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Running
on
Zero
| import os | |
| import numpy as np | |
| import base64 | |
| import io | |
| import requests | |
| from io import BytesIO | |
| os.system("pip install gradio==4.37.2") | |
| os.system("pip install opencv-python") | |
| import cv2 | |
| import gradio as gr | |
| import random | |
| import warnings | |
| import spaces | |
| from PIL import Image | |
| from S2I import Sketch2ImageController, css, scripts | |
| dark_mode_theme = """ | |
| function refresh() { | |
| const url = new URL(window.location); | |
| if (url.searchParams.get('__theme') !== 'dark') { | |
| url.searchParams.set('__theme', 'dark'); | |
| window.location.href = url.href; | |
| } | |
| } | |
| """ | |
| os.environ["TOKENIZERS_PARALLELISM"] = "false" | |
| warnings.filterwarnings("ignore") | |
| controller = Sketch2ImageController(gr) | |
| clear_flag = False | |
| 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, input_type): | |
| 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, input_type) | |
| 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, input_type): | |
| 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, input_type) | |
| def get_dark_mode(): | |
| return """ | |
| () => { | |
| document.body.classList.toggle('dark'); | |
| } | |
| """ | |
| def pil_image_to_data_uri(img, format="PNG"): | |
| buffered = BytesIO() | |
| img.save(buffered, format=format) | |
| img_str = base64.b64encode(buffered.getvalue()).decode() | |
| return f"data:image/{format.lower()};base64,{img_str}" | |
| def clear_session(image): | |
| global clear_flag | |
| clear_flag = True | |
| return gr.update(value=None), gr.update(value=None) | |
| 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, input_type): | |
| global clear_flag | |
| if clear_flag: | |
| clear_flag = False # Reset the flag after handling the clear action | |
| return gr.update(value=None) | |
| else: | |
| if options == 'GPU': | |
| decorated_run = spaces.GPU(run_gpu) | |
| 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, input_type) | |
| else: | |
| 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, input_type) | |
| def read_temp_file(temp_file_wrapper): | |
| name = temp_file_wrapper.name | |
| with open(temp_file_wrapper.name, 'rb') as f: | |
| # Read the content of the file | |
| file_content = f.read() | |
| return file_content, name | |
| def convert_to_pencil_sketch(image): | |
| if image is None: | |
| raise ValueError(f"Image at path {image} could not be loaded.") | |
| # Converting it into grayscale | |
| gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| # Inverting the image | |
| inverted_image = 255 - gray_image | |
| # Blurring the image | |
| blurred = cv2.GaussianBlur(inverted_image, (25, 25), 0) | |
| inverted_blurred = 255 - blurred | |
| # Creating the pencil sketch | |
| pencil_sketch = cv2.divide(gray_image, inverted_blurred, scale=256.0) | |
| return pencil_sketch | |
| def get_meta_from_image(input_img, type_image): | |
| global clear_flag | |
| if clear_flag: | |
| clear_flag = False # Reset the flag after handling the clear action | |
| return gr.update(value=None) # Ensure nothing is processed if clear flag is true | |
| else: | |
| if input_img is None: | |
| return gr.update(value=None) | |
| img = Image.open(BytesIO(requests.get(input_img).content)).convert('RGB') | |
| # Read the image using Pillow | |
| img_np = np.array(img) | |
| if type_image == 'RGB': | |
| sketch = convert_to_pencil_sketch(img_np) | |
| processed_img = 255 - sketch | |
| elif type_image == 'SKETCH': | |
| processed_img = 255 - img_np | |
| # Convert the processed image back to PIL Image | |
| img_pil = Image.fromarray(processed_img.astype('uint8')) | |
| return img_pil | |
| with gr.Blocks(css=css, theme="NoCrypt/miku@1.2.1") as demo: | |
| gr.HTML( | |
| """ | |
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>S2I-Artwork Animation</title> | |
| <style> | |
| @keyframes blinkCursor { | |
| from { border-right-color: rgba(255, 255, 255, 0.75); } | |
| to { border-right-color: transparent; } | |
| } | |
| @keyframes fadeIn { | |
| 0% { opacity: 0; transform: translateY(-10px); } | |
| 100% { opacity: 1; transform: translateY(0); } | |
| } | |
| @keyframes bounce { | |
| 0%, 20%, 50%, 80%, 100% { | |
| transform: translateY(0); | |
| } | |
| 40% { | |
| transform: translateY(-10px); | |
| } | |
| 60% { | |
| transform: translateY(-5px); | |
| } | |
| } | |
| .typewriter h1 { | |
| overflow: hidden; | |
| border-right: .15em solid rgba(255, 255, 255, 0.75); | |
| white-space: nowrap; | |
| margin: 0 auto; | |
| letter-spacing: .15em; | |
| animation: | |
| zoomInOut 4s infinite; | |
| } | |
| .animated-heading { | |
| animation: fadeIn 2s ease-in-out; | |
| } | |
| .animated-link { | |
| display: inline-block; | |
| animation: bounce 3s infinite; | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <div> | |
| <div class="typewriter"> | |
| <h1 style="display: flex; align-items: center; justify-content: center; margin-bottom: 10px; text-align: center;"> | |
| <img src="https://imgur.com/H2SLps2.png" alt="icon" style="margin-left: 10px; height: 30px;"> | |
| S2I-Artwork | |
| <img src="https://imgur.com/cNMKSAy.png" alt="icon" style="margin-left: 10px; height: 30px;">: | |
| Personalized Sketch-to-Art 🧨 Diffusion Models | |
| <img src="https://imgur.com/yDnDd1p.png" alt="icon" style="margin-left: 10px; height: 30px;"> | |
| </h1> | |
| </div> | |
| <h3 class="animated-heading" style="text-align: center; margin-bottom: 10px;">Authors: Vo Nguyen An Tin, Nguyen Thiet Su</h3> | |
| <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> | |
| <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> | |
| <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> | |
| <h4 class="animated-heading" style="margin-bottom: 10px;">* View the full code project: | |
| <a class="animated-link" href="https://github.com/aihacker111/S2I-Artwork-Sketch-to-Image/" target="_blank">GitHub Repository</a> | |
| </h4> | |
| <h4 class="animated-heading" style="margin-bottom: 10px;"> | |
| <a class="animated-link" href="https://github.com/aihacker111/S2I-Artwork-Sketch-to-Image/" target="_blank"> | |
| <img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="100"> | |
| </a> | |
| </h4> | |
| </div> | |
| </body> | |
| </html> | |
| """ | |
| ) | |
| with gr.Row(elem_id="main_row"): | |
| with gr.Column(elem_id="column_input"): | |
| gr.Markdown("## SKETCH", elem_id="input_header") | |
| image = gr.Sketchpad( | |
| type="pil", | |
| height=512, | |
| width=512, | |
| min_width=512, | |
| image_mode="RGBA", | |
| show_label=False, | |
| mirror_webcam=False, | |
| show_download_button=True, | |
| elem_id='input_image', | |
| brush=gr.Brush(colors=["#000000"], color_mode="fixed", default_size=4), | |
| canvas_size=(1024, 1024), | |
| layers=False | |
| ) | |
| with gr.Group(): | |
| with gr.Row(): | |
| url_image = gr.Textbox(label="Image URLS", value="") | |
| type_image = gr.Radio( | |
| choices=["RGB", "SKETCH"], | |
| value="SKETCH", | |
| label="Type of Image (Color Image or Sketch Image)", | |
| interactive=True) | |
| with gr.Row(): | |
| ui_mode = gr.Radio( | |
| choices=["Light Mode", "Dark Mode"], | |
| value="Light Mode", | |
| label="Switch Light/Dark Mode UI", | |
| interactive=True) | |
| zero_gpu_options = gr.Radio( | |
| choices=["GPU", "CPU"], | |
| value="GPU", | |
| label="GPU & CPU Options Spaces", | |
| interactive=True) | |
| model_options = gr.Radio( | |
| choices=["350k", "350k-adapter"], | |
| value="350k-adapter", | |
| label="Type Sketch2Image models", | |
| interactive=True) | |
| half_model = gr.Radio( | |
| choices=["float32", "float16"], | |
| value="float16", | |
| label="Demo Speed", | |
| interactive=True) | |
| with gr.Column(elem_id="column_output"): | |
| gr.Markdown("## IMAGE GENERATE", elem_id="output_header") | |
| result = gr.Image( | |
| label="Result", | |
| height=440, | |
| width=440, | |
| elem_id="output_image", | |
| show_label=False, | |
| show_download_button=True, | |
| ) | |
| with gr.Group(): | |
| prompt = gr.Textbox(label="Personalized Text", value="", show_label=True) | |
| with gr.Row(): | |
| run_button = gr.Button("Generate 🪄", min_width=5, variant='primary') | |
| randomize_seed = gr.Button(value='\U0001F3B2', variant='primary') | |
| clear_button = gr.Button("Reset Sketch Session", min_width=10, variant='primary') | |
| with gr.Accordion("S2I Advances Option", open=True): | |
| with gr.Row(): | |
| input_type = gr.Radio( | |
| choices=["live-sketch", "url-sketch"], | |
| value="live-sketch", | |
| label="Type Sketch2Image models", | |
| interactive=True) | |
| style = gr.Dropdown( | |
| label="Style", | |
| choices=controller.STYLE_NAMES, | |
| value=controller.DEFAULT_STYLE_NAME, | |
| scale=1, | |
| ) | |
| prompt_temp = gr.Textbox( | |
| label="Prompt Style Template", | |
| value=controller.styles[controller.DEFAULT_STYLE_NAME], | |
| scale=2, | |
| max_lines=1, | |
| ) | |
| seed = gr.Textbox(label="Seed", value='42', scale=1, min_width=50) | |
| val_r = gr.Slider( | |
| label="Sketch guidance: ", | |
| show_label=True, | |
| minimum=0, | |
| maximum=1, | |
| value=0.4, | |
| step=0.01, | |
| scale=3, | |
| ) | |
| demo.load(None, None, None, js=scripts) | |
| ui_mode.change(None, [], [], js=get_dark_mode()) | |
| randomize_seed.click( | |
| lambda x: random.randint(0, controller.MAX_SEED), | |
| inputs=[], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ) | |
| inputs = [zero_gpu_options, image, prompt, prompt_temp, style, seed, val_r, half_model, model_options, input_type] | |
| outputs = [result] | |
| prompt.submit(fn=assign_gpu, inputs=inputs, outputs=outputs, api_name=False) | |
| style.change( | |
| lambda x: controller.styles[x], | |
| inputs=[style], | |
| outputs=[prompt_temp], | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=assign_gpu, | |
| inputs=inputs, | |
| outputs=outputs, | |
| api_name=False, | |
| ) | |
| clear_button.click(fn=clear_session, inputs=[image], outputs=[image, result]) | |
| val_r.change(assign_gpu, inputs=inputs, outputs=outputs, queue=False, api_name=False) | |
| run_button.click(fn=assign_gpu, inputs=inputs, outputs=outputs, api_name=False) | |
| image.change(assign_gpu, inputs=inputs, outputs=outputs, queue=False, api_name=False) | |
| url_image.submit(fn=get_meta_from_image, inputs=[url_image, type_image], outputs=[image]) | |
| url_image.change(fn=get_meta_from_image, inputs=[url_image, type_image], outputs=[image]) | |
| if __name__ == '__main__': | |
| demo.queue() | |
| demo.launch(debug=True, share=False) | |