---
title: multimodal_multiview_avatar_gen
canonical_url: "https://www.modelscope.cn/models/iic/multimodal_multiview_avatar_gen"
md_url: "https://www.modelscope.cn/models/iic/multimodal_multiview_avatar_gen.md"
repository: iic/multimodal_multiview_avatar_gen
chinese_name: "基于扩散模型的人物多视图生成模型"
last_updated: 2023-11-22
license: "Apache License 2.0"
library_name:
  - pytorch
frameworks:
  - pytorch
domain:
  - multi-modal
downloads: 168
stars: 7
tags:
  - "multiview avatar"
  - "diffusion model"
  - "人物多视图"
  - "文到图"
  - "文生图"
  - "生成"
  - "图片生成"
  - "生成图片"
  - "ai作画"
  - "作画ai"
  - "作画"
---

# multimodal_multiview_avatar_gen

> multimodal_multiview_avatar_gen - iic 在 ModelScope 开源的模型。基于扩散模型的人物多视图生成模型

iic/multimodal_multiview_avatar_gen 是 ModelScope 魔搭社区上的机器学习模型，采用 Apache License 2.0 许可。

- **Repository**: iic/multimodal_multiview_avatar_gen
- **License**: Apache License 2.0
- **Tags**: multiview avatar, diffusion model, 人物多视图, 文到图, 文生图, 生成, 图片生成, 生成图片, ai作画, 作画ai, 作画
- **Downloads**: 168
- **Stars**: 7
- **Last updated**: 2023-11-22

Source: https://www.modelscope.cn/models/iic/multimodal_multiview_avatar_gen

---

# Stable Diffusion for Multiview Avatar Generation 文本生成人物多视图模型
该模型为文本生成人物多视图模型，输入描述文本，实现端到端人物多视图生成。

# 文本多视图大模型


<img src="./data/test-iron-man.jpg"
     alt="Iron Man."
     style="width: 3920px;" />


<img src="./data/test-spider-man.jpg"
     alt="Spider Man."
     style="width: 3920px;" />


## 模型描述
该模型基于Stable Diffusion v1.5， ControlNet v1.1 与diffusers进行构建。
## 模型期望使用方式和适用范围

该模型推理时对机器GPU显存有一定要求；在FP16模式下并开启enable_xformers_memory_efficient_attention选项时，要求显存16GB。

### 如何使用Pipeline
在 ModelScope 框架上，提供输入文本，即可以通过简单的 Pipeline 调用来使用该模型。

#### 安装说明
```
git clone https://github.com/ArcherFMY/Multiview-Avatar.git
cd Multiview-Avatar
pip install -r requirements.txt
python setup.py develop
pip install modelscope
```

#### 推理代码范例
```python
from mvavatar import ms_wrapper
from modelscope.pipelines import pipeline
input = "a girl"
inference = pipeline('my-multiview-avatar-task', model='damo/multimodal_multiview_avatar_gen', model_revision='v1.0.0')
output = inference(input)
output.save('test.jpg')
```


如果你觉得这个模型对你有所帮助，请考虑引用下面的相关论文：
```
@article{ruiz2022dreambooth,
  title={DreamBooth: Fine Tuning Text-to-image Diffusion Models for Subject-Driven Generation},
  author={Ruiz, Nataniel and Li, Yuanzhen and Jampani, Varun and Pritch, Yael and Rubinstein, Michael and Aberman, Kfir},
  booktitle={arXiv preprint arxiv:2208.12242},
  year={2022}
}
@misc{von-platen-etal-2022-diffusers,
  author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf},
  title = {Diffusers: State-of-the-art diffusion models},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/huggingface/diffusers}}
}
@misc{zhang2023adding,
  title={Adding Conditional Control to Text-to-Image Diffusion Models}, 
  author={Lvmin Zhang and Maneesh Agrawala},
  year={2023},
  eprint={2302.05543},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}
```