app.py

import gradio as gr
import torch
import numpy as np
import diffusers
import os
from PIL import Image
hf_token = os.environ.get("HF_TOKEN")
from diffusers import AutoPipelineForText2Image


device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = AutoPipelineForText2Image.from_pretrained("briaai/BRIA-2.3", torch_dtype=torch.float16, force_zeros_for_empty_prompt=False).to(device)
pipe.load_ip_adapter("briaai/DEV-Image-Prompt", subfolder='models', weight_name="ip_adapter_bria.bin")

default_negative_prompt= "" #"Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"


def read_content(file_path: str) -> str:
    """read the content of target file
    """
    with open(file_path, 'r', encoding='utf-8') as f:
        content = f.read()

    return content

def predict(image, prompt="high quality, best quality", negative_prompt="", guidance_scale=5, steps=30, ip_adapter_scale = 1.0, width=1024, height=1024, seed=0):

    pipe.set_ip_adapter_scale(ip_adapter_scale)

    if negative_prompt == "":
        negative_prompt = None
    
    init_image = image.convert("RGB")
    # if center_crop is False:
    init_image = init_image.resize((224, 224))
    
    generator = torch.Generator(device="cpu").manual_seed(int(seed))
        
    output = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    ip_adapter_image=init_image,
    num_inference_steps=int(steps),
    generator=generator,
    height=int(height), width=int(width),
    guidance_scale=guidance_scale
    )

    torch.cuda.empty_cache
    return output.images[0] #, gr.update(visible=True)


css = '''
.gradio-container{max-width: 1100px !important}
#image_upload{min-height:400px}
#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
#mask_radio .gr-form{background:transparent; border: none}
#word_mask{margin-top: .75em !important}
#word_mask textarea:disabled{opacity: 0.3}
.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
.dark .footer {border-color: #303030}
.dark .footer>p {background: #0b0f19}
.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
#image_upload .touch-none{display: flex}
@keyframes spin {
    from {
        transform: rotate(0deg);
    }
    to {
        transform: rotate(360deg);
    }
}
#share-btn-container {padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; max-width: 13rem; margin-left: auto;}
div#share-btn-container > div {flex-direction: row;background: black;align-items: center}
#share-btn-container:hover {background-color: #060606}
#share-btn {all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.5rem !important; padding-bottom: 0.5rem !important;right:0;}
#share-btn * {all: unset}
#share-btn-container div:nth-child(-n+2){width: auto !important;min-height: 0px !important;}
#share-btn-container .wrap {display: none !important}
#share-btn-container.hidden {display: none!important}
#prompt input{width: calc(100% - 160px);border-top-right-radius: 0px;border-bottom-right-radius: 0px;}
#run_button{position:absolute;margin-top: 11px;right: 0;margin-right: 0.8em;border-bottom-left-radius: 0px;
    border-top-left-radius: 0px;}
#prompt-container{margin-top:-18px;}
#prompt-container .form{border-top-left-radius: 0;border-top-right-radius: 0}
#image_upload{border-bottom-left-radius: 0px;border-bottom-right-radius: 0px}
'''

image_blocks = gr.Blocks(css=css, elem_id="total-container")
with image_blocks as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("## BRIA 2.3")
        gr.HTML('''
          <p style="margin-bottom: 10px; font-size: 94%">
            This is a demo for 
            <a href="https://huggingface.co/briaai/BRIA-2.3" target="_blank">BRIA 2.3 text-to-image </a>. 
            BRIA 2.3 improve the generation of humans and illustrations compared to BRIA 2.2 while still trained on licensed data, and so provide full legal liability coverage for copyright and privacy infringement.
          </p>
        ''')
    with gr.Row():
                with gr.Column():
                    image = gr.Image(elem_id="image_upload", type="pil", label="Upload", height=400)
                    # image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
                    
                    
                    with gr.Row(elem_id="prompt-container", equal_height=True):
                        with gr.Row():
                            prompt = gr.Textbox(placeholder="Your prompt (you can leave it empty if you only want the image prompt as input)", show_label=False, elem_id="prompt")
                            btn = gr.Button("Generate!", elem_id="run_button")

                    with gr.Accordion(label="Settings", open=True):
                        with gr.Row(equal_height=True):
                            ip_adapter_scale = gr.Number(value=1.0, minimum=0.01, maximum=1.0, step=0.01, label="ip_adapter_scale")
                            width = gr.Number(value=1024, minimum=300, maximum=2000, step=1, label="width")
                            height = gr.Number(value=1024, minimum=300, maximum=2000, step=1, label="height")                    
                    
                    with gr.Accordion(label="Advanced Settings", open=False):
                        with gr.Row(equal_height=True):
                            guidance_scale = gr.Number(value=5, minimum=1.0, maximum=10.0, step=0.5, label="guidance_scale")
                            steps = gr.Number(value=30, minimum=10, maximum=100, step=1, label="steps")
                            seed = gr.Number(value=0, minimum=0, maximum=100000, step=1, label="seed")                                                        
                            negative_prompt = gr.Textbox(label="negative_prompt", value=default_negative_prompt, placeholder=default_negative_prompt, info="what you don't want to see in the image")
                            center_crop = gr.Checkbox(label="center_crop", info="If not checked, the image would be resized to square before it's fed to the model."),

                        
                with gr.Column():
                    image_out = gr.Image(label="Output", elem_id="output-img", height=400)

            

    btn.click(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, ip_adapter_scale, width, height, seed], outputs=[image_out], api_name='run')
    prompt.submit(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, ip_adapter_scale, width, height, seed], outputs=[image_out])

    # gr.Examples(
    #             examples=[
    #                 ["./imgs/example.png"],
    #             ],
    #             fn=predict,
    #             inputs=[image],
    #             cache_examples=False,
    # )
    gr.HTML(
        """
            <div class="footer">
                <p>Model by <a href="https://huggingface.co/diffusers" style="text-decoration: underline;" target="_blank">Diffusers</a> - Gradio Demo by 🤗 Hugging Face
                </p>
            </div>
        """
    )

image_blocks.queue(max_size=25,api_open=False).launch(show_api=False)