---
title: alikaTEST
canonical_url: "https://www.modelscope.ai/models/dotakhm/alikaTEST"
md_url: "https://www.modelscope.ai/models/dotakhm/alikaTEST.md"
repository: dotakhm/alikaTEST
last_updated: 2026-06-07
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: 238.1M
tensor_type:
  - F16
library_name:
  - pytorch
  - lora
  - safetensors
frameworks:
  - pytorch
supports_inference: txt2img
downloads: 302
stars: 0
tags:
  - LoRA
---

# alikaTEST

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

dotakhm/alikaTEST is a 238.1M-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**: dotakhm/alikaTEST
- **License**: Apache License 2.0
- **Tasks**: text-to-image-synthesis
- **Parameters**: 238.1M
- **Base model**: Tongyi-MAI/Z-Image-Turbo
- **Online inference**: txt2img
- **Tags**: LoRA
- **Downloads**: 302
- **Stars**: 0
- **Last updated**: 2026-06-07

Source: https://www.modelscope.ai/models/dotakhm/alikaTEST

---

# 

## 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：46
* 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="dotakhm/alikaTEST", origin_file_pattern="alikaTEST_c1-st6000.safetensors"))
prompt = "a cat"
image = pipe(prompt=prompt, num_inference_steps=8, cfg_scale=1)
image.save("image.jpg")
```