import gradio as gr import spaces import torch from diffusers import AutoencoderKL, TCDScheduler from diffusers.models.model_loading_utils import load_state_dict from gradio_imageslider import ImageSlider from huggingface_hub import hf_hub_download from controlnet_union import ControlNetModel_Union from pipeline_fill_sd_xl import StableDiffusionXLFillPipeline from PIL import Image, ImageDraw import numpy as np config_file = hf_hub_download( "xinsir/controlnet-union-sdxl-1.0", filename="config_promax.json", ) config = ControlNetModel_Union.load_config(config_file) controlnet_model = ControlNetModel_Union.from_config(config) model_file = hf_hub_download( "xinsir/controlnet-union-sdxl-1.0", filename="diffusion_pytorch_model_promax.safetensors", ) state_dict = load_state_dict(model_file) model, _, _, _, _ = ControlNetModel_Union._load_pretrained_model( controlnet_model, state_dict, model_file, "xinsir/controlnet-union-sdxl-1.0" ) model.to(device="cuda", dtype=torch.float16) vae = AutoencoderKL.from_pretrained( "madebbyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16 ).to("cuda") pipe = StableDiffusionXLFillPipeline.from_pretrained( "SG161222/RealVisXL_V5.0_Lightning", torch_dtype=torch.float16, vae=vae, controlnet=model, variant="fp16", ).to("cuda") pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) def can_expand(source_width, source_height, target_width, target_height, alignment): """Checks if the image can be expanded based on the alignment.""" if alignment in ("Left", "Right") and source_width>=target_width: return False if alignment in ("Top","Bottom") and source_height>=target_height: return False return True def prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom): target_size = (width, height) #Calculate the scaling factor to fit the image within the target size scale_factor = min(target_size[0]/image.width, target_size[1]/image.height) new_width = int(image.width * scale_factor) new_height = int(image.height * scale_factor) #Resize the source image to fit within target size source = image.resize((new_width, new_height), Image.LANCZOS) #Apply resize option using percentages if resize_option == "Full": resize_percentage = 100 elif resize_option == "50%": resize_percentage = 50 elif resize_option == "33%": resize_percentage = 33 elif resize_option == "25%": resize_option = 25 else: resize_percentage = custom_resize_percentage #calculate new dimensions based on percentage resize_factor = resize_percentage/100 new_width = int(source.width * resize_factor) new_height = int(source.height * resize_factor) #Ensure minimum size of 64 pixels new_width = max(new_width, 64) new_height = max(new_height, 64) #Resize the image source = source.resize((new_width, new_height), Image.LANCZOS) #Calculate the overlap in pixels based on the percentage overlap_x = int(new_width * (overlap_percentage/100)) overlap_y = int(new_height * (overlap_percentage/100)) #Ensure minimum overlap of 1 pixel overlap_x = max(overlap_x, 1) overlap_y = max(overlap_y, 1) #Calculate margins based on alignment if alignment == "Middle": margin_x = (target_size[0]-new_width)//2 margin_y = (target_size[1]-new_height)//2 elif alignment == "Left": margin_x = 0 margin_y = (target_size[1]-new_height)//2 elif alignment == "Right": margin_x = target_size[0] - new_width margin_y = (target_size[1]-new_height)//2 elif alignment == "Top": margin_x = (target_size[0]-new_width)//2 margin_y = 0 elif alignment == "Bottom": margin_x = (target_size[0]-new_width)//2 margin_y = target_size[1] - new_height #adjust margins to eliminate gaps margin_x = max(0, min(margin_x, target_size[0]-new_width)) margin_y = max(0, min(margin_y, target_size[1]-new_height)) #Create a new background image and paste the resized source image background = Image.new('RGB', target_size, (255,255,255)) background.paste(source, (margin_x, margin_y)) #Create the mask mask = Image.new('L', target_size, 255) mask_draw = ImageDraw.Draw(mask) #Calculate overlap areas white_gaps_patch = 2 left_overlap = margin_x + overlap_x if overlap_left else margin_x+white_gaps_patch right_overlap = margin_x + new_width-overlap_x if overlap_right else margin_x+new_width-white_gaps_patch top_overlap = margin_y + overlap_y if overlap_top else margin_y + white_gaps_patch bottom_overlap = margin_y + new_height - overlap_y if overlap_bottom else margin_y+new_height-white_gaps_patch if alignment == "Left": left_overlap = margin_x + overlap_x if overlap_left else margin_x elif alignment == "Right": right_overlap = margin_x + new_width - overlap_x if overlap_right else margin_x + new_width elif alignment == "Top": top_overlap = margin_y + overlap_y if overlap_top else margin_y elif alignment == "Bottom": bottom_overlap = margin_y + new_height - overlap_y if overlap_bottom else margin_y + new_height #Draw the mask mask_draw.rectangle([ (left_overlap, top_overlap), (right_overlap, bottom_overlap) ], fill=0) return background, mask def preview_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom): background, mask = prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom) #Create a preview image showing the mask preview = background.copy().convert('RGBA') #Create a semi-transparent red overlay red_overlay = Image.new('RGBA', background.size, (255, 0, 0, 64)) #Reduced alpha to 64(25% opacity) #Convert black pixels in the mask to semi-transparent red red_mask = Image.new('RGBA', background.size, (0,0,0,0)) red_mask.paste(red_overlay, (0,0), mask) #Overlay the red mask on the background preview = Image.alpha_composite(preview, red_mask) return preview @spaces.GPU(duration=24) def infer(image, width, height, overlap_percentage, num_inference_steps, resize_option, custom_resize_percentage, prompt_input, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom): background, mask = prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom) if not can_expand(background.width, background.height, width, height, alignment): alignment = "Middle" cnet_image = background.copy() cnet_image.paste(0, (0,0), mask) final_prompt = f"{prompt_input}, high quality, 4k" ( prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, ) = pipe.encode_prompt(final_prompt, "cuda", True) for image in pipe( prompt_embeds = prompt_embeds, negative_prompt_embeds = negative_prompt_embeds, pooled_prompt_embeds = pooled_prompt_embeds, negative_pooled_prompt_embeds = negative_pooled_prompt_embeds, image = cnet_image, num_inference_steps=num_inference_steps ): yield cnet_image, image image = image.convert('RGBA') cnet_image.paste(image, (0,0), mask) yield background, cnet_image def clear_result(): """Clears the result ImageSlider.""" return gr.update(value=None) def preload_presets(target_ratio, ui_width, ui_height): """Updates the width and height sliders based on the selected aspect ratio.""" if target_ratio == "9:16": changed_width = 720 changed_height = 1280 return changed_width, changed_height, gr.update() elif target_ratio == "16:9": changed_width = 1280 changed_height = 720 return changed_width, changed_height, gr.update() elif target_ratio == "1:1": changed_width = 1024 changed_height = 1024 return ui_width, ui_height, gr.update(open=True) def select_the_right_preset(user_width, user_height): if user_width == 720 and user_height == 1280: return "9:16" elif user_width == 1280 and user_height == 720: return "16:9" elif user_width == 1024 and user_height == 1024: return "1:1" else: return "Custom" def toggle_custom_resize_slider(resize_option): return gr.update(visible=(resize_option=="Custom")) def update_history(new_image, history): """Updates the history gallery with the new image.""" if history is None: history = [] history.insert(0, new_image) return history with gr.Blocks() as demo: with gr.Column(): with gr.Row(): with gr.Column(): input_image = gr.Image( type="pil", label="Input Image" ) with gr.Row(): with gr.Column(scale=2): prompt_input = gr.Textbox(label="Prompt (Optional)") with gr.Column(scale=1): run_button = gr.Button('Generate') with gr.Row(): target_ratio = gr.Ratio( label="Expected Ratio", choices=["9:16", "16:9", "1:1", "Custom"], value="9:16", scale=2 ) alignment_dropdown = gr.Dropdown( choices=['Middle','Left','Right','Top','Bottom'], value='Middle', label='Alignment' ) #高级配置,当选择custom的时候会自动打开 with gr.Accordion(label="Advanced settings", open=False) as settings_panel: with gr.Column(): #自定义的宽高 with gr.Row(): width_slider = gr.Slider( label="Target Width", minimum=720, maximum=1536, step=8, value=720, #Set a default value ) height_slider = gr.Slider( label="Target Height", minimum=720, maximum=1536, step=8, value=1280, #Set a default value ) #生成步数 num_inference_steps = gr.Slider(label="Steps", minimum=4, maximum=12, step=1, value=8) #组件组 with gr.Group(): overlap_percentage = gr.Slider( label="Mask overlap (%)", minimum=1, maximum=50, value=10, step=1 ) with gr.Row(): overlap_top = gr.Checkbox(label="Overlap Top", value=True) overlap_right = gr.Checkbox(label="Overlap Right", value=True) with gr.Row(): overlap_left = gr.Checkbox(label="Overlap Left", value=True) overlap_bottom = gr.Checkbox(label="Overlap Bottom", value=True) with gr.Row(): resize_option = gr.Radio( label = "Resize input image", choices = ["Full", "50%", "33%", "25%", "Custom"], value="Full" ) custom_resize_percentage = gr.Slider( label="Custom resize (%)", minimum = 1, maximum = 100, step = 1, value = 50, visible = False ) with gr.Column(): preview_button = gr.Button("Preview alignment and mask") with gr.Column(): result = gr.Image(label="Generate Image", interactive=False) history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False) preview_image = gr.Image(label="Preview") target_ratio.change( fn=preload_presets, #选择ratio aspect 的单选框时,调用这个函数 inputs=[target_ratio, width_slider, height_slider], outputs=[width_slider, height_slider, settings_panel], queue=False ) width_slider.change( fn=select_the_right_preset, inputs=[width_slider, height_slider], outputs=[target_ratio], queue=False ) height_slider.change( fn=select_the_right_preset, inputs=[width_slider, height_slider], outputs=[target_ratio], queue=False ) resize_option.change( fn=toggle_custom_resize_slider, inputs=[resize_option], outputs=[custom_resize_percentage], queue=False ) run_button.click(#Clear the result fn=clear_result, inputs=None, outputs=result, ).then( #Generate the new image fn=infer, inputs=[input_image, width_slider, height_slider, overlap_percentage, num_inference_steps, resize_option, custom_resize_percentage, prompt_input, alignment_dropdown, overlap_left, overlap_right, overlap_top, overlap_bottom], outputs=result, ).then(#update the history gallery fn=lambda x, history: update_history(x[1], history), inputs=[result, history_gallery], outputs=history_gallery, ) preview_button.click( fn=preview_image_and_mask, inputs=[input_image, width_slider, height_slider, overlap_percentage, resize_option, custom_resize_percentage, alignment_dropdown, overlap_left, overlap_right, overlap_top, overlap_bottom], outputs=preview_image, queue=False ) demo.queue(max_size=12).launch(share=False)