import os
import math
import gradio as gr
import numpy as np
import requests
import json
import base64
from PIL import Image
from io import BytesIO
import runpod
from enum import Enum
api_key = os.getenv("FAI_API_KEY")
api = os.getenv("FAI_API")
def image_to_base64(image):
# Open the image file
with image:
# Create a buffer to hold the binary data
buffered = BytesIO()
# Save the image in its original format to the buffer
#print(image.format)
image.save(buffered, format="PNG")
# Get the byte data from the buffer
binary_image_data = buffered.getvalue()
# Encode the binary data to a base64 string
base64_image = base64.b64encode(binary_image_data).decode("utf-8")
return base64_image
def process(data, api, api_key):
runpod.api_key = api_key
input_payload = {"input": data }
try:
endpoint = runpod.Endpoint(api)
run_request = endpoint.run(input_payload)
# Initial check without blocking, useful for quick tasks
status = run_request.status()
print(f"Initial job status: {status}")
if status != "COMPLETED":
# Polling with timeout for long-running tasks
output = run_request.output(timeout=60)
else:
output = run_request.output()
print(f"Job output: {output}")
except Exception as e:
print(f"An error occurred: {e}")
image_data = output['image']
# Decode the Base64 string
image_bytes = base64.b64decode(image_data)
# Convert binary data to image
image = Image.open(BytesIO(image_bytes))
return image
def process_generate(fore, prompt, image_width, image_height, intensity, mode, refprompt):
forestr = image_to_base64(fore.convert("RGBA"))
'''
data = {
"foreground_image64": forestr,
"prompt" : prompt,
"mode" : mode,
"intensity" : float(intensity),
"width" : int(image_width),
"height" : int(image_height),
"refprompt" : refprompt
}
'''
data = {
"foreground_image64": forestr,
"prompt" : "There is Perfume, nestled on a crystalline cliff of glistening snow, under a celestial night sky adorned with constellations and swirling galaxies, framed by ethereal, blue flames that dance gracefully in the icy air",
"mode" : "full", #refiner, full
"intensity" : 3.0,
"width" : 1000,
"height" : 1000,
"refprompt" : " transparent glass "
}
image = process(data, api, api_key)
return image
class Stage(Enum):
FIRST_STAGE = "first-stage"
SECOND_STAGE = "refiner"
FULL = "full"
css="""#disp_image {
text-align: center; /* Horizontally center the content */
}
#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}
#duplicate-button {
margin-left: auto;
color: #fff;
background: #1565c0;
}
"""
block = gr.Blocks(css=css, title="## F.ai Fuzer").queue()
with block:
gr.HTML("""
Fotographer AI Fuzer
""")
gr.HTML('''
''')
with gr.Row():
gr.Markdown("### F.ai Fuzer: Real Composite Photography in 2 minutes!")
with gr.Row():
fore = gr.Image(image_mode='RGBA', type="pil", label="Foreground Image", height=400)
with gr.Column():
result_gallery = gr.Image(label='Output') #gr.Gallery(height=400, object_fit='contain', label='Outputs')
with gr.Row():
prompt = gr.Textbox(label="Prompt")
with gr.Column():
refprompt = gr.Textbox(label="Refiner Prompt")
with gr.Row():
mode = gr.Radio(choices=[e.value for e in Stage],
value=Stage.FULL.value,
label="Generation Mode", type='value')
with gr.Column():
image_width = gr.Slider(label="Image Width", minimum=256, maximum=1500, value=1024, step=64)
image_height = gr.Slider(label="Image Height", minimum=256, maximum=1500, value=1024, step=64)
with gr.Row():
intensity = gr.Slider(label="Refiner Strength", minimum=1, maximum=7, value=3, step=0.5)
generate_button = gr.Button(value="Generate")
ips = [fore, prompt, image_width, image_height, intensity, mode, refprompt]
generate_button.click(fn=process_generate, inputs=ips, outputs=[result_gallery])
block.launch()
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