feat: 新增 K3 动作控制节点，移除启动更新弹框通知
- 新增 K3MotionControl / K3MotionVideoCheck 节点并注册 - K3_video: 生成音频参数改为 metadata.sound - r2_uploader: 新增 upload_image 工具函数 - 移除启动时更新检查弹框
This commit is contained in:
Jony
+5
-10
@@ -9,20 +9,11 @@ Comfyui_o1key - ComfyUI 自定义节点集合
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└── __init__.py # 节点注册入口
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"""
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# 检查更新（仅在启动时检查一次）
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try:
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from .utils.update_checker import check_for_updates, notify_new_version
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if check_for_updates():
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notify_new_version()
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except Exception:
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# 静默失败，不影响插件加载
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pass
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import ssl
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from .nodes import NanoBananaPro, BatchNanoBananaPro, GoogleGemini, LoadFile, ImageStitchPro, SaveCleanImage, BatchCleanMetadata, VideoPreview, GoogleVeo, FluxImageEdit, UniversalLLMChat, KlingVideo, KlingFirstLastFrame, KlingMotionControlTest, AspectRatioPreset, MultiResPreview, BatchImagesO1key, Seedance, SeedanceMultiModal, StreamPreview, DoubaoImage, O1keyGPTImage, KVideo
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from .nodes import K3Video, K3VideoFirstLast
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from .nodes import K3Video, K3VideoFirstLast, K3MotionControl, K3MotionVideoCheck
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# 报错弹框友好文案（不修改原节点代码，仅在外层统一处理）
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_MSG_TIMEOUT = "API 请求超时，请稍后重试或检查网络。"
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@@ -84,6 +75,8 @@ NODE_CLASS_MAPPINGS = {
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"KVideo": KVideo,
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"K3Video": K3Video,
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"K3VideoFirstLast": K3VideoFirstLast,
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"K3MotionControl": K3MotionControl,
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"K3MotionVideoCheck": K3MotionVideoCheck,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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@@ -112,6 +105,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"KVideo": "K26 图生视频",
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"K3Video": "K3 图生视频 自研",
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"K3VideoFirstLast": "首尾帧 K3 自研",
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"K3MotionControl": "动作控制 K3 自研",
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"K3MotionVideoCheck": "视频时长检测 K3",
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}
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WEB_DIRECTORY = "./web"
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@@ -0,0 +1,387 @@
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"""
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K3 动作控制 自研节点
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用参考视频驱动参考图中人物动作，生成视频。
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视频通过 R2 上传后传 URL，图片转 base64 直传。
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"""
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import asyncio
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import io
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import json
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import os
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import re
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import struct
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import aiohttp
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from ..utils.config import get_api_key_or_raise, get_api_base_url
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from ..utils.r2_uploader import upload_video, upload_image
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from ..utils.image_utils import tensor_to_pil
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try:
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from comfy_api.latest import InputImpl
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import folder_paths
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_FOLDER_PATHS_OK = True
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except Exception:
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_FOLDER_PATHS_OK = False
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# ── 常量 ──────────────────────────────────────────────────────────────────────
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_ENDPOINT_CREATE = "/kling/v1/videos/motion-control"
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_ENDPOINT_STATUS = "/kling/v1/videos/motion-control/{task_id}"
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_POLL_INIT = 5
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_POLL_MAX = 15
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# ── 工具函数 ───────────────────────────────────────────────────────────────────
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def _get_video_dir() -> str:
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if _FOLDER_PATHS_OK:
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base = folder_paths.get_output_directory()
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else:
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plugin = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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base = os.path.join(os.path.dirname(os.path.dirname(plugin)), "output")
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d = os.path.join(base, "video")
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os.makedirs(d, exist_ok=True)
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return d
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def _next_counter(directory: str, prefix: str) -> int:
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pattern = re.compile(rf"^{re.escape(prefix)}_(\d+)")
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max_n = 0
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if os.path.exists(directory):
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for f in os.listdir(directory):
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m = pattern.match(f)
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if m:
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max_n = max(max_n, int(m.group(1)))
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return max_n + 1
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# ── 视频时长检测（纯标准库，跨平台） ──────────────────────────────────────────
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def _parse_video_duration(data: bytes) -> float | None:
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"""从 MP4/MOV 原始字节解析时长（秒）。读取 mvhd box。"""
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idx = data.find(b"mvhd")
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if idx == -1:
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return None
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box = data[idx + 4:]
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if len(box) < 32:
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return None
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version = box[0]
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try:
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if version == 0:
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timescale = struct.unpack(">I", box[12:16])[0]
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duration = struct.unpack(">I", box[16:20])[0]
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else: # version == 1
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timescale = struct.unpack(">I", box[20:24])[0]
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duration = struct.unpack(">Q", box[24:32])[0]
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except struct.error:
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return None
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return (duration / timescale) if timescale > 0 else None
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def _get_video_duration(reference_video) -> float | None:
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"""从 ComfyUI VIDEO 对象获取视频时长（秒），失败返回 None。"""
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try:
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source = reference_video.get_stream_source()
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if isinstance(source, str) and os.path.isfile(source):
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with open(source, "rb") as f:
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data = f.read()
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elif isinstance(source, io.BytesIO):
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source.seek(0)
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data = source.read()
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else:
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return None
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return _parse_video_duration(data)
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except Exception:
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return None
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def _validate_video_duration(reference_video, character_orientation: str):
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"""校验视频时长，超限时抛出 ValueError。解析失败时静默跳过。"""
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duration = _get_video_duration(reference_video)
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if duration is None:
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print("[K3 动作控制] 无法解析视频时长，跳过校验。")
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return
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limit = 10 if character_orientation == "image" else 30
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print(f"[K3 动作控制] 检测到视频时长: {duration:.2f}s（限制: 3~{limit}s）")
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if not (3 <= duration <= limit):
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orientation_label = "图片" if character_orientation == "image" else "视频"
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raise ValueError(
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f"参考视频时长 {duration:.1f}s 不符合要求。\n"
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f"角色朝向为「{orientation_label}」时，时长须在 3~{limit}s 之间。"
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)
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# ── 节点 ──────────────────────────────────────────────────────────────────────
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class K3MotionControl:
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"""K3 动作控制 自研 —— 用参考视频驱动参考图人物动作"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"参考图片": ("IMAGE",),
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"参考视频": ("VIDEO",),
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"提示词": ("STRING", {"multiline": True, "default": ""}),
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"模型": (["v3", "v2-6"], {"default": "v3"}),
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"模式": (["标准", "专家"], {"default": "专家"}),
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"时长": ([5, 10, 15, 20, 25, 30], {"default": 5}),
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"角色朝向": (["图片", "视频"], {"default": "图片"}),
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"保留原声": (["打开", "关闭"], {"default": "打开"}),
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"seed": ("INT", {
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"default": 0, "min": 0, "max": 2147483647,
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"tooltip": "seed 仅控制节点是否重新运行，结果本身不可复现。",
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}),
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},
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}
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RETURN_TYPES = ("VIDEO",)
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RETURN_NAMES = ("视频",)
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FUNCTION = "generate"
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CATEGORY = "comfyui_o1key/KVideo"
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async def generate(self, 参考图片, 参考视频, 提示词, 保留原声, 角色朝向, 模式, 模型, 时长, seed, **kwargs):
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api_key = get_api_key_or_raise()
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base_url = get_api_base_url()
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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}
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# ── 参数映射 ──────────────────────────────────────────────────
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mode_api = "std" if 模式 == "标准" else "pro"
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model_name = f"kling-{模型}-motion-{mode_api}-{时长}s"
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character_orientation = "image" if 角色朝向 == "图片" else "video"
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keep_sound = "yes" if 保留原声 == "打开" else "no"
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prompt = 提示词.strip()
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if len(prompt) > 2500:
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raise ValueError(f"提示词长度（{len(prompt)}）超过上限 2500 个字符，请缩短后重试。")
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# ── 进度条 ────────────────────────────────────────────────────
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try:
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from comfy.utils import ProgressBar
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pbar = ProgressBar(100)
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except Exception:
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pbar = None
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def _stage(s: str):
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if s == "uploading":
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print("[K3 动作控制] 上传视频到 R2...")
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if pbar: pbar.update_absolute(0, 100)
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elif s == "submitting":
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print("[K3 动作控制] 提交任务...")
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if pbar: pbar.update_absolute(10, 100)
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elif s.startswith("submitted:"):
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print(f"[K3 动作控制] 任务已提交 → {s.split(':', 1)[1]}")
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if pbar: pbar.update_absolute(15, 100)
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elif s == "downloading":
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print("[K3 动作控制] 下载视频...")
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if pbar: pbar.update_absolute(99, 100)
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elif s == "done":
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print("[K3 动作控制] 完成")
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if pbar: pbar.update_absolute(100, 100)
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def _progress(pct: int):
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if pbar: pbar.update_absolute(15 + int(pct * 0.84), 100)
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# ── 视频时长校验 ──────────────────────────────────────────────
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_validate_video_duration(参考视频, character_orientation)
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# 参考视频时长不得超过所选时长（防止用长视频生成短计费）
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_dur = _get_video_duration(参考视频)
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if _dur is not None and _dur > 时长 + 0.5:
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raise ValueError(
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f"参考视频时长 {_dur:.1f}s 超过所选时长 {时长}s。\n"
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f"请将时长调整为 ≥{_dur:.0f}s 的档位，或更换更短的参考视频。"
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)
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# ── 图片 & 视频上传 R2 → 获取公网 URL ────────────────────────
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_stage("uploading")
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pil_list = tensor_to_pil(参考图片)
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image_url = await upload_image(pil_list[0].convert("RGB"))
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video_url = await upload_video(参考视频)
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# ── 构建请求体 ────────────────────────────────────────────────
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body: dict = {
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"model_name": model_name,
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"model": model_name,
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"image_url": image_url,
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"video_url": video_url,
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"character_orientation": character_orientation,
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"mode": mode_api,
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"keep_original_sound": keep_sound,
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}
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if prompt:
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body["prompt"] = prompt
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# ── 保存路径 ──────────────────────────────────────────────────
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video_dir = _get_video_dir()
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counter = _next_counter(video_dir, "k3_motion")
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save_path = os.path.join(video_dir, f"k3_motion_{counter:05d}.mp4")
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connector = aiohttp.TCPConnector(ssl=False, force_close=True)
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async with aiohttp.ClientSession(connector=connector) as session:
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# 1. 提交任务
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_stage("submitting")
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create_url = f"{base_url}{_ENDPOINT_CREATE}"
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async with session.post(
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create_url,
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data=json.dumps(body, ensure_ascii=False).encode("utf-8"),
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headers=headers,
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) as resp:
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text = await resp.text()
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if resp.status != 200:
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try:
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err = json.loads(text)
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msg = err.get("message") or text
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except Exception:
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msg = text
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raise RuntimeError(f"K3 动作控制提交失败 ({resp.status}): {msg}")
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create_resp = json.loads(text)
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# task_id 兼容扁平结构和 data 嵌套结构
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task_id = (
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create_resp.get("task_id")
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or create_resp.get("id")
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or create_resp.get("data", {}).get("task_id")
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)
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if not task_id:
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raise RuntimeError(f"API 未返回任务 ID，响应：{create_resp}")
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_stage(f"submitted:{task_id}")
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# 2. 轮询
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status_url = f"{base_url}{_ENDPOINT_STATUS.format(task_id=task_id)}"
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interval = _POLL_INIT
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video_result_url = None
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while True:
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await asyncio.sleep(interval)
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async with session.get(status_url, headers=headers) as resp:
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text = await resp.text()
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if resp.status != 200:
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try:
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err = json.loads(text)
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msg = err.get("message") or text
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except Exception:
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msg = text
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raise RuntimeError(f"状态查询失败 ({resp.status}): {msg}")
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sr = json.loads(text)
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# 兼容扁平结构和 data 嵌套结构
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data = sr.get("data", sr)
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status = (data.get("status") or sr.get("status") or "").lower()
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pct_raw = data.get("progress", 0)
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try:
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pct = int(str(pct_raw).rstrip("%").strip())
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except (ValueError, AttributeError):
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pct = 0
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print(f"[K3 动作控制] 生成中 {pct}%")
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_progress(pct)
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if status in ("success", "completed", "done", "finished", "succeed"):
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video_result_url = (
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data.get("video_url")
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or data.get("result_url")
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or data.get("url")
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or (data.get("result", {}) or {}).get("url")
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or sr.get("video_url")
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or sr.get("url")
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)
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break
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elif status in ("failed", "fail"):
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err_info = data.get("error") or sr.get("error") or {}
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err_msg = (err_info.get("message", "未知错误")
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if isinstance(err_info, dict) else str(err_info))
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raise RuntimeError(f"K3 动作控制生成失败：{err_msg}")
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interval = min(interval * 1.3, _POLL_MAX)
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if not video_result_url:
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raise RuntimeError(f"API 未返回视频 URL，响应：{sr}")
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# 3. 下载视频
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_stage("downloading")
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async with session.get(video_result_url, allow_redirects=True) as resp:
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if resp.status != 200:
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raise RuntimeError(f"视频下载失败 ({resp.status})")
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os.makedirs(os.path.dirname(save_path), exist_ok=True)
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with open(save_path, "wb") as f:
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async for chunk in resp.content.iter_chunked(8192):
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f.write(chunk)
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_stage("done")
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if _FOLDER_PATHS_OK:
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return (InputImpl.VideoFromFile(save_path),)
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return (save_path,)
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# ── 视频时长检测测试节点 ──────────────────────────────────────────────────────
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class K3MotionVideoCheck:
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"""检测视频时长并校验是否满足动作控制的限制，不调用 API。"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"参考视频": ("VIDEO",),
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"角色朝向": (["图片", "视频"], {"default": "图片"}),
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},
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("检测结果",)
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FUNCTION = "check"
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CATEGORY = "comfyui_o1key/KVideo"
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OUTPUT_NODE = True
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def check(self, 参考视频, 角色朝向):
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character_orientation = "image" if 角色朝向 == "图片" else "video"
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duration = _get_video_duration(参考视频)
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if duration is None:
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result = "❌ 无法解析视频时长（格式不支持或文件损坏）"
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print(f"[K3 视频检测] {result}")
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return (result,)
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limit = 10 if character_orientation == "image" else 30
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orientation_label = 角色朝向
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ok = 3 <= duration <= limit
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if ok:
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result = (
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f"✅ 时长检测通过\n"
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f"视频时长: {duration:.2f}s\n"
|
||||
f"角色朝向: {orientation_label}（限制 3~{limit}s）"
|
||||
)
|
||||
else:
|
||||
result = (
|
||||
f"❌ 时长检测不通过\n"
|
||||
f"视频时长: {duration:.2f}s\n"
|
||||
f"角色朝向: {orientation_label}（限制 3~{limit}s）\n"
|
||||
f"请更换时长在 3~{limit}s 之间的视频。"
|
||||
)
|
||||
|
||||
print(f"[K3 视频检测] {result}")
|
||||
return (result,)
|
||||
|
||||
|
||||
# ── 节点注册 ──────────────────────────────────────────────────────────────────
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"K3MotionControl": K3MotionControl,
|
||||
"K3MotionVideoCheck": K3MotionVideoCheck,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"K3MotionControl": "动作控制 K3 自研",
|
||||
"K3MotionVideoCheck": "视频时长检测 K3",
|
||||
}
|
||||
+1
-1
@@ -231,7 +231,7 @@ class K3Video:
|
||||
if 负向提示词.strip():
|
||||
body["negative_prompt"] = 负向提示词.strip()
|
||||
if 生成音频 == "打开":
|
||||
body["generate_audio"] = True
|
||||
body["metadata"] = {"sound": "on"}
|
||||
|
||||
# ── 进度条 ────────────────────────────────────────────────────
|
||||
try:
|
||||
|
||||
+2
-1
@@ -23,5 +23,6 @@ from .gpt_image import O1keyGPTImage
|
||||
from .K_video import KVideo
|
||||
from .K3_video import K3Video
|
||||
from .K3_video_firstlast import K3VideoFirstLast
|
||||
from .K3_motion_control import K3MotionControl, K3MotionVideoCheck
|
||||
|
||||
__all__ = ['NanoBananaPro', 'BatchNanoBananaPro', 'GoogleGemini', 'LoadFile', 'ImageStitchPro', 'SaveCleanImage', 'BatchCleanMetadata', 'VideoPreview', 'KlingVideo', 'KlingFirstLastFrame', 'KlingMotionControlTest', 'AspectRatioPreset', 'GoogleVeo', 'FluxImageEdit', 'UniversalLLMChat', 'MultiResPreview', 'BatchImagesO1key', 'Seedance', 'SeedanceMultiModal', 'StreamPreview', 'DoubaoImage', 'O1keyGPTImage', 'KVideo', 'K3Video', 'K3VideoFirstLast']
|
||||
__all__ = ['NanoBananaPro', 'BatchNanoBananaPro', 'GoogleGemini', 'LoadFile', 'ImageStitchPro', 'SaveCleanImage', 'BatchCleanMetadata', 'VideoPreview', 'KlingVideo', 'KlingFirstLastFrame', 'KlingMotionControlTest', 'AspectRatioPreset', 'GoogleVeo', 'FluxImageEdit', 'UniversalLLMChat', 'MultiResPreview', 'BatchImagesO1key', 'Seedance', 'SeedanceMultiModal', 'StreamPreview', 'DoubaoImage', 'O1keyGPTImage', 'KVideo', 'K3Video', 'K3VideoFirstLast', 'K3MotionControl', 'K3MotionVideoCheck']
|
||||
|
||||
@@ -47,6 +47,23 @@ async def _put_upload(upload_url: str, data: bytes, content_type: str):
|
||||
raise RuntimeError(f"文件上传失败 ({resp.status}): {text}")
|
||||
|
||||
|
||||
async def upload_image(pil_image) -> str:
|
||||
"""
|
||||
接受 PIL Image 对象，编码为 PNG 上传到 R2，返回公网 URL。
|
||||
"""
|
||||
import io as _io
|
||||
buf = _io.BytesIO()
|
||||
pil_image.save(buf, format="PNG")
|
||||
data = buf.getvalue()
|
||||
filename = f"{uuid.uuid4()}.png"
|
||||
|
||||
upload_url, public_url = await _presign(filename, "image/png")
|
||||
await _put_upload(upload_url, data, "image/png")
|
||||
|
||||
print(f"[R2] 图片已上传: {public_url}")
|
||||
return public_url
|
||||
|
||||
|
||||
async def upload_video(video) -> str:
|
||||
"""
|
||||
接受 ComfyUI VIDEO 对象，上传到 R2，返回公网 URL。
|
||||
|
||||
Reference in New Issue
Block a user