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fal kling

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nofunstudio
2025-06-17 19:57:44 -06:00
parent 76083b4331
commit fa970a551a
2 changed files with 310 additions and 2 deletions
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__init__.py
+5 -2
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@@ -7,6 +7,7 @@ from .queue_counter_reset import DynamicQueueCounter
from .custom_name_selector import IndexedStringSelector
from .ksampler_fun import KSampler
from .fal_recraft_upscale import FalAPI_recraft_upscale
from .fal_kling_video import FalAPI_kling_video
WEB_DIRECTORY = "./js"
NODE_CLASS_MAPPINGS = {
@@ -17,7 +18,8 @@ NODE_CLASS_MAPPINGS = {
"DynamicQueueCounter": DynamicQueueCounter,
"IndexedStringSelector": IndexedStringSelector,
"Fun KSampler": KSampler,
"FalAPI_recraft_upscale": FalAPI_recraft_upscale
"FalAPI_recraft_upscale": FalAPI_recraft_upscale,
"FalAPI_kling_video": FalAPI_kling_video
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LayeredInfiniteZoom": "Layered Infinite Zoom",
@@ -27,7 +29,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"DynamicQueueCounter": "Dynamic Queue Counter",
"IndexedStringSelector": "Indexed String Selector",
"Fun KSampler": "Fun KSampler",
"FalAPI_recraft_upscale": "Fal API Recraft Upscale"
"FalAPI_recraft_upscale": "Fal API Recraft Upscale",
"FalAPI_kling_video": "Fal API Kling Video"
}
__all__ = [
fal_kling_video.py
+305
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@@ -0,0 +1,305 @@
import os
import time
import requests
import tempfile
import json
import numpy as np
import torch
from io import BytesIO
from PIL import Image
import fal_client
class FalAPI_kling_video:
@classmethod
def INPUT_TYPES(cls):
"""
A video generation node for `fal-ai/kling-video/v2.1/standard/image-to-video`.
Takes an image input, prompt, and generates a video using Kling Video API.
"""
return {
"required": {
"api_token": ("STRING", {
"multiline": False,
"default": "",
"display": "FAL API Token"
}),
"image": ("IMAGE", {
"display": "Source Image"
}),
"prompt": ("STRING", {
"multiline": True,
"default": "A cinematic video transformation",
"display": "Video Prompt"
}),
}
}
RETURN_TYPES = ("STRING", "STRING",)
RETURN_NAMES = ("video_path", "generation_info",)
FUNCTION = "generate_video"
CATEGORY = "FAL"
def __init__(self):
# Directory structure for saving generated videos
self.output_dir = "output/API/FAL/kling-video"
self.metadata_dir = os.path.join(self.output_dir, "metadata")
os.makedirs(self.output_dir, exist_ok=True)
os.makedirs(self.metadata_dir, exist_ok=True)
def get_next_number(self):
"""
Looks at existing .mp4/.mov files and picks the next integer
filename index (e.g. 001, 002, etc.).
"""
valid_exts = {".mp4", ".mov", ".avi"}
files = [f for f in os.listdir(self.output_dir)
if os.path.splitext(f)[1].lower() in valid_exts]
if not files:
return 1
numbers = []
for file_name in files:
base, _ = os.path.splitext(file_name)
try:
numbers.append(int(base))
except ValueError:
pass
if numbers:
return max(numbers) + 1
else:
return 1
def create_filename(self, number):
"""
Zero-padded filenames ending in .mp4 (e.g., '001.mp4').
"""
return f"{number:03d}.mp4"
def save_video_and_metadata(self, video_content, generation_info, number):
"""
Saves the generated video as .mp4, plus metadata as a .json.
"""
filename = self.create_filename(number)
filepath = os.path.join(self.output_dir, filename)
# Save video content
with open(filepath, 'wb') as f:
f.write(video_content)
# Create metadata filename (001_metadata.json)
metadata_filename = f"{number:03d}_metadata.json"
metadata_filepath = os.path.join(self.metadata_dir, metadata_filename)
# Write out metadata
with open(metadata_filepath, "w", encoding="utf-8") as f:
json.dump(generation_info, f, indent=4, ensure_ascii=False)
return filepath, metadata_filepath
def on_queue_update(self, update):
"""Handle queue updates and log messages"""
try:
if isinstance(update, fal_client.InProgress):
for log in update.logs:
print(f"[Kling Video] {log['message']}")
except Exception as e:
print(f"[Kling Video] Error in queue update: {e}")
def generate_video(self, api_token, image, prompt):
"""
Generate video using fal-ai/kling-video/v2.1/standard/image-to-video
"""
print(f"[Kling Video] Starting video generation process...")
# For errors, return empty strings
empty_path = ""
# Make sure we have an API token
if not api_token:
error_msg = "A FAL API token is required."
print(f"[Kling Video] Error: {error_msg}")
raise ValueError(error_msg)
# Validate inputs
if image is None:
error_msg = "Image input is required."
print(f"[Kling Video] Error: {error_msg}")
raise ValueError(error_msg)
if not prompt or prompt.strip() == "":
error_msg = "Prompt input is required."
print(f"[Kling Video] Error: {error_msg}")
raise ValueError(error_msg)
print(f"[Kling Video] Input image shape: {image.shape}")
print(f"[Kling Video] Prompt: {prompt}")
try:
# 1) Set up FAL client with API token
os.environ["FAL_KEY"] = api_token
print(f"[Kling Video] API token set, processing image...")
# Check if fal_client is working
try:
print(f"[Kling Video] Testing fal_client import: {fal_client.__version__ if hasattr(fal_client, '__version__') else 'version unknown'}")
except Exception as e:
print(f"[Kling Video] fal_client check failed: {str(e)}")
# 2) Convert ComfyUI image tensor to temporary file
try:
temp_file_path = self.tensor_to_tempfile(image)
print(f"[Kling Video] Created temp file: {temp_file_path}")
except Exception as e:
print(f"[Kling Video] Error creating temp file: {str(e)}")
raise
# 3) Upload image to FAL storage and get URL
try:
print(f"[Kling Video] Uploading image to FAL storage...")
image_url = fal_client.upload_file(temp_file_path)
print(f"[Kling Video] Image uploaded successfully: {image_url}")
except Exception as e:
print(f"[Kling Video] Error uploading file: {str(e)}")
# Clean up file before re-raising
try:
os.remove(temp_file_path)
except:
pass
raise
# 4) Build the input arguments
arguments = {
"prompt": prompt,
"image_url": image_url
}
print(f"[Kling Video] Starting video generation...")
# 5) Call fal_client.subscribe()
try:
result = fal_client.subscribe(
"fal-ai/kling-video/v2.1/standard/image-to-video",
arguments=arguments,
with_logs=True,
on_queue_update=self.on_queue_update,
)
print(f"[Kling Video] Video generation completed successfully")
except Exception as e:
print(f"[Kling Video] Error during video generation: {str(e)}")
# Clean up file before re-raising
try:
os.remove(temp_file_path)
except:
pass
raise
# 6) Clean up temporary file
os.remove(temp_file_path)
print(f"[Kling Video] Cleaned up temp file")
if not result or not result.get("video"):
raise ValueError("No valid video result from fal_client.subscribe().")
# 7) Get the generated video URL
video_data = result["video"]
video_url = video_data["url"]
# 8) Download the generated video
print(f"[Kling Video] Downloading video from: {video_url}")
resp = requests.get(video_url)
if resp.status_code != 200:
raise ConnectionError(f"Failed to download video. HTTP {resp.status_code}")
# 9) Save the video & metadata
number = self.get_next_number()
generation_info = {
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
"model": "fal-ai/kling-video/v2.1/standard/image-to-video",
"parameters": {
"prompt": prompt
},
"input_image_url": image_url,
"output_video_info": video_data,
"fal_result": result
}
video_path, metadata_path = self.save_video_and_metadata(resp.content, generation_info, number)
print(f"[Kling Video] Saved video -> {video_path}")
print(f"[Kling Video] Saved metadata -> {metadata_path}")
# 10) Return the video path & metadata JSON string
return (video_path, json.dumps(generation_info, indent=2))
except Exception as e:
# Return empty path and an error message in JSON
error_info = {
"error": f"Kling video generation failed: {str(e)}",
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
"model": "fal-ai/kling-video/v2.1/standard/image-to-video"
}
print(f"[Kling Video] Error: {str(e)}")
return (empty_path, json.dumps(error_info, indent=2))
def tensor_to_tempfile(self, tensor):
"""
Convert a ComfyUI IMAGE tensor to a PNG file
Return the file path (caller must delete).
"""
pil_img = self.tensor_to_pil(tensor)
fd, filename = tempfile.mkstemp(suffix=".png")
os.close(fd)
pil_img.save(filename, format="PNG")
return filename
def tensor_to_pil(self, tensor):
"""
Convert tensor to PIL Image: handle (B, H, W, C) or (C, H, W).
"""
print(f"[Kling Video] Converting tensor to PIL - input shape: {tensor.shape}")
if len(tensor.shape) == 4:
tensor = tensor[0] # remove batch dimension
print(f"[Kling Video] Removed batch dimension, new shape: {tensor.shape}")
arr = tensor.cpu().numpy()
print(f"[Kling Video] Converted to numpy array, shape: {arr.shape}")
# If shape is (C, H, W), transpose it
if arr.ndim == 3 and arr.shape[0] <= 4:
arr = np.transpose(arr, (1, 2, 0))
print(f"[Kling Video] Transposed array, new shape: {arr.shape}")
arr = (arr * 255).clip(0, 255).astype("uint8")
print(f"[Kling Video] Converted to uint8, shape: {arr.shape}")
pil_img = Image.fromarray(arr)
print(f"[Kling Video] Created PIL image: {pil_img.size} mode: {pil_img.mode}")
return pil_img
@classmethod
def IS_CHANGED(cls, **kwargs):
"""
Return NaN means no caching for this node.
"""
return float("NaN")
def interrupt(self):
"""
Interrupt method for consistency.
"""
print("[Kling Video] Interrupt called.")
# Register with ComfyUI
NODE_CLASS_MAPPINGS = {
"FalAPI_kling_video": FalAPI_kling_video
}
NODE_DISPLAY_NAME_MAPPINGS = {
"FalAPI_kling_video": "FAL Kling Video Generation"
}
__all__ = ["FalAPI_kling_video"]