import gradio as gr import re import subprocess import time import threading from tqdm import tqdm from huggingface_hub import snapshot_download #Download model snapshot_download( repo_id = "Wan-AI/Wan2.1-T2V-1.3B", local_dir = "./Wan2.1-T2V-1.3B" ) def infer(prompt, progress=gr.Progress(track_tqdm=True)): total_process_steps = 11 irrelevant_steps = 4 relevant_steps = total_process_steps - irrelevant_steps # 7 steps # Overall progress bar for the process steps. overall_bar = tqdm(total=relevant_steps, desc="Overall Process", position=1, ncols=120, dynamic_ncols=False, leave=True) processed_steps = 0 # Regex for detecting video generation progress lines (e.g., "10%|...| 5/50") progress_pattern = re.compile(r"(\d+)%\|.*\| (\d+)/(\d+)") gen_progress_bar = None # Variables for managing the sub-progress bar for each step. current_sub_bar = None current_sub_thread = None current_cancel_event = None sub_lock = threading.Lock() def update_sub_bar(sub_bar, cancel_event): # This function updates the sub_bar once per second up to 20 seconds, # unless the cancel_event is set. for i in range(20): if cancel_event.is_set(): break time.sleep(1) sub_bar.update(1) sub_bar.refresh() # When done (or canceled), do nothing here; # closing will be done in close_sub_bar() from the main thread. def close_sub_bar(): nonlocal current_sub_bar, current_sub_thread, current_cancel_event with sub_lock: if current_sub_bar is not None: try: # Complete any remaining ticks (if any) remaining = current_sub_bar.total - current_sub_bar.n if remaining > 0: current_sub_bar.update(remaining) except Exception: pass current_sub_bar.close() overall_bar.update(1) overall_bar.refresh() current_sub_bar = None if current_sub_thread is not None: current_sub_thread.join() current_sub_thread = None if current_cancel_event is not None: current_cancel_event = None command = [ "python", "-u", "-m", "generate", # using -u for unbuffered output and module name without .py "--task", "t2v-1.3B", "--size", "832*480", "--ckpt_dir", "./Wan2.1-T2V-1.3B", "--sample_shift", "8", "--sample_guide_scale", "6", "--prompt", prompt, "--save_file", "generated_video.mp4" ] process = subprocess.Popen( command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, bufsize=1 # line-buffered ) for line in iter(process.stdout.readline, ''): stripped_line = line.strip() if not stripped_line: continue # Check if this is a video generation progress line. progress_match = progress_pattern.search(stripped_line) if progress_match: current = int(progress_match.group(2)) total = int(progress_match.group(3)) if gen_progress_bar is None: gen_progress_bar = tqdm(total=total, desc="Video Generation", position=0, ncols=120, dynamic_ncols=True, leave=True) gen_progress_bar.update(current - gen_progress_bar.n) gen_progress_bar.refresh() continue # Check for INFO lines. if "INFO:" in stripped_line: parts = stripped_line.split("INFO:", 1) msg = parts[1].strip() if len(parts) > 1 else "" tqdm.write(stripped_line) if processed_steps < irrelevant_steps: processed_steps += 1 else: with sub_lock: # If a sub-bar is already active, cancel its update and close it. if current_sub_bar is not None: if current_cancel_event is not None: current_cancel_event.set() close_sub_bar() # Now create a new sub-bar for the current step. current_sub_bar = tqdm(total=20, desc=msg, position=2, ncols=120, dynamic_ncols=False, leave=True) current_cancel_event = threading.Event() current_sub_thread = threading.Thread(target=update_sub_bar, args=(current_sub_bar, current_cancel_event)) current_sub_thread.start() continue else: tqdm.write(stripped_line) process.wait() # After process ends, if a sub-bar is still active, cancel and close it. with sub_lock: if current_cancel_event is not None: current_cancel_event.set() if current_sub_bar is not None: close_sub_bar() if gen_progress_bar: gen_progress_bar.close() overall_bar.close() if process.returncode == 0: print("Command executed successfully.") return "generated_video.mp4" else: print("Error executing command.") raise Exception("Error executing command") with gr.Blocks() as demo: with gr.Column(): gr.Markdown("# Wan 2.1") prompt = gr.Textbox(label="Prompt") submit_btn = gr.Button("Submit") 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, ssr_mode=False)