import gradio as gr import spaces import torch from diffusers import AutoencoderKL, TCDScheduler from diffusers.models.model_loading_utils import load_state_dict # Removed: 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 # --- Model Loading (unchanged) --- 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", ) sstate_dict = load_state_dict(model_file) model, _, _, _, _ = ControlNetModel_Union._load_pretrained_model( controlnet_model, sstate_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) # --- Helper Functions (unchanged, except infer) --- 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_percentage = 25 else: # Custom 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" # Default to middle if expansion not possible with current alignment cnet_image = background.copy() # Prepare the controlnet input image (original image with blacked-out mask area) # Note: The pipeline expects the original image content where the mask is 0 (black) # and the area to be filled where the mask is 255 (white). # However, the current pipeline_fill_sd_xl seems to use the mask differently internally. # Let's prepare the input image as per the original logic, which pastes black over the masked area. black_fill = Image.new('RGB', cnet_image.size, (0, 0, 0)) # Invert the mask: white (255) becomes the area to keep, black (0) the area to fill inverted_mask = Image.eval(mask, lambda x: 255 - x) cnet_image.paste(black_fill, (0, 0), inverted_mask) # Paste black where the inverted mask is white (original mask was 0) 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) # Generate the image content for the masked area # The pipeline yields the generated content for the masked area # We only need the final result from the generator generated_content = None for res 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, # Pass the image with blacked-out area mask_image=mask, # Pass the mask (white = area to fill) num_inference_steps=num_inference_steps ): generated_content = res # Keep updating until the last step # The pipeline directly returns the final composite image in recent versions # If it returns only the filled part, we need to composite it # Let's assume the pipeline returns the final composited image based on its name "FillPipeline" final_image = generated_content # --- OLD compositing logic (keep commented in case pipeline behavior differs) --- # # Convert generated content to RGBA to handle potential transparency # generated_content = generated_content.convert("RGBA") # # Create the final composite image by pasting the generated content onto the background # final_image = background.copy().convert("RGBA") # # Paste the generated content using the original mask (white area = where to paste) # final_image.paste(generated_content, (0, 0), mask) # final_image = final_image.convert("RGB") # Convert back to RGB if needed # Yield only the final composited image yield final_image def clear_result(): """Clears the result Image.""" 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 changed_width, changed_height, gr.update() elif target_ratio == "Custom": # When switching to custom, keep current slider values but open the accordion 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 = [] # Ensure new_image is a PIL Image before inserting if isinstance(new_image, Image.Image): history.insert(0, new_image) # Handle cases where the input might be None or not an image (e.g., during clearing) elif new_image is not None: print(f"Warning: Attempted to add non-image type to history: {type(new_image)}") return history # --- Gradio UI --- css = """ .gradio-container { width: 1200px !important; } h1 { text-align: center; } footer { visibility: hidden; } """ title = """

Diffusers Image Outpaint Lightning

""" 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" ) 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="Alignment" ) with gr.Accordion(label="Advanced settings", open=False) as settings_panel: with gr.Column(): with gr.Row(): width_slider = gr.Slider( label="Target Width", minimum=720, maximum=1536, step=8, value=720, # Default for 9:16 ) height_slider = gr.Slider( label="Target Height", minimum=720, maximum=1536, step=8, value=1280, # Default for 9:16 ) num_inference_steps = gr.Slider(label="Steps", minimum=4, maximum=12, step=1, value=8) 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(): # Changed nesting for better layout 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(): # Keep preview button separate preview_button = gr.Button("Preview alignment and mask") gr.Examples( examples=[ ["./examples/example_1.webp", 1280, 720, "Middle"], ["./examples/example_2.jpg", 1440, 810, "Left"], ["./examples/example_3.jpg", 1024, 1024, "Top"], ["./examples/example_3.jpg", 1024, 1024, "Bottom"], ], inputs=[input_image, width_slider, height_slider, alignment_dropdown], # Ensure examples don't try to set output components directly # outputs=[result], # Remove output mapping from examples # fn=infer, # Don't run infer on example click, just load inputs ) with gr.Column(): # *** MODIFICATION: Changed ImageSlider to Image *** result = gr.Image(label="Generated Image", interactive=False, type="pil") use_as_input_button = gr.Button("Use as Input Image", visible=False) history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False, type="pil") preview_image = gr.Image(label="Preview", type="pil") # Ensure preview is also PIL # --- Event Handlers --- def use_output_as_input(output_image): """Sets the generated output as the new input image.""" # *** MODIFICATION: Access the image directly, not output_image[1] *** return gr.update(value=output_image) use_as_input_button.click( fn=use_output_as_input, inputs=[result], # Input is the single result image 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 ) # Link sliders change to update the ratio selection to "Custom" width_slider.change( fn=select_the_right_preset, inputs=[width_slider, height_slider], outputs=[target_ratio], queue=False ).then( fn=lambda: gr.update(open=True), # Also open accordion on slider change inputs=None, outputs=settings_panel, queue=False ) height_slider.change( fn=select_the_right_preset, inputs=[width_slider, height_slider], outputs=[target_ratio], queue=False ).then( fn=lambda: gr.update(open=True), # Also open accordion on slider change inputs=None, outputs=settings_panel, queue=False ) resize_option.change( fn=toggle_custom_resize_slider, inputs=[resize_option], outputs=[custom_resize_percentage], queue=False ) # Combine run logic for Button and Textbox submission run_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 ] def run_generation(img, w, h, ov_perc, steps, res_opt, cust_res_perc, prompt, align, ov_l, ov_r, ov_t, ov_b, history): # The infer function is a generator, we need to iterate to get the final value final_image = None for res_img in infer(img, w, h, ov_perc, steps, res_opt, cust_res_perc, prompt, align, ov_l, ov_r, ov_t, ov_b): final_image = res_img # Update history with the final image updated_history = update_history(final_image, history) # Return the final image for the result component and the updated history return final_image, updated_history, gr.update(visible=True) # Also make button visible run_button.click( fn=clear_result, # First clear the previous result inputs=None, outputs=result, queue=False # Clearing should be fast ).then( fn=run_generation, # Then run the generation and history update inputs=run_inputs + [history_gallery], # Pass current history outputs=[result, history_gallery, use_as_input_button], # Update result, history, and button visibility ) prompt_input.submit( fn=clear_result, # First clear the previous result inputs=None, outputs=result, queue=False # Clearing should be fast ).then( fn=run_generation, # Then run the generation and history update inputs=run_inputs + [history_gallery], # Pass current history outputs=[result, history_gallery, use_as_input_button], # Update result, history, and button visibility ) 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 # Preview should be fast ) # Launch the demo demo.queue(max_size=20).launch(share=False, ssr_mode=False, show_error=True)