| import gradio as gr | |
| import torch | |
| from diffusers import AutoPipelineForInpainting | |
| import diffusers | |
| from share_btn import community_icon_html, loading_icon_html, share_js | |
| from sdxl import sdxl_diffusion_loop | |
| from sdxl_models import SDXLUNet, SDXLVae, SDXLControlNetPreEncodedControlnetCond | |
| import torchvision.transforms.functional as TF | |
| from diffusion import make_sigmas, set_with_tqdm | |
| from huggingface_hub import hf_hub_download | |
| import gc | |
| set_with_tqdm(True) | |
| pipe = AutoPipelineForInpainting.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16, variant="fp16") | |
| pipe.text_encoder.to("cuda") | |
| pipe.text_encoder_2.to("cuda") | |
| comparing_unet = SDXLUNet.load(hf_hub_download("stabilityai/stable-diffusion-xl-base-1.0", "unet/diffusion_pytorch_model.fp16.safetensors")) | |
| comparing_vae = SDXLVae.load(hf_hub_download("madebyollin/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors")) | |
| comparing_vae.to(torch.float16) | |
| comparing_controlnet = SDXLControlNetPreEncodedControlnetCond.load(hf_hub_download("williamberman/sdxl_controlnet_inpainting", "sdxl_controlnet_inpaint_pre_encoded_controlnet_cond_checkpoint_200000.safetensors")) | |
| comparing_controlnet.to(torch.float16) | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| 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=20, strength=1.0, scheduler="EulerDiscreteScheduler"): | |
| if negative_prompt == "": | |
| negative_prompt = None | |
| scheduler_class_name = scheduler.split("-")[0] | |
| add_kwargs = {} | |
| if len(scheduler.split("-")) > 1: | |
| add_kwargs["use_karras"] = True | |
| if len(scheduler.split("-")) > 2: | |
| add_kwargs["algorithm_type"] = "sde-dpmsolver++" | |
| scheduler = getattr(diffusers, scheduler_class_name) | |
| pipe.scheduler = scheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler", **add_kwargs) | |
| init_image = dict["image"].convert("RGB").resize((1024, 1024)) | |
| mask = dict["mask"].convert("RGB").resize((1024, 1024)) | |
| pipe.vae.to('cuda') | |
| pipe.unet.to('cuda') | |
| output = pipe(prompt = prompt, negative_prompt=negative_prompt, image=init_image, mask_image=mask, guidance_scale=guidance_scale, num_inference_steps=int(steps), strength=strength) | |
| pipe.vae.to('cpu') | |
| pipe.unet.to('cpu') | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| comparing_vae.to('cuda') | |
| comparing_unet.to('cuda') | |
| comparing_controlnet.to('cuda') | |
| image = TF.to_tensor(dict["image"].convert("RGB").resize((1024, 1024))) | |
| mask = TF.to_tensor(dict["mask"].convert("L").resize((1024, 1024))) | |
| image = image * (mask < 0.5) | |
| image = TF.normalize(image, [0.5], [0.5]) | |
| image = comparing_vae.encode(image[None, :, :, :].to(dtype=comparing_vae.dtype, device=comparing_vae.device)).to(dtype=comparing_controlnet.dtype, device=comparing_controlnet.device) | |
| mask = TF.resize(mask, (1024 // 8, 1024 // 8))[None, :, :, :].to(dtype=image.dtype, device=image.device) | |
| image = torch.concat((image, mask), dim=1) | |
| sigmas = make_sigmas(device=comparing_unet.device).to(dtype=comparing_unet.dtype) | |
| timesteps = torch.linspace(0, sigmas.numel() - 1, int(steps), dtype=torch.long, device=comparing_unet.device) | |
| out = sdxl_diffusion_loop( | |
| prompts=prompt, negative_prompts=negative_prompt, images=image, guidance_scale=guidance_scale, sigmas=sigmas, timesteps=timesteps, | |
| text_encoder_one=pipe.text_encoder, text_encoder_two=pipe.text_encoder_2, unet=comparing_unet, controlnet=comparing_controlnet | |
| ) | |
| comparing_unet.to('cpu') | |
| comparing_controlnet.to('cpu') | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| out = comparing_vae.output_tensor_to_pil(comparing_vae.decode(out)) | |
| comparing_vae.to('cpu') | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| return output.images[0], out[0], 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;} | |
| #run_button{position:absolute;margin-top: 11px;right: 0;margin-right: 0.8em;border-bottom-left-radius: 0px; | |
| border-top-left-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=400) | |
| with gr.Row(elem_id="prompt-container", mobile_collapse=False, equal_height=True): | |
| with gr.Row(): | |
| prompt = gr.Textbox(placeholder="Your prompt (what you want in place of what is erased)", show_label=False, elem_id="prompt") | |
| btn = gr.Button("Inpaint!", elem_id="run_button") | |
| with gr.Accordion(label="Advanced Settings", open=False): | |
| with gr.Row(mobile_collapse=False, 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=20, minimum=1, maximum=1000, step=1, label="steps") | |
| strength = gr.Number(value=0.99, 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(mobile_collapse=False, equal_height=True): | |
| schedulers = ["DEISMultistepScheduler", "HeunDiscreteScheduler", "EulerDiscreteScheduler", "DPMSolverMultistepScheduler", "DPMSolverMultistepScheduler-Karras", "DPMSolverMultistepScheduler-Karras-SDE"] | |
| scheduler = gr.Dropdown(label="Schedulers", choices=schedulers, value="EulerDiscreteScheduler") | |
| with gr.Column(): | |
| image_out = gr.Image(label="Output diffusers full finetune 0.1", elem_id="output-img", height=400) | |
| image_out_comparing = gr.Image(label="Output controlnet + vae", elem_id="output-img-comparing", height=400) | |
| 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, scheduler], outputs=[image_out, image_out_comparing, share_btn_container], api_name='run') | |
| prompt.submit(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, image_out_comparing, share_btn_container]) | |
| share_button.click(None, [], [], _js=share_js) | |
| gr.Examples( | |
| examples=[ | |
| ["./imgs/aaa (8).png"], | |
| ["./imgs/download (1).jpeg"], | |
| ["./imgs/0_oE0mLhfhtS_3Nfm2.png"], | |
| ["./imgs/02_HubertyBlog-1-1024x1024.jpg"], | |
| ["./imgs/jdn_jacques_de_nuce-1024x1024.jpg"], | |
| ["./imgs/c4ca473acde04280d44128ad8ee09e8a.jpg"], | |
| ["./imgs/canam-electric-motorcycles-scaled.jpg"], | |
| ["./imgs/e8717ce80b394d1b9a610d04a1decd3a.jpeg"], | |
| ["./imgs/Nature___Mountains_Big_Mountain_018453_31.jpg"], | |
| ["./imgs/Multible-sharing-room_ccexpress-2-1024x1024.jpeg"], | |
| ], | |
| fn=predict, | |
| inputs=[image], | |
| cache_examples=False, | |
| ) | |
| gr.HTML( | |
| """ | |
| <div class="footer"> | |
| <p>Model by <a href="https://huggingface.co/diffusers" style="text-decoration: underline;" target="_blank">Diffusers</a> - Gradio Demo by 🤗 Hugging Face | |
| </p> | |
| </div> | |
| """ | |
| ) | |
| image_blocks.queue(max_size=25).launch() |