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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 | |
import logging | |
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): | |
logging.debug(f"Inside update_selection, selected_state: {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") | |
post_processed_result = gr.Image(interactive=False, label="Post-Processed Image") | |
# Accordion moved here, inside the same gr.Blocks context | |
with gr.Accordion(label="Pixel art", open=True): | |
with gr.Row(): | |
enabled = gr.Checkbox(label="Enable", value=False) | |
downscale = gr.Slider(label="Downscale", minimum=1, maximum=32, step=2, value=8) | |
need_rescale = gr.Checkbox(label="Rescale to original size", value=True) | |
with gr.Tabs(): | |
with gr.TabItem("Color"): | |
enable_color_limit = gr.Checkbox(label="Enable", value=False) | |
palette_size_color = gr.Slider(label="Palette Size", minimum=1, maximum=256, step=1, value=16) | |
quantization_methods_color = gr.Radio(choices=["Median Cut", "Maximum Coverage", "Fast Octree"], label="Colors Quantization Method") | |
dither_methods_color = gr.Radio(choices=["None", "Floyd-Steinberg"], label="Colors Dither Method") | |
k_means_color = gr.Checkbox(label="Enable k-means for color quantization", value=True) | |
with gr.TabItem("Grayscale"): | |
enable_grayscale = gr.Checkbox(label="Enable", value=False) | |
palette_size_gray = gr.Slider(label="Palette Size", minimum=1, maximum=256, step=1, value=16) | |
quantization_methods_gray = gr.Radio(choices=["Median Cut", "Maximum Coverage", "Fast Octree"], label="Colors Quantization Method") | |
dither_methods_gray = gr.Radio(choices=["None", "Floyd-Steinberg"], label="Colors Dither Method") | |
k_means_gray = gr.Checkbox(label="Enable k-means for color quantization", value=True) | |
with gr.TabItem("Black and white"): | |
enable_black_and_white = gr.Checkbox(label="Enable", value=False) | |
inverse_black_and_white = gr.Checkbox(label="Inverse", value=False) | |
threshold_black_and_white = gr.Slider(label="Threshold", minimum=1, maximum=256, step=1, value=128) | |
with gr.TabItem("Custom color palette"): | |
enable_custom_palette = gr.Checkbox(label="Enable", value=False) | |
palette_image = gr.Image(label="Color palette image", type="pil") | |
palette_size_custom = gr.Slider(label="Palette Size", minimum=1, maximum=256, step=1, value=16) | |
dither_methods_custom = gr.Radio(choices=["None", "Floyd-Steinberg"], label="Colors Dither Method") | |
post_process_button = gr.Button("Apply Post-Processing") | |
# The rest of your code for setting up the app | |
gallery.select(update_selection, outputs=[prompt, selected_info, selected_state]) | |
prompt.submit(fn=run_lora, inputs=[prompt, selected_state], outputs=[result, post_processed_result]) | |
post_process_button.click(fn=apply_post_processing, inputs=[post_processed_result, downscale, enable_color_limit, enable_grayscale, enable_black_and_white], outputs=[post_processed_result]) | |
app.queue(max_size=20, concurrency_count=5) | |
app.launch() | |