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 import cv2 import tempfile import os # Load models and configurations 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 def infer(image, width=1024, height=1024, overlap_width=18, num_inference_steps=8, resize_option="custom", custom_resize_size=768, 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 create_zoom_animation(previous_frame, next_frame, steps): # List to store all frames interpolated_frames = [] for i in range(steps): t = i / (steps - 1) # Normalized time between 0 and 1 # Compute zoom factor (from 1 to 2) z = 1 + t # Zoom factor increases from 1 to 2 if i < steps - 1: # Compute crop size crop_size = int(1024 / z) # Compute crop coordinates to center the crop x0 = (1024 - crop_size) // 2 y0 = (1024 - crop_size) // 2 x1 = x0 + crop_size y1 = y0 + crop_size # Crop the previous_frame cropped_prev = previous_frame.crop((x0, y0, x1, y1)) # Resize to 512x512 resized_frame = cropped_prev.resize((512, 512), Image.LANCZOS) interpolated_frames.append(resized_frame) else: # For the last frame, use the next_frame resized to 512x512 resized_frame = next_frame.resize((512, 512), Image.LANCZOS) return interpolated_frames def create_video_from_images(image_list, fps=24): if not image_list: return None with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as temp_video_file: video_path = temp_video_file.name frame = np.array(image_list[0]) height, width, layers = frame.shape fourcc = cv2.VideoWriter_fourcc(*'mp4v') video = cv2.VideoWriter(video_path, fourcc, fps, (width, height)) for image in image_list: video.write(cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)) video.release() return video_path @spaces.GPU(duration=70) def loop_outpainting(image, width=1024, height=1024, overlap_width=6, num_inference_steps=8, resize_option="custom", custom_resize_size=512, prompt_input=None, alignment="Middle", num_iterations=6, fps=24, num_interpolation_frames=18, progress=gr.Progress()): current_image = image if(current_image.width != 1024 or current_image.height != 1024): for first_result in infer(current_image, 1024, 1024, overlap_width, num_inference_steps, resize_option, 1024, prompt_input, alignment): pass current_image = first_result[1] image_list = [current_image] for _ in progress.tqdm(range(num_iterations), desc="Generating frames"): # Generate new image for step_result in infer(current_image, width, height, overlap_width, num_inference_steps, resize_option, custom_resize_size, prompt_input, alignment): pass # Process all steps new_image = step_result[1] # Get the final image from the last step image_list.append(new_image) # Use new image as input for next iteration current_image = new_image # Reverse the image list to create a zoom-in effect reverse_image_list = image_list[::-1] # Create interpolated frames final_frame_list = [] for i in range(len(reverse_image_list) - 1): larger_frame = reverse_image_list[i] smaller_frame = reverse_image_list[i + 1] interpolated_frames = create_zoom_animation(larger_frame, smaller_frame, num_interpolation_frames) if i == 0: # Include all frames for the first sequence final_frame_list.extend(interpolated_frames) else: # Exclude the first frame to avoid duplication final_frame_list.extend(interpolated_frames[1:]) # Create video from the final frame list video_path = create_video_from_images(final_frame_list, fps) return video_path loop_outpainting.zerogpu = True 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 = """ .gradio-container { width: 1200px !important; } """ title = """

Outpaint Video Zoom-In

""" with gr.Blocks(css=css) as demo: with gr.Column(): gr.HTML(title) with gr.Row(): with gr.Column(): input_image = gr.Image( type="pil", label="Input Image" ) prompt_input = gr.Textbox(label="Prompt (Optional)", visible=True) with gr.Row(): with gr.Column(scale=1): run_button = gr.Button("Generate", visible=False) loop_button = gr.Button("Create outpainting video") with gr.Row(): target_ratio = gr.Radio( label="Expected Ratio", choices=["9:16", "16:9", "1:1", "Custom"], value="1:1", scale=2, visible=False ) alignment_dropdown = gr.Dropdown( choices=["Middle", "Left", "Right", "Top", "Bottom"], value="Middle", label="Alignment", visible=False ) with gr.Accordion(label="Advanced settings", open=False, visible=False) as settings_panel: with gr.Column(): with gr.Row(): width_slider = gr.Slider( label="Width", minimum=720, maximum=1536, step=8, value=1024, ) height_slider = gr.Slider( label="Height", minimum=720, maximum=1536, step=8, value=1024, ) 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=1, step=1 ) with gr.Row(): resize_option = gr.Radio( label="Resize input image", choices=["Full", "1/2", "1/3", "1/4", "Custom"], value="Custom" ) custom_resize_size = gr.Slider( label="Custom resize size", minimum=64, maximum=1024, step=8, value=512, visible=False ) with gr.Row(): num_iterations = gr.Slider(label="Number of iterations", minimum=2, maximum=24, step=1, value=6) fps = gr.Slider(label="fps", minimum=1, maximum=24, value=24) with gr.Row(): num_interpolation_frames = gr.Slider(label="Interpolation frames", minimum=0, maximum=10, step=1, value=18) with gr.Column(): result = ImageSlider( interactive=False, label="Generated Image", visible=False ) use_as_input_button = gr.Button("Use as Input Image", visible=False) video_output = gr.Video(label="Outpainting Video") gr.Examples( examples=["hide.png", "disaster.png"], fn=loop_outpainting, inputs=input_image, outputs=video_output, cache_examples="lazy", ) 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, ) loop_button.click( fn=loop_outpainting, inputs=[input_image, width_slider, height_slider, overlap_width, num_inference_steps, resize_option, custom_resize_size, prompt_input, alignment_dropdown, num_iterations, fps, num_interpolation_frames], outputs=video_output, ) demo.queue(max_size=12).launch(share=False)