Trained on about 75+ random art images, including anime, digital art, and other styles I found on Pinterest.I recommend setting the strength to 0.7 as increasing it further would give anatomical artifacts.A LoRA strength between 0.5 and 0.7 is a good range. This LoRA also works well as an enhancer for Z images, especially at low strengths. Use tags like Vivid, art, digital art, painterly

Z-Image-Art

ZIB-Aesthetic-v4_c2-st6000.safetensors

v6.0

v7.0

v5.5

v5.0

v4.0

v3.0

v2.0

v1.0


# 

## 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](https://modelscope.ai/models/Tongyi-MAI/Z-Image)
* Training Code: [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)
* Data amount：95
* 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", 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="hopper21/ZIB-Aesthetic-v4", origin_file_pattern="ZIB-Aesthetic-v4_c2-st6000.safetensors"))
prompt = "a cat"
image = pipe(prompt=prompt, num_inference_steps=50, cfg_scale=4)
image.save("image.jpg")
```


Files with SHA256: f2f48215f6038d866e48226a8668e41cb06b1e6b82f143515d565e7a1325cabe