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| import gradio as gr | |
| import time | |
| import numpy as np | |
| from PIL import Image | |
| import random | |
| # Mock Pipeline Class to simulate the processing stages described | |
| # In a real scenario, this would interface with Diffusers/PyTorch models | |
| class VideoGenerationPipeline: | |
| def __init__(self): | |
| self.device = "cpu" # Mock device | |
| def preprocess(self, image): | |
| """Simulate Stage 1: Upscaling and Preprocessing""" | |
| time.sleep(1) | |
| return image | |
| def generate_sequence(self, image, steps): | |
| """Simulate Stage 2: Frame Generation""" | |
| time.sleep(2) | |
| return [image] * steps | |
| def render_video(self, frames, fps): | |
| """Simulate Stage 3: Video Rendering""" | |
| time.sleep(1) | |
| # Return a dummy video path or placeholder | |
| return "output_video.mp4" | |
| # Initialize the mock pipeline | |
| pipeline = VideoGenerationPipeline() | |
| def process_video( | |
| reference_image, | |
| pace_slider, | |
| motion_slider, | |
| num_frames, | |
| guidance_scale, | |
| progress=gr.Progress() | |
| ): | |
| """ | |
| Main inference function simulating a complex multi-stage pipeline. | |
| Uses gr.Progress to provide user feedback during long operations. | |
| """ | |
| if reference_image is None: | |
| raise gr.Error("Please upload a reference image first.") | |
| try: | |
| # Stage 1: Preprocessing | |
| progress(0.1, desc="Stage 1: Preprocessing & Upscaling...") | |
| _ = pipeline.preprocess(reference_image) | |
| # Stage 2: Sequence Generation | |
| progress(0.4, desc="Stage 2: Generating Frame Sequence...") | |
| # Simulate variable processing time based on complexity | |
| time.sleep(1 + (motion_slider * 0.5)) | |
| # Stage 3: Choreography/Motion | |
| progress(0.7, desc="Stage 3: Applying Motion & Choreography...") | |
| _ = pipeline.generate_sequence(reference_image, num_frames) | |
| # Stage 4: Final Render | |
| progress(0.9, desc="Stage 4: Rendering Final Video...") | |
| output_path = pipeline.render_video([], fps=25) | |
| progress(1.0, desc="Complete!") | |
| # In a real app, return the actual video file path | |
| # Here we return the input as a placeholder for the demo | |
| return reference_image | |
| except Exception as e: | |
| raise gr.Error(f"Pipeline failed: {str(e)}") | |
| # Gradio 6 Application Structure | |
| # CRITICAL: gr.Blocks() takes NO parameters in Gradio 6 | |
| with gr.Blocks() as demo: | |
| # Header with required attribution | |
| gr.HTML(""" | |
| <div style="text-align: center; margin-bottom: 20px;"> | |
| <h1>Pro-Video Img2Vid Pipeline</h1> | |
| <p>Advanced Image-to-Video Generation Workflow</p> | |
| <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="color: #007bff; text-decoration: none;">Built with anycoder</a> | |
| </div> | |
| """) | |
| # Layout: Sidebar for controls, Main area for IO | |
| with gr.Row(): | |
| # Sidebar for Configuration | |
| with gr.Sidebar(width=320): | |
| gr.Markdown("## Configuration") | |
| gr.Markdown("### Generation Settings") | |
| pace_slider = gr.Slider( | |
| minimum=0.6, | |
| maximum=1.4, | |
| value=0.9, | |
| step=0.05, | |
| label="Generation Pace", | |
| info="Lower is faster processing" | |
| ) | |
| motion_slider = gr.Slider( | |
| minimum=0.8, | |
| maximum=2.0, | |
| value=1.2, | |
| step=0.1, | |
| label="Motion Intensity", | |
| info="Controls movement magnitude" | |
| ) | |
| num_frames = gr.Slider( | |
| minimum=16, | |
| maximum=64, | |
| value=24, | |
| step=4, | |
| label="Frame Count", | |
| info="Total frames in output" | |
| ) | |
| guidance_scale = gr.Slider( | |
| minimum=5.0, | |
| maximum=15.0, | |
| value=7.5, | |
| step=0.5, | |
| label="Guidance Scale", | |
| info="Adherence to prompt" | |
| ) | |
| gr.Markdown("---") | |
| gr.Markdown("### System Info") | |
| system_status = gr.Textbox( | |
| value="System Ready (CUDA: Available)", | |
| label="Status", | |
| interactive=False | |
| ) | |
| # Main Content Area | |
| with gr.Column(scale=1): | |
| gr.Markdown("## Input / Output") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image( | |
| type="pil", | |
| label="Reference Image", | |
| sources=["upload", "clipboard"], | |
| height=400 | |
| ) | |
| # Action Buttons | |
| with gr.Row(): | |
| generate_btn = gr.Button("Generate Video", variant="primary", size="lg") | |
| clear_btn = gr.ClearButton([input_image], variant="stop") | |
| with gr.Column(): | |
| output_video = gr.Video( | |
| label="Generated Video Output (25fps)", | |
| autoplay=True, | |
| height=400 | |
| ) | |
| # Advanced Settings Accordion | |
| with gr.Accordion("Advanced Settings", open=False): | |
| seed = gr.Number(label="Seed (Random for -1)", value=-1, precision=0) | |
| negative_prompt = gr.Textbox( | |
| label="Negative Prompt", | |
| placeholder="blur, distortion, low quality...", | |
| lines=2 | |
| ) | |
| enable_lora = gr.Checkbox(label="Enable Custom LoRA", value=False) | |
| # Event Listeners | |
| # Gradio 6 uses api_visibility instead of just api_name | |
| generate_btn.click( | |
| fn=process_video, | |
| inputs=[ | |
| input_image, | |
| pace_slider, | |
| motion_slider, | |
| num_frames, | |
| guidance_scale | |
| ], | |
| outputs=output_video, | |
| api_visibility="public" | |
| ) | |
| # Gradio 6 Launch Method | |
| # CRITICAL: All parameters (theme, css, etc.) go here, NOT in gr.Blocks() | |
| demo.launch( | |
| theme=gr.themes.Soft( | |
| primary_hue="indigo", | |
| secondary_hue="blue", | |
| neutral_hue="slate", | |
| font=gr.themes.GoogleFont("Inter"), | |
| text_size="lg", | |
| spacing_size="lg", | |
| radius_size="md" | |
| ), | |
| # Custom CSS for additional polish | |
| css=""" | |
| .gradio-container { | |
| max-width: 1400px !important; | |
| } | |
| h1 { | |
| font-weight: 700 !important; | |
| color: #1f2937 !important; | |
| } | |
| """, | |
| footer_links=[ | |
| {"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"} | |
| ] | |
| ) |