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StoryMaker

(★ 722)

StoryMaker: Towards consistent characters in text-to-image generation

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- compare.png
- day1.png
- diverse.png
- two.png
- - 001.jpg
  - 001_mask.png
  - 001_pose.jpg
  - test.json
- - ldh666_0.jpg
  - ldh666_1.jpg
  - ldh666_2.jpg
  - ldh666_3.jpg
  - ldh_cloth_0.jpg
  - ldh_cloth_1.jpg
  - ldh_cloth_2.jpg
  - ldh_cloth_3.jpg
  - ldh_tsy666_0.jpg
  - ldh_tsy666_1.jpg
  - ldh_tsy666_2.jpg
  - ldh_tsy666_3.jpg
- cloth1.png
- cloth2.png
- ldh.png
- ldh_mask.png
- tsy.png
- tsy_mask.png
- attention_processor.py
- attention_processor_faceid.py
- ip_adapter.py
- ip_adapter_faceid.py
- resampler.py
- utils.py
infer.py
mp_dataset.py
pipeline_sdxl_storymaker.py
README.md
train.sh
train_storymaker.py

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// repository documentation

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<div align="center">
<h1>StoryMaker: Towards consistent characters in text-to-image generation</h1>

<a href='https://arxiv.org/pdf/2409.12576'><img src='https://img.shields.io/badge/Technique-Report-red'></a>
<a href='https://huggingface.co/RED-AIGC/StoryMaker'><img src='https://img.shields.io/static/v1?label=Paper&message=Huggingface&color=orange'></a>

</div>
StoryMaker is a personalization solution preserves not only the consistency of faces but also clothing, hairstyles and bodies in the multiple characters scene, enabling the potential to make a story consisting of a series of images.
<p align="center">
  <img src="assets/day1.png">
  Visualization of generated images by StoryMaker. First three rows tell a story about a day in the life of a "office worker" and the last two rows tell a story about a movie of "Before Sunrise".
</p>

## News
- [2024/11/09] 🔥 We release the training code.
- [2024/09/20] 🔥 We release the [technical report](https://arxiv.org/pdf/2409.12576).
- [2024/09/02] 🔥 We release the [model weights](https://huggingface.co/RED-AIGC/StoryMaker).

## Demos

### Two Portraits Synthesis

<p align="center">
  <img src="assets/two.png">
</p>

### Diverse application

<p align="center">
  <img src="assets/diverse.png">
</p>

## Download

You can directly download the model from [Huggingface](https://huggingface.co/RED-AIGC/StoryMaker).

If you cannot access to Huggingface, you can use [hf-mirror](https://hf-mirror.com/) to download models.
```python
export HF_ENDPOINT=https://hf-mirror.com
huggingface-cli download --resume-download RED-AIGC/StoryMaker --local-dir checkpoints --local-dir-use-symlinks False
```

For face encoder, you need to manually download via this [URL](https://github.com/deepinsight/insightface/issues/1896#issuecomment-1023867304) to `models/buffalo_l` as the default link is invalid. Once you have prepared all models, the folder tree should be like:

```
  .
  ├── models
  ├── checkpoints/mask.bin
  ├── pipeline_sdxl_storymaker.py
  └── README.md
```

## Usage

```python
import diffusers

import cv2
import torch
import numpy as np
from PIL import Image

from insightface.app import FaceAnalysis
from diffusers import UniPCMultistepScheduler
from pipeline_sdxl_storymaker import StableDiffusionXLStoryMakerPipeline

# prepare 'buffalo_l' under ./models
app = FaceAnalysis(name='buffalo_l', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
app.prepare(ctx_id=0, det_size=(640, 640))

# prepare models under ./checkpoints
face_adapter = f'./checkpoints/mask.bin'
image_encoder_path = 'laion/CLIP-ViT-H-14-laion2B-s32B-b79K'  #  from https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K

base_model = 'huaquan/YamerMIX_v11'  # from https://huggingface.co/huaquan/YamerMIX_v11
pipe = StableDiffusionXLStoryMakerPipeline.from_pretrained(
    base_model,
    torch_dtype=torch.float16
)
pipe.cuda()

# load adapter
pipe.load_storymaker_adapter(image_encoder_path, face_adapter, scale=0.8, lora_scale=0.8)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
```

Then, you can customized your own images

```python
# load an image and mask
face_image = Image.open("examples/ldh.png").convert('RGB')
mask_image = Image.open("examples/ldh_mask.png").convert('RGB')
    
face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))
face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1] # only use the maximum face

prompt = "a person is taking a selfie, the person is wearing a red hat, and a volcano is in the distance"
n_prompt = "bad quality, NSFW, low quality, ugly, disfigured, deformed"

generator = torch.Generator(device='cuda').manual_seed(666)
for i in range(4):
    output = pipe(
        image=face_image, mask_image=mask_image, face_info=face_info,
        prompt=prompt,
        negative_prompt=n_prompt,
        ip_adapter_scale=0.8, lora_scale=0.8,
        num_inference_steps=25,
        guidance_scale=7.5,
        height=1280, width=960,
        generator=generator,
    ).images[0]
    output.save(f'examples/results/ldh666_new_{i}.jpg')
```

## Acknowledgements
- Our work is highly inspired by [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter) and [InstantID](https://github.com/instantX-research/InstantID). Thanks for their great works!
- Thanks [Yamer](https://civitai.com/user/Yamer) for developing [YamerMIX](https://civitai.com/models/84040?modelVersionId=309729), we use it as base model in our demo.