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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()
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