<p align="center">
  <img src="https://photo-maker.github.io/assets/logo.png" height=100>

</p>

<!-- ## <div align="center"><b>PhotoMaker</b></div> -->
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## PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
[[Paper](https://huggingface.co/papers/2312.04461)] &emsp; [[Project Page](https://photo-maker.github.io)] &emsp; [[Model Card](https://huggingface.co/TencentARC/PhotoMaker)] <br>

[[🤗 Demo (Realistic)](https://huggingface.co/spaces/TencentARC/PhotoMaker)] &emsp; [[🤗 Demo (Stylization)](https://huggingface.co/spaces/TencentARC/PhotoMaker-Style)] <br>

</div>


---

Official implementation of **[PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding](https://huggingface.co/papers/2312.04461)**.

### 🌠  **Key Features:**

1. Rapid customization **within seconds**, with no additional LoRA training.
2. Ensures impressive ID fidelity, offering diversity, promising text controllability, and high-quality generation.
3. Can serve as an **Adapter** to collaborate with other Base Models alongside LoRA modules in community.


---

<div style="text-align: center;">
  <video width=80% autoplay muted loop controls>
    <source src="https://photo-maker.github.io/assets/photomaker_demo.mp4" type="video/mp4">
  </video>
  <br>
  Note: The prompt displayed in the video is a simplified version.
</div>


<!-- <div align="center"> -->

<!-- ![photomaker_demo_fast](https://github.com/TencentARC/PhotoMaker/assets/21050959/e72cbf4d-938f-417d-b308-55e76a4bc5c8) -->
<!-- </div> -->


## 🚩 **New Features/Updates**
- ✅ Jan. 15, 2024. We release PhotoMaker.

---

## 🔥 **Examples**


### Realistic generation 

- [![Huggingface PhotoMaker](https://img.shields.io/static/v1?label=Demo&message=Huggingface%20Gradio&color=orange)](https://huggingface.co/spaces/TencentARC/PhotoMaker)
- [**PhotoMaker notebook demo**](photomaker_demo.ipynb)

<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/6285a9133ab6642179158944/BYBZNyfmN4jBKBxxt4uxz.jpeg" height=450>
</p>

<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/6285a9133ab6642179158944/9KYqoDxfbNVLzVKZzSzwo.jpeg" height=450>
</p>

### Stylization generation 

Note: only change the base model and add the LoRA modules for better stylization

- [![Huggingface PhotoMaker-Style](https://img.shields.io/static/v1?label=Demo&message=Huggingface%20Gradio&color=orange)](https://huggingface.co/spaces/TencentARC/PhotoMaker-Style)
- [**PhotoMaker-Style notebook demo**](photomaker_style_demo.ipynb) 

<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/6285a9133ab6642179158944/du884lcjpqqjnJIxpATM2.jpeg" height=450>
</p>
  
<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/6285a9133ab6642179158944/-AC7Hr5YL4yW1zXGe_Izl.jpeg" height=450>
</p>

# 🔧 Dependencies and Installation

- Python >= 3.8 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
- [PyTorch >= 2.0.0](https://pytorch.org/)
```bash
pip install -r requirements.txt
```

# ⏬ Download Models 
The model will be automatically downloaded through following two lines:

```python
from huggingface_hub import hf_hub_download
photomaker_path = hf_hub_download(repo_id="TencentARC/PhotoMaker", filename="photomaker-v1.bin", repo_type="model")
```

You can also choose to download manually from this [url](https://huggingface.co/TencentARC/PhotoMaker).

# 💻 How to Test

## Use like [diffusers](https://github.com/huggingface/diffusers)

- Dependency
```py
import torch
import os
from diffusers.utils import load_image
from diffusers import EulerDiscreteScheduler
from photomaker.pipeline import PhotoMakerStableDiffusionXLPipeline

### Load base model
pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
    base_model_path,  # can change to any base model based on SDXL
    torch_dtype=torch.bfloat16, 
    use_safetensors=True, 
    variant="fp16"
).to(device)

### Load PhotoMaker checkpoint
pipe.load_photomaker_adapter(
    os.path.dirname(photomaker_path),
    subfolder="",
    weight_name=os.path.basename(photomaker_path),
    trigger_word="img"  # define the trigger word
)     

pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)

### Also can cooperate with other LoRA modules
# pipe.load_lora_weights(os.path.dirname(lora_path), weight_name=lora_model_name, adapter_name="xl_more_art-full")
# pipe.set_adapters(["photomaker", "xl_more_art-full"], adapter_weights=[1.0, 0.5])

pipe.fuse_lora()
```

- Input ID Images
```py
### define the input ID images
input_folder_name = './examples/newton_man'
image_basename_list = os.listdir(input_folder_name)
image_path_list = sorted([os.path.join(input_folder_name, basename) for basename in image_basename_list])

input_id_images = []
for image_path in image_path_list:
    input_id_images.append(load_image(image_path))
```

<div align="center">

<a href="https://github.com/TencentARC/PhotoMaker/assets/21050959/01d53dfa-7528-4f09-a1a5-96b349ae7800" align="center"><img style="margin:0;padding:0;" src="https://github.com/TencentARC/PhotoMaker/assets/21050959/01d53dfa-7528-4f09-a1a5-96b349ae7800"/></a>
</div>

- Generation
```py
# Note that the trigger word `img` must follow the class word for personalization
prompt = "a half-body portrait of a man img wearing the sunglasses in Iron man suit, best quality"
negative_prompt = "(asymmetry, worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth, grayscale"
generator = torch.Generator(device=device).manual_seed(42)
images = pipe(
    prompt=prompt,
    input_id_images=input_id_images,
    negative_prompt=negative_prompt,
    num_images_per_prompt=1,
    num_inference_steps=num_steps,
    start_merge_step=10,
    generator=generator,
).images[0]
gen_images.save('out_photomaker.png')
```

<div align="center">

<a href="https://github.com/TencentARC/PhotoMaker/assets/21050959/703c00e1-5e50-4c19-899e-25ee682d2c06" align="center"><img width=400 style="margin:0;padding:0;" src="https://github.com/TencentARC/PhotoMaker/assets/21050959/703c00e1-5e50-4c19-899e-25ee682d2c06"/></a>

</div>

## Start a local gradio demo
Run the following command:

```python
python gradio_demo/app.py
```

You could customize this script in [this file](gradio_demo/app.py).

## Usage Tips:
- Upload more photos of the person to be customized to improve ID fidelty. If the input is Asian face(s), maybe consider adding 'asian' before the class word, e.g., `asian woman img`
- When stylizing, does the generated face look too realistic? Adjust the Style strength to 30-50, the larger the number, the less ID fidelty, but the stylization ability will be better. You could also try out other base models or LoRAs with good stylization effects.
- For faster speed, reduce the number of generated images and sampling steps. However, please note that reducing the sampling steps may compromise the ID fidelity.

# 🤗 Acknowledgements
- T2I-Adapter is co-hosted by Tencent ARC Lab and Nankai University [MCG-NKU](https://mmcheng.net/cmm/).
- Inspired from many excellent demos and repos, including [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter), [multimodalart/Ip-Adapter-FaceID](https://huggingface.co/spaces/multimodalart/Ip-Adapter-FaceID), [FastComposer](https://github.com/mit-han-lab/fastcomposer), and [T2I-Adapter](https://github.com/TencentARC/T2I-Adapter). Thanks for their great works!

# Disclaimer
This project strives to positively impact the domain of AI-driven image generation. Users are granted the freedom to create images using this tool, but they are expected to comply with local laws and utilize it in a responsible manner. The developers do not assume any responsibility for potential misuse by users.

# BibTeX
If you find PhotoMaker useful for your research and applications, please cite using this BibTeX:

```bibtex
@article{li2023photomaker,
  title={PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding},
  author={Li, Zhen and Cao, Mingdeng and Wang, Xintao and Qi, Zhongang and Cheng, Ming-Ming and Shan, Ying},
  booktitle={arXiv preprint arxiv:2312.04461},
  year={2023}
}