This LoRA aims to emulate the artstyle and visual style of 'Kawaii - Kawai' the S2 outro of anime series 'My Dress Up Darling'.Pics for training the LoRA pulled from Twitter/X thread by @PasTPourChien - <a target="_blank" rel="noopener noreferrer nofollow" href="https://x.com/PasTPourChien/status/1944072379694514248">https://x.com/PasTPourChien/status/1944072379694514248</a>'Kawaii - Kawai' outro is created by Korean artists Vivinos and Qmeng. Check them out.Credit: This model received technical support from ModelScope.Author: D2 at ModelScope

Kawaii - Vivinos

Kawaii-a-ZIT_c1-st8000.safetensors

V2 ZIT

V2 Krea2


# 

## 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：54
* Total training steps：8000
* 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="Jagerfly/Kawaii-a-ZIT", origin_file_pattern="Kawaii-a-ZIT_c1-st8000.safetensors"))
prompt = "a cat"
image = pipe(prompt=prompt, num_inference_steps=8, cfg_scale=1)
image.save("image.jpg")
```


Files with SHA256: 3c6ef1e558d3dd2f104560de28a3c410233d3d7b0f3def0d61dbb3048f4c87e4