import gradio as gr import requests import io from PIL import Image import json import os import logging import time from tqdm import tqdm from image_processing import downscale_image, limit_colors, resize_image, convert_to_grayscale, convert_to_black_and_white # Placeholder class for processed images class SomeClass: def __init__(self): self.images = [] with open('loras.json', 'r') as f: loras = json.load(f) def update_selection(selected_state: gr.SelectData): selected_lora_index = selected_state.index selected_lora = loras[selected_lora_index] new_placeholder = f"Type a prompt for {selected_lora['title']}" lora_repo = selected_lora["repo"] updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨" return ( gr.update(placeholder=new_placeholder), updated_text, selected_state ) def run_lora(prompt, selected_state, pixel_art_options, postprocess_options, progress=gr.Progress(track_tqdm=True)): selected_lora_index = selected_state.index selected_lora = loras[selected_lora_index] api_url = f"https://api-inference.huggingface.co/models/{selected_lora['repo']}" payload = { "inputs": f"{prompt} {selected_lora['trigger_word']}", "parameters": {"negative_prompt": "bad art, ugly, watermark, deformed"}, } response = requests.post(api_url, json=payload) if response.status_code == 200: original_image = Image.open(io.BytesIO(response.content)) processed = SomeClass() processed.images = [original_image] pixel_art_script = PixelArtScript() postprocess_script = ScriptPostprocessingUpscale() pixel_art_script.postprocess( processed, **pixel_art_options ) postprocess_script.process( processed, **postprocess_options ) refined_image = processed.images[-1] return original_image, refined_image def apply_post_processing(image, image_processing_options): processed_image = image.copy() if image_processing_options['downscale'] > 1: processed_image = downscale_image(processed_image, image_processing_options['downscale']) if image_processing_options['limit_colors']: processed_image = limit_colors(processed_image) if image_processing_options['grayscale']: processed_image = convert_to_grayscale(processed_image) if image_processing_options['black_and_white']: processed_image = convert_to_black_and_white(processed_image) return processed_image with gr.Blocks() as app: title = gr.Markdown("# artificialguybr LoRA portfolio") description = gr.Markdown("### This is a Pixel Art Generator using SD Loras.") selected_state = gr.State() with gr.Row(): gallery = gr.Gallery( [(item["image"], item["title"]) for item in loras], label="LoRA Gallery", allow_preview=False, columns=3 ) with gr.Column(): prompt_title = gr.Markdown("### Click on a LoRA in the gallery to create with it") selected_info = gr.Markdown("") with gr.Row(): prompt = gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1, placeholder="Type a prompt after selecting a LoRA") button = gr.Button("Run") result = gr.Image(interactive=False, label="Generated Image") refined_result = gr.Image(interactive=False, label="Refined Generated Image") # New Output for Post-Processed Image post_processed_result = gr.Image(interactive=False, label="Post-Processed Image") # New UI elements for pixel art options with gr.Row(): pixel_art_options = PixelArtScript().ui(True) postprocess_options = ScriptPostprocessingUpscale().ui() # New UI elements for image processing options with gr.Row(): downscale = gr.Slider(minimum=1, maximum=10, step=1, label="Downscale") limit_colors = gr.Checkbox(label="Limit Colors") grayscale = gr.Checkbox(label="Grayscale") black_and_white = gr.Checkbox(label="Black and White") image_processing_options = { 'downscale': downscale, 'limit_colors': limit_colors, 'grayscale': grayscale, 'black_and_white': black_and_white } post_process_button = gr.Button("Apply Post-Processing") gallery.select( update_selection, outputs=[prompt, selected_info, selected_state] ) prompt.submit( fn=run_lora, inputs=[prompt, selected_state, pixel_art_options, postprocess_options], outputs=[result, refined_result] ) post_process_button.click( fn=apply_post_processing, inputs=[refined_result, image_processing_options], outputs=[post_processed_result] ) app.queue(max_size=20, concurrency_count=5) app.launch()