Update app.py

app.py

@@ -1,521 +1,21 @@
 import gradio as gr
-import spaces
-import time
-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
-
-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()
-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
-):
-    """
-    Generate an outpainted image using Stable Diffusion XL with ControlNet guidance.
-    
-    This function performs intelligent image outpainting by expanding the input image 
-    according to the specified target dimensions and alignment, generating new content 
-    guided by a textual prompt. It uses a ControlNet-enabled diffusion pipeline to ensure 
-    coherent image extension.
-
-    Args:
-        image (PIL.Image): The input image to be outpainted.
-        width (int): The target width of the output image.
-        height (int): The target height of the output image.
-        overlap_percentage (int): Percentage of overlap between original and outpainted regions for seamless blending.
-        num_inference_steps (int): Number of inference steps for image generation. Higher values yield better results.
-        resize_option (str): Predefined or custom percentage to resize the input image ("Full", "50%", "33%", "25%", or "Custom").
-        custom_resize_percentage (int): Custom resize percentage if resize_option is "Custom".
-        prompt_input (str): A text prompt describing desired content for the generated region.
-        alignment (str): Alignment of the original image within the canvas ("Middle", "Left", "Right", "Top", "Bottom").
-        overlap_left (bool): Whether to allow blending on the left edge.
-        overlap_right (bool): Whether to allow blending on the right edge.
-        overlap_top (bool): Whether to allow blending on the top edge.
-        overlap_bottom (bool): Whether to allow blending on the bottom edge.
-
-    Yields:
-        Tuple[PIL.Image, PIL.Image]: 
-        - The intermediate ControlNet input image (showing the masked area).
-        - The final generated image with the inpainted region.
-    """
-    #gr.Info("10 seconds will be used from your daily ZeroGPU time credits.")
-    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
-
-    #time.sleep(1)
-    #image = image.convert("RGBA")
-    #cnet_image.paste(image, (0, 0), mask)
-
-    #return 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 changed_width, changed_height, gr.update()
-    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"))
-
-def update_history(new_image, history):
-    """Updates the history gallery with the new image."""
-    time.sleep(1)
-    if history is None:
-        history = []
-    history.insert(0, new_image)
-    return history
-
-css = """
-.gradio-container {
-    max-width: 1200px !important;
-    margin: 0 auto;
-}
-"""
-
-title = """<h1 align="center">Diffusers Image Outpaint</h1>
-<div align="center">Drop an image you would like to extend, pick your expected ratio and hit Generate.</div>
-<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
-    <p style="display: flex;gap: 6px;">
-         <a href="https://huggingface.co/spaces/fffiloni/diffusers-image-outpaint?duplicate=true">
-            <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-md.svg" alt="Duplicate this Space">
-        </a> to skip the queue and enjoy faster inference on the GPU of your choice 
-    </p>
-</div>
-"""
-
-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,  # Set a default value
-                            )
-                            height_slider = gr.Slider(
-                                label="Target Height",
-                                minimum=720,
-                                maximum=1536,
-                                step=8,
-                                value=1280,  # Set a default value
-                            )
-                        
-                        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():
-                                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")
-                            
-                            
-                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],
-                )
-
-                
-
-            with gr.Column():
-                result = ImageSlider(
-                    interactive=False,
-                    label="Generated Image",
-                )
-                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)
-                preview_image = gr.Image(label="Preview")
-
-        
-
-    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],
-        show_api=False
-    )
-    
-    target_ratio.change(
-        fn=preload_presets,
-        inputs=[target_ratio, width_slider, height_slider],
-        outputs=[width_slider, height_slider, settings_panel],
-        queue=False,
-        show_api=False
-    )
-
-    width_slider.change(
-        fn=select_the_right_preset,
-        inputs=[width_slider, height_slider],
-        outputs=[target_ratio],
-        queue=False,
-        show_api=False
-    )
-
-    height_slider.change(
-        fn=select_the_right_preset,
-        inputs=[width_slider, height_slider],
-        outputs=[target_ratio],
-        queue=False,
-        show_api=False
-    )
-
-    resize_option.change(
-        fn=toggle_custom_resize_slider,
-        inputs=[resize_option],
-        outputs=[custom_resize_percentage],
-        queue=False,
-        show_api=False
-    )
-    
-    run_button.click(  # Clear the result
-        fn=clear_result,
-        inputs=None,
-        outputs=result,
-        show_api=False
-    ).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(  # Show the "Use as Input Image" button
-        fn=lambda: gr.update(visible=True),
-        inputs=None,
-        outputs=use_as_input_button,
-        show_api=False
-    ).then(  # Update the history gallery
-        fn=lambda x, history: update_history(x[1], history),
-        inputs=[result, history_gallery],
-        outputs=history_gallery,
-        show_api=False
-    )
-
-    prompt_input.submit(  # Clear the result
-        fn=clear_result,
-        inputs=None,
-        outputs=result,
-        show_api=False
-    ).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,
-        show_api=False
-    ).then(  # Update the history gallery
-        fn=lambda x, history: update_history(x[1], history),
-        inputs=[result, history_gallery],
-        outputs=history_gallery,
-        show_api=False
-    ).then(  # Show the "Use as Input Image" button
-        fn=lambda: gr.update(visible=True),
-        inputs=None,
-        outputs=use_as_input_button,
-        show_api=False
-    )
-
-    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,
-        show_api=False
-    )
-
-demo.queue(max_size=12).launch(share=False, show_error=True, mcp_server=True)
+import os
+import subprocess
+
+def remove_subtitles(video):
+    input_path = video
+    output_path = "output.mp4"
+    cmd = f"python main.py --input_video {input_path} --output_video {output_path}"
+    subprocess.run(cmd, shell=True)
+    return output_path
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🎬 Video Subtitle Remover (VSR)")
+    video_input = gr.Video(label="上传视频")
+    run_btn = gr.Button("去字幕")
+    video_output = gr.Video(label="处理后视频")
+    download = gr.File(label="下载结果")
+    run_btn.click(remove_subtitles, inputs=video_input, outputs=[video_output, download])
+
+if __name__ == "__main__":
+    demo.launch()