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import gradio as gr | |
import numpy as np | |
import os | |
from PIL import Image | |
import requests | |
from io import BytesIO | |
import io | |
import base64 | |
hf_token = os.environ.get("HF_TOKEN_API_DEMO") # we get it from a secret env variable, such that it's private | |
auth_headers = {"api_token": hf_token} | |
def convert_mask_image_to_base64_string(mask_image): | |
buffer = io.BytesIO() | |
mask_image.save(buffer, format="PNG") # You can choose the format (e.g., "JPEG", "PNG") | |
# Encode the buffer in base64 | |
image_base64_string = base64.b64encode(buffer.getvalue()).decode('utf-8') | |
return f",{image_base64_string}" # for some reason the funciton which downloads image from base64 expects prefix of "," which is redundant in the url | |
def download_image(url): | |
response = requests.get(url) | |
return Image.open(BytesIO(response.content)).convert("RGB") | |
def eraser_api_call(image_base64_file, mask_base64_file, mask_type): | |
url = "http://engine.prod.bria-api.com/v1/eraser" | |
payload = { | |
"file": image_base64_file, | |
"mask_file": mask_base64_file, | |
"mask_type": mask_type, | |
} | |
response = requests.post(url, json=payload, headers=auth_headers) | |
response = response.json() | |
res_image = download_image(response["result_url"]) | |
return res_image | |
def predict(dict): | |
init_image = Image.fromarray(dict['background'][:, :, :3], 'RGB') #dict['background'].convert("RGB")#.resize((1024, 1024)) | |
mask = Image.fromarray(dict['layers'][0][:,:,3], 'L') #dict['layers'].convert("RGB")#.resize((1024, 1024)) | |
image_base64_file = convert_mask_image_to_base64_string(init_image) | |
mask_base64_file = convert_mask_image_to_base64_string(mask) | |
mask_type = "manual" | |
gen_img = eraser_api_call(image_base64_file, mask_base64_file, mask_type) | |
return gen_img | |
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 { | |
width: 100%; | |
height: 50px; /* Set a fixed height for the button */ | |
display: flex; | |
align-items: center; | |
justify-content: center; | |
} | |
#output-img img, #image_upload img { | |
object-fit: contain; /* Ensure aspect ratio is preserved */ | |
width: 100%; | |
height: auto; /* Let height adjust automatically */ | |
} | |
#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 Eraser API") | |
gr.HTML(''' | |
<p style="margin-bottom: 10px; font-size: 94%"> | |
This demo showcases the BRIA Eraser capability, which allows users to remove specific elements or objects from images.<br> | |
The pipeline comprises multiple components, including <a href="https://huggingface.co/briaai/BRIA-2.3" target="_blank">briaai/BRIA-2.3</a>, | |
<a href="https://huggingface.co/briaai/BRIA-2.3-ControlNet-Inpainting" target="_blank">briaai/BRIA-2.3-ControlNet-Inpainting</a>, | |
and <a href="https://huggingface.co/briaai/BRIA-2.3-FAST-LORA" target="_blank">briaai/BRIA-2.3-FAST-LORA</a>, all trained on licensed data.<br> | |
This ensures full legal liability coverage for copyright and privacy infringement.<br> | |
Notes:<br> | |
- High-resolution images may take longer to process.<br> | |
- For multiple masks, results are better if all masks are included in inference.<br> | |
</p> | |
''') | |
with gr.Row(): | |
with gr.Column(): | |
image = gr.ImageEditor(sources=["upload"], layers=False, transforms=[], | |
brush=gr.Brush(colors=["#000000"], color_mode="fixed"), | |
) | |
with gr.Row(elem_id="prompt-container", equal_height=True): | |
with gr.Column(): # Wrap the button inside a Column | |
btn = gr.Button("Erase!", elem_id="run_button") | |
with gr.Column(): | |
image_out = gr.Image(label="Output", elem_id="output-img") | |
# Button click will trigger the inpainting function (no prompt required) | |
btn.click(fn=predict, inputs=[image], outputs=[image_out], api_name='run') | |
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) |