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| # This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_seg2image.py | |
| # The original license file is LICENSE.ControlNet in this repo. | |
| import gradio as gr | |
| from PIL import Image | |
| #first elem of gallery is ^^ - {'name': '/tmp/tmpw60bbw6k.png', 'data': 'file=/tmp/tmpw60bbw6k.png', 'is_file': True} | |
| #first elem of gallery is ^^ - {'name': '/tmp/tmpba0d5dt5.png', 'data': 'file=/tmp/tmpba0d5dt5.png', 'is_file': True} | |
| import numpy as np | |
| import base64 | |
| def encode(input_image): | |
| print(f"type of input_image ^^ - {type(input_image)}") | |
| # Convert NumPy array to bytes | |
| img_bytes = np.ndarray.tobytes(input_image) | |
| # Encode the bytes using Base64 | |
| encoded_string = base64.b64encode(img_bytes).decode('utf-8') | |
| # Print and return the encoded string | |
| #print(encoded_string) | |
| return encoded_string | |
| def create_imgcomp(input_image, filename): | |
| encoded_string = encode(input_image) | |
| htmltag = '<img src= "data:image/jpeg;base64,' + encoded_string + '" alt="Original Image"/></div> <img src= "https://ysharma-ControlNetSegmentation.hf.space/file=' + filename + '" alt="Control Net Image"/>' | |
| #https://ysharma-controlnetsegmentation.hf.space/file=/tmp/tmpqcz9yeta.png | |
| print(f"htmltag is ^^ - {htmltag}") | |
| desc = """ | |
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <style> | |
| body { | |
| background: rgb(17, 17, 17); | |
| } | |
| .image-slider { | |
| margin-left: 3rem; | |
| position: relative; | |
| display: inline-block; | |
| line-height: 0; | |
| } | |
| .image-slider img { | |
| user-select: none; | |
| max-width: 400px; | |
| } | |
| .image-slider > div { | |
| position: absolute; | |
| width: 25px; | |
| max-width: 100%; | |
| overflow: hidden; | |
| resize: horizontal; | |
| } | |
| .image-slider > div:before { | |
| content: ''; | |
| display: block; | |
| width: 13px; | |
| height: 13px; | |
| overflow: hidden; | |
| position: absolute; | |
| resize: horizontal; | |
| right: 3px; | |
| bottom: 3px; | |
| background-clip: content-box; | |
| background: linear-gradient(-45deg, black 50%, transparent 0); | |
| -webkit-filter: drop-shadow(0 0 2px black); | |
| filter: drop-shadow(0 0 2px black); | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <div style="margin: 3rem; | |
| font-family: Roboto, sans-serif"> | |
| <h1 style="color: green"> Testing image comp</h1> | |
| </div> <div> <div class="image-slider"> <div> """ + htmltag + "</div> </div> </body> </html> " | |
| return desc | |
| def dummyfun(result_gallery): | |
| print(f"type of gallery is ^^ - {type(result_gallery)}") | |
| print(f"length of gallery is ^^ - {len(result_gallery)}") | |
| print(f"first elem of gallery is ^^ - {result_gallery[0]}") | |
| print(f"first elem of gallery is ^^ - {result_gallery[1]}") | |
| # Load the image | |
| #image = result_gallery[1] #Image.open("example.jpg") | |
| # Get the filename | |
| #filename = image.filename | |
| # Print the filename | |
| #print(f"filename is ^^ - {filename}") | |
| return result_gallery[1]['name'] #+ ',' + result_gallery[1]['name'] #filename | |
| def create_demo(process, max_images=12): | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| gr.Markdown('## Control Stable Diffusion with Segmentation Maps') | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(source='upload', type='numpy') | |
| prompt = gr.Textbox(label='Prompt') | |
| run_button = gr.Button(label='Run') | |
| with gr.Accordion('Advanced options', open=False): | |
| num_samples = gr.Slider(label='Images', | |
| minimum=1, | |
| maximum=max_images, | |
| value=1, | |
| step=1) | |
| image_resolution = gr.Slider(label='Image Resolution', | |
| minimum=256, | |
| maximum=768, | |
| value=512, | |
| step=256) | |
| detect_resolution = gr.Slider( | |
| label='Segmentation Resolution', | |
| minimum=128, | |
| maximum=1024, | |
| value=512, | |
| step=1) | |
| ddim_steps = gr.Slider(label='Steps', | |
| minimum=1, | |
| maximum=100, | |
| value=20, | |
| step=1) | |
| scale = gr.Slider(label='Guidance Scale', | |
| minimum=0.1, | |
| maximum=30.0, | |
| value=9.0, | |
| step=0.1) | |
| seed = gr.Slider(label='Seed', | |
| minimum=-1, | |
| maximum=2147483647, | |
| step=1, | |
| randomize=True, | |
| queue=False) | |
| eta = gr.Number(label='eta (DDIM)', value=0.0) | |
| a_prompt = gr.Textbox( | |
| label='Added Prompt', | |
| value='best quality, extremely detailed') | |
| n_prompt = gr.Textbox( | |
| label='Negative Prompt', | |
| value= | |
| 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality' | |
| ) | |
| with gr.Column(): | |
| result_gallery = gr.Gallery(label='Output', #visible= False, | |
| show_label=False, | |
| elem_id='gallery').style( | |
| grid=2, height='auto') | |
| b1 = gr.Button('Get filenames') | |
| filename = gr.Textbox(label="image file names") | |
| b2 = gr.Button('Show Image-Comparison') | |
| imagecomp = gr.HTML() | |
| ips = [ | |
| input_image, prompt, a_prompt, n_prompt, num_samples, | |
| image_resolution, detect_resolution, ddim_steps, scale, seed, eta | |
| ] | |
| run_button.click(fn=process, | |
| inputs=ips, | |
| outputs=[result_gallery], | |
| api_name='seg') | |
| b1.click(dummyfun, [result_gallery], [filename]) | |
| b2.click(create_imgcomp, [input_image, filename], [imagecomp]) | |
| return demo | |