305 lines
11 KiB
Python
305 lines
11 KiB
Python
import os
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import time
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import requests
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import tempfile
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import json
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import numpy as np
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import torch
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from io import BytesIO
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from PIL import Image
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import fal_client
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class FalAPI_kling_video:
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@classmethod
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def INPUT_TYPES(cls):
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"""
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A video generation node for `fal-ai/kling-video/v2.1/standard/image-to-video`.
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Takes an image input, prompt, and generates a video using Kling Video API.
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"""
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return {
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"required": {
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"api_token": ("STRING", {
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"multiline": False,
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"default": "",
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"display": "FAL API Token"
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}),
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"image": ("IMAGE", {
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"display": "Source Image"
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}),
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"prompt": ("STRING", {
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"multiline": True,
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"default": "A cinematic video transformation",
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"display": "Video Prompt"
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}),
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}
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}
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RETURN_TYPES = ("STRING", "STRING",)
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RETURN_NAMES = ("video_path", "generation_info",)
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FUNCTION = "generate_video"
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CATEGORY = "FAL"
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def __init__(self):
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# Directory structure for saving generated videos
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self.output_dir = "output/API/FAL/kling-video"
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self.metadata_dir = os.path.join(self.output_dir, "metadata")
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os.makedirs(self.output_dir, exist_ok=True)
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os.makedirs(self.metadata_dir, exist_ok=True)
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def get_next_number(self):
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"""
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Looks at existing .mp4/.mov files and picks the next integer
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filename index (e.g. 001, 002, etc.).
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"""
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valid_exts = {".mp4", ".mov", ".avi"}
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files = [f for f in os.listdir(self.output_dir)
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if os.path.splitext(f)[1].lower() in valid_exts]
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if not files:
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return 1
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numbers = []
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for file_name in files:
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base, _ = os.path.splitext(file_name)
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try:
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numbers.append(int(base))
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except ValueError:
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pass
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if numbers:
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return max(numbers) + 1
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else:
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return 1
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def create_filename(self, number):
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"""
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Zero-padded filenames ending in .mp4 (e.g., '001.mp4').
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"""
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return f"{number:03d}.mp4"
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def save_video_and_metadata(self, video_content, generation_info, number):
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"""
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Saves the generated video as .mp4, plus metadata as a .json.
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"""
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filename = self.create_filename(number)
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filepath = os.path.join(self.output_dir, filename)
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# Save video content
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with open(filepath, 'wb') as f:
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f.write(video_content)
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# Create metadata filename (001_metadata.json)
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metadata_filename = f"{number:03d}_metadata.json"
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metadata_filepath = os.path.join(self.metadata_dir, metadata_filename)
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# Write out metadata
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with open(metadata_filepath, "w", encoding="utf-8") as f:
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json.dump(generation_info, f, indent=4, ensure_ascii=False)
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return filepath, metadata_filepath
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def on_queue_update(self, update):
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"""Handle queue updates and log messages"""
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try:
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if isinstance(update, fal_client.InProgress):
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for log in update.logs:
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print(f"[Kling Video] {log['message']}")
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except Exception as e:
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print(f"[Kling Video] Error in queue update: {e}")
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def generate_video(self, api_token, image, prompt):
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"""
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Generate video using fal-ai/kling-video/v2.1/standard/image-to-video
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"""
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print(f"[Kling Video] Starting video generation process...")
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# For errors, return empty strings
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empty_path = ""
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# Make sure we have an API token
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if not api_token:
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error_msg = "A FAL API token is required."
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print(f"[Kling Video] Error: {error_msg}")
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raise ValueError(error_msg)
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# Validate inputs
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if image is None:
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error_msg = "Image input is required."
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print(f"[Kling Video] Error: {error_msg}")
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raise ValueError(error_msg)
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if not prompt or prompt.strip() == "":
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error_msg = "Prompt input is required."
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print(f"[Kling Video] Error: {error_msg}")
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raise ValueError(error_msg)
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print(f"[Kling Video] Input image shape: {image.shape}")
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print(f"[Kling Video] Prompt: {prompt}")
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try:
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# 1) Set up FAL client with API token
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os.environ["FAL_KEY"] = api_token
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print(f"[Kling Video] API token set, processing image...")
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# Check if fal_client is working
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try:
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print(f"[Kling Video] Testing fal_client import: {fal_client.__version__ if hasattr(fal_client, '__version__') else 'version unknown'}")
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except Exception as e:
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print(f"[Kling Video] fal_client check failed: {str(e)}")
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# 2) Convert ComfyUI image tensor to temporary file
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try:
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temp_file_path = self.tensor_to_tempfile(image)
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print(f"[Kling Video] Created temp file: {temp_file_path}")
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except Exception as e:
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print(f"[Kling Video] Error creating temp file: {str(e)}")
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raise
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# 3) Upload image to FAL storage and get URL
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try:
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print(f"[Kling Video] Uploading image to FAL storage...")
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image_url = fal_client.upload_file(temp_file_path)
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print(f"[Kling Video] Image uploaded successfully: {image_url}")
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except Exception as e:
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print(f"[Kling Video] Error uploading file: {str(e)}")
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# Clean up file before re-raising
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try:
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os.remove(temp_file_path)
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except:
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pass
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raise
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# 4) Build the input arguments
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arguments = {
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"prompt": prompt,
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"image_url": image_url
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}
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print(f"[Kling Video] Starting video generation...")
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# 5) Call fal_client.subscribe()
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try:
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result = fal_client.subscribe(
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"fal-ai/kling-video/v2.1/standard/image-to-video",
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arguments=arguments,
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with_logs=True,
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on_queue_update=self.on_queue_update,
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)
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print(f"[Kling Video] Video generation completed successfully")
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except Exception as e:
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print(f"[Kling Video] Error during video generation: {str(e)}")
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# Clean up file before re-raising
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try:
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os.remove(temp_file_path)
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except:
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pass
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raise
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# 6) Clean up temporary file
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os.remove(temp_file_path)
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print(f"[Kling Video] Cleaned up temp file")
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if not result or not result.get("video"):
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raise ValueError("No valid video result from fal_client.subscribe().")
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# 7) Get the generated video URL
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video_data = result["video"]
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video_url = video_data["url"]
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# 8) Download the generated video
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print(f"[Kling Video] Downloading video from: {video_url}")
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resp = requests.get(video_url)
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if resp.status_code != 200:
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raise ConnectionError(f"Failed to download video. HTTP {resp.status_code}")
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# 9) Save the video & metadata
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number = self.get_next_number()
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generation_info = {
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"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
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"model": "fal-ai/kling-video/v2.1/standard/image-to-video",
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"parameters": {
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"prompt": prompt
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},
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"input_image_url": image_url,
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"output_video_info": video_data,
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"fal_result": result
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}
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video_path, metadata_path = self.save_video_and_metadata(resp.content, generation_info, number)
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print(f"[Kling Video] Saved video -> {video_path}")
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print(f"[Kling Video] Saved metadata -> {metadata_path}")
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# 10) Return the video path & metadata JSON string
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return (video_path, json.dumps(generation_info, indent=2))
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except Exception as e:
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# Return empty path and an error message in JSON
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error_info = {
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"error": f"Kling video generation failed: {str(e)}",
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"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
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"model": "fal-ai/kling-video/v2.1/standard/image-to-video"
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}
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print(f"[Kling Video] Error: {str(e)}")
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return (empty_path, json.dumps(error_info, indent=2))
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def tensor_to_tempfile(self, tensor):
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"""
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Convert a ComfyUI IMAGE tensor to a PNG file
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Return the file path (caller must delete).
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"""
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pil_img = self.tensor_to_pil(tensor)
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fd, filename = tempfile.mkstemp(suffix=".png")
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os.close(fd)
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pil_img.save(filename, format="PNG")
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return filename
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def tensor_to_pil(self, tensor):
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"""
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Convert tensor to PIL Image: handle (B, H, W, C) or (C, H, W).
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"""
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print(f"[Kling Video] Converting tensor to PIL - input shape: {tensor.shape}")
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if len(tensor.shape) == 4:
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tensor = tensor[0] # remove batch dimension
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print(f"[Kling Video] Removed batch dimension, new shape: {tensor.shape}")
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arr = tensor.cpu().numpy()
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print(f"[Kling Video] Converted to numpy array, shape: {arr.shape}")
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# If shape is (C, H, W), transpose it
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if arr.ndim == 3 and arr.shape[0] <= 4:
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arr = np.transpose(arr, (1, 2, 0))
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print(f"[Kling Video] Transposed array, new shape: {arr.shape}")
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arr = (arr * 255).clip(0, 255).astype("uint8")
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print(f"[Kling Video] Converted to uint8, shape: {arr.shape}")
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pil_img = Image.fromarray(arr)
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print(f"[Kling Video] Created PIL image: {pil_img.size} mode: {pil_img.mode}")
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return pil_img
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@classmethod
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def IS_CHANGED(cls, **kwargs):
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"""
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Return NaN means no caching for this node.
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"""
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return float("NaN")
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def interrupt(self):
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"""
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Interrupt method for consistency.
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"""
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print("[Kling Video] Interrupt called.")
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# Register with ComfyUI
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NODE_CLASS_MAPPINGS = {
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"FalAPI_kling_video": FalAPI_kling_video
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"FalAPI_kling_video": "FAL Kling Video Generation"
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}
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__all__ = ["FalAPI_kling_video"] |