feat: sync latest local version as authoritative codebase
Complete rewrite/sync of comfyui_o1key custom nodes. Treat this commit as the current canonical version. Co-Authored-By: Claude Sonnet 4.5 <[email protected]>
This commit is contained in:
o1key
@@ -0,0 +1,526 @@
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"""
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Sora 视频生成节点
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ComfyUI 自定义节点，调用 Sora API 生成视频
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"""
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import os
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import re
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import time
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from math import gcd
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from typing import Optional, Tuple
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import torch
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from ..utils.image_utils import tensor_to_pil
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from ..clients.sora_client import SoraClient
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from ..models_config import (
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get_enabled_sora_models,
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get_all_sora_seconds,
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get_all_sora_sizes,
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get_sora_supported_seconds,
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get_sora_supported_sizes,
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get_sora_seconds_with_labels,
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get_sora_sizes_with_labels,
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SORA_MODELS,
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)
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try:
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import folder_paths
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FOLDER_PATHS_AVAILABLE = True
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except ImportError:
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FOLDER_PATHS_AVAILABLE = False
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try:
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from comfy.utils import ProgressBar
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PROGRESS_BAR_AVAILABLE = True
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except ImportError:
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PROGRESS_BAR_AVAILABLE = False
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print("⚠️ SoraVideo: comfy.utils.ProgressBar 不可用，将只使用终端进度显示")
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def _size_to_display(size: str) -> str:
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"""
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将 'WxH' 格式的分辨率转换为友好显示名。
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例如：
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"720x1280" → "720P 9:16"
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"1280x720" → "720P 16:9"
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"1024x1792" → "1K 4:7"
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"1792x1024" → "1K 7:4"
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Args:
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size: 分辨率字符串，格式 "WxH"
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Returns:
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友好显示名字符串
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"""
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parts = size.lower().split("x")
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w, h = int(parts[0]), int(parts[1])
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short_side = min(w, h)
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if short_side >= 3840:
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res = "4K"
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elif short_side >= 1920:
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res = "2K"
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elif short_side >= 1080:
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res = "1K"
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elif short_side >= 720:
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res = "720P"
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elif short_side >= 480:
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res = "480P"
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else:
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res = f"{short_side}P"
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g = gcd(w, h)
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ratio = f"{w // g}:{h // g}"
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return f"{res} {ratio} ({size})"
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def _build_size_display_map(sizes: list) -> dict:
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"""
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构建 显示名 → 实际值 映射字典。
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Args:
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sizes: 实际分辨率列表，如 ["720x1280", "1280x720"]
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Returns:
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字典，key 为显示名，value 为实际分辨率字符串
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"""
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mapping = {}
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for size in sizes:
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display = _size_to_display(size)
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if display in mapping:
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# 极少数情况下防止重名
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display = f"{display} ({size})"
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mapping[display] = size
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return mapping
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def _get_video_output_dir() -> str:
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"""获取视频输出目录: ComfyUI/output/video"""
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if FOLDER_PATHS_AVAILABLE:
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base = folder_paths.get_output_directory()
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else:
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plugin_dir = 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_dir)), "output")
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video_dir = os.path.join(base, "video")
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os.makedirs(video_dir, exist_ok=True)
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return video_dir
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def _get_next_counter(directory: str, prefix: str) -> int:
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"""扫描目录，获取下一个可用的文件计数器"""
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if not os.path.exists(directory):
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return 1
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pattern = re.compile(rf"^{re.escape(prefix)}_(\d+)")
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max_counter = 0
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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_counter = max(max_counter, int(m.group(1)))
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return max_counter + 1
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def _fit_image_to_target(image, target_size: str):
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"""
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将参考图片按 "等比缩放覆盖 + 居中裁剪" 策略适配到目标分辨率。
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策略 (Cover Crop)：
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1. 比较图片宽高比和目标宽高比
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2. 等比缩放，使图片最短边刚好覆盖目标对应边（图片完全覆盖目标区域）
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3. 居中裁剪多余部分，得到精确目标尺寸
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Args:
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image: PIL Image 对象
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target_size: 目标分辨率字符串，格式 "WxH"（如 "720x1280"）
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Returns:
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适配后的 PIL Image 对象
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"""
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from PIL import Image as PILImage
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# 解析目标尺寸
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parts = target_size.lower().split("x")
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target_w, target_h = int(parts[0]), int(parts[1])
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src_w, src_h = image.size
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src_ratio = src_w / src_h
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target_ratio = target_w / target_h
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# 宽高比一致且尺寸不超过目标，无需处理
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if abs(src_ratio - target_ratio) < 0.01 and src_w <= target_w and src_h <= target_h:
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return image
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print(f"Sora: 参考图片 {src_w}x{src_h} (比例 {src_ratio:.2f}) → 目标 {target_w}x{target_h} (比例 {target_ratio:.2f})")
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# 获取高质量重采样滤波器
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resample = PILImage.Resampling.LANCZOS if hasattr(PILImage, "Resampling") else PILImage.LANCZOS
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# Cover Crop: 缩放使图片完全覆盖目标区域，然后居中裁剪
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if src_ratio > target_ratio:
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# 图片更宽：以高度为基准缩放，裁左右
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scale = target_h / src_h
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new_w = round(src_w * scale)
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new_h = target_h
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image = image.resize((new_w, new_h), resample=resample)
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# 居中裁剪宽度
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left = (new_w - target_w) // 2
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image = image.crop((left, 0, left + target_w, target_h))
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else:
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# 图片更高（或一样）：以宽度为基准缩放，裁上下
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scale = target_w / src_w
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new_w = target_w
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new_h = round(src_h * scale)
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image = image.resize((new_w, new_h), resample=resample)
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# 居中裁剪高度
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top = (new_h - target_h) // 2
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image = image.crop((0, top, target_w, top + target_h))
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print(f"Sora: 参考图片已适配为 {image.size[0]}x{image.size[1]}")
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return image
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def _compress_image_for_upload(
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image,
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target_size: Optional[str] = None,
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) -> bytes:
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"""
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将 PIL Image 适配目标分辨率并编码为 PNG 字节，用于上传。
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============================================================
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⚠️ 已验证可用的标准做法，请勿随意修改以下编码逻辑！
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============================================================
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经过多轮调试（2026-02-28），以下参数组合为唯一验证成功的方案：
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1. 图片格式：PNG（format="PNG"）
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- 不可改为 JPEG —— API 会校验 Content-Type，抓包确认服务端使用 image/png
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- 不可使用 base64 字符串 —— 会报 "expected a file, got a string"
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- 不可使用 data URI —— 服务端不识别，返回 500
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2. 图片尺寸：必须与视频分辨率完全一致（target_size）
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- 不可缩放降采样 —— 会报 "Inpaint image must match the requested width and height"
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- 尺寸由 _fit_image_to_target() 保证（等比缩放 + 居中裁剪）
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3. 上传方式：由调用方（sora_client.py）以 multipart/form-data 文件字段上传
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- filename="reference.png", content_type="image/png"
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- 不可改回 application/json —— 服务端校验 input_reference 必须为 file 类型
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============================================================
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Args:
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image: PIL Image 对象
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target_size: 目标分辨率字符串 "WxH"（如 "720x1280"）
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Returns:
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PNG 格式的二进制字节
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"""
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from io import BytesIO
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# 统一转换为 RGB（去除透明通道及其他模式）
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if image.mode != "RGB":
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image = image.convert("RGB")
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# 适配到目标分辨率（等比缩放 + 居中裁剪）
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# ⚠️ 必须保持此尺寸不变，API 强制要求参考图片与视频分辨率完全一致
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if target_size:
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image = _fit_image_to_target(image, target_size)
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# ⚠️ 必须使用 PNG 格式，不可改为 JPEG 或其他格式
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buffered = BytesIO()
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image.save(buffered, format="PNG")
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size_kb = buffered.tell() / 1024
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print(f"Sora: 参考图片编码为 PNG，{size_kb:.0f} KB ({image.size[0]}x{image.size[1]})")
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return buffered.getvalue()
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class SoraVideo:
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"""
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Sora 视频生成节点
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功能：
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- 文生视频：基于提示词生成视频
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- 图生视频：基于参考图片和提示词生成视频
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- 异步轮询：自动等待生成完成并下载
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"""
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def __init__(self):
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self.client = None
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@classmethod
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def INPUT_TYPES(cls):
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from ..models_config import SECONDS_DISPLAY_MAP, RESOLUTION_DISPLAY_MAP
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enabled_models = get_enabled_sora_models()
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if not enabled_models:
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enabled_models = ["请在 models_config.py 中启用至少一个 Sora 模型"]
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# 构建秒数选项列表（按数字顺序排序）
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# 格式: ["4", "8", "10", "12", "15", "25(pro)"]
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all_seconds_display = []
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seen_seconds = set()
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for model_id in enabled_models:
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supported = get_sora_supported_seconds(model_id)
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for s in supported:
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if s not in seen_seconds:
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seen_seconds.add(s)
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display = SECONDS_DISPLAY_MAP.get(s, str(s))
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all_seconds_display.append((s, display))
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# 按秒数数值排序
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all_seconds_display = sorted(all_seconds_display, key=lambda x: x[0])
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seconds_options = [d for _, d in all_seconds_display] if all_seconds_display else ["4", "8", "12"]
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# 构建分辨率选项列表（去重）
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# 格式: ["720P", "1080P"]
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seen_resolutions = set()
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for model_id in enabled_models:
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supported = get_sora_supported_sizes(model_id)
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for size in supported:
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if size in RESOLUTION_DISPLAY_MAP:
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res_name, _ = RESOLUTION_DISPLAY_MAP[size]
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seen_resolutions.add(res_name)
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resolution_options = sorted(list(seen_resolutions)) if seen_resolutions else ["720P"]
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return {
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"required": {
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"prompt": ("STRING", {
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"default": "A calico cat playing a piano on stage",
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"multiline": True,
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}),
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"模型": (enabled_models, {
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"default": enabled_models[0],
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}),
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"分辨率": (resolution_options, {
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"default": resolution_options[0] if resolution_options else "720P",
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}),
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"宽高比": (["竖屏", "横屏"], {
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"default": "竖屏",
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}),
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"视频时长": (seconds_options, {
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"default": seconds_options[0] if seconds_options else "4",
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}),
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"生成数量": ("INT", {
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"default": 1,
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"min": 1,
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"max": 10,
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"step": 1,
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}),
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"seed": ("INT", {
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"default": 0,
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"min": 0,
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"max": 0xffffffffffffffff
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}),
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},
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"optional": {
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"参考图片": ("IMAGE",),
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},
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("预览视频",)
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FUNCTION = "generate_video"
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CATEGORY = "video/generation"
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DESCRIPTION = (
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"Sora 视频生成节点。\n"
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"支持文生视频和图生视频，自动轮询任务状态并下载视频。\n"
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"视频保存到 ComfyUI/output/video/ 目录。\n\n"
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"【模型说明】\n"
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"• sora-2：官方模型，支持 4/8/12秒、720P 分辨率\n"
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"• sora-2-pro：增强模型，支持全时长（含25秒）、1080P 分辨率\n\n"
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"【时长说明】\n"
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"• 25(pro)：仅 sora-2-pro 支持的25秒时长\n\n"
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"【分辨率说明】\n"
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"• 720P：sora-2 和 sora-2-pro 均支持\n"
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"• 1080P：仅 sora-2-pro 支持的高清分辨率"
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)
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def generate_video(
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self,
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prompt: str,
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模型: str,
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**kwargs,
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) -> Tuple[str]:
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from ..models_config import SECONDS_DISPLAY_MAP, RESOLUTION_DISPLAY_MAP
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视频时长_display = kwargs.pop("视频时长", "4")
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分辨率_display = kwargs.pop("分辨率", "720P")
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宽高比 = kwargs.pop("宽高比", "竖屏")
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生成数量 = kwargs.pop("生成数量", 1)
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seed = kwargs.pop("seed", 0)
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start_time = time.time()
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# 解析秒数显示值（如 "25(pro)" → 25）
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seconds = 4 # 默认
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for actual, display in SECONDS_DISPLAY_MAP.items():
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if display == 视频时长_display:
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seconds = actual
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break
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# 如果找不到映射，尝试直接解析数字
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if seconds == 4 and 视频时长_display != "4":
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try:
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seconds = int(视频时长_display.replace("(pro)", ""))
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except ValueError:
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seconds = 4
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# 根据分辨率和宽高比确定实际分辨率值
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分辨率 = "720x1280" # 默认
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for actual, (res_name, orientation) in RESOLUTION_DISPLAY_MAP.items():
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if res_name == 分辨率_display and orientation == 宽高比:
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分辨率 = actual
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break
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# 检查参考图片
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ref_image = kwargs.get("参考图片")
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ref_image_bytes = None
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if ref_image is not None:
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pil_images = tensor_to_pil(ref_image)
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if pil_images:
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ref_image_bytes = _compress_image_for_upload(pil_images[0], target_size=分辨率)
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mode_str = "图生视频 (含参考图)" if ref_image_bytes else "文生视频"
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# 获取用户友好的显示值用于日志
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seconds_display = SECONDS_DISPLAY_MAP.get(seconds, str(seconds))
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res_display = f"{分辨率_display} {宽高比}"
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if 生成数量 > 1:
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print(f"Sora: {mode_str} | 并发{生成数量}个 | {模型} | {seconds_display} | {res_display}")
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else:
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print(f"Sora: {mode_str} | {模型} | {seconds_display} | {res_display}")
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# 校验参数兼容性
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supported_seconds = get_sora_supported_seconds(模型)
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if supported_seconds and seconds not in supported_seconds:
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# 构建带标签的支持时长列表
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supported_labels = []
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for s in supported_seconds:
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display = SECONDS_DISPLAY_MAP.get(s, str(s))
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supported_labels.append(display)
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raise ValueError(
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f"时长 {SECONDS_DISPLAY_MAP.get(seconds, str(seconds))} 与模型 \"{模型}\" 不兼容！\n"
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f"该模型支持的时长: {', '.join(supported_labels)}"
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)
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supported_sizes = get_sora_supported_sizes(模型)
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if supported_sizes and 分辨率 not in supported_sizes:
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# 检查该分辨率是否为Pro独占
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pro_only_sizes = ["1024x1792", "1792x1024"]
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_, orientation = RESOLUTION_DISPLAY_MAP.get(分辨率, (分辨率, ""))
|
||||
extra_hint = f"\n提示：1080P {orientation} 为 sora-2-pro 独占，请切换模型或选择720P。" if 分辨率 in pro_only_sizes else ""
|
||||
raise ValueError(
|
||||
f"分辨率 \"{分辨率_display} {宽高比}\" 与模型 \"{模型}\" 不兼容！"
|
||||
f"支持的分辨率: {', '.join(supported_sizes)}" + extra_hint
|
||||
)
|
||||
|
||||
# 准备保存路径
|
||||
video_dir = _get_video_output_dir()
|
||||
counter = _get_next_counter(video_dir, "sora")
|
||||
|
||||
# ProgressBar
|
||||
pbar = None
|
||||
if PROGRESS_BAR_AVAILABLE:
|
||||
pbar = ProgressBar(生成数量 if 生成数量 > 1 else 100)
|
||||
|
||||
try:
|
||||
if self.client is None:
|
||||
self.client = SoraClient()
|
||||
|
||||
if 生成数量 == 1:
|
||||
# ── 单个视频：保留详细进度（提交→轮询→下载）
|
||||
save_path = os.path.join(video_dir, f"sora_{counter:05d}.mp4")
|
||||
last_progress = [0]
|
||||
|
||||
def progress_callback(progress_pct: int):
|
||||
print(
|
||||
f"\rSora: 生成中... 进度: {progress_pct}%",
|
||||
end="", flush=True
|
||||
)
|
||||
if pbar is not None and progress_pct > last_progress[0]:
|
||||
pbar.update(progress_pct - last_progress[0])
|
||||
last_progress[0] = progress_pct
|
||||
|
||||
def on_stage(stage: str):
|
||||
if stage == "submitting":
|
||||
print("Sora: 正在提交视频生成任务...")
|
||||
elif stage.startswith("submitted:"):
|
||||
vid = stage.split(":", 1)[1]
|
||||
print(f"Sora: 视频任务已提交，ID: {vid}")
|
||||
elif stage == "polling":
|
||||
print("Sora: 等待视频生成...")
|
||||
elif stage == "downloading":
|
||||
print("") # 换行（结束 \r 行）
|
||||
print("Sora: 视频生成完成，正在下载...")
|
||||
|
||||
result_path = self.client.generate_video_sync(
|
||||
prompt=prompt,
|
||||
model=模型,
|
||||
seconds=seconds,
|
||||
size=分辨率,
|
||||
save_path=save_path,
|
||||
input_reference_bytes=ref_image_bytes,
|
||||
seed=seed,
|
||||
progress_callback=progress_callback,
|
||||
on_stage=on_stage,
|
||||
)
|
||||
result_paths = [result_path]
|
||||
|
||||
else:
|
||||
# ── 批量并发：同时提交多个任务
|
||||
save_paths = [
|
||||
os.path.join(video_dir, f"sora_{counter + i:05d}.mp4")
|
||||
for i in range(生成数量)
|
||||
]
|
||||
success_count = [0]
|
||||
fail_count = [0]
|
||||
|
||||
def batch_progress_callback(current: int, total: int, success: bool, error_msg):
|
||||
if success:
|
||||
success_count[0] += 1
|
||||
print(f"Sora: 第 {current}/{total} 个视频完成 ✓")
|
||||
else:
|
||||
fail_count[0] += 1
|
||||
print(f"Sora: 第 {current}/{total} 个视频失败 ✗")
|
||||
if error_msg:
|
||||
print(f"原始错误详情:\n{error_msg}")
|
||||
if pbar is not None:
|
||||
pbar.update(1)
|
||||
|
||||
print(f"Sora: 正在并发提交 {生成数量} 个视频任务，请耐心等待...")
|
||||
result_paths = self.client.generate_batch_videos_sync(
|
||||
prompt=prompt,
|
||||
model=模型,
|
||||
seconds=seconds,
|
||||
size=分辨率,
|
||||
save_paths=save_paths,
|
||||
input_reference_bytes=ref_image_bytes,
|
||||
seed=seed,
|
||||
progress_callback=batch_progress_callback,
|
||||
)
|
||||
|
||||
elapsed = time.time() - start_time
|
||||
time_str = f"{elapsed:.2f}s" if elapsed >= 1 else f"{elapsed:.3f}s"
|
||||
print(f"Sora: 完成！总耗时 {time_str} | 已生成 {len(result_paths)} 个视频")
|
||||
for p in result_paths:
|
||||
print(f" → {p}")
|
||||
|
||||
output_path = "\n".join(result_paths)
|
||||
return (output_path,)
|
||||
|
||||
except ValueError as e:
|
||||
error_msg = str(e)
|
||||
print(f"\nSora: ❌ {error_msg}")
|
||||
raise ValueError(error_msg) from None
|
||||
|
||||
except RuntimeError as e:
|
||||
error_msg = str(e)
|
||||
print(f"\nSora: ❌ {error_msg}")
|
||||
raise RuntimeError(error_msg) from None
|
||||
|
||||
except Exception as e:
|
||||
error_msg = str(e)
|
||||
print(f"\nSora: ❌ {error_msg}")
|
||||
raise type(e)(error_msg) from None
|
||||
|
||||
finally:
|
||||
if self.client is not None:
|
||||
try:
|
||||
balance_data = self.client.query_balance_sync()
|
||||
balance_info = self.client.format_balance_info(balance_data)
|
||||
print(f"Sora: {balance_info}")
|
||||
except Exception:
|
||||
pass
|
||||
Reference in New Issue
Block a user