feat: Seedance 视频/音频改用 R2 预签名上传,移除插件内凭证

- 新增 utils/r2_uploader.py:通过 o1key 后端预签名接口上传视频/音频,零 R2 凭证
- seedance_video.py:视频、音频均改为 await upload_video/upload_audio,移除 base64 inline 编码
- 删除 doubao-seedance-2-0-fast 模型选项
- .config 加入 .gitignore,停止 git 追踪,防止 API Key 泄露
- base_client.py:新增系统代理自动检测(Windows 注册表 / macOS networksetup)
This commit is contained in:
Jony
2026-04-21 00:10:55 +08:00
parent c4bb8d9724
commit 07f0c5ed5f
9 changed files with 276 additions and 173 deletions
-1
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@@ -1 +0,0 @@
O1KEY_API_KEY=
+3
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@@ -21,3 +21,6 @@ venv/
# OS
.DS_Store
Thumbs.db
# 用户配置(含 API Key,不提交)
.config
+123 -3
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@@ -9,9 +9,124 @@ import threading
from abc import ABC, abstractmethod
from typing import Any, Callable, Dict, List, Optional
import os
import platform
import time
import aiohttp
def _detect_proxy() -> str | None:
"""
检测当前系统的 HTTP 代理地址。
只读取,不修改任何环境变量或系统设置。
检测顺序:
1. 环境变量 HTTPS_PROXY / HTTP_PROXY(用户/启动脚本已配置时直接用)
2. Windows 注册表 Internet Settings
3. macOS networksetup
Returns:
代理 URL 字符串(如 "http://127.0.0.1:10808"),未检测到返回 None。
"""
# 1. 环境变量优先
for key in ("HTTPS_PROXY", "HTTP_PROXY", "https_proxy", "http_proxy", "ALL_PROXY", "all_proxy"):
val = os.environ.get(key)
if val:
return val
system = platform.system()
# 2. Windows 注册表
if system == "Windows":
try:
import winreg
reg_key = winreg.OpenKey(
winreg.HKEY_CURRENT_USER,
r"Software\Microsoft\Windows\CurrentVersion\Internet Settings",
)
enabled = winreg.QueryValueEx(reg_key, "ProxyEnable")[0]
if enabled:
server = winreg.QueryValueEx(reg_key, "ProxyServer")[0]
if server:
# 过滤掉 "http=...;https=..." 多协议格式,取第一个可用地址
if "=" in server:
# 例: "http=127.0.0.1:10808;https=127.0.0.1:10808"
for part in server.split(";"):
if "=" in part:
addr = part.split("=", 1)[1].strip()
if addr:
return f"http://{addr}"
else:
return f"http://{server}"
except Exception:
pass
# 3. macOS networksetup
elif system == "Darwin":
try:
import subprocess
for iface in ("Wi-Fi", "Ethernet", "USB 10/100/1000 LAN"):
for flag, proto in (("-getsecurewebproxy", "https"), ("-getwebproxy", "http"),
("-getsocksfirewallproxy", "socks5")):
out = subprocess.run(
["networksetup", flag, iface],
capture_output=True, text=True, timeout=3,
).stdout
if "Enabled: Yes" in out:
lines = {l.split(":")[0].strip(): l.split(":", 1)[1].strip()
for l in out.splitlines() if ":" in l}
host = lines.get("Server", "")
port = lines.get("Port", "")
if host and port and port != "0":
return f"{proto}://{host}:{port}"
except Exception:
pass
return None
# ── 带 TTL 的代理缓存 ──────────────────────────────────────────────────────────
# Windows 注册表读取 < 1msTTL 设短;macOS 需要子进程,TTL 设长一些。
_PROXY_TTL = 3.0 if platform.system() == "Windows" else 10.0
_proxy_cache_value: str | None = None
_proxy_cache_expires: float = 0.0
_proxy_cache_lock = threading.Lock()
def _get_proxy() -> str | None:
"""
返回当前生效的代理地址(带 TTL 缓存)。
缓存过期后重新检测,代理状态变化时自动打印日志。
"""
global _proxy_cache_value, _proxy_cache_expires
now = time.monotonic()
if now < _proxy_cache_expires: # 缓存命中,零开销
return _proxy_cache_value
with _proxy_cache_lock:
if now < _proxy_cache_expires: # 双重检查,防止并发重复检测
return _proxy_cache_value
new_value = _detect_proxy()
if new_value != _proxy_cache_value:
if new_value:
print("[o1key] 检测到代理已开启")
else:
print("[o1key] 代理已关闭,切换为直连模式")
_proxy_cache_value = new_value
_proxy_cache_expires = time.monotonic() + _PROXY_TTL
return _proxy_cache_value
# 启动时打印一次初始状态
_initial = _get_proxy()
print(f"[o1key] 启动代理检测: {'已开启' if _initial else '未开启,直连模式'}")
class BaseAPIClient(ABC):
"""
API 客户端抽象基类
@@ -195,8 +310,9 @@ class BaseAPIClient(ABC):
)
async def _do_request():
_proxy = _get_proxy()
connect_start = time.time()
async with session.post(url, json=request_body, headers=headers, timeout=_aiohttp_timeout) as response:
async with session.post(url, json=request_body, headers=headers, timeout=_aiohttp_timeout, proxy=_proxy) as response:
connect_time = time.time() - connect_start
if response.status != 200:
@@ -308,7 +424,10 @@ class BaseAPIClient(ABC):
close_session = True
try:
async with session.get(url, headers=headers) as response:
_proxy = _get_proxy()
_get_start = time.time()
async with session.get(url, headers=headers, proxy=_proxy) as response:
_get_elapsed = time.time() - _get_start
if response.status != 200:
error_text = await response.text()
@@ -375,7 +494,8 @@ class BaseAPIClient(ABC):
f"API 返回错误:{error_message}"
)
return await response.json()
_resp_data = await response.json()
return _resp_data
finally:
if close_session:
+10 -2
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@@ -577,7 +577,14 @@ class GeminiAPIClient(BaseAPIClient):
download_info = f"{img_size_str}"
# 单行输出
print(f"{task_prefix}请求 {size_str} → API {request_time:.1f}s → {download_info}")
timing = response.get("_timing", {})
net_connect = timing.get("connect_time")
net_download = timing.get("download_time")
if net_connect is not None and net_download is not None:
net_str = f" | 连接 {net_connect:.2f}s | 下载 {net_download:.2f}s"
else:
net_str = ""
print(f"{task_prefix}请求 {size_str} → API {request_time:.1f}s → {download_info}{net_str}")
# 返回结果和计时信息
total_time = time.time() - total_start
@@ -721,7 +728,8 @@ class GeminiAPIClient(BaseAPIClient):
raise first_error
raise RuntimeError(f"批量生成失败,{fail_count} 个请求全部失败")
print(f"GeminiClient: 批量生成完成,成功 {success_count}/{batch_size},失败 {fail_count}")
if batch_size > 1:
print(f"GeminiClient: 批量生成完成,成功 {success_count}/{batch_size},失败 {fail_count}")
return all_images
def generate_sync(
+8 -1
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@@ -632,7 +632,14 @@ class OpenAIAPIClient(BaseAPIClient):
else:
download_info = img_size_str
print(f"{task_prefix} 请求 {size_str} → API {request_time:.1f}s → {download_info}")
timing = response.get("_timing", {})
net_connect = timing.get("connect_time")
net_download = timing.get("download_time")
if net_connect is not None and net_download is not None:
net_str = f" | 连接 {net_connect:.2f}s | 下载 {net_download:.2f}s"
else:
net_str = ""
print(f"{task_prefix} 请求 {size_str} → API {request_time:.1f}s → {download_info}{net_str}")
total_time = time.time() - total_start
timing_info = {
-76
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@@ -43,14 +43,6 @@ VIDEO_MIME_TYPES = {
".3gpp": "video/3gpp"
}
# 尝试导入视频处理库
try:
import cv2
CV2_AVAILABLE = True
except ImportError:
CV2_AVAILABLE = False
print("⚠️ Google Gemini: OpenCV (cv2) 不可用,视频压缩功能将受限")
try:
import subprocess
FFMPEG_AVAILABLE = True
@@ -328,63 +320,6 @@ class GoogleGemini:
print(f"Google Gemini: 视频压缩异常: {str(e)}")
return False
def _compress_video_with_opencv(self, input_path: str, output_path: str, scale: float = 0.5) -> bool:
"""
使用 OpenCV 压缩视频(备用方案)
Args:
input_path: 输入视频路径
output_path: 输出视频路径
scale: 尺寸缩放比例
Returns:
是否压缩成功
"""
if not CV2_AVAILABLE:
return False
try:
cap = cv2.VideoCapture(input_path)
if not cap.isOpened():
return False
# 获取原视频参数
fps = cap.get(cv2.CAP_PROP_FPS)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# 计算新尺寸
new_width = int(width * scale)
new_height = int(height * scale)
# 创建视频写入器
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_path, fourcc, fps, (new_width, new_height))
print(f"Google Gemini: 使用 OpenCV 压缩视频,分辨率 {width}x{height} -> {new_width}x{new_height}")
while True:
ret, frame = cap.read()
if not ret:
break
# 缩放帧
resized = cv2.resize(frame, (new_width, new_height))
out.write(resized)
cap.release()
out.release()
if os.path.exists(output_path):
final_size = os.path.getsize(output_path)
print(f"Google Gemini: 视频压缩完成,最终大小 {final_size / 1024 / 1024:.2f}MB")
return True
return False
except Exception as e:
print(f"Google Gemini: OpenCV 压缩失败: {str(e)}")
return False
def _compress_video(self, video_path: str) -> str:
"""
压缩视频到 1-10MB 之间
@@ -425,17 +360,6 @@ class GoogleGemini:
# 如果仍然太大,继续降低目标
os.remove(output_path)
# FFmpeg 失败或不可用,尝试 OpenCV
if CV2_AVAILABLE:
scales = [0.7, 0.5, 0.4, 0.3, 0.25]
for scale in scales:
if self._compress_video_with_opencv(video_path, output_path, scale):
final_size = os.path.getsize(output_path)
if final_size <= MAX_FILE_SIZE:
return output_path
# 如果仍然太大,继续降低分辨率
os.remove(output_path)
# 所有压缩方法都失败
raise ValueError(
f"视频文件过大 ({original_size / 1024 / 1024:.2f}MB) 且无法压缩到 20MB 以下。"
+4 -88
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@@ -14,6 +14,7 @@ import torch
from ..clients.seedance_client import SeedanceClient
from ..clients.gemini_client import GeminiAPIClient
from ..utils.image_utils import tensor_to_pil, encode_image_to_base64, pil_to_tensor
from ..utils.r2_uploader import upload_video, upload_audio
from comfy_api.latest import InputImpl
@@ -28,7 +29,6 @@ except ImportError:
_MODELS = [
"doubao-seedance-2-0-260128",
"doubao-seedance-2-0-fast-260128",
]
_RESOLUTIONS = ["720p", "480p"]
@@ -73,78 +73,6 @@ def _tensor_to_base64_url(tensor) -> str:
return f"data:image/png;base64,{b64}"
def _video_to_base64_url(video) -> str:
"""ComfyUI VIDEO 对象 → data:video/<ext>;base64,xxx"""
import base64
import io as _io
source = video.get_stream_source()
if isinstance(source, _io.BytesIO):
source.seek(0)
data = source.read()
ext = "mp4"
else:
video_path = source
if not video_path or not os.path.isfile(video_path):
raise ValueError(f"无法获取参考视频文件路径(当前路径:{video_path}")
ext = os.path.splitext(video_path)[1].lower().lstrip(".")
if ext not in ("mp4", "mov"):
raise ValueError(f"参考视频格式须为 mp4 或 mov,当前为 .{ext}")
with open(video_path, "rb") as f:
data = f.read()
b64 = base64.b64encode(data).decode("utf-8")
return f"data:video/{ext};base64,{b64}"
def _audio_to_base64_url(audio) -> str:
"""ComfyUI AUDIO dictwaveform tensor + sample_rate)→ data:audio/wav;base64,xxx"""
import base64
import io
import struct
import numpy as np
waveform = audio["waveform"] # shape: [B, C, N] or [C, N]
sample_rate = int(audio["sample_rate"])
# 统一为 [C, N]
if waveform.dim() == 3:
waveform = waveform[0]
# 转为 numpy float32,然后转 int16 PCM
wav_np = waveform.cpu().numpy()
if wav_np.ndim == 2:
# 多声道 → 单声道(取均值)
wav_np = wav_np.mean(axis=0)
wav_np = np.clip(wav_np, -1.0, 1.0)
pcm = (wav_np * 32767).astype(np.int16)
# 写 WAV 文件到内存
buf = io.BytesIO()
num_samples = len(pcm)
num_channels = 1
bits_per_sample = 16
byte_rate = sample_rate * num_channels * bits_per_sample // 8
block_align = num_channels * bits_per_sample // 8
data_size = num_samples * block_align
# RIFF header
buf.write(b"RIFF")
buf.write(struct.pack("<I", 36 + data_size))
buf.write(b"WAVE")
# fmt chunk
buf.write(b"fmt ")
buf.write(struct.pack("<IHHIIHH", 16, 1, num_channels, sample_rate,
byte_rate, block_align, bits_per_sample))
# data chunk
buf.write(b"data")
buf.write(struct.pack("<I", data_size))
buf.write(pcm.tobytes())
b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
return f"data:audio/wav;base64,{b64}"
async def _url_to_tensor(url: str) -> torch.Tensor:
"""从 URL 下载图片并转为 ComfyUI IMAGE tensor,失败时返回 None"""
@@ -201,6 +129,7 @@ def _make_callbacks(tag: str, pbar):
return on_stage, on_progress
# ── 统一节点 ─────────────────────────────────────────────────────────────────
#
# 模式由图片输入自动判断:
@@ -454,7 +383,7 @@ class SeedanceMultiModal:
# 参考视频(最多3个)
for v in ref_videos:
url = _video_to_base64_url(v)
url = await upload_video(v)
content.append({
"type": "video_url",
"video_url": {"url": url},
@@ -463,7 +392,7 @@ class SeedanceMultiModal:
# 参考音频(最多3段)
for a in ref_audios:
url = _audio_to_base64_url(a)
url = await upload_audio(a)
content.append({
"type": "audio_url",
"audio_url": {"url": url},
@@ -511,19 +440,6 @@ class SeedanceMultiModal:
if first_image_url:
body["image"] = first_image_url
# ── 打印请求体结构(base64 截断显示)────────────────────────────────
import json as _json, copy as _copy
def _truncate_body(obj):
if isinstance(obj, dict):
return {k: _truncate_body(v) for k, v in obj.items()}
if isinstance(obj, list):
return [_truncate_body(i) for i in obj]
if isinstance(obj, str) and obj.startswith("data:") and len(obj) > 80:
return obj[:60] + f"...[{len(obj)}chars]"
return obj
print("[SeedanceMultiModal] 请求体预览:")
print(_json.dumps(_truncate_body(_copy.deepcopy(body)), ensure_ascii=False, indent=2))
# ── 保存路径 ──────────────────────────────────────────────────────
video_dir = _get_video_output_dir()
counter = _get_next_counter(video_dir, "seedance_mm")
+1 -1
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@@ -15,7 +15,7 @@ CONFIG_FILE = os.path.join(PLUGIN_ROOT, ".config")
# ============ API 基础配置 ============
# 所有 API 客户端的统一基础 URL
# 可通过环境变量 O1KEY_API_BASE_URL 覆盖
DEFAULT_API_BASE_URL = "https://vip.o1key.com"
DEFAULT_API_BASE_URL = "https://api.o1key.com"
def load_config(config_path: Optional[str] = None) -> Dict[str, str]:
+126
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@@ -0,0 +1,126 @@
"""
R2 文件上传工具(通过 o1key 后端预签名接口)
- 插件内零 R2 凭证,仅使用用户的 O1KEY_API_KEY
- 流程:请求预签名 URL → PUT 直传 R2 → 返回公网 URL
"""
import io
import os
import uuid
import aiohttp
from .config import get_api_key_or_raise, get_api_base_url
async def _presign(filename: str, content_type: str) -> tuple:
"""向 o1key 后端请求预签名 URL,返回 (upload_url, public_url)"""
api_key = get_api_key_or_raise()
base_url = get_api_base_url()
async with aiohttp.ClientSession() as session:
async with session.post(
f"{base_url}/v1/storage/presign",
headers={"Authorization": f"Bearer {api_key}"},
json={"filename": filename, "content_type": content_type},
timeout=aiohttp.ClientTimeout(total=10),
) as resp:
if resp.status != 200:
text = await resp.text()
raise RuntimeError(f"预签名请求失败 ({resp.status}): {text}")
data = await resp.json()
return data["upload_url"], data["public_url"]
async def _put_upload(upload_url: str, data: bytes, content_type: str):
"""用预签名 URL 直传文件到 R2(不带 Authorization"""
async with aiohttp.ClientSession() as session:
async with session.put(
upload_url,
data=data,
headers={"Content-Type": content_type},
timeout=aiohttp.ClientTimeout(total=120),
) as resp:
if resp.status not in (200, 204):
text = await resp.text()
raise RuntimeError(f"文件上传失败 ({resp.status}): {text}")
async def upload_video(video) -> str:
"""
接受 ComfyUI VIDEO 对象,上传到 R2,返回公网 URL。
支持 mp4 / mov 格式。
"""
source = video.get_stream_source()
if isinstance(source, io.BytesIO):
source.seek(0)
data = source.read()
ext = "mp4"
else:
video_path = source
if not video_path or not os.path.isfile(video_path):
raise ValueError(f"无法获取参考视频文件路径(当前路径:{video_path}")
ext = os.path.splitext(video_path)[1].lower().lstrip(".")
if ext not in ("mp4", "mov"):
raise ValueError(f"参考视频格式须为 mp4 或 mov,当前为 .{ext}")
with open(video_path, "rb") as f:
data = f.read()
content_type = "video/mp4" if ext == "mp4" else "video/quicktime"
filename = f"{uuid.uuid4()}.{ext}"
upload_url, public_url = await _presign(filename, content_type)
await _put_upload(upload_url, data, content_type)
print(f"[R2] 视频已上传: {public_url}")
return public_url
async def upload_audio(audio) -> str:
"""
接受 ComfyUI AUDIO dictwaveform tensor + sample_rate),
编码为 WAV 后上传到 R2,返回公网 URL。
"""
import struct
import numpy as np
waveform = audio["waveform"] # shape: [B, C, N] or [C, N]
sample_rate = int(audio["sample_rate"])
if waveform.dim() == 3:
waveform = waveform[0]
wav_np = waveform.cpu().numpy()
if wav_np.ndim == 2:
wav_np = wav_np.mean(axis=0)
wav_np = np.clip(wav_np, -1.0, 1.0)
pcm = (wav_np * 32767).astype(np.int16)
num_samples = len(pcm)
num_channels = 1
bits_per_sample = 16
byte_rate = sample_rate * num_channels * bits_per_sample // 8
block_align = num_channels * bits_per_sample // 8
data_size = num_samples * block_align
buf = io.BytesIO()
buf.write(b"RIFF")
buf.write(struct.pack("<I", 36 + data_size))
buf.write(b"WAVE")
buf.write(b"fmt ")
buf.write(struct.pack("<IHHIIHH", 16, 1, num_channels, sample_rate,
byte_rate, block_align, bits_per_sample))
buf.write(b"data")
buf.write(struct.pack("<I", data_size))
buf.write(pcm.tobytes())
data = buf.getvalue()
filename = f"{uuid.uuid4()}.wav"
upload_url, public_url = await _presign(filename, "audio/wav")
await _put_upload(upload_url, data, "audio/wav")
print(f"[R2] 音频已上传: {public_url}")
return public_url