""" K26 图生视频节点 """ import asyncio import json import math import os import tempfile import aiohttp from ..utils.config import get_api_key_or_raise, get_api_base_url, NETWORK_ROUTE_OPTIONS, get_base_url_by_route from ..utils.image_utils import tensor_to_pil, encode_image_to_base64 from ..utils.http_error import async_request_with_retry from ..utils.video_task import ( check_interrupt, extract_error_message, extract_progress, extract_status, extract_video_url, interruptible_sleep, is_failure_status, is_success_status, run_with_interrupt, ) try: from comfy_api.latest import InputImpl import folder_paths _FOLDER_PATHS_OK = True except ImportError: _FOLDER_PATHS_OK = False # 模型基础名，运行时动态拼接完整名称 _MODEL_BASE = "kling-v2-6" # API 端点 _ENDPOINT_CREATE = "/v1/video/generations" _ENDPOINT_STATUS = "/v1/video/generations/{task_id}" _POLL_INIT = 3 _POLL_MAX = 15 def _image_to_base64(tensor, scale=1.0) -> str: from PIL import Image pil = tensor_to_pil(tensor) img = pil[0] if scale < 1.0: w, h = img.size new_w = max(1, int(w * scale)) new_h = max(1, int(h * scale)) img = img.resize((new_w, new_h), Image.LANCZOS) return encode_image_to_base64(img, format="PNG") class KVideoFirstLast: """K26 图生视频节点（首尾帧）""" @classmethod def INPUT_TYPES(cls): return { "required": { "起始帧": ("IMAGE",), "提示词": ("STRING", {"multiline": True, "default": ""}), "模式": (["1080p"],), "时长": ([5, 10],), "生成音频": (["关闭", "打开"], {"default": "关闭"}), "网络线路": (NETWORK_ROUTE_OPTIONS, {"default": "全球加速"}), "seed": ("INT", { "default": 0, "min": 0, "max": 2147483647, "tooltip": "seed 仅控制节点是否重新运行，结果本身不可复现。", }), }, "optional": { "尾帧": ("IMAGE",), }, } RETURN_TYPES = ("VIDEO",) RETURN_NAMES = ("视频",) FUNCTION = "generate" CATEGORY = "comfyui_o1key/KVideo" async def generate(self, 起始帧, 提示词, 模式, 时长, 生成音频="关闭", 网络线路="全球加速", 尾帧=None, seed=0): api_key = get_api_key_or_raise() base_url = get_base_url_by_route(网络线路) headers = { "Authorization": f"Bearer {api_key}", "Content-Type": "application/json", } # ── 动态拼接模型名 ──────────────────────────────────────────── mode_api = "pro" # 1080p 映射为 pro voice = "voice" if 生成音频 == "打开" else "novoice" model_name = f"{_MODEL_BASE}-{mode_api}-{时长}s-{voice}" # ── 构建请求体（超过 10MB 自动缩放图片）──────────────────────── MAX_BODY = 10 * 1024 * 1024 scale = 1.0 print(f"[K26 图生视频] 请求体大小限制: 10MB，超出将自动缩放图片") while True: body = { "model": model_name, "prompt": 提示词.strip(), "image": _image_to_base64(起始帧, scale), "mode": mode_api, "duration": 时长, } metadata = {} if 尾帧 is not None: metadata["image_tail"] = _image_to_base64(尾帧, scale) if 生成音频 == "打开": metadata["sound"] = "on" if metadata: body["metadata"] = metadata body_str = json.dumps(body, ensure_ascii=False) body_size = len(body_str.encode("utf-8")) if body_size <= MAX_BODY: print(f"[K26 图生视频] 请求体大小: {body_size / 1024 / 1024:.2f}MB" + (f"（已缩放至 {scale:.1%}）" if scale < 1.0 else "")) break # 等比缩放：图片像素面积与 base64 长度近似线性 target_ratio = MAX_BODY / body_size scale = scale * math.sqrt(target_ratio) * 0.95 # 5% 安全余量 if scale < 0.01: raise RuntimeError("图片缩放后仍超过10MB限制，请使用更小的参考图") w, h = tensor_to_pil(起始帧)[0].size print(f"[K26 图生视频] 请求体 {body_size / 1024 / 1024:.2f}MB 超限，" f"自动缩放至 {scale:.1%}（{int(w * scale)}x{int(h * scale)}）") # ── 进度条 ──────────────────────────────────────────────────── try: from comfy.utils import ProgressBar pbar = ProgressBar(100) except Exception: pbar = None def _stage(s: str): if s == "submitting": print("[K26 图生视频] 提交中...") if pbar: pbar.update_absolute(0, 100) elif s.startswith("submitted:"): print(f"[K26 图生视频] 任务已提交 → {s.split(':', 1)[1]}") if pbar: pbar.update_absolute(5, 100) elif s == "downloading": print("[K26 图生视频] 下载视频...") if pbar: pbar.update_absolute(99, 100) elif s == "done": print("[K26 图生视频] 完成") if pbar: pbar.update_absolute(100, 100) def _progress(pct: int): if pbar: pbar.update_absolute(5 + int(pct * 0.94), 100) # ── 保存路径（临时文件，避免与下游保存节点重复落盘）────────────────── tmp_fd, save_path = tempfile.mkstemp(suffix=".mp4", prefix="k26_") connector = aiohttp.TCPConnector(ssl=False, force_close=True) async with aiohttp.ClientSession(connector=connector) as session: # 1. 提交 check_interrupt() _stage("submitting") create_url = f"{base_url}{_ENDPOINT_CREATE}" resp = await run_with_interrupt(async_request_with_retry( session, "POST", create_url, json=body, headers=headers, prefix="K26 提交: " )) check_interrupt() text = await resp.text() create_resp = json.loads(text) task_id = ( create_resp.get("task_id") or create_resp.get("id") or create_resp.get("data", {}).get("task_id") ) if not task_id: raise RuntimeError(f"API 未返回任务 ID，响应：{create_resp}") _stage(f"submitted:{task_id}") # 2. 轮询 status_url = f"{base_url}{_ENDPOINT_STATUS.format(task_id=task_id)}" interval = _POLL_INIT video_url = None while True: check_interrupt() async with session.get(status_url, headers=headers) as resp: text = await resp.text() if resp.status != 200: try: err = json.loads(text) msg = err.get("error", {}).get("message") or err.get("message") or text except Exception: msg = text raise RuntimeError(f"状态查询失败 ({resp.status}): {msg}") sr = json.loads(text) data = sr.get("data", sr) status = extract_status(sr) pct = extract_progress(sr) print(f"[K26 图生视频] 生成中 {pct}%") _progress(pct) if is_success_status(status): # 提取视频 URL video_url = extract_video_url(sr) break if is_failure_status(status, sr): err_msg = extract_error_message(sr) raise RuntimeError(f"K26 生成失败：{err_msg}") await interruptible_sleep(interval) interval = min(interval * 1.5, _POLL_MAX) if not video_url: raise RuntimeError(f"API 未返回视频 URL，响应：{sr}") # 3. 下载 check_interrupt() _stage("downloading") async with session.get(video_url, allow_redirects=True) as resp: if resp.status != 200: raise RuntimeError(f"视频下载失败 ({resp.status})") os.close(tmp_fd) with open(save_path, "wb") as f: async for chunk in resp.content.iter_chunked(8192): check_interrupt() f.write(chunk) _stage("done") if _FOLDER_PATHS_OK: return (InputImpl.VideoFromFile(save_path),) return (save_path,) NODE_CLASS_MAPPINGS = { "KVideoFirstLast": KVideoFirstLast, } NODE_DISPLAY_NAME_MAPPINGS = { "KVideoFirstLast": "K26 图生视频（首尾帧）", }