PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding

[Paper]   [Project Page]   [Model Card]

[🤗 Demo (Realistic)]   [🤗 Demo (Stylization)]

If the ID fidelity is not enough for you, please try our stylization application, you may be pleasantly surprised.

---

Official implementation of PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding.

🌠 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.

---

🚩 New Features/Updates

• ✅ Jan. 15, 2024. We release PhotoMaker.

---

🔥 Examples

Realistic generation

• 
• PhotoMaker notebook demo

Stylization generation

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

• 
• PhotoMaker-Style notebook demo

🔧 Dependencies and Installation

• Python >= 3.8 (Recommend to use Anaconda or Miniconda)
• PyTorch >= 2.0.0

pip install -r requirements.txt

⏬ Download Models

The model will be automatically downloaded through following two lines:

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.

💻 How to Test

Use like diffusers

• Dependency

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

### 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))

• Generation

# 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')

Start a local gradio demo

Run the following command:

python gradio_demo/app.py

You could customize this script in this file.

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.
• Inspired from many excellent demos and repos, including IP-Adapter, multimodalart/Ip-Adapter-FaceID, FastComposer, and T2I-Adapter. Thanks for their great works!
• Thanks for Venus team in Tencent PCG for their feedback and suggestions.

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:

@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}
}