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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()
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