8.3 KiB
8.3 KiB
Documentation
- Class name: LoadVideoFromUrl
- Category: Art Venture/Loaders
- Output node: False
LoadVideoFromUrl节点旨在从URL加载视频内容，并将其转换为适合进一步处理或分析的格式。它支持处理各种URL格式，包括视频文件的直接链接，并将其转换为系统内使用的标准化视频格式。
Input types
Required
- video
- "video"参数是一个字符串，表示要加载的视频的URL。它在确定视频获取和处理的来源方面起着关键作用。
- Comfy dtype: STRING
- Python dtype: str
- force_rate
- 指定加载视频的目标帧率，允许控制播放速度或帧率标准化。
- Comfy dtype: INT
- Python dtype: Optional[float]
- force_size
- 定义加载视频的目标分辨率，实现调整大小或纵横比。
- Comfy dtype: COMBO[STRING]
- Python dtype: Optional[str]
- custom_width
- 为加载的视频设置自定义宽度，覆盖默认或指定的'force_size'宽度。
- Comfy dtype: INT
- Python dtype: Optional[int]
- custom_height
- 为加载的视频设置自定义高度，覆盖默认或指定的'force_size'高度。
- Comfy dtype: INT
- Python dtype: Optional[int]
- frame_load_cap
- 限制从视频中加载的帧数，适用于处理长视频或采样。
- Comfy dtype: INT
- Python dtype: Optional[int]
- skip_first_frames
- 跳过视频开头指定数量的帧，用于跳过不需要的内容或片头。
- Comfy dtype: INT
- Python dtype: Optional[int]
- select_every_nth
- 从视频中加载每第n帧，允许降低帧率或选择性帧分析。
- Comfy dtype: INT
- Python dtype: Optional[int]
Optional
- meta_batch
- 启用加载视频的元数据批处理，优化视频元数据至关重要的场景。
- Comfy dtype: VHS_BatchManager
- Python dtype: Optional[bool]
Output types
- frames
- 表示加载视频的张量列表。
- Comfy dtype: IMAGE
- Python dtype: List[torch.Tensor]
- frame_count
- 包含每个加载视频的帧数的列表。
- Comfy dtype: INT
- Python dtype: List[int]
- has_video
- 指示是否成功加载任何视频的布尔值。
- Comfy dtype: BOOLEAN
- Python dtype: bool
Usage tips
- Infra type:
CPU - Common nodes: unknown
Source code
class UtilLoadVideoFromUrl(LoadVideoPath):
@classmethod
def INPUT_TYPES(s):
inputs = LoadVideoPath.INPUT_TYPES()
inputs["required"]["video"] = ("STRING", {"default": "", "multiline": True, "dynamicPrompts": False})
return inputs
CATEGORY = "Art Venture/Loaders"
FUNCTION = "load"
RETURN_TYPES = ("IMAGE", "INT", "BOOLEAN")
RETURN_NAMES = ("frames", "frame_count", "has_video")
OUTPUT_IS_LIST = (True, True, False)
def load_gif(
self,
gif_path: str,
force_rate: int,
force_size: str,
skip_first_frames: int,
frame_load_cap: int,
select_every_nth: int,
):
from PIL import Image, ImageSequence
image = Image.open(gif_path)
frames = []
total_frames_evaluated = -1
if force_rate != 0:
print(f"Force rate is not supported for gifs/webps")
if frame_load_cap == 0:
frame_load_cap = 999999999
for i, frame in enumerate(ImageSequence.Iterator(image)):
if i < skip_first_frames:
continue
elif i >= skip_first_frames + frame_load_cap:
break
else:
total_frames_evaluated += 1
if total_frames_evaluated % select_every_nth == 0:
frames.append(pil2tensor(frame.copy().convert("RGB")))
images = torch.cat(frames, dim=0)
if force_size != "Disabled":
height = images.shape[1]
width = images.shape[2]
new_size = target_size(width, height, force_size)
if new_size[0] != width or new_size[1] != height:
s = images.movedim(-1, 1)
s = common_upscale(s, new_size[0], new_size[1], "lanczos", "disabled")
images = s.movedim(1, -1)
return (images, len(frames))
def load_url(self, video: str, **kwargs):
url = video.strip('"')
if url == "":
return (None, 0)
if os.path.isfile(url):
pass
elif url.startswith("file://"):
url = url[7:]
url = os.path.abspath(url)
if not os.path.isfile(url):
raise Exception(f"File {url} does not exist")
if url.startswith(input_dir):
video = url[len(input_dir) + 1 :] + " [input]"
elif url.startswith(output_dir):
video = url[len(output_dir) + 1 :] + " [output]"
elif url.startswith(temp_dir):
video = url[len(temp_dir) + 1 :] + " [temp]"
else:
# move file to temp_dir
import shutil
tempdir = os.path.join(temp_dir, "video")
if not os.path.exists(tempdir):
os.makedirs(tempfile, exist_ok=True)
filename = os.path.basename(url)
filepath = os.path.join(tempdir, filename)
i = 1
split = os.path.splitext(filename)
while os.path.exists(filepath):
filename = f"{split[0]} ({i}){split[1]}"
filepath = os.path.join(tempdir, filename)
i += 1
shutil.copy(url, filepath)
video = "video/" + filename + " [temp]"
elif url.startswith("http://") or url.startswith("https://"):
from torch.hub import download_url_to_file
from urllib.parse import urlparse
parts = urlparse(url)
filename = os.path.basename(parts.path)
tempfile = os.path.join(temp_dir, "video")
if not os.path.exists(tempfile):
os.makedirs(tempfile, exist_ok=True)
tempfile = os.path.join(tempfile, filename)
print(f'Downloading: "{url}" to {tempfile}\n')
download_url_to_file(url, tempfile, progress=True)
video = "video/" + filename + " [temp]"
elif url.startswith("/view?"):
from urllib.parse import parse_qs
qs = parse_qs(url[6:])
filename = qs.get("name", qs.get("filename", None))
if filename is None:
raise Exception(f"Invalid url: {url}")
filename = filename[0]
subfolder = qs.get("subfolder", None)
if subfolder is not None:
filename = os.path.join(subfolder[0], filename)
dirtype = qs.get("type", ["input"])
video = f"{filename} [{dirtype[0]}]"
else:
raise Exception(f"Invalid url: {url}")
if ".gif [" in video.lower() or ".webp [" in video.lower():
gif_path = folder_paths.get_annotated_filepath(video.strip('"'))
res = self.load_gif(gif_path, **kwargs)
else:
res = self.load_video(video=video, **kwargs)
return res
def load(self, video: str, **kwargs):
urls = video.strip().split("\n")
videos = []
frame_counts = []
for url in urls:
images, frame_count = self.load_url(url, **kwargs)
if images is not None and frame_count > 0:
videos.append(images)
frame_counts.append(frame_count)
has_video = len(videos) > 0
if not has_video:
image = torch.zeros((1, 64, 64, 3), dtype=torch.float32, device="cpu")
videos.append(image)
frame_counts.append(1)
return (videos, frame_counts, has_video)
@classmethod
def IS_CHANGED(s, video: str, **kwargs):
return video
@classmethod
def VALIDATE_INPUTS(s, **kwargs):
return True