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import gradio as gr
from gradio_imageslider import ImageSlider
import os
from comfydeploy import ComfyDeploy
import requests
from PIL import Image
from io import BytesIO
from dotenv import load_dotenv
import base64
from typing import Optional, Tuple, Union
import glob
load_dotenv()
# Initialize ComfyDeploy client
client: ComfyDeploy = ComfyDeploy(bearer_auth=os.environ.get("COMFY_DEPLOY_API_KEY"))
deployment_id: str = os.environ.get("COMFY_DEPLOYMENT_ID")
# Add these global variables at the top of the file, after imports
global_input_image = None
global_image_slider = None
def clear_output():
return None
def process_image(
image: Optional[Union[str, Image.Image]],
denoise: float,
steps: int,
tile_size: int,
downscale: float,
upscale: float,
color_match: float,
controlnet_tile_end: float,
controlnet_tile_strength: float,
) -> Tuple[Optional[Image.Image], Optional[Image.Image]]:
# Convert image to base64
if image is not None:
if isinstance(image, str):
with open(image, "rb") as img_file:
image_base64: str = base64.b64encode(img_file.read()).decode("utf-8")
else:
buffered: BytesIO = BytesIO()
image.save(buffered, format="PNG")
image_base64: str = base64.b64encode(buffered.getvalue()).decode("utf-8")
else:
return None, None
# Prepare inputs
inputs: dict = {
"image": f"data:image/png;base64,{image_base64}",
"denoise": str(denoise),
"steps": str(steps),
"tile_size": str(tile_size),
"downscale": str(downscale),
"upscale": str(upscale),
"color_match": str(color_match),
"controlnet_tile_end": str(controlnet_tile_end),
"controlnet_tile_strength": str(controlnet_tile_strength),
}
# Call ComfyDeploy API
try:
result = client.run.create(
request={"deployment_id": deployment_id, "inputs": inputs}
)
if result and result.object:
run_id: str = result.object.run_id
# Wait for the result
while True:
run_result = client.run.get(run_id=run_id)
if run_result.object.status == "success":
for output in run_result.object.outputs:
if output.data and output.data.images:
image_url: str = output.data.images[0].url
# Download and return both the original and processed images
response: requests.Response = requests.get(image_url)
processed_image: Image.Image = Image.open(
BytesIO(response.content)
)
return image, processed_image
return None, None
elif run_result.object.status == "failed":
return None, None
except Exception as e:
print(f"Error: {e}")
return None, None
def run(
denoise,
steps,
tile_size,
downscale,
upscale,
color_match,
controlnet_tile_end,
controlnet_tile_strength,
):
global global_input_image
global global_image_slider
if not global_input_image:
return None
# Set image_slider to None before processing
global_image_slider = None
# Process the image
original, processed = process_image(
global_input_image,
denoise,
steps,
tile_size,
downscale,
upscale,
color_match,
controlnet_tile_end,
controlnet_tile_strength,
)
if original and processed:
global_image_slider = [original, processed]
return global_image_slider
# Function to load preset images
def load_preset_images():
image_files = glob.glob("images/inputs/*")
return [
{"name": img, "image": Image.open(img)}
for img in image_files
if Image.open(img).format.lower()
in ["png", "jpg", "jpeg", "gif", "bmp", "webp"]
]
def set_input_image(images, evt: gr.SelectData):
global global_input_image
global_input_image = images[evt.index][0]
return global_input_image
# Define Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# 🌊 Creative Image Upscaler")
with gr.Row():
with gr.Column():
input_image = gr.Image(
type="pil",
label="Input Image",
value=lambda: global_input_image,
interactive=True,
)
# Add preset images
gr.Markdown("### Preset Images")
preset_images = load_preset_images()
gallery = gr.Gallery(
[img["image"] for img in preset_images],
label="Preset Images",
columns=5,
height=130,
allow_preview=False,
)
gallery.select(set_input_image, gallery, input_image)
with gr.Accordion("Advanced Parameters", open=False):
denoise: gr.Slider = gr.Slider(0, 1, value=0.4, label="Denoise")
steps: gr.Slider = gr.Slider(1, 40, value=10, step=1, label="Steps")
tile_size: gr.Slider = gr.Slider(
64, 2048, value=1024, step=8, label="Tile Size"
)
downscale: gr.Slider = gr.Slider(
1, 4, value=1, step=1, label="Downscale"
)
upscale: gr.Slider = gr.Slider(1, 4, value=4, step=0.1, label="Upscale")
color_match: gr.Slider = gr.Slider(0, 1, value=0, label="Color Match")
controlnet_tile_end: gr.Slider = gr.Slider(
0, 1, value=1, label="ControlNet Tile End"
)
controlnet_tile_strength: gr.Slider = gr.Slider(
0, 1, value=0.7, label="ControlNet Tile Strength"
)
with gr.Column():
image_slider = ImageSlider(
label="Compare Original and Processed",
type="pil",
value=lambda: global_image_slider,
interactive=True,
)
process_btn: gr.Button = gr.Button("Run")
process_btn.click(
fn=run,
inputs=[
denoise,
steps,
tile_size,
downscale,
upscale,
color_match,
controlnet_tile_end,
controlnet_tile_strength,
],
outputs=[image_slider],
)
def build_example(input_image_path):
output_image_path = input_image_path.replace("inputs", "outputs")
return [
input_image_path,
0.4, # denoise
10, # steps
1024, # tile_size
1, # downscale
4, # upscale
0, # color_match
1, # controlnet_tile_end
0.7, # controlnet_tile_strength
(input_image_path, output_image_path),
]
# Build examples
input_images = glob.glob("images/inputs/*")
examples = [build_example(img) for img in input_images]
# Update the gr.Examples call
gr.Examples(
examples=examples,
inputs=[
input_image,
denoise,
steps,
tile_size,
downscale,
upscale,
color_match,
controlnet_tile_end,
controlnet_tile_strength,
image_slider,
],
)
if __name__ == "__main__":
demo.launch(debug=True, share=True)
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