conda create --name photomaker python=3.10conda activate photomakerpip install -U pip
# Install requirementspip install -r requirements.txt
# Install photomakerpip install git+https://github.com/TencentARC/PhotoMaker.git

from photomaker import PhotoMakerStableDiffusionXLPipeline

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

import torchimport osfrom diffusers.utils import load_imagefrom diffusers import EulerDiscreteSchedulerfrom photomaker import PhotoMakerStableDiffusionXLPipeline
### Load base modelpipe = 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 checkpointpipe.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()

### define the input ID imagesinput_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))

# Note that the trigger word `img` must follow the class word for personalizationprompt = "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')