This Python script harnesses the power of Gradio's client API to transform text prompts into images. It's designed to connect to a specific Hugging Face model, request image generation based on detailed text and parameter inputs, then neatly organize the resulting files. It's like a tidy butler for your AI-generated art, minus the white gloves.

TextDiffuser.py

from gradio_client import Client
import os
import shutil
# https://huggingface.co/spaces/JingyeChen22/TextDiffuser-2
destination_directory = 'd:\\downloads\\'
client = Client("https://jingyechen22-textdiffuser-2.hf.space/--replicas/snjqb/")
result = client.predict(
		"-1",	# str  in 'guest_id' Textbox component
		"<|startoftext|>poster of four players playing beach volleyball and net photorealistic <|endoftext|><|startoftext|> l36 t18 r90 b35 [M] [a] [l] [i] [b] [u] <|endoftext|> l38 t32 r87 b52 [B] [e] [a] [c] [h] <|endoftext|> l21 t51 r105 b76 [V] [o] [l] [l] [e] [y] [b] [a] [l] [l] <|endoftext|><|endoftext|>",	# str  in 'Prompt. You can let language model automatically identify keywords, or provide them below' Textbox component
		"Malibu/Beach/Volleyball",	# str  in '(Optional) Keywords. Should be separated by / (e.g., keyword1/keyword2/...)' Textbox component
		"Wide-format",	# str  in '(Optional) Positive prompt' Textbox component
		"TextDiffuser-2",	# Literal[TextDiffuser-2, TextDiffuser-2-LCM]  in 'Choice of models' Radio component
		30,	# float (numeric value between 1 and 50) in 'Sampling step' Slider component
		7,	# float (numeric value between 1 and 13) in 'Scale of classifier-free guidance' Slider component
		8,	# float (numeric value between 1 and 6) in 'Batch size' Slider component
		1.7,	# float (numeric value between 0.1 and 2) in 'Temperature' Slider component
		False,	# bool  in 'Natural image generation' Checkbox component
							api_name="/text_to_image"
)
r = result
# Parse the JSON to extract the file paths
# Check if the first element of the tuple contains the data
if isinstance(result[0], list):
    # Loop through each entry in the data
    for item in result[0]:
        # Extract the image path
        image_path = item.get('image')
        if image_path:
            # Extract the parent folder name
            parent_folder = os.path.dirname(image_path)
            parent_folder_name = os.path.basename(parent_folder)
            # Create a new filename using the parent folder name
            new_filename = f"{parent_folder_name}.png"
            # Define the destination path
            destination_path = os.path.join(destination_directory, new_filename)
            # Move the file
            shutil.move(image_path, destination_path)
            print(f"Moved: {image_path} to {destination_path}")
            # Check if the parent folder is empty and delete it
            if not os.listdir(parent_folder):
                os.rmdir(parent_folder)
                print(f"Deleted empty folder: {parent_folder}")
else:
    print("The result structure is not as expected.")
print(result)

Sample output: