import spaces import os import io import IPython.display from PIL import Image import base64 import io from PIL import Image import gradio as gr import requests import time import random import numpy as np import torch import os from transformers import ViTModel, ViTImageProcessor from utils import text_encoder_forward from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler from utils import latents_to_images, downsampling, merge_and_save_images from omegaconf import OmegaConf # from accelerate.utils import set_seed from tqdm import tqdm from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput from PIL import Image from models.celeb_embeddings import embedding_forward import models.embedding_manager import importlib import time import os # os.environ['GRADIO_TEMP_DIR'] = 'qinghewang/tmp' title = r"""

CharacterFactory: Sampling Consistent Characters with GANs for Diffusion Models

""" description = r""" Official Gradio demo for CharacterFactory: Sampling Consistent Characters with GANs for Diffusion Models.
How to use:
1. Enter prompts with the trigger words `a person`, and each line will generate an image. 2. You can choose to `Create a new character` or `Still use this character`. We have provided some examples, click on the examples below to use. 3. You can choose to use the Normal version (the gender is random), the Man version, and the Woman version. 4. Click the Generate button to begin (Images are generated one by one). 5. Our method can be applied to illustrating books and stories, creating brand ambassadors, developing presentations, art design, identity-consistent data construction and more. Looking forward to your explorations!๐Ÿ˜Š 6. If CharacterFactory is helpful, please help to โญ the Github Repo. Thanks! """ article = r""" --- ๐Ÿ“ **Citation**
If our work is helpful for your research or applications, please cite us via: ```bibtex @article{wang2024characterfactory, title={CharacterFactory: Sampling Consistent Characters with GANs for Diffusion Models}, author={Wang, Qinghe and Li, Baolu and Li, Xiaomin and Cao, Bing and Ma, Liqian and Lu, Huchuan and Jia, Xu}, journal={arXiv preprint arXiv:2404.15677}, year={2024} } ``` ๐Ÿ“ง **Contact**
If you have any questions, please feel free to open an issue or directly reach us out at qinghewang@mail.dlut.edu.cn. """ css = ''' #color-bg{display:flex;justify-content: center;align-items: center;} .color-bg-item{width: 100%; height: 32px} #main_button{width:100%}