app.py

import gradio as gr
import torch
import os
import glob
import spaces

from datetime import datetime
from PIL import Image
from diffusers.utils import load_image
from diffusers import EulerDiscreteScheduler
from pipline_StableDiffusion_ConsistentID import ConsistentIDStableDiffusionPipeline
from huggingface_hub import hf_hub_download
### Model can be imported from https://github.com/zllrunning/face-parsing.PyTorch?tab=readme-ov-file
### We use the ckpt of 79999_iter.pth: https://drive.google.com/open?id=154JgKpzCPW82qINcVieuPH3fZ2e0P812
### Thanks for the open source of face-parsing model.
from models.BiSeNet.model import BiSeNet

# zero = torch.Tensor([0]).cuda()
# print(zero.device) # <-- 'cpu' 🤔
# device = zero.device # "cuda"
device = "cuda"

# Gets the absolute path of the current script
script_directory = os.path.dirname(os.path.realpath(__file__))

# download ConsistentID checkpoint to cache
base_model_path = "SG161222/Realistic_Vision_V6.0_B1_noVAE"
consistentID_path = hf_hub_download(repo_id="JackAILab/ConsistentID", filename="ConsistentID-v1.bin", repo_type="model")

### Load base model
pipe = ConsistentIDStableDiffusionPipeline.from_pretrained(
    base_model_path, 
    torch_dtype=torch.float16, 
    safety_checker=None, # use_safetensors=True, 
    variant="fp16"
).to(device)

### Load other pretrained models
## BiSenet
bise_net_cp_path = hf_hub_download(repo_id="JackAILab/ConsistentID", filename="face_parsing.pth", local_dir="./checkpoints")
bise_net = BiSeNet(n_classes = 19)
bise_net.load_state_dict(torch.load(bise_net_cp_path, map_location=device))

### Load consistentID_model checkpoint
pipe.load_ConsistentID_model(
    os.path.dirname(consistentID_path),
    bise_net,
    subfolder="",
    weight_name=os.path.basename(consistentID_path),
    trigger_word="img",
)
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)


# @torch.inference_mode()
# def Enhance_prompt(prompt,select_images):
    
#     llva_prompt = f'Please ignore the image. Enhance the following text prompt for me. You can associate more details with the character\'s gesture, environment, and decent clothing:"{prompt}".' 
#     args = type('Args', (), {
#         "model_path": llva_model_path,
#         "model_base": None,
#         "model_name": get_model_name_from_path(llva_model_path),
#         "query": llva_prompt,
#         "conv_mode": None,
#         "image_file": select_images,
#         "sep": ",",
#         "temperature": 0,
#         "top_p": None,
#         "num_beams": 1,
#         "max_new_tokens": 512
#     })() 
#     Enhanced_prompt = eval_model(args, llva_tokenizer, llva_model, llva_image_processor)

#     return Enhanced_prompt
    
@spaces.GPU
def process(inputImage,prompt,negative_prompt):

    # hyper-parameter
    select_images = load_image(Image.fromarray(inputImage))
    num_steps = 50
    merge_steps = 30
    
    if prompt == "":
        prompt = "A man, in a forest"
        prompt = "A man, with backpack, in a raining tropical forest, adventuring, holding a flashlight, in mist, seeking animals"
        prompt = "A person, in a sowm, wearing santa hat and a scarf, with a cottage behind"
    else:
        # prompt=Enhance_prompt(prompt,blank_image) # TODO
        prompt=prompt
        print(prompt)
        pass
        
    if negative_prompt == "":
        negative_prompt = ",monochrome, lowres, bad anatomy, worst quality, low quality, blurry"

    # Extend Prompt
    prompt = "cinematic photo," + prompt + ", 50mm photograph, half-length portrait, film, bokeh, professional, 4k, highly detailed"

    negtive_prompt_group="((((ugly)))), (((duplicate))), ((morbid)), ((mutilated)), [out of frame], extra fingers, mutated hands, ((poorly drawn hands)), ((poorly drawn face)), (((mutation))), (((deformed))), ((ugly)), blurry, ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))). out of frame, ugly, extra limbs, (bad anatomy), gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), mutated hands, (fused fingers), (too many fingers), (((long neck)))"
    negative_prompt = negative_prompt + negtive_prompt_group
    
    seed = torch.randint(0, 1000, (1,)).item()
    generator = torch.Generator(device=device).manual_seed(seed)

    images = pipe(
        prompt=prompt,
        width=512,    
        height=768,
        input_id_images=select_images,
        negative_prompt=negative_prompt,
        num_images_per_prompt=1,
        num_inference_steps=num_steps,
        start_merge_step=merge_steps,
        generator=generator,
    ).images[0]

    current_date = datetime.today()

    output_dir = script_directory + f"/images/gradio_outputs"
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    images.save(os.path.join(output_dir, f"{current_date}-{seed}.jpg"))

    return os.path.join(output_dir, f"{current_date}-{seed}.jpg")

# # Gets the templates
# script_directory = os.path.dirname(os.path.realpath(__file__))
# # preset_template = glob.glob(script_directory+"/images/templates/*.png")
# preset_template = glob.glob("./images/templates/*.png")
# preset_template = preset_template + glob.glob("./images/templates/*.jpg")

# # Use Blocks Create Gradio 
# with gr.Blocks(title="ConsistentID Demo") as demo:
#     gr.Markdown("# ConsistentID Demo")
#     gr.Markdown("\
#         Put the reference figure to be redrawn into the box below (There is a small probability of referensing failure. You can submit it repeatedly)")
#     gr.Markdown("\
#         If you find our work interesting, please leave a star in GitHub for us!<br>\
#         https://github.com/JackAILab/ConsistentID")
#     with gr.Row():
#         with gr.Column():
#             model_selected_tab = gr.State(0)
#             with gr.TabItem("template images") as template_images_tab:
#                 template_gallery_list = [(i, i) for i in preset_template]
#                 gallery = gr.Gallery(template_gallery_list,columns=[4], rows=[2], object_fit="contain", height="auto",show_label=False)
                
#                 def select_function(evt: gr.SelectData):
#                     return preset_template[evt.index]

#                 selected_template_images = gr.Text(show_label=False, visible=False, placeholder="Selected")
#                 gallery.select(select_function, None, selected_template_images)
#             with gr.TabItem("Upload Image") as upload_image_tab:
#                 costum_image = gr.Image(label="Upload Image")

#             model_selected_tabs = [template_images_tab, upload_image_tab]
#             for i, tab in enumerate(model_selected_tabs):
#                 tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[model_selected_tab])

#             with gr.Column():
#                 prompt_selected_tab = gr.State(0)
#                 with gr.TabItem("template prompts") as template_prompts_tab:
#                     prompt_selected = gr.Dropdown(value="A person, police officer, half body shot", elem_id='dropdown', choices=[
#                         "A woman in a wedding dress",
#                         "A woman, queen, in a gorgeous palace",
#                         "A man sitting at the beach with sunset", 
#                         "A person, police officer, half body shot", 
#                         "A man, sailor, in a boat above ocean",
#                         "A women wearing headphone, listening music", 
#                         "A man, firefighter, half body shot"], label=f"prepared prompts")

#                 with gr.TabItem("custom prompt") as custom_prompt_tab:
#                     prompt = gr.Textbox(label="prompt",placeholder="A man/woman wearing a santa hat")
#                     nagetive_prompt = gr.Textbox(label="negative prompt",placeholder="monochrome, lowres, bad anatomy, worst quality, low quality, blurry")
            
#                 prompt_selected_tabs = [template_prompts_tab, custom_prompt_tab]
#                 for i, tab in enumerate(prompt_selected_tabs):
#                     tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[prompt_selected_tab])
            
#             retouching = gr.Checkbox(label="face retouching",value=False)
#             width = gr.Slider(label="image width",minimum=256,maximum=768,value=512,step=8)
#             height = gr.Slider(label="image height",minimum=256,maximum=768,value=768,step=8)
#             width.release(lambda x,y: min(1280-x,y), inputs=[width,height], outputs=[height])
#             height.release(lambda x,y: min(1280-y,x), inputs=[width,height], outputs=[width])
#             merge_steps = gr.Slider(label="step starting to merge facial details(30 is recommended)",minimum=10,maximum=50,value=30,step=1)
            
#             btn = gr.Button("Run")
#         with gr.Column():
#             out = gr.Image(label="Output")
#             gr.Markdown('''
#                 N.B.:<br/>
#                 - If the proportion of face in the image is too small, the probability of an error will be slightly higher, and the similarity will also significantly decrease.)
#                 - At the same time, use prompt with \"man\" or \"woman\" instead of \"person\" as much as possible, as that may cause the model to be confused whether the protagonist is male or female.
#                 - Due to insufficient graphics memory on the demo server, there is an upper limit on the resolution for generating samples. We will support the generation of SDXL as soon as possible<br/><br/>
#                 ''')
#         btn.click(fn=process, inputs=[selected_template_images,costum_image,prompt,nagetive_prompt,prompt_selected,retouching
#             ,model_selected_tab,prompt_selected_tab,width,height,merge_steps], outputs=out)

iface = gr.Interface(
    fn=process,
    inputs=[
        gr.Image(label="Upload Image"), 
        gr.Textbox(label="prompt",placeholder="A man, in a forest, adventuring"),
        gr.Textbox(label="negative prompt",placeholder="monochrome, lowres, bad anatomy, worst quality, low quality, blurry"),
    ],
    outputs=[
        gr.Image(label="Output"), 
    ],
    title="ConsistentID Demo",
    description="Put reference portrait below" ,
    allow_flagging="never"
)

iface.launch() # zero.device

# @spaces.GPU
# def greet(n):
#     print(zero.device) # <-- 'cuda:0' 🤗
#     return f"Hello {zero + n} Tensor"

# demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
# demo.launch()