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| import gradio as gr | |
| import re | |
| import subprocess | |
| import select | |
| from huggingface_hub import snapshot_download | |
| # Download model (for demonstration, adjust based on actual model needs) | |
| snapshot_download( | |
| repo_id="Wan-AI/Wan2.1-T2V-1.3B", | |
| local_dir="./Wan2.1-T2V-1.3B" | |
| ) | |
| # Function to generate video | |
| def infer(prompt, progress=gr.Progress(track_tqdm=True)): | |
| # Reduced progress output and simplified structure | |
| command = [ | |
| "python", "-u", "-m", "generate", # Using unbuffered output | |
| "--task", "t2v-1.3B", | |
| "--size", "832*480", # You can try reducing resolution further for CPU | |
| "--ckpt_dir", "./Wan2.1-T2V-1.3B", | |
| "--sample_shift", "8", | |
| "--sample_guide_scale", "6", | |
| "--prompt", prompt, | |
| "--save_file", "generated_video.mp4" | |
| ] | |
| # Run the model inference in a subprocess | |
| process = subprocess.Popen(command, | |
| stdout=subprocess.PIPE, | |
| stderr=subprocess.PIPE, | |
| text=True, | |
| bufsize=1) | |
| # Monitor progress with a minimal progress bar | |
| progress_pattern = re.compile(r"(\d+)%\|.*\| (\d+)/(\d+)") | |
| video_progress_bar = None | |
| overall_steps = 0 | |
| while True: | |
| rlist, _, _ = select.select([process.stdout], [], [], 0.04) | |
| if rlist: | |
| line = process.stdout.readline() | |
| if not line: | |
| break | |
| stripped_line = line.strip() | |
| if not stripped_line: | |
| continue | |
| # Check for video generation progress | |
| progress_match = progress_pattern.search(stripped_line) | |
| if progress_match: | |
| current = int(progress_match.group(2)) | |
| total = int(progress_match.group(3)) | |
| if video_progress_bar is None: | |
| video_progress_bar = gr.Progress() | |
| video_progress_bar.update(current / total) | |
| video_progress_bar.update(current / total) | |
| continue | |
| # Process info messages (simplified) | |
| if "INFO:" in stripped_line: | |
| overall_steps += 1 | |
| continue | |
| else: | |
| print(stripped_line) | |
| if process.poll() is not None: | |
| break | |
| # Clean up and finalize the progress bar | |
| process.wait() | |
| if video_progress_bar: | |
| video_progress_bar.close() | |
| # Return the video file path if successful | |
| if process.returncode == 0: | |
| return "generated_video.mp4" | |
| else: | |
| raise Exception("Error executing command") | |
| # Gradio UI | |
| with gr.Blocks() as demo: | |
| with gr.Column(): | |
| gr.Markdown("# Wan 2.1 1.3B Video Generation") | |
| prompt = gr.Textbox(label="Prompt") | |
| submit_btn = gr.Button("Generate Video") | |
| video_res = gr.Video(label="Generated Video") | |
| submit_btn.click( | |
| fn=infer, | |
| inputs=[prompt], | |
| outputs=[video_res] | |
| ) | |
| demo.queue().launch(show_error=True, show_api=False) | |