Emma Veil Style is a fine-tuned LoRA model designed to replicate the distinctive artistic style of Emma Veil. This style LoRA captures the unique visual characteristics of the artist's work, enabling users to generate images with a consistent and recognizable aesthetic.<h3 id="recommended-settings:">Recommended Settings:</h3><ul><li>Weight: 50 - 80 (start at 50 and adjust based on desired intensity)</li><li>Trigger Word: <span style="background-color:rgb(38, 38, 38);color:rgba(255, 255, 255, 0.9);font-family:&quot;SF Pro Display&quot;, &quot;SF Pro Text&quot;, &quot;PingFang SC&quot;, &quot;Helvetica Neue&quot;, &quot;Noto Sans SC&quot;, &quot;Microsoft YaHei&quot;, sans-serif;font-size:15px">Emma-veil-lora</li></ul>Base Model: Compatible with Illustrious / SD 1.5 / SDXL / Z-Image (z_image best results)

Emma Veil Style

First Realease Only 30% Emma Veil Style.

Emma-veil-v1_c1-st2000.safetensors

Emma-veil-v1_c1-st1000.safetensors

Emma-veil-v1_c1-st3000.safetensors

Ev-v1

EV-v2


# 

## 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：44
* Total training steps：3000
* 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="Nik8nik/Emma-veil-v1", origin_file_pattern="Emma-veil-v1_c1-st3000.safetensors"))
prompt = "a cat"
image = pipe(prompt=prompt, num_inference_steps=50, cfg_scale=4)
image.save("image.jpg")
```


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