Spaces:
Running
Running
File size: 3,962 Bytes
291e480 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
import gradio as gr
import asyncio
import fal_client
from dotenv import load_dotenv
import os
from pathlib import Path
import time
load_dotenv()
os.environ["FAL_KEY"] = os.getenv("FAL_API_KEY")
async def generate_paris_images(image1_path: str, image2_path: str, woman_prompt: str, man_prompt: str, batch_size: int, progress=gr.Progress()):
start_time = time.time()
print("Progress: 5% - Starting Paris image generation...")
progress(0.05, desc="Starting Paris image generation...")
# Upload all images in parallel
upload_tasks = [
fal_client.upload_file_async(str(image1_path)),
fal_client.upload_file_async(str(image2_path)),
fal_client.upload_file_async("test_images/woman.png"),
fal_client.upload_file_async("test_images/man.png"),
fal_client.upload_file_async("test_images/clipspace-mask-4736783.png"),
fal_client.upload_file_async("test_images/clipspace-mask-4722992.png")
]
[image1_url, image2_url, woman_img, man_img, mask1_img, mask2_img] = await asyncio.gather(*upload_tasks)
print("Progress: 40% - Uploaded all images")
progress(0.4, desc="Uploaded all images")
handler = await fal_client.submit_async(
"comfy/LVE/paris-couple",
arguments={
"loadimage_1": image1_url,
"loadimage_2": image2_url,
"loadimage_3": woman_img,
"loadimage_4": mask1_img,
"loadimage_5": mask2_img,
"loadimage_6": man_img,
"woman_prompt": woman_prompt,
"man_prompt": man_prompt,
"batch_size": batch_size
}
)
print("Progress: 60% - Processing images...")
progress(0.6, desc="Processing images...")
result = await handler.get()
print(result)
end_time = time.time()
processing_time = end_time - start_time
print(f"Progress: 100% - Generation completed in {processing_time:.2f} seconds")
progress(1.0, desc=f"Generation completed in {processing_time:.2f} seconds")
# Return all generated image URLs and processing time
# Get the first key from outputs dynamically
return (
[img["url"] for img in result["outputs"][next(iter(result["outputs"]))]["images"]] if "outputs" in result and result["outputs"] else [],
f"Processing time: {processing_time:.2f} seconds"
)
with gr.Blocks() as demo:
with gr.Row():
image1_input = gr.Image(label="Upload Woman Image", type="filepath", value="test_images/user3-f.jpg")
image2_input = gr.Image(label="Upload Man Image", type="filepath", value="test_images/user3.jpg")
with gr.Row():
woman_prompt = gr.Textbox(
label="Woman Prompt",
value="Close-up, portrait photo, a woman, Paris nighttime romance scene, wearing an elegant black dress with a shawl, standing beneath the same canopy of twinkling lights along the Champs-Élysées, the Eiffel Tower glowing bright in the distance, soft mist rising from the street, looking at the camera."
)
man_prompt = gr.Textbox(
label="Man Prompt",
value="Close-up, portrait photo, a man, Paris nighttime romance scene, wearing a tailored suit with a crisp white shirt, standing beneath a canopy of twinkling lights along the Champs-Élysées, the Eiffel Tower glowing bright in the distance, soft mist rising from the street, looking at the camera."
)
batch_size = gr.Slider(minimum=1, maximum=8, value=4, step=1, label="Batch Size")
generate_btn = gr.Button("Generate")
image_output = gr.Gallery(label="Generated Image")
time_output = gr.Textbox(label="Processing Time")
generate_btn.click(
fn=generate_paris_images,
inputs=[image1_input, image2_input, woman_prompt, man_prompt, batch_size],
outputs=[image_output, time_output]
)
if __name__ == "__main__":
print("Starting Gradio interface...")
demo.launch()
|