import spaces import gradio as gr from diffusers import AutoPipelineForInpainting, AutoencoderKL import torch from PIL import Image, ImageOps vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) pipeline = AutoPipelineForInpainting.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True).to("cuda") def get_select_index(evt: gr.SelectData): return evt.index @spaces.GPU() def squarify_image(img): if(img.height > img.width): bg_size = img.height else: bg_size = img.width bg = Image.new(mode="RGB", size=(bg_size,bg_size), color="white") bg.paste(img, ( int((bg.width - bg.width)/2), 0) ) return bg @spaces.GPU() def divisible_by_8(image): width, height = image.size # Calculate the new width and height that are divisible by 8 new_width = (width // 8) * 8 new_height = (height // 8) * 8 # Resize the image resized_image = image.resize((new_width, new_height)) return resized_image @spaces.GPU() def restore_version(index, versions): print('restore version:', index) final_dict = {'background': versions[index][0], 'layers': None, 'composite': versions[index][0]} return final_dict @spaces.GPU() def generate(image_editor, prompt, neg_prompt, versions): image = image_editor['background'].convert('RGB') # Resize image image.thumbnail((1024, 1024)) image = divisible_by_8(image) original_image_size = image.size # Mask layer layer = image_editor["layers"][0].resize(image.size) # Make image a square image = squarify_image(image) # Make sure mask is white with a black background mask = Image.new("RGBA", image.size, "WHITE") mask.paste(layer, (0, 0), layer) mask = ImageOps.invert(mask.convert('L')) # Inpaint pipeline.to("cuda") final_image = pipeline(prompt=prompt, image=image, mask_image=mask).images[0] # Make sure the longest side of image is 1024 if (original_image_size[0] > original_image_size[1]): original_image_size = ( original_image_size[0] * (1024/original_image_size[0]) , original_image_size[1] * (1024/original_image_size[0])) else: original_image_size = (original_image_size[0] * (1024/original_image_size[1]), original_image_size[1] * (1024/original_image_size[1])) # Crop image to original aspect ratio final_image = final_image.crop((0, 0, original_image_size[0], original_image_size[1])) # gradio.ImageEditor requires a diction final_dict = {'background': final_image, 'layers': None, 'composite': final_image} # Add generated image to version gallery if(versions==None): final_gallery = [image_editor['background'] ,final_image] else: final_gallery = versions final_gallery.append(final_image) return final_dict, gr.Gallery(value=final_gallery, visible=True), gr.update(visible=True) with gr.Blocks() as demo: gr.Markdown(""" # Inpainting SDXL Sketch Pad by [Tony Assi](https://www.tonyassi.com/) Please ❤️ this Space. I build custom AI apps for companies. Email me for business inquiries. """) with gr.Row(): with gr.Column(): sketch_pad = gr.ImageMask(type='pil', label='Inpaint') prompt = gr.Textbox(label="Prompt") generate_button = gr.Button("Generate") with gr.Accordion("Advanced Settings", open=False): neg_prompt = gr.Textbox(label='Negative Prompt', value='ugly, deformed') with gr.Column(): version_gallery = gr.Gallery(label="Versions", type="pil", object_fit='contain', visible=False) restore_button = gr.Button("Restore Version", visible=False) selected = gr.Number(show_label=False, visible=False) gr.Examples( [[{'background':'./tony.jpg', 'layers':['./tony-mask.jpg'], 'composite':'./tony.jpg'}, 'tuexedo', 'ugly', None]], [sketch_pad, prompt, neg_prompt, version_gallery], [sketch_pad, version_gallery, restore_button], generate, cache_examples=True, ) version_gallery.select(get_select_index, None, selected) generate_button.click(fn=generate, inputs=[sketch_pad,prompt, neg_prompt, version_gallery], outputs=[sketch_pad, version_gallery, restore_button]) restore_button.click(fn=restore_version, inputs=[selected, version_gallery], outputs=sketch_pad) demo.launch()