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fix: 图生视频节点进度解析兼容百分号格式，K26/K3/Seedance统一走cf-api异步接口，K26新增seed参数

Browse Source 
- 修复 K_video_image2video.py 进度值 "10%" 解析报错：rstrip('%') 后 float→int 安全转换
- K26 节点拆分为图生视频/首尾帧两个独立节点，删除旧的合并节点 K_video.py
- K26/K3 视频节点及 Seedance 客户端统一改用 get_async_api_base_url (cf-api.o1key.com)
- K3 节点模式选项从 标准/专家 改为 720p/1080p，与后端一致
- K_video_image2video.py 和 K_video_firstlast.py 新增 ComfyUI 原生 seed 参数

Co-Authored-By: Claude Opus 4.6 <[email protected]>
This commit is contained in:
o1key
2026-05-07 09:48:24 +08:00
co-authored by Claude Opus 4.6
parent 844401dbb2
commit 1a813bfd1d
10 changed files with 333 additions and 49 deletions
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nodes/K3_motion_control.py
+4 -4
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@@ -13,7 +13,7 @@ import tempfile
import aiohttp
from ..utils.config import get_api_key_or_raise, get_api_base_url
from ..utils.config import get_api_key_or_raise, get_async_api_base_url
from ..utils.r2_uploader import upload_video, upload_image
from ..utils.image_utils import tensor_to_pil
@@ -107,7 +107,7 @@ class K3MotionControl:
"参考视频": ("VIDEO",),
"提示词": ("STRING", {"multiline": True, "default": ""}),
"模型": (["v3", "v2-6"], {"default": "v3"}),
"模式": (["标准", "专家"], {"default": "专家"}),
"模式": (["720p", "1080p"], {"default": "1080p"}),
"时长": ([5, 10, 15, 20, 25, 30], {"default": 5}),
"角色朝向": (["图片", "视频"], {"default": "图片"}),
"保留原声": (["打开", "关闭"], {"default": "打开"}),
@@ -125,14 +125,14 @@ class K3MotionControl:
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):
api_key = get_api_key_or_raise()
base_url = get_api_base_url()
base_url = get_async_api_base_url()
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
}
# ── 参数映射 ──────────────────────────────────────────────────
mode_api = "std" if 模式 == "标准" else "pro"
mode_api = "std" if 模式 == "720p" else "pro"
model_name = f"kling-{模型}-motion-{mode_api}-{时长}s"
character_orientation = "image" if 角色朝向 == "图片" else "video"
keep_sound = "yes" if 保留原声 == "打开" else "no"
nodes/K3_video.py
+5 -5
View File
@@ -11,7 +11,7 @@ import tempfile
import aiohttp
from ..utils.config import get_api_key_or_raise, get_api_base_url
from ..utils.config import get_api_key_or_raise, get_async_api_base_url
from ..utils.image_utils import tensor_to_pil, encode_image_to_base64
try:
@@ -25,8 +25,8 @@ 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"}
_MODES = ["720p", "1080p", "4K"]
_MODE_MAP = {"720p": "std", "1080p": "pro", "4K": "4k"}
_MULTI_SHOT_OPTIONS = [
"禁用",
@@ -112,7 +112,7 @@ class K3Video:
"负向提示词": ("STRING", {"multiline": True, "default": ""}),
"时长": ([5, 10, 15], {"default": 5}),
"生成音频": (["关闭", "打开"], {"default": "关闭"}),
"模式": (_MODES, {"default": "标准"}),
"模式": (_MODES, {"default": "720p"}),
"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。",
@@ -139,7 +139,7 @@ class K3Video:
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):
api_key = get_api_key_or_raise()
base_url = get_api_base_url()
base_url = get_async_api_base_url()
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
nodes/K3_video_firstlast.py
+5 -5
View File
@@ -10,7 +10,7 @@ import tempfile
import aiohttp
from ..utils.config import get_api_key_or_raise, get_api_base_url
from ..utils.config import get_api_key_or_raise, get_async_api_base_url
from ..utils.image_utils import tensor_to_pil, encode_image_to_base64
try:
@@ -24,8 +24,8 @@ except Exception:
# ── 常量 ──────────────────────────────────────────────────────────────────────
_MODEL_BASE = "kling-v3"
_MODES = ["标准", "专家", "4K"]
_MODE_MAP = {"标准": "std", "专家": "pro", "4K": "4k"}
_MODES = ["720p", "1080p", "4K"]
_MODE_MAP = {"720p": "std", "1080p": "pro", "4K": "4k"}
_ENDPOINT_CREATE = "/v1/video/generations"
_ENDPOINT_STATUS = "/v1/video/generations/{task_id}"
@@ -93,7 +93,7 @@ class K3VideoFirstLast:
"负向提示词": ("STRING", {"multiline": True, "default": ""}),
"时长": ([5, 10, 15], {"default": 5}),
"生成音频": (["关闭", "打开"], {"default": "关闭"}),
"模式": (_MODES, {"default": "标准"}),
"模式": (_MODES, {"default": "720p"}),
"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。",
@@ -111,7 +111,7 @@ class K3VideoFirstLast:
async def generate(self, 起始帧, 提示词, 负向提示词, 时长, 生成音频, 模式, seed, 尾帧=None):
api_key = get_api_key_or_raise()
base_url = get_api_base_url()
base_url = get_async_api_base_url()
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
nodes/K_video.py → nodes/K_video_firstlast.py
+61 -23
View File
@@ -4,12 +4,13 @@ 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
from ..utils.config import get_api_key_or_raise, get_async_api_base_url
from ..utils.image_utils import tensor_to_pil, encode_image_to_base64
try:
@@ -30,13 +31,20 @@ _POLL_INIT = 3
_POLL_MAX = 15
def _image_to_base64(tensor) -> str:
def _image_to_base64(tensor, scale=1.0) -> str:
from PIL import Image
pil = tensor_to_pil(tensor)
return encode_image_to_base64(pil[0], format="PNG")
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 KVideo:
"""K26 图生视频节点"""
class KVideoFirstLast:
"""K26 图生视频节点（首尾帧）"""
@classmethod
def INPUT_TYPES(cls):
@@ -44,9 +52,13 @@ class KVideo:
"required": {
"起始帧": ("IMAGE",),
"提示词": ("STRING", {"multiline": True, "default": ""}),
"模式": (["pro"],),
"模式": (["1080p"],),
"时长": ([5, 10],),
"生成音频": (["关闭", "打开"], {"default": "关闭"}),
"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",),
@@ -58,30 +70,56 @@ class KVideo:
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", 尾帧=None):
async def generate(self, 起始帧, 提示词, 模式, 时长, 生成音频="关闭", 尾帧=None, seed=0):
api_key = get_api_key_or_raise()
base_url = get_api_base_url()
base_url = get_async_api_base_url()
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}-{模式}-{时长}s-{voice}"
model_name = f"{_MODEL_BASE}-{mode_api}-{时长}s-{voice}"
# ── 构建请求体 ────────────────────────────────────────────────
body = {
"model": model_name,
"prompt": 提示词.strip(),
"image": _image_to_base64(起始帧),
"mode": 模式,
"duration": 时长,
}
if 生成音频 == "打开":
body["generate_audio"] = True
if 尾帧 is not None:
body["metadata"] = {"image_tail": _image_to_base64(尾帧)}
# ── 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,
}
if 生成音频 == "打开":
body["generate_audio"] = True
if 尾帧 is not None:
body["metadata"] = {"image_tail": _image_to_base64(尾帧, scale)}
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:
@@ -205,9 +243,9 @@ class KVideo:
NODE_CLASS_MAPPINGS = {
"KVideo": KVideo,
"KVideoFirstLast": KVideoFirstLast,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"KVideo": "K26 图生视频",
"KVideoFirstLast": "K26 图生视频（首尾帧）",
}
nodes/K_video_image2video.py
+236
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@@ -0,0 +1,236 @@
"""
K26 图生视频节点
支持 720p 和 1080p 模式
"""
import asyncio
import json
import math
import os
import tempfile
import aiohttp
from ..utils.config import get_api_key_or_raise, get_async_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
_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 KVideoImage2Video:
"""K26 图生视频节点"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"起始帧": ("IMAGE",),
"提示词": ("STRING", {"multiline": True, "default": ""}),
"模式": (["720p", "1080p"], {"default": "720p"}),
"时长": ([5, 10], {"default": 5}),
"生成音频": (["关闭", "打开"], {"default": "关闭"}),
"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。",
}),
},
}
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", seed=0):
api_key = get_api_key_or_raise()
base_url = get_async_api_base_url()
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
}
# ── 动态拼接模型名 ────────────────────────────────────────────
mode_api = "std" if 模式 == "720p" else "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,
}
if 生成音频 == "打开":
body["generate_audio"] = True
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
_stage("submitting")
create_url = f"{base_url}{_ENDPOINT_CREATE}"
async with session.post(create_url, json=body, headers=headers) as resp:
if resp.status != 200:
err_text = await resp.text()
raise RuntimeError(f"提交失败 ({resp.status}): {err_text}")
sr = await resp.json()
task_id = sr.get("task_id") or sr.get("id")
if not task_id:
raise RuntimeError(f"API 未返回 task_id，响应：{sr}")
_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:
await asyncio.sleep(interval)
async with session.get(status_url, headers=headers) as resp:
if resp.status != 200:
err_text = await resp.text()
raise RuntimeError(f"查询失败 ({resp.status}): {err_text}")
sr = await resp.json()
data = sr.get("data", {}) or {}
status = (sr.get("status") or data.get("status") or "").lower()
pct_raw = str(data.get("progress", 0)).strip().rstrip('%')
try:
pct = max(0, min(100, int(float(pct_raw))))
except (ValueError, TypeError):
pct = 0
print(f"[K26 图生视频] 生成中 {pct}%")
_progress(pct)
if status in ("success", "completed", "done", "finished", "succeed"):
# 提取视频 URL
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"K26 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.close(tmp_fd)
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,)
NODE_CLASS_MAPPINGS = {
"KVideoImage2Video": KVideoImage2Video,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"KVideoImage2Video": "K26 图生视频",
}
nodes/__init__.py
+3 -2
View File
@@ -21,9 +21,10 @@ from .seedance_video import Seedance, SeedanceMultiModal
from .nano_banana_v2 import NanoBananaV2, NanoBananaV2Batch, AsyncImageGenerator, BatchAsyncImageGenerator
from .doubao_image import DoubaoImage
from .gpt_image import O1keyGPTImage
from .K_video import KVideo
from .K_video_firstlast import KVideoFirstLast
from .K_video_image2video import KVideoImage2Video
from .K3_video import K3Video
from .K3_video_firstlast import K3VideoFirstLast
from .K3_motion_control import K3MotionControl, K3MotionVideoCheck
__all__ = ['NanoBananaV2', 'NanoBananaV2Batch', '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', 'AsyncImageGenerator', 'BatchAsyncImageGenerator']
__all__ = ['NanoBananaV2', 'NanoBananaV2Batch', 'NanoBananaPro', 'BatchNanoBananaPro', 'GoogleGemini', 'LoadFile', 'ImageStitchPro', 'SaveCleanImage', 'BatchCleanMetadata', 'VideoPreview', 'KlingVideo', 'KlingFirstLastFrame', 'KlingMotionControlTest', 'AspectRatioPreset', 'GoogleVeo', 'FluxImageEdit', 'UniversalLLMChat', 'MultiResPreview', 'BatchImagesO1key', 'Seedance', 'SeedanceMultiModal', 'StreamPreview', 'DoubaoImage', 'O1keyGPTImage', 'KVideoFirstLast', 'KVideoImage2Video', 'K3Video', 'K3VideoFirstLast', 'K3MotionControl', 'K3MotionVideoCheck', 'AsyncImageGenerator', 'BatchAsyncImageGenerator']
nodes/nano_banana_v2.py
+8 -3
View File
@@ -62,8 +62,9 @@ except ImportError:
DEBUG_LOG_ENABLED = False
REQUEST_LOG_ENABLED = False
_POLL_INTERVAL = 2 # 轮询间隔（秒）
_MAX_WAIT_TIME = 900 # 单任务最大等待时间（秒）
_POLL_INTERVAL = 2 # 轮询间隔（秒）
_INTERRUPT_CHECK_INTERVAL = 0.1 # 取消检查间隔（秒）
_MAX_WAIT_TIME = 900 # 单任务最大等待时间（秒）
def _images_to_tensor_safe(images: List[Image.Image], node_label: str) -> torch.Tensor:
@@ -374,7 +375,11 @@ class NanoBananaV2:
friendly_msg = self._friendly_error(error_msg)
raise RuntimeError(f"任务失败: {friendly_msg}")
elif status in ("SUBMITTED", "IN_PROGRESS"):
await asyncio.sleep(_POLL_INTERVAL)
# 分段 sleep，每 0.1 秒检查一次取消信号
sleep_iterations = int(_POLL_INTERVAL / _INTERRUPT_CHECK_INTERVAL)
for _ in range(sleep_iterations):
self._check_interrupt()
await asyncio.sleep(_INTERRUPT_CHECK_INTERVAL)
else:
raise RuntimeError(f"未知任务状态: {status}")