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( "madebyollin/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 @spaces.GPU(duration=24) def infer(image, width, height, overlap_width, num_inference_steps, resize_option, custom_resize_size, prompt_input=None, alignment="Middle"): source = image target_size = (width, height) overlap = overlap_width # Upscale if source is smaller than target in both dimensions if source.width < target_size[0] and source.height < target_size[1]: scale_factor = min(target_size[0] / source.width, target_size[1] / source.height) new_width = int(source.width * scale_factor) new_height = int(source.height * scale_factor) source = source.resize((new_width, new_height), Image.LANCZOS) if source.width > target_size[0] or source.height > target_size[1]: scale_factor = min(target_size[0] / source.width, target_size[1] / source.height) new_width = int(source.width * scale_factor) new_height = int(source.height * scale_factor) source = source.resize((new_width, new_height), Image.LANCZOS) if resize_option == "Full": resize_size = max(source.width, source.height) elif resize_option == "1/2": resize_size = max(source.width, source.height) // 2 elif resize_option == "1/3": resize_size = max(source.width, source.height) // 3 elif resize_option == "1/4": resize_size = max(source.width, source.height) // 4 else: # Custom resize_size = custom_resize_size aspect_ratio = source.height / source.width new_width = resize_size new_height = int(resize_size * aspect_ratio) source = source.resize((new_width, new_height), Image.LANCZOS) if not can_expand(source.width, source.height, target_size[0], target_size[1], alignment): alignment = "Middle" # Calculate margins based on alignment if alignment == "Middle": margin_x = (target_size[0] - source.width) // 2 margin_y = (target_size[1] - source.height) // 2 elif alignment == "Left": margin_x = 0 margin_y = (target_size[1] - source.height) // 2 elif alignment == "Right": margin_x = target_size[0] - source.width margin_y = (target_size[1] - source.height) // 2 elif alignment == "Top": margin_x = (target_size[0] - source.width) // 2 margin_y = 0 elif alignment == "Bottom": margin_x = (target_size[0] - source.width) // 2 margin_y = target_size[1] - source.height background = Image.new('RGB', target_size, (255, 255, 255)) background.paste(source, (margin_x, margin_y)) mask = Image.new('L', target_size, 255) mask_draw = ImageDraw.Draw(mask) # Adjust mask generation based on alignment if alignment == "Middle": mask_draw.rectangle([ (margin_x + overlap, margin_y + overlap), (margin_x + source.width - overlap, margin_y + source.height - overlap) ], fill=0) elif alignment == "Left": mask_draw.rectangle([ (margin_x, margin_y), (margin_x + source.width - overlap, margin_y + source.height) ], fill=0) elif alignment == "Right": mask_draw.rectangle([ (margin_x + overlap, margin_y), (margin_x + source.width, margin_y + source.height) ], fill=0) elif alignment == "Top": mask_draw.rectangle([ (margin_x, margin_y), (margin_x + source.width, margin_y + source.height - overlap) ], fill=0) elif alignment == "Bottom": mask_draw.rectangle([ (margin_x, margin_y + overlap), (margin_x + source.width, margin_y + source.height) ], fill=0) 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(open=False) elif target_ratio == "16:9": changed_width = 1280 changed_height = 720 return changed_width, changed_height, gr.update(open=False) elif target_ratio == "1:1": changed_width = 1024 changed_height = 1024 return changed_width, changed_height, gr.update(open=False) elif target_ratio == "Custom": 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")) css = """ footer { visibility: hidden; } """ with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css=css) 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.Radio( 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="고정 위치 선택" ) with gr.Accordion(label="Advanced settings", open=False) as settings_panel: with gr.Column(): with gr.Row(): width_slider = gr.Slider( label="Width", minimum=256, maximum=1536, step=8, value=720, # Set a default value ) height_slider = gr.Slider( label="Height", minimum=256, maximum=1536, step=8, value=480, # Set a default value ) with gr.Row(): num_inference_steps = gr.Slider(label="Steps", minimum=4, maximum=12, step=1, value=8) overlap_width = gr.Slider( label="Mask overlap width", minimum=1, maximum=50, value=42, step=1 ) with gr.Row(): resize_option = gr.Radio( label="Resize input image", choices=["Full", "1/2", "1/3", "1/4", "Custom"], value="Full" ) custom_resize_size = gr.Slider( label="Custom resize size", minimum=256, # 이전에는 720이었습니다. maximum=1024, step=8, value=512, # 초기 값이므로 이 값도 적절히 조정할 수 있습니다. visible=False # 기본적으로 이 슬라이더는 숨겨져 있으며, 'Custom'이 선택될 때만 보입니다. ) gr.Examples( examples=[ ["./examples/example_2.jpg", 1440, 810, "Middle"], ["./examples/example_3.jpg", 1024, 1024, "Bottom"], ["./examples/example_4.png", 1024, 1024, "Top"], ], inputs=[input_image, width_slider, height_slider, alignment_dropdown], ) with gr.Column(): result = ImageSlider( interactive=False, label="Generated Image", ) use_as_input_button = gr.Button("Use as Input Image", visible=False) def use_output_as_input(output_image): """Sets the generated output as the new input image.""" return gr.update(value=output_image[1]) use_as_input_button.click( fn=use_output_as_input, inputs=[result], outputs=[input_image] ) target_ratio.change( fn=preload_presets, 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_size], queue=False ) run_button.click( fn=clear_result, inputs=None, outputs=result, ).then( fn=infer, inputs=[input_image, width_slider, height_slider, overlap_width, num_inference_steps, resize_option, custom_resize_size, prompt_input, alignment_dropdown], outputs=result, ).then( fn=lambda: gr.update(visible=True), inputs=None, outputs=use_as_input_button, ) prompt_input.submit( fn=clear_result, inputs=None, outputs=result, ).then( fn=infer, inputs=[input_image, width_slider, height_slider, overlap_width, num_inference_steps, resize_option, custom_resize_size, prompt_input, alignment_dropdown], outputs=result, ).then( fn=lambda: gr.update(visible=True), inputs=None, outputs=use_as_input_button, ) demo.queue(max_size=12).launch(share=False)