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| import gradio as gr | |
| import requests | |
| import io | |
| from PIL import Image | |
| import json | |
| from image_processing import downscale_image, limit_colors, convert_to_grayscale, convert_to_black_and_white | |
| class SomeClass: | |
| def __init__(self): | |
| self.images = [] | |
| with open('loras.json', 'r') as f: | |
| loras = json.load(f) | |
| def update_selection(selected_state): | |
| 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, 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] | |
| refined_image = processed.images[-1] | |
| return original_image, refined_image | |
| def apply_post_processing(image, downscale, limit_colors, grayscale, black_and_white): | |
| processed_image = image.copy() | |
| if downscale > 1: | |
| processed_image = downscale_image(processed_image, downscale) | |
| if limit_colors: | |
| processed_image = limit_colors(processed_image) | |
| if grayscale: | |
| processed_image = convert_to_grayscale(processed_image) | |
| if 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") | |
| post_processed_result = gr.Image(interactive=False, label="Post-Processed Image") | |
| 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") | |
| 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], outputs=[result, refined_result]) | |
| post_process_button.click(fn=apply_post_processing, inputs=[refined_result, downscale, limit_colors, grayscale, black_and_white], outputs=[post_processed_result]) | |
| app.queue(max_size=20, concurrency_count=5) | |
| app.launch() | |