import sys sys.path.insert(0, "ComfyUI") import os import torch import gc import gradio as gr from wan22_style import VideoProcessor from huggingface_hub import hf_hub_download import cv2 import shutil import os def convert_video_for_gradio(video_path): """ Convert video to web-compatible format for Gradio display. This fixes the 'video not playable' issue with OpenCV-generated videos. """ if video_path is None: return None try: # Create output in a Gradio-friendly location output_dir = "video" os.makedirs(output_dir, exist_ok=True) base_name = os.path.splitext(os.path.basename(video_path))[0] converted_path = os.path.join(output_dir, f"{base_name}_gradio_compatible.mp4") # Read the original video cap = cv2.VideoCapture(video_path) if not cap.isOpened(): print(f"Could not open video: {video_path}") return video_path # Return original if can't process # Get video properties fps = cap.get(cv2.CAP_PROP_FPS) width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # Try different codecs in order of preference codecs_to_try = ['mp4v', 'XVID', 'MJPG', 'X264'] out = None for codec in codecs_to_try: try: fourcc = cv2.VideoWriter_fourcc(*codec) out = cv2.VideoWriter(converted_path, fourcc, fps, (width, height)) if out.isOpened(): print(f"Using codec: {codec}") break else: out.release() out = None except: continue if out is None or not out.isOpened(): print("No suitable codec found, copying original file") shutil.copy2(video_path, converted_path) return converted_path # Read and write frames while True: ret, frame = cap.read() if not ret: break out.write(frame) # Clean up cap.release() out.release() print(f"Video converted for Gradio: {converted_path}") return converted_path except Exception as e: print(f"Error converting video: {e}") # Fallback: just copy the original file try: output_dir = "video" os.makedirs(output_dir, exist_ok=True) base_name = os.path.splitext(os.path.basename(video_path))[0] fallback_path = os.path.join(output_dir, f"{base_name}_fallback.mp4") shutil.copy2(video_path, fallback_path) return fallback_path except: return video_path # Return original if all else fails def video_output_wrapper(func): """ Wrapper function that automatically converts video outputs to Gradio-compatible format. Use this to wrap any function that returns a video path. """ def wrapper(*args, **kwargs): # Call the original function result = func(*args, **kwargs) # If result is a video path, convert it for Gradio if result is not None and isinstance(result, str) and result.endswith(('.mp4', '.avi', '.mov', '.mkv')): print(f"Converting output video for Gradio: {result}") converted_path = convert_video_for_gradio(result) return converted_path return result return wrapper #@video_output_wrapper #def generate_image(*args, **kwargs): # """ # Example function that would return a video path. # Replace this with your actual video processing function. # """ # # This is just a placeholder - replace with your actual processing # return "video/processed_rgbfast.mp4" video_processor = VideoProcessor() @video_output_wrapper def process_video_wrapper(structure_video, prompt_video, prompt_negative, structure_option, prompt_style, num_frames, fps, seed): result = video_processor._process_single_video( video_file_path=structure_video, output_prefix="video/gradio_output", positive_prompt=prompt_video, negative_prompt=prompt_negative, preprocess_option=structure_option, flux_positive_prompt=prompt_style, num_frames=int(num_frames), fps=int(fps), seed=int(seed) ) return result if __name__ == "__main__": #hf_hub_download(repo_id="Comfy-Org/Lumina_Image_2.0_Repackaged", filename="split_files/vae/ae.safetensors",local_dir="temp") #os.system("mv temp/split_files/vae/ae.safetensors ComfyUI/models/vae/ae.safetensors") #hf_hub_download(repo_id="Comfy-Org/Wan_2.1_ComfyUI_repackaged", filename="split_files/vae/wan_2.1_vae.safetensors",local_dir="temp") #os.system("mv temp/split_files/vae/wan_2.1_vae.safetensors ComfyUI/models/vae/wan_2.1_vae.safetensors") #hf_hub_download(repo_id="openai/clip-vit-large-patch14", filename="model.safetensors",local_dir="temp") #os.system("mv temp/model.safetensors ComfyUI/models/clip/clip-vit-large-patch14.safetensors") #hf_hub_download(repo_id="comfyanonymous/flux_text_encoders", filename="t5xxl_fp8_e4m3fn_scaled.safetensors",local_dir="temp") #os.system("mv temp/t5xxl_fp8_e4m3fn_scaled.safetensors ComfyUI/models/clip/t5xxl_fp8_e4m3fn_scaled.safetensors") #hf_hub_download(repo_id="QuantStack/Wan2.2-Fun-A14B-Control-GGUF", filename="HighNoise/Wan2.2-Fun-A14B-Control_HighNoise-Q8_0.gguf",local_dir="temp") #os.system("mv temp/HighNoise/Wan2.2-Fun-A14B-Control_HighNoise-Q8_0.gguf ComfyUI/models/unet/Wan2.2-Fun-A14B-Control_HighNoise-Q8_0.gguf") #hf_hub_download(repo_id="QuantStack/Wan2.2-Fun-A14B-Control-GGUF", filename="LowNoise/Wan2.2-Fun-A14B-Control_LowNoise-Q8_0.gguf",local_dir="temp") #os.system("mv temp/LowNoise/Wan2.2-Fun-A14B-Control_LowNoise-Q8_0.gguf ComfyUI/models/unet/Wan2.2-Fun-A14B-Control_LowNoise-Q8_0.gguf") #hf_hub_download(repo_id="QuantStack/FLUX.1-Kontext-dev-GGUF", filename="flux1-kontext-dev-Q8_0.gguf",local_dir="temp") #os.system("mv temp/flux1-kontext-dev-Q8_0.gguf ComfyUI/models/unet/flux1-kontext-dev-Q8_0.gguf") # Start your Gradio app with gr.Blocks() as app: # Add a title gr.Markdown("# Video Style Shaping with Flux and WAN2.2") gr.Markdown("## Click the convert button if getting the not playable error") with gr.Row(): with gr.Column(): # Add an input prompt_style = gr.Textbox(label="Style Prompt for Flux", placeholder="Enter your prompt here...") prompt_video = gr.Textbox(label="Content Prompt for WAN Video", placeholder="Enter your prompt here...") # Add a `Row` to include the groups side by side with gr.Row(): # First group includes structure image and depth strength with gr.Group(): structure_video = gr.Video(label="Structure Video (quality: 720 x 1280, decent quality: 480 x 832)") convert_btn = gr.Button("Convert Video for Gradio", size="sm") structure_option = gr.Radio(label="Preprocessing Option", choices=["Intensity", "Canny", "None"], value="Intensity") # Advanced Settings Section (collapsible) with gr.Accordion("⚙️ Advanced Settings", open=False): with gr.Row(): with gr.Column(): # Video generation parameters num_frames = gr.Slider( label="Number of Frames", minimum=17, maximum=121, value=81, step=1, info="Number of frames to generate" ) fps = gr.Slider( label="FPS", minimum=8, maximum=30, value=16, step=1, info="Frames per second" ) seed = gr.Number( label="Seed", value=-1, precision=0, info="Random seed (-1 for random)" ) prompt_negative = gr.Textbox( label="Negative Prompt for WAN Video", value="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，CG, game, cartoon, anime, render, 渲染，游戏，卡通", lines=4 ) # The generate button generate_btn = gr.Button("Generate") with gr.Column(): # The output video output_video = gr.Video(label="Generated Video") # Convert video button (manual only to avoid infinite loops) convert_btn.click( fn=convert_video_for_gradio, inputs=[structure_video], outputs=[structure_video] ) # When clicking the button, it will trigger the `generate_image` function, with the respective inputs # and the output an image generate_btn.click( fn=process_video_wrapper, inputs=[ structure_video, prompt_video, prompt_negative, structure_option, prompt_style, num_frames, fps, seed, ], outputs=[output_video] ) app.launch(share=True)