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| 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() | |