI/Z-Image](https://modelscope.cn/models/Tongyi-MAI/Z-Image)
* 训练代码：[DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)
* 训练数据量：100
* 总训练步数：10000
* 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.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", origin_file_pattern="transformer/*.safetensors"),
        ModelConfig(model_id="Tongyi-MAI/Z-Image", origin_file_pattern="text_encoder/*.safetensors"),
        ModelConfig(model_id="Tongyi-MAI/Z-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
    ],
    tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image", origin_file_pattern="tokenizer/"),
)
pipe.load_lora(pipe.dit, ModelConfig(model_id="begoshensaid/zit-hashimotokanna-char-r1", origin_file_pattern="zit-hashimotokanna-char-r1_c1-st10000.safetensors"))
prompt = "a cat"
image = pipe(prompt=prompt, num_inference_steps=50, cfg_scale=4)
image.save("image.jpg")
```