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| import gradio as gr | |
| import torch | |
| import numpy as np | |
| from PIL import Image,ImageFilter | |
| from diffusers.models import AutoencoderKL | |
| from diffusers import AutoPipelineForInpainting, UNet2DConditionModel, DiffusionPipeline, StableDiffusionInpaintPipeline | |
| import diffusers | |
| from share_btn import community_icon_html, loading_icon_html, share_js | |
| vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse") | |
| def read_content(file_path: str) -> str: | |
| """read the content of target file | |
| """ | |
| with open(file_path, 'r', encoding='utf-8') as f: | |
| content = f.read() | |
| return content | |
| def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=30, strength=0.8,model="Realistic_V5.0", scheduler="DPMSolverMultistepScheduler-Karras"): | |
| pipe = AutoPipelineForInpainting.from_pretrained("SG161222/Realistic_Vision_V5.0_noVAE",vae=vae).to("cuda") | |
| if model == "Realistic_V5.1": | |
| pipe = AutoPipelineForInpainting.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", vae=vae).to("cuda") | |
| if model == "EpicRealism": | |
| pipe = AutoPipelineForInpainting.from_pretrained("emilianJR/epiCRealism", vae=vae).to("cuda") | |
| if model == "Realistic_V6.0": | |
| pipe = AutoPipelineForInpainting.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", vae=vae).to("cuda") | |
| pipe.safety_checker = lambda images, **kwargs: (images, [False] * len(images)) | |
| if negative_prompt == "": | |
| negative_prompt = None | |
| scheduler_class_name = scheduler.split("-")[0] | |
| add_kwargs = {} | |
| if len(scheduler.split("-")) > 1: | |
| add_kwargs["use_karras_sigmas"] = True | |
| if len(scheduler.split("-")) > 2: | |
| add_kwargs["algorithm_type"] = "sde-dpmsolver++" | |
| scheduler = getattr(diffusers, scheduler_class_name) | |
| pipe.scheduler = scheduler.from_pretrained("emilianJR/epiCRealism", subfolder="scheduler", **add_kwargs) | |
| init_image = dict["image"] | |
| mask_image = dict["mask"] | |
| width, height = init_image.size | |
| mask_image = mask_image.convert("RGBA") | |
| data = mask_image.getdata() | |
| new_data = [] | |
| for item in data: | |
| if item[:3] == (0, 0, 0): # Check if the pixel is black | |
| new_data.append((0, 0, 0, 0)) # Add transparent pixel | |
| else: | |
| new_data.append(item) | |
| mask_image.putdata(new_data) | |
| mask_image = mask_image.resize(init_image.size, resample=Image.LANCZOS) | |
| mask_image = mask_image.filter(ImageFilter.GaussianBlur(5)) | |
| #mask_image = pipe.mask_processor.blur(mask_image, blur_factor=15) | |
| with torch.cuda.amp.autocast(): | |
| output = pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| image=init_image, | |
| mask_image=mask_image, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=int(steps), | |
| strength=strength, | |
| clip_skip=1 | |
| ) | |
| inpainted_image = output.images[0] | |
| inpainted_image = inpainted_image.resize(init_image.size, resample=Image.LANCZOS) | |
| # Combine the original and inpainted images using the mask | |
| combined_image = Image.composite(inpainted_image, init_image, mask_image.split()[3]) | |
| print("Positive:", prompt) | |
| print("Negative:", negative_prompt) | |
| print("Guidance_scale:", guidance_scale) | |
| print("Steps:", steps) | |
| print("Strength:", strength) | |
| print("Scheduler:", scheduler) | |
| return inpainted_image, combined_image, gr.update(visible=True) | |
| css = ''' | |
| .gradio-container{max-width: 1100px !important} | |
| #image_upload{min-height:400px} | |
| #image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px} | |
| #mask_radio .gr-form{background:transparent; border: none} | |
| #word_mask{margin-top: .75em !important} | |
| #word_mask textarea:disabled{opacity: 0.3} | |
| .footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5} | |
| .footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white} | |
| .dark .footer {border-color: #303030} | |
| .dark .footer>p {background: #0b0f19} | |
| .acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%} | |
| #image_upload .touch-none{display: flex} | |
| @keyframes spin { | |
| from { | |
| transform: rotate(0deg); | |
| } | |
| to { | |
| transform: rotate(360deg); | |
| } | |
| } | |
| #share-btn-container {padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; max-width: 13rem; margin-left: auto;} | |
| div#share-btn-container > div {flex-direction: row;background: black;align-items: center} | |
| #share-btn-container:hover {background-color: #060606} | |
| #share-btn {all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.5rem !important; padding-bottom: 0.5rem !important;right:0;} | |
| #share-btn * {all: unset} | |
| #share-btn-container div:nth-child(-n+2){width: auto !important;min-height: 0px !important;} | |
| #share-btn-container .wrap {display: none !important} | |
| #share-btn-container.hidden {display: none!important} | |
| #prompt input{width: calc(100% - 160px);border-top-right-radius: 0px;border-bottom-right-radius: 0px;} | |
| #prompt-container{margin-top:-18px;} | |
| #prompt-container .form{border-top-left-radius: 0;border-top-right-radius: 0} | |
| #image_upload{border-bottom-left-radius: 0px;border-bottom-right-radius: 0px} | |
| ''' | |
| image_blocks = gr.Blocks(css=css, elem_id="total-container") | |
| with image_blocks as demo: | |
| gr.HTML(read_content("header.html")) | |
| with gr.Row(): | |
| with gr.Column(): | |
| image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Upload",height=512) | |
| with gr.Row(elem_id="prompt-container",equal_height=True): | |
| with gr.Row(): | |
| prompt = gr.Textbox(placeholder="Your prompt", show_label=False, elem_id="prompt", lines=5) | |
| with gr.Row(equal_height=True): | |
| btn = gr.Button("Inpaint!", elem_id="run_button") | |
| with gr.Accordion(label="Advanced Settings", open=False): | |
| with gr.Row(equal_height=True): | |
| guidance_scale = gr.Number(value=7.5, minimum=1.0, maximum=20.0, step=0.1, label="guidance_scale") | |
| steps = gr.Number(value=40, minimum=10, maximum=100, step=1, label="steps") | |
| strength = gr.Number(value=0.8, minimum=0.01, maximum=1.0, step=0.01, label="strength") | |
| negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt", info="what you don't want to see in the image") | |
| with gr.Row(equal_height=True): | |
| models = ["Realistic_V5.0","Realistic_V5.1","Realistic_V6.0","Epic_Realism"] | |
| model = gr.Dropdown(label="Models",choices=models,value="Realistic_V5.0") | |
| with gr.Row(equal_height=True): | |
| schedulers = ["DEISMultistepScheduler", "HeunDiscreteScheduler", "EulerDiscreteScheduler", "DPMSolverMultistepScheduler", "DPMSolverMultistepScheduler-Karras", "DPMSolverMultistepScheduler-Karras-SDE"] | |
| scheduler = gr.Dropdown(label="Schedulers", choices=schedulers, value="DPMSolverMultistepScheduler-Karras") | |
| with gr.Column(): | |
| image_out = gr.Image(label="Output", elem_id="output-img", height=512, width=512) | |
| image_out1 = gr.Image(label="Output", elem_id="output-img", height=512, width=512) | |
| with gr.Group(elem_id="share-btn-container", visible=False) as share_btn_container: | |
| community_icon = gr.HTML(community_icon_html) | |
| loading_icon = gr.HTML(loading_icon_html) | |
| share_button = gr.Button("Share to community", elem_id="share-btn",visible=True) | |
| btn.click(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, model, scheduler], outputs=[image_out,image_out1,share_btn_container], api_name='run') | |
| prompt.submit(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, model, scheduler], outputs=[image_out,image_out1,share_btn_container]) | |
| share_button.click(None, [], [], _js=share_js) | |
| image_blocks.queue(max_size=25,api_open=True).launch(show_api=True) |