shadow
@@ -10,6 +10,8 @@ For business cooperation, please contact email 389570357@qq.com
|
||||
|
||||
##### `最新`：
|
||||
|
||||
- 新增[fal.ai](https://fal.ai/dashboard)的视频生成:Kling、RunwayGen3、LumaDreamMachine，[工作流下载](./workflow/video-all-in-one-test-workflow.json)
|
||||
|
||||
- 新增 SimulateDevDesignDiscussions，需要安装[swarm](https://github.com/openai/swarm)和[Comfyui-ChatTTS](https://github.com/shadowcz007/Comfyui-ChatTTS)，[工作流下载](./workflow/swarm制作的播客节点workflow.json)
|
||||
|
||||
- 新增 SenseVoice
|
||||
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||||
+19
@@ -1455,4 +1455,23 @@ try:
|
||||
except Exception as e:
|
||||
logging.info('Whisper.available False' )
|
||||
|
||||
|
||||
try:
|
||||
from .nodes.FalVideo import VideoGenKlingNode,VideoGenLumaDreamMachineNode,VideoGenRunwayGen3Node,LoadVideoFromURL
|
||||
logging.info('FalVideo.available')
|
||||
# Update Node class mappings
|
||||
NODE_CLASS_MAPPINGS['VideoGenKlingNode']=VideoGenKlingNode
|
||||
NODE_CLASS_MAPPINGS['VideoGenRunwayGen3Node']=VideoGenRunwayGen3Node
|
||||
NODE_CLASS_MAPPINGS['VideoGenLumaDreamMachineNode']=VideoGenLumaDreamMachineNode
|
||||
NODE_CLASS_MAPPINGS['LoadVideoFromURL']=LoadVideoFromURL
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS["VideoGenKlingNode"]= "Kling Video Generation @fal"
|
||||
NODE_DISPLAY_NAME_MAPPINGS["VideoGenRunwayGen3Node"]= "Runway Gen3 Image-to-Video @fal"
|
||||
NODE_DISPLAY_NAME_MAPPINGS["VideoGenLumaDreamMachineNode"]= "Luma Dream Machine @fal"
|
||||
NODE_DISPLAY_NAME_MAPPINGS["LoadVideoFromURL"]= "Load Video from URL"
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logging.info('FalVideo.available False' )
|
||||
|
||||
logging.info('\033[93m -------------- \033[0m')
|
||||
|
||||
@@ -0,0 +1,332 @@
|
||||
# 修改自 https://github.com/gokayfem/ComfyUI-fal-API/blob/main/nodes/video_node.py
|
||||
# image-to-video all in one
|
||||
|
||||
import os,sys
|
||||
import torch
|
||||
from PIL import Image
|
||||
import tempfile
|
||||
import numpy as np
|
||||
import requests
|
||||
import cv2
|
||||
import subprocess
|
||||
import importlib.util
|
||||
python = sys.executable
|
||||
|
||||
def is_installed(package, package_overwrite=None,auto_install=True):
|
||||
is_has=False
|
||||
try:
|
||||
spec = importlib.util.find_spec(package)
|
||||
is_has=spec is not None
|
||||
except ModuleNotFoundError:
|
||||
pass
|
||||
|
||||
package = package_overwrite or package
|
||||
|
||||
if spec is None:
|
||||
if auto_install==True:
|
||||
print(f"Installing {package}...")
|
||||
# 清华源 -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
command = f'"{python}" -m pip install {package}'
|
||||
|
||||
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ)
|
||||
|
||||
is_has=True
|
||||
|
||||
if result.returncode != 0:
|
||||
print(f"Couldn't install\nCommand: {command}\nError code: {result.returncode}")
|
||||
is_has=False
|
||||
else:
|
||||
print(package+'## OK')
|
||||
|
||||
return is_has
|
||||
|
||||
|
||||
try:
|
||||
if is_installed('fal_client','fal-client')==True:
|
||||
from fal_client import submit, upload_file
|
||||
except:
|
||||
print("#install fal-client error")
|
||||
|
||||
|
||||
def upload_image(image):
|
||||
try:
|
||||
# Convert the image tensor to a numpy array
|
||||
if isinstance(image, torch.Tensor):
|
||||
image_np = image.cpu().numpy()
|
||||
else:
|
||||
image_np = np.array(image)
|
||||
|
||||
# Ensure the image is in the correct format (H, W, C)
|
||||
if image_np.ndim == 4:
|
||||
image_np = image_np.squeeze(0) # Remove batch dimension if present
|
||||
if image_np.ndim == 2:
|
||||
image_np = np.stack([image_np] * 3, axis=-1) # Convert grayscale to RGB
|
||||
elif image_np.shape[0] == 3:
|
||||
image_np = np.transpose(image_np, (1, 2, 0)) # Change from (C, H, W) to (H, W, C)
|
||||
|
||||
# Normalize the image data to 0-255 range
|
||||
if image_np.dtype == np.float32 or image_np.dtype == np.float64:
|
||||
image_np = (image_np * 255).astype(np.uint8)
|
||||
|
||||
# Convert to PIL Image
|
||||
pil_image = Image.fromarray(image_np)
|
||||
|
||||
# Save the image to a temporary file
|
||||
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_file:
|
||||
pil_image.save(temp_file, format="PNG")
|
||||
temp_file_path = temp_file.name
|
||||
|
||||
# Upload the temporary file
|
||||
image_url = upload_file(temp_file_path)
|
||||
return image_url
|
||||
except Exception as e:
|
||||
print(f"Error uploading image: {str(e)}")
|
||||
return None
|
||||
finally:
|
||||
# Clean up the temporary file
|
||||
if 'temp_file_path' in locals():
|
||||
os.unlink(temp_file_path)
|
||||
|
||||
|
||||
class VideoGenKlingNode:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"prompt": ("STRING", {"default": "", "multiline": True}),
|
||||
"duration": (["5", "10"], {"default": "5"}),
|
||||
"aspect_ratio": (["16:9", "9:16", "1:1"], {"default": "16:9"}),
|
||||
"mode": (["standard", "pro"], {"default": "standard"}),
|
||||
"fal_key":("STRING", {"forceInput": True,}),
|
||||
},
|
||||
"optional": {
|
||||
"image": ("IMAGE",),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING",)
|
||||
FUNCTION = "generate_video"
|
||||
CATEGORY = "♾️Mixlab/Video"
|
||||
|
||||
def generate_video(self, prompt, duration, aspect_ratio,mode,fal_key, image=None):
|
||||
arguments = {
|
||||
"prompt": prompt,
|
||||
"duration": duration,
|
||||
"aspect_ratio": aspect_ratio,
|
||||
}
|
||||
|
||||
os.environ["FAL_KEY"] = fal_key
|
||||
|
||||
api_url="fal-ai/kling-video/v1/"+mode
|
||||
|
||||
try:
|
||||
if image is not None:
|
||||
image_url = upload_image(image)
|
||||
if image_url:
|
||||
arguments["image_url"] = image_url
|
||||
handler = submit(api_url+"/image-to-video", arguments=arguments)
|
||||
else:
|
||||
return ("Error: Unable to upload image.",)
|
||||
else:
|
||||
handler = submit(api_url+"/text-to-video", arguments=arguments)
|
||||
|
||||
result = handler.get()
|
||||
video_url = result["video"]["url"]
|
||||
return (video_url,)
|
||||
except Exception as e:
|
||||
print(f"Error generating video: {str(e)}")
|
||||
return ("Error: Unable to generate video.",)
|
||||
|
||||
|
||||
class VideoGenRunwayGen3Node:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"prompt": ("STRING", {"default": "", "multiline": True}),
|
||||
"image": ("IMAGE",),
|
||||
"duration": (["5", "10"], {"default": "5"}),
|
||||
"aspect_ratio": (["16:9", "9:16"], {"default": "16:9"}),
|
||||
"fal_key":("STRING", {"forceInput": True,}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING",)
|
||||
FUNCTION = "generate_video"
|
||||
CATEGORY = "♾️Mixlab/Video"
|
||||
|
||||
def generate_video(self, prompt, image, duration,aspect_ratio,fal_key):
|
||||
os.environ["FAL_KEY"] = fal_key
|
||||
try:
|
||||
image_url = upload_image(image)
|
||||
if not image_url:
|
||||
return ("Error: Unable to upload image.",)
|
||||
|
||||
arguments = {
|
||||
"prompt": prompt,
|
||||
"image_url": image_url,
|
||||
"duration": duration,
|
||||
"ratio":aspect_ratio
|
||||
}
|
||||
|
||||
handler = submit("fal-ai/runway-gen3/turbo/image-to-video", arguments=arguments)
|
||||
result = handler.get()
|
||||
video_url = result["video"]["url"]
|
||||
return (video_url,)
|
||||
except Exception as e:
|
||||
print(f"Error generating video: {str(e)}")
|
||||
return ("Error: Unable to generate video.",)
|
||||
|
||||
class VideoGenLumaDreamMachineNode:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"prompt": ("STRING", {"default": "", "multiline": True}),
|
||||
"aspect_ratio": (["16:9", "9:16", "4:3", "3:4", "21:9", "9:21"], {"default": "16:9"}),
|
||||
"fal_key":("STRING", {"forceInput": True,}),
|
||||
},
|
||||
"optional": {
|
||||
"image": ("IMAGE",),
|
||||
"loop": ("BOOLEAN", {"default": True}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING",)
|
||||
FUNCTION = "generate_video"
|
||||
CATEGORY = "♾️Mixlab/Video"
|
||||
|
||||
def generate_video(self, prompt, aspect_ratio,fal_key, image=None, loop=True):
|
||||
|
||||
os.environ["FAL_KEY"] = fal_key
|
||||
|
||||
arguments = {
|
||||
"prompt": prompt,
|
||||
"aspect_ratio": aspect_ratio,
|
||||
"loop": loop,
|
||||
}
|
||||
|
||||
try:
|
||||
if image is not None:
|
||||
image_url = upload_image(image)
|
||||
if not image_url:
|
||||
return ("Error: Unable to upload image.",)
|
||||
arguments["image_url"] = image_url
|
||||
endpoint = "fal-ai/luma-dream-machine/image-to-video"
|
||||
else:
|
||||
endpoint = "fal-ai/luma-dream-machine"
|
||||
|
||||
handler = submit(endpoint, arguments=arguments)
|
||||
result = handler.get()
|
||||
video_url = result["video"]["url"]
|
||||
return (video_url,)
|
||||
except Exception as e:
|
||||
print(f"Error generating video: {str(e)}")
|
||||
return ("Error: Unable to generate video.",)
|
||||
|
||||
class LoadVideoFromURL:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"url": ("STRING", {"default": "https://example.com/video.mp4"}),
|
||||
"force_rate": ("INT", {"default": 0, "min": 0, "max": 60, "step": 1}),
|
||||
"force_size": (["Disabled", "Custom Height", "Custom Width", "Custom", "256x?", "?x256", "256x256", "512x?", "?x512", "512x512"],),
|
||||
"custom_width": ("INT", {"default": 512, "min": 0, "max": 8192, "step": 8}),
|
||||
"custom_height": ("INT", {"default": 512, "min": 0, "max": 8192, "step": 8}),
|
||||
"frame_load_cap": ("INT", {"default": 0, "min": 0, "max": 1000000, "step": 1}),
|
||||
"skip_first_frames": ("INT", {"default": 0, "min": 0, "max": 1000000, "step": 1}),
|
||||
"select_every_nth": ("INT", {"default": 1, "min": 1, "max": 1000000, "step": 1}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE", "INT", "VHS_VIDEOINFO")
|
||||
RETURN_NAMES = ("frames", "frame_count", "video_info")
|
||||
FUNCTION = "load_video_from_url"
|
||||
CATEGORY = "♾️Mixlab/Video"
|
||||
|
||||
def load_video_from_url(self, url, force_rate, force_size, custom_width, custom_height, frame_load_cap, skip_first_frames, select_every_nth):
|
||||
# Download the video to a temporary file
|
||||
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_file:
|
||||
response = requests.get(url, stream=True)
|
||||
for chunk in response.iter_content(chunk_size=8192):
|
||||
temp_file.write(chunk)
|
||||
temp_file_path = temp_file.name
|
||||
|
||||
# Load the video using OpenCV
|
||||
cap = cv2.VideoCapture(temp_file_path)
|
||||
|
||||
# Get video properties
|
||||
fps = cap.get(cv2.CAP_PROP_FPS)
|
||||
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
duration = total_frames / fps
|
||||
|
||||
# Calculate target size
|
||||
if force_size != "Disabled":
|
||||
if force_size == "Custom Width":
|
||||
new_height = int(height * (custom_width / width))
|
||||
new_width = custom_width
|
||||
elif force_size == "Custom Height":
|
||||
new_width = int(width * (custom_height / height))
|
||||
new_height = custom_height
|
||||
elif force_size == "Custom":
|
||||
new_width, new_height = custom_width, custom_height
|
||||
else:
|
||||
target_width, target_height = map(int, force_size.replace("?", "0").split("x"))
|
||||
if target_width == 0:
|
||||
new_width = int(width * (target_height / height))
|
||||
new_height = target_height
|
||||
else:
|
||||
new_height = int(height * (target_width / width))
|
||||
new_width = target_width
|
||||
else:
|
||||
new_width, new_height = width, height
|
||||
|
||||
frames = []
|
||||
frame_count = 0
|
||||
|
||||
for i in range(total_frames):
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
break
|
||||
|
||||
if i < skip_first_frames:
|
||||
continue
|
||||
|
||||
if (i - skip_first_frames) % select_every_nth != 0:
|
||||
continue
|
||||
|
||||
if force_size != "Disabled":
|
||||
frame = cv2.resize(frame, (new_width, new_height))
|
||||
|
||||
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||||
frame = torch.from_numpy(frame).float() / 255.0
|
||||
frames.append(frame)
|
||||
|
||||
frame_count += 1
|
||||
|
||||
if frame_load_cap > 0 and frame_count >= frame_load_cap:
|
||||
break
|
||||
|
||||
cap.release()
|
||||
os.unlink(temp_file_path)
|
||||
|
||||
frames = torch.stack(frames)
|
||||
|
||||
video_info = {
|
||||
"source_fps": fps,
|
||||
"source_frame_count": total_frames,
|
||||
"source_duration": duration,
|
||||
"source_width": width,
|
||||
"source_height": height,
|
||||
"loaded_fps": fps if force_rate == 0 else force_rate,
|
||||
"loaded_frame_count": frame_count,
|
||||
"loaded_duration": frame_count / (fps if force_rate == 0 else force_rate),
|
||||
"loaded_width": new_width,
|
||||
"loaded_height": new_height,
|
||||
}
|
||||
|
||||
return (frames, frame_count, video_info)
|
||||
|
||||
+1
-1
@@ -1,7 +1,7 @@
|
||||
[project]
|
||||
name = "comfyui-mixlab-nodes"
|
||||
description = "3D, ScreenShareNode & FloatingVideoNode, SpeechRecognition & SpeechSynthesis, GPT, LoadImagesFromLocal, Layers, Other Nodes, ..."
|
||||
version = "0.45.0"
|
||||
version = "0.46.0"
|
||||
license = "MIT"
|
||||
dependencies = ["numpy", "pyOpenSSL", "watchdog", "opencv-python-headless", "matplotlib", "openai", "simple-lama-inpainting", "clip-interrogator==0.6.0", "transformers>=4.36.0", "lark-parser", "imageio-ffmpeg", "rembg[gpu]", "omegaconf==2.3.0", "Pillow>=9.5.0", "einops==0.7.0", "trimesh>=4.0.5", "huggingface-hub", "scikit-image"]
|
||||
|
||||
|
||||
@@ -3,7 +3,7 @@ import { app } from '../../../scripts/app.js'
|
||||
const repoOwner = 'shadowcz007' // 替换为仓库的所有者
|
||||
const repoName = 'comfyui-mixlab-nodes' // 替换为仓库的名称
|
||||
|
||||
const version = 'v0.45.0'
|
||||
const version = 'v0.46.0'
|
||||
|
||||
fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
|
||||
.then(response => response.json())
|
||||
|
||||
@@ -0,0 +1,775 @@
|
||||
{
|
||||
"last_node_id": 18,
|
||||
"last_link_id": 15,
|
||||
"nodes": [
|
||||
{
|
||||
"id": 6,
|
||||
"type": "LoadImage",
|
||||
"pos": [
|
||||
-32,
|
||||
79
|
||||
],
|
||||
"size": {
|
||||
"0": 315,
|
||||
"1": 314
|
||||
},
|
||||
"flags": {},
|
||||
"order": 0,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
1,
|
||||
2,
|
||||
3
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
},
|
||||
{
|
||||
"name": "MASK",
|
||||
"type": "MASK",
|
||||
"links": null,
|
||||
"shape": 3
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LoadImage"
|
||||
},
|
||||
"widgets_values": [
|
||||
"1.jpg",
|
||||
"image"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 7,
|
||||
"type": "KeyInput",
|
||||
"pos": [
|
||||
-34,
|
||||
585
|
||||
],
|
||||
"size": {
|
||||
"0": 315,
|
||||
"1": 94
|
||||
},
|
||||
"flags": {},
|
||||
"order": 1,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "key",
|
||||
"type": "STRING",
|
||||
"links": [
|
||||
4,
|
||||
5,
|
||||
6
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "KeyInput"
|
||||
},
|
||||
"widgets_values": [
|
||||
null,
|
||||
null
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 10,
|
||||
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"groups": [],
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"config": {},
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"extra": {
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"ds": {
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"scale": 0.7247295000000012,
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"offset": [
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}
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},
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"version": 0.4
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}
|
||||
Reference in New Issue
Block a user