![]() ![]() Object Removal in Photos: Mark the unwanted content, then tap “Go” to remove it once and for all. Iface = gr.With this Photo Inpaint ReTouch app, you can just mark any unwanted content or background, then perfectly remove it from your photos with just one touch! This is not only an excellent photo editor, but also an eraser tool for photos. 2) For the 'Input Image with Mask', draw a black-colored mask (either manually with something like Paint, or by using gradio's built-in image editor & add a black-colored shape) IT MUST BE BLACK COLOR, but doesn't have to be rectangular! This is because it auto-detects the mask based on 0 (black) pixel values! 3) For the Conditional Text, type something you'd like to see the black region get filled in with :)"Īrticle = "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models | Github Repo | " Gr.outputs.Image(label= 'Inpainted Image')]Įxamples = ]ĭescription = " Using GLIDE to inpaint black regions of an input image! Instructions: 1) For the 'Input Image', upload an image. Gradio_outputs = [gr.outputs.Image(label= 'Auto-Detected Mask (From drawn black pixels)'), Gr.inputs.Textbox(label= 'Conditional Text to Inpaint')] Gradio_inputs = [gr.inputs.Image( type= 'pil', Return source_mask_64_img, show_images(up_samples) Noise=th.randn(up_shape, device=device) * upsample_temp, Up_shape = (batch_size, 3, options_up, options_up) Inpaint_mask=source_mask_256.repeat(batch_size, 1, 1, 1).to(device), Tokens, mask = model_up.tokenizer.padded_tokens_and_mask( Tokens = model_up.tokenizer.encode(prompt) Inpaint_mask=source_mask_64.repeat(full_batch_size, 1, 1, 1).to(device), Inpaint_image=(source_image_64 * source_mask_64).repeat(full_batch_size, 1, 1, 1).to(device), * batch_size + * batch_size, device=device # Pack the tokens together into model kwargs. Uncond_tokens, uncond_mask = _tokens_and_mask( # Create the classifier-free guidance tokens (empty) # Create the text tokens to feed to the model. Source_mask_64 = source_mask_64.unsqueeze( 0) # The mask should always be a boolean 64圆4 mask, and then we Source_mask_64_img = tensor_to_image(source_mask_64) Source_mask_64 = (gray_scale_source_image!= 0). Gray_scale_source_image = image_to_tensor(input_img_with_mask_64) Input_img_with_mask_64 = input_img_with_nvert( 'L').resize(( 64, 64), resample=Image.BICUBIC) # Assuming that all black pixels are meant for inpainting. # Since gradio doesn't supply which pixels were drawn, we need to find it ourselves! Source_image_64 = pil_to_numpy(input_img_64) Input_img_64 = input_img.convert( 'RGB').resize(( 64, 64), resample=Image.BICUBIC) # Save as png for later mask detection :) float() / 127.5 - 1ĭef pil_to_numpy( pil_img: Image) -> Tuple:ĭef inpaint( input_img, input_img_with_mask, prompt): ![]() Return th.from_numpy(img).permute( 0, 3, 1, 2). Pil_img = pil_img.resize((size, size), resample=Image.BICUBIC) # for the part of the image which is known. # Force the model to have the exact right x_start predictions Half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps)Įps = th.cat(, dim= 0) Model_out = model(combined, ts, **kwargs)Įps, rest = model_out, model_outĬond_eps, uncond_eps = th.split(eps, len(eps) // 2, dim= 0) # Create an classifier-free guidance sampling function # A value of 1.0 is sharper, but sometimes results in grainy artifacts. # Tune this parameter to control the sharpness of 256x256 images. Print( 'total upsampler parameters', sum(x.numel() for x in model_up.parameters())) Model_up.load_state_dict(load_checkpoint( 'upsample-inpaint', device)) Model_up, diffusion_up = create_model_and_diffusion(**options_up) Options_up = 'fast27' # use 27 diffusion steps for very fast sampling Options_up = model_and_diffusion_defaults_upsampler() Print( 'total base parameters', sum(x.numel() for x in model.parameters())) Model.load_state_dict(load_checkpoint( 'base-inpaint', device)) Model, diffusion = create_model_and_diffusion(**options) ![]() Options = '100' # use 100 diffusion steps for fast sampling # On CPU, generating one sample may take on the order of 20 minutes.ĭevice = th.device( 'cpu' if not has_cuda else 'cuda') # This notebook supports both CPU and GPU. ', shell= True)įrom glide_text2im.download import load_checkpointįrom glide_text2im.model_creation import (įrom ansforms import ToTensor, ToPILImage ![]()
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