🧔 壮实亚洲熊爸 —— 亚洲中年 专属以写实壮实亚洲熊系大叔为核心，专注于刻画厚重有力、毛发浓密、肌肉与脂肪完美融合的中年熊爸形象。本 LoRA 通过针对性微调，强化亚洲中年男性的成熟体型、生活痕迹与雄性魅力，目标是生成既有力量感又温暖亲切的真实熊爸形象。🔑 触发词 / Trigger Word *<code>as1da2</code>**- 兼容底模： Z-Image-turbo - 强化亚洲中年男性特征 + 熊系壮实体型📋 推荐 Prompt 示例1. 参考图片风格（shirtless 壮实熊爸）： as1da2 stands shirtless, exuding strength and presence, his muscular build is prominent, with broad shoulders and a well-defined chest, complemented by a thick beard and short hair, he wears dark gray sweatpants with a drawstring, standing confidently with his hands relaxed at his sides, the neutral background highlights his powerful physique, and the lighting accentuates his skin tone and body contours, his expression is serious and composed, conveying a sense of quiet intensity, highly detailed, photorealistic推荐生成设置： - Sampler: Euler - Steps: 9 - CFG Scale: 1 - LoRA 权重: 0.8-1.0 - 分辨率: 1024x1024 💡 实用小贴士 1. 自然语言提示推荐：Z-Image-turbo 适合自然语言描述，可用 ChatGPT、Claude、Gemini、Grok 或 Qwen 润色 prompt，直接说“你想要的壮实亚洲熊爸场景”即可获得高质量扩展。 2. Civitai 在线生图注意：Z-Image-turbo 在本地生成效果最佳，在线生成可能对 LoRA 调用有一定限制，推荐本地使用以充分发挥身材与细节优势。 3. 触发词小提醒：使用 <code>as1da2</code> 作为核心触发，结合 <code>thick asian bear daddy, mature, dad bod</code> 等词可进一步强化效果。 4. 想强化亚洲感 → 在 prompt 最后加 <code>east asian features, black hair, realistic asian face</code> 5. 想更壮实/厚重 → 加 <code>prominent dad bod, thick waist, dense body hair, massive chest, strong arms</code> 6. 姿势推荐：在描述中直接加入 <code>standing confidently, relaxed pose, looking at viewer</code> 等 7. 如果细节不足 → 加入 <code>highly detailed, 8k, ultra realistic, photorealistic, extremely detailed skin and hair</code> 或适当调整 LoRA 权重

壮实亚洲熊爸  as1da2

zimageturbo-as1da2_c1-st3000.safetensors

v1.0 (ZImageTurbo)


# 

## 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：20
* Total training steps：4000
* 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="jlhe2016/zimageturbo-as1da2", origin_file_pattern="zimageturbo-as1da2_c1-st4000.safetensors"))
prompt = "a cat"
image = pipe(prompt=prompt, num_inference_steps=8, cfg_scale=1)
image.save("image.jpg")
```


Files with SHA256: 9f14abdfa2e84929e7385de92c44b73a76da487184479aa4c5dc7b219777a7b9