feat: 实现 K3 图生视频 / 多镜头后端请求

- 模型名动态拼接（模式/时长/音频），不暴露于前端
- 模式前端重命名为 标准/专家/4K；4K 模式不拼接 voice 后缀
- 起始帧改为必填，仅作图生视频；移除尾帧参数
- 图片预处理：宽高比校验、等比缩放（最小 300px / 最大 10MB）
- 多镜头模式：metadata 携带 multi_shot/shot_type/multi_prompt
- 多镜头前端选项重命名为 禁用/N个故事板
- 多镜头提示词为空时 fallback 为空格，规避 API prompt is required

nodes/K3_video.py

@@ -1,9 +1,19 @@
 """
-K3 图生视频 自研节点（方案A：经典 INPUT_TYPES 前端壳子）
+K3 图生视频 自研节点（图生视频 / 多镜头）
-复刻 Kling 3.0 Video 的前端参数，多镜头通过下拉选择控制分镜数量。
+模型名根据 模式/时长/音频 动态拼接，不暴露在前端。
-后端请求尚未实现。
+起始帧为必填，仅作图生视频；多镜头功能待实现。
 """
 
+import asyncio
+import json
+import os
+import re
+
+import aiohttp
+
+from ..utils.config import get_api_key_or_raise, get_api_base_url
+from ..utils.image_utils import tensor_to_pil, encode_image_to_base64
+
 try:
     from comfy_api.latest import InputImpl
     import folder_paths
@@ -14,51 +24,123 @@ except Exception:
 
 # ── 常量 ──────────────────────────────────────────────────────────────────────
 
+_MODEL_BASE = "kling-v3"          # 动态拼接为 kling-v3-{模式}-{时长}s-{voice}
+_MODES      = ["标准", "专家", "4K"]
+_MODE_MAP   = {"标准": "std", "专家": "pro", "4K": "4k"}
+
 _MULTI_SHOT_OPTIONS = [
-    "disabled",
+    "禁用",
-    "1 storyboard",
+    "1个故事板",
-    "2 storyboards",
+    "2个故事板",
-    "3 storyboards",
+    "3个故事板",
-    "4 storyboards",
+    "4个故事板",
-    "5 storyboards",
+    "5个故事板",
-    "6 storyboards",
+    "6个故事板",
 ]
 
-_MODELS        = ["kling-v3"]
+_ENDPOINT_CREATE = "/v1/video/generations"
-_MODES         = ["std", "pro", "4k"]
+_ENDPOINT_STATUS = "/v1/video/generations/{task_id}"
+
+_POLL_INIT = 3
+_POLL_MAX  = 15
+
+
+# ── 工具函数 ───────────────────────────────────────────────────────────────────
+
+def _get_video_dir() -> str:
+    if _FOLDER_PATHS_OK:
+        base = folder_paths.get_output_directory()
+    else:
+        plugin = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+        base = os.path.join(os.path.dirname(os.path.dirname(plugin)), "output")
+    d = os.path.join(base, "video")
+    os.makedirs(d, exist_ok=True)
+    return d
+
+
+def _next_counter(directory: str, prefix: str) -> int:
+    pattern = re.compile(rf"^{re.escape(prefix)}_(\d+)")
+    max_n = 0
+    if os.path.exists(directory):
+        for f in os.listdir(directory):
+            m = pattern.match(f)
+            if m:
+                max_n = max(max_n, int(m.group(1)))
+    return max_n + 1
+
+
+def _image_to_base64(tensor) -> str:
+    pil = tensor_to_pil(tensor)
+    return encode_image_to_base64(pil[0], format="PNG")
+
+
+def _prepare_image_base64(tensor) -> str:
+    """转换并校验图片，不符合约束时自动等比缩放后返回 base64。"""
+    import io
+    import base64
+
+    pil_list = tensor_to_pil(tensor)
+    img = pil_list[0].convert("RGB")
+    w, h = img.size
+
+    # 1. 宽高比校验（无法通过等比缩放修复，直接报错）
+    ratio = w / h
+    if ratio < 1 / 2.5 or ratio > 2.5:
+        raise RuntimeError(
+            f"图片宽高比 {w}:{h}（{ratio:.2f}）超出允许范围 1:2.5 ~ 2.5:1，请裁剪后重试。"
+        )
+
+    # 2. 最小尺寸：任意边 < 300px 时等比放大
+    if w < 300 or h < 300:
+        scale = max(300 / w, 300 / h)
+        img = img.resize((int(w * scale), int(h * scale)), resample=1)  # LANCZOS=1
+
+    # 3. 文件大小：循环等比缩小直到 ≤ 10MB
+    MAX_BYTES = 10 * 1024 * 1024
+    for _ in range(20):                      # 最多迭代 20 次，防止死循环
+        buf = io.BytesIO()
+        img.save(buf, format="PNG")
+        if buf.tell() <= MAX_BYTES:
+            break
+        scale = (MAX_BYTES / buf.tell()) ** 0.5 * 0.95   # 留 5% 余量
+        new_w = int(img.width * scale)
+        new_h = int(img.height * scale)
+        if new_w < 300 or new_h < 300:
+            raise RuntimeError(
+                f"图片压缩至 10MB 以内后尺寸（{new_w}x{new_h}）低于最小限制 300px，无法同时满足两项约束。"
+            )
+        img = img.resize((new_w, new_h), resample=1)
+    else:
+        raise RuntimeError("图片经过 20 次缩放仍超过 10MB，请检查原始图片。")
+
+    buf.seek(0)
+    return base64.b64encode(buf.read()).decode("utf-8")
 
 
 # ── 节点 ──────────────────────────────────────────────────────────────────────
 
 class K3Video:
-    """K3 图生视频 自研（前端壳子，后端待实现）"""
+    """K3 图生视频 自研"""
 
     @classmethod
     def INPUT_TYPES(cls):
         required = {
-            # 多镜头模式选择
             "多镜头": (_MULTI_SHOT_OPTIONS, {
-                "default": "disabled",
+                "default": "禁用",
-                "tooltip": "disabled：单段模式；N storyboards：启用 N 段分镜。",
+                "tooltip": "禁用：单段模式；N个故事板：启用 N 段分镜。",
             }),
-            # 单段模式字段（多镜头 disabled 时使用）
+            "起始帧":    ("IMAGE",),
             "提示词":    ("STRING", {"multiline": True, "default": ""}),
             "负向提示词": ("STRING", {"multiline": True, "default": ""}),
-            "时长":      ([5, 10, 15], {
+            "时长":      ([5, 10, 15], {"default": 5}),
-                "default": 5,
-                "tooltip": "单段模式时长（秒）；多镜头模式下由各分镜时长决定。",
-            }),
-            # 通用参数
             "生成音频":  (["关闭", "打开"], {"default": "关闭"}),
-            "模型":     (_MODELS, {"default": "kling-v3"}),
+            "模式":      (_MODES, {"default": "标准"}),
-            "模式":     (_MODES,  {"default": "std"}),
             "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。",
             }),
         }
 
-        # 分镜字段放最后，按 分镜N_提示词 / 分镜N_时长 交替排列
         for i in range(1, 7):
             required[f"分镜{i}_提示词"] = ("STRING", {
                 "multiline": True, "default": "",
@@ -70,22 +152,209 @@ class K3Video:
                 "tooltip": f"第 {i} 段分镜时长（秒）。",
             })
 
-        optional = {
+        return {"required": required}
-            "起始帧": ("IMAGE",),
-            "尾帧":   ("IMAGE",),
-        }
-
-        return {"required": required, "optional": optional}
 
     RETURN_TYPES  = ("VIDEO",)
     RETURN_NAMES  = ("视频",)
     FUNCTION      = "generate"
     CATEGORY      = "comfyui_o1key/KVideo"
 
-    async def generate(self, 多镜头, 提示词, 负向提示词, 时长,
+    async def generate(self, 多镜头, 起始帧, 提示词, 负向提示词, 时长, 生成音频, 模式, seed, **kwargs):
-                       生成音频, 模型, 模式, seed,
+        api_key  = get_api_key_or_raise()
-                       起始帧=None, 尾帧=None, **kwargs):
+        base_url = get_api_base_url()
-        raise NotImplementedError("K3 图生视频 自研：后端尚未实现。")
+        headers  = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type":  "application/json",
+        }
+
+        is_multi  = 多镜头 != "禁用"
+        voice     = "voice" if 生成音频 == "打开" else "novoice"
+        mode_api  = _MODE_MAP[模式]
+        if mode_api == "4k":
+            model_name = f"{_MODEL_BASE}-4k-{时长}s"
+        else:
+            model_name = f"{_MODEL_BASE}-{mode_api}-{时长}s-{voice}"
+
+        # ── 多镜头模式 ────────────────────────────────────────────────
+        if is_multi:
+            shot_count = int(多镜头[0])  # "3个故事板" → 3
+
+            # 收集分镜参数
+            multi_prompt = []
+            for i in range(1, shot_count + 1):
+                p = kwargs.get(f"分镜{i}_提示词", "").strip()
+                d = kwargs.get(f"分镜{i}_时长", 0)
+                if not p:
+                    raise RuntimeError(f"多镜头模式错误：第 {i} 段分镜提示词不能为空。")
+                if d < 1:
+                    raise RuntimeError(f"多镜头模式错误：第 {i} 段分镜时长不能小于 1 秒。")
+                multi_prompt.append({"index": i, "prompt": p, "duration": str(d)})
+
+            # 校验时长总和
+            total = sum(int(s["duration"]) for s in multi_prompt)
+            if total != 时长:
+                raise RuntimeError(
+                    f"多镜头模式错误：各分镜时长之和（{total}s）必须等于总时长（{时长}s）。"
+                )
+
+            metadata: dict = {
+                "multi_shot": "true",
+                "shot_type":  "customize",
+                "multi_prompt": multi_prompt,
+            }
+            if 生成音频 == "打开":
+                metadata["sound"] = "on"
+
+            body: dict = {
+                "model":    model_name,
+                "prompt":   提示词.strip() or " ",
+                "mode":     mode_api,
+                "duration": 时长,
+                "image":    _prepare_image_base64(起始帧),
+                "metadata": metadata,
+            }
+            if 负向提示词.strip():
+                body["negative_prompt"] = 负向提示词.strip()
+
+        # ── 单段图生视频模式 ──────────────────────────────────────────
+        else:
+            if not 提示词.strip():
+                raise RuntimeError("单段模式错误：提示词不能为空。")
+
+            body = {
+                "model":    model_name,
+                "prompt":   提示词.strip(),
+                "mode":     mode_api,
+                "duration": 时长,
+                "image":    _prepare_image_base64(起始帧),
+            }
+            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
+
+        # ── 进度条 ────────────────────────────────────────────────────
+        try:
+            from comfy.utils import ProgressBar
+            pbar = ProgressBar(100)
+        except Exception:
+            pbar = None
+
+        tag = "多镜头" if is_multi else "图生视频"
+
+        def _stage(s: str):
+            if s == "submitting":
+                print(f"[K3 {tag}] 提交中...")
+                if pbar: pbar.update_absolute(0, 100)
+            elif s.startswith("submitted:"):
+                print(f"[K3 {tag}] 任务已提交 → {s.split(':', 1)[1]}")
+                if pbar: pbar.update_absolute(5, 100)
+            elif s == "downloading":
+                print(f"[K3 {tag}] 下载视频...")
+                if pbar: pbar.update_absolute(99, 100)
+            elif s == "done":
+                print(f"[K3 {tag}] 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 ──────────────────────────────────────────────────
+        video_dir = _get_video_dir()
+        counter   = _next_counter(video_dir, "k3")
+        save_path = os.path.join(video_dir, f"k3_{counter:05d}.mp4")
+
+        connector = aiohttp.TCPConnector(ssl=False, force_close=True)
+        async with aiohttp.ClientSession(connector=connector) as session:
+
+            # 1. 提交
+            _stage("submitting")
+            create_url = f"{base_url}{_ENDPOINT_CREATE}"
+            async with session.post(create_url, json=body, 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"K3 提交失败 ({resp.status}): {msg}")
+                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:
+                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 = (data.get("status") or sr.get("status") or "").lower()
+
+                pct_raw = data.get("progress", 0)
+                try:
+                    pct = int(str(pct_raw).rstrip("%").strip())
+                except (ValueError, AttributeError):
+                    pct = 0
+                print(f"[K3 {tag}] 生成中 {pct}%")
+                _progress(pct)
+
+                if status in ("success", "completed", "done", "finished", "succeed"):
+                    video_url = (
+                        data.get("video_url")
+                        or data.get("result_url")
+                        or data.get("url")
+                        or (data.get("result", {}) or {}).get("url")
+                        or sr.get("video_url")
+                        or sr.get("url")
+                    )
+                    break
+                if status in ("failed", "fail"):
+                    err_info = data.get("error") or sr.get("error") or {}
+                    err_msg  = (err_info.get("message", "未知错误")
+                                if isinstance(err_info, dict) else str(err_info))
+                    raise RuntimeError(f"K3 生成失败：{err_msg}")
+
+                await asyncio.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
+            _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.makedirs(os.path.dirname(save_path), exist_ok=True)
+                with open(save_path, "wb") as f:
+                    async for chunk in resp.content.iter_chunked(8192):
+                        f.write(chunk)
+
+        _stage("done")
+
+        if _FOLDER_PATHS_OK:
+            return (InputImpl.VideoFromFile(save_path),)
+        return (save_path,)
 
 
 # ── 节点注册 ──────────────────────────────────────────────────────────────────