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import os |
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import numpy as np |
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import io |
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os.system("pip install gradio==4.29.0") |
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os.system("pip install opencv-python") |
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import cv2 |
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import gradio as gr |
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import random |
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import warnings |
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import spaces |
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from PIL import Image |
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from S2I import Sketch2ImageController, css, scripts |
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dark_mode_theme = """ |
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function refresh() { |
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const url = new URL(window.location); |
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if (url.searchParams.get('__theme') !== 'dark') { |
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url.searchParams.set('__theme', 'dark'); |
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window.location.href = url.href; |
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} |
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} |
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""" |
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os.environ["TOKENIZERS_PARALLELISM"] = "false" |
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warnings.filterwarnings("ignore") |
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controller = Sketch2ImageController(gr) |
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def run_gpu(options, img_init, text_init, prompt_template_init, style_name_init, seeds_init, val_r_values_init, faster_init, model_name_init, clear_flag): |
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return controller.artwork(options, img_init, text_init, prompt_template_init, style_name_init, seeds_init, val_r_values_init, faster_init, model_name_init, clear_flag) |
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def run_cpu(options, img_init, text_init, prompt_template_init, style_name_init, seeds_init, val_r_values_init, faster_init, model_name_init, clear_flag): |
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return controller.artwork(options, img_init, text_init, prompt_template_init, style_name_init, seeds_init, val_r_values_init, faster_init, model_name_init, clear_flag) |
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def get_dark_mode(): |
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return """ |
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() => { |
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document.body.classList.toggle('dark'); |
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} |
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""" |
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def clear_session(): |
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return gr.update(value=None), gr.update(value=None) |
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def assign_gpu(options, img_init, text_init, prompt_template_init, style_name_init, seeds_init, val_r_values_init, faster_init, model_name_init, clear_flag): |
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if options == 'GPU': |
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decorated_run = spaces.GPU(run_gpu) |
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return decorated_run(options, img_init, text_init, prompt_template_init, style_name_init, seeds_init, val_r_values_init, faster_init, model_name_init, clear_flag) |
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else: |
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return run_cpu(options, img_init, text_init, prompt_template_init, style_name_init, seeds_init, val_r_values_init, faster_init, model_name_init, clear_flag) |
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def read_temp_file(temp_file_wrapper): |
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name = temp_file_wrapper.name |
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with open(temp_file_wrapper.name, 'rb') as f: |
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file_content = f.read() |
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return file_content, name |
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def convert_to_pencil_sketch(image): |
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if image is None: |
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raise ValueError(f"Image at path {image} could not be loaded.") |
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gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) |
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inverted_image = 255 - gray_image |
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blurred = cv2.GaussianBlur(inverted_image, (25, 25), 0) |
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inverted_blurred = 255 - blurred |
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pencil_sketch = cv2.divide(gray_image, inverted_blurred, scale=256.0) |
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return pencil_sketch |
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def get_meta_from_image(input_img, type_image): |
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if input_img is None: |
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return gr.update(value=None) |
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file_content, _ = read_temp_file(input_img) |
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img = Image.open(io.BytesIO(file_content)).convert("RGB") |
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img_np = np.array(img) |
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if type_image == 'RGB': |
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sketch = convert_to_pencil_sketch(img_np) |
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processed_img = 255 - sketch |
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elif type_image == 'SKETCH': |
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processed_img = 255 - img_np |
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img_pil = Image.fromarray(processed_img.astype('uint8')) |
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return img_pil |
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with gr.Blocks(css=css, theme="NoCrypt/miku@1.2.1") as demo: |
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gr.HTML( |
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""" |
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<!DOCTYPE html> |
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<html lang="en"> |
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<head> |
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<meta charset="UTF-8"> |
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<meta name="viewport" content="width=device-width, initial-scale=1.0"> |
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<title>S2I-Artwork Animation</title> |
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<style> |
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@keyframes blinkCursor { |
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from { border-right-color: rgba(255, 255, 255, 0.75); } |
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to { border-right-color: transparent; } |
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} |
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@keyframes fadeIn { |
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0% { opacity: 0; transform: translateY(-10px); } |
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100% { opacity: 1; transform: translateY(0); } |
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} |
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@keyframes bounce { |
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0%, 20%, 50%, 80%, 100% { |
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transform: translateY(0); |
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} |
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40% { |
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transform: translateY(-10px); |
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} |
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60% { |
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transform: translateY(-5px); |
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} |
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} |
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.typewriter h1 { |
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overflow: hidden; |
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border-right: .15em solid rgba(255, 255, 255, 0.75); |
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white-space: nowrap; |
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margin: 0 auto; |
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letter-spacing: .15em; |
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animation: |
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zoomInOut 4s infinite; |
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} |
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.animated-heading { |
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animation: fadeIn 2s ease-in-out; |
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} |
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.animated-link { |
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display: inline-block; |
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animation: bounce 3s infinite; |
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} |
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</style> |
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</head> |
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<body> |
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<div> |
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<div class="typewriter"> |
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<h1 style="display: flex; align-items: center; justify-content: center; margin-bottom: 10px; text-align: center;"> |
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<img src="https://imgur.com/H2SLps2.png" alt="icon" style="margin-left: 10px; height: 30px;"> |
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S2I-Artwork |
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<img src="https://imgur.com/cNMKSAy.png" alt="icon" style="margin-left: 10px; height: 30px;">: |
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Personalized Sketch-to-Art 🧨 Diffusion Models |
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<img src="https://imgur.com/yDnDd1p.png" alt="icon" style="margin-left: 10px; height: 30px;"> |
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</h1> |
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</div> |
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<h3 class="animated-heading" style="text-align: center; margin-bottom: 10px;">Authors: Vo Nguyen An Tin, Nguyen Thiet Su</h3> |
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<h4 class="animated-heading" style="margin-bottom: 10px;">*This project is the fine-tuning task with LorA on large datasets included: COCO-2017, LHQ, Danbooru, LandScape and Mid-Journey V6</h4> |
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<h4 class="animated-heading" style="margin-bottom: 10px;">* We public 2 sketch2image-models-lora training on 30K and 60K steps with skip-connection and Transformers Super-Resolution variables</h4> |
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<h4 class="animated-heading" style="margin-bottom: 10px;">* The inference and demo time of model is faster, you can slowly in the first runtime, but after that, the time process over 1.5 ~ 2s</h4> |
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<h4 class="animated-heading" style="margin-bottom: 10px;">* View the full code project: |
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<a class="animated-link" href="https://github.com/aihacker111/S2I-Artwork-Sketch-to-Image/" target="_blank">GitHub Repository</a> |
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</h4> |
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<h4 class="animated-heading" style="margin-bottom: 10px;"> |
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<a class="animated-link" href="https://github.com/aihacker111/S2I-Artwork-Sketch-to-Image/" target="_blank"> |
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<img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="100"> |
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</a> |
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</h4> |
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</div> |
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</body> |
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</html> |
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""" |
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) |
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with gr.Row(elem_id="main_row"): |
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with gr.Column(elem_id="column_input"): |
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gr.Markdown("## SKETCH", elem_id="input_header") |
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image = gr.Sketchpad( |
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type="pil", |
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height=512, |
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width=512, |
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min_width=512, |
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image_mode="RGBA", |
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show_label=False, |
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mirror_webcam=False, |
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show_download_button=True, |
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elem_id='input_image', |
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brush=gr.Brush(colors=["#000000"], color_mode="fixed", default_size=4), |
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canvas_size=(1024, 1024), |
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layers=False |
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) |
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input_image = gr.File(label='Input image') |
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download_sketch = gr.Button( |
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"Download sketch", scale=1, elem_id="download_sketch" |
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) |
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with gr.Column(elem_id="column_output"): |
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gr.Markdown("## IMAGE GENERATE", elem_id="output_header") |
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result = gr.Image( |
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label="Result", |
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height=440, |
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width=440, |
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elem_id="output_image", |
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show_label=False, |
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show_download_button=True, |
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) |
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with gr.Row(): |
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run_button = gr.Button("Generate 🪄", min_width=5, variant='primary') |
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randomize_seed = gr.Button(value='\U0001F3B2', variant='primary') |
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clear_button = gr.Button("Reset Sketch Session", min_width=10, variant='primary') |
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prompt = gr.Textbox(label="Personalized Text", value="", show_label=True) |
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with gr.Accordion("S2I Advances Option", open=True): |
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with gr.Row(): |
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ui_mode = gr.Radio( |
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choices=["Light Mode", "Dark Mode"], |
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value="Light Mode", |
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label="Switch Light/Dark Mode UI", |
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interactive=True) |
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type_image = gr.Radio( |
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choices=["RGB", "SKETCH"], |
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value="SKETCH", |
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label="Type of Image (Color Image or Sketch Image)", |
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interactive=True) |
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input_type = gr.Radio( |
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choices=["live-sketch", "upload"], |
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value="live-sketch", |
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label="Type Sketch2Image models", |
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interactive=True) |
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style = gr.Dropdown( |
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label="Style", |
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choices=controller.STYLE_NAMES, |
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value=controller.DEFAULT_STYLE_NAME, |
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scale=1, |
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) |
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prompt_temp = gr.Textbox( |
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label="Prompt Style Template", |
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value=controller.styles[controller.DEFAULT_STYLE_NAME], |
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scale=2, |
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max_lines=1, |
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) |
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seed = gr.Textbox(label="Seed", value='42', scale=1, min_width=50) |
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zero_gpu_options = gr.Radio( |
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choices=["GPU", "CPU"], |
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value="GPU", |
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label="GPU & CPU Options Spaces", |
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interactive=True) |
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half_model = gr.Radio( |
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choices=["float32", "float16"], |
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value="float16", |
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label="Demo Speed", |
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interactive=True) |
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model_options = gr.Radio( |
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choices=["100k", "350k"], |
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value="350k", |
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label="Type Sketch2Image models", |
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interactive=True) |
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val_r = gr.Slider( |
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label="Sketch guidance: ", |
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show_label=True, |
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minimum=0, |
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maximum=1, |
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value=0.4, |
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step=0.01, |
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scale=3, |
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) |
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demo.load(None, None, None, js=scripts) |
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ui_mode.change(None, [], [], js=get_dark_mode()) |
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randomize_seed.click( |
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lambda x: random.randint(0, controller.MAX_SEED), |
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inputs=[], |
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outputs=seed, |
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queue=False, |
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api_name=False, |
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) |
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inputs = [zero_gpu_options, image, prompt, prompt_temp, style, seed, val_r, half_model, model_options, input_type] |
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outputs = [result, download_sketch] |
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prompt.submit(fn=assign_gpu, inputs=inputs, outputs=outputs, api_name=False) |
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input_image.change( |
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fn=get_meta_from_image, |
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inputs=[ |
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input_image, type_image |
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], |
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outputs=[ |
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image |
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] |
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) |
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style.change( |
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lambda x: controller.styles[x], |
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inputs=[style], |
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outputs=[prompt_temp], |
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queue=False, |
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api_name=False, |
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).then( |
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fn=assign_gpu, |
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inputs=inputs, |
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outputs=outputs, |
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api_name=False, |
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) |
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clear_button.click(fn=clear_session, inputs=[], outputs=[image, result]).then( |
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fn=assign_gpu, |
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inputs=inputs, |
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outputs=outputs, |
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api_name=False, |
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) |
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val_r.change(assign_gpu, inputs=inputs, outputs=outputs, queue=False, api_name=False) |
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run_button.click(fn=assign_gpu, inputs=inputs, outputs=outputs, api_name=False) |
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image.change(assign_gpu, inputs=inputs, outputs=outputs, queue=False, api_name=False) |
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if __name__ == '__main__': |
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demo.queue() |
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demo.launch(debug=True, share=False) |
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