diff --git a/V3_DEV_GUIDE.py b/V3_DEV_GUIDE.py
new file mode 100644
index 0000000..6723124
--- /dev/null
+++ b/V3_DEV_GUIDE.py
@@ -0,0 +1,148 @@
+"""
+ComfyUI V3 节点开发参考
+========================
+
+本文件记录了将 V1 节点迁移到 V3 的关键经验，供后续节点开发快速参考。
+基于 nano_banana.py 的实际迁移总结。
+
+核心发现：V3 节点可以直接放入 V1 的 NODE_CLASS_MAPPINGS 中注册，
+ComfyUI 通过 issubclass(obj_class, _ComfyNodeInternal) 自动识别并
+调用 GET_NODE_INFO_V1() 生成前端所需的节点信息。无需 comfy_entrypoint。
+
+=== 最小 V3 节点模板 ===
+
+ from comfy_api.latest import io
+
+ class MyNode(io.ComfyNode):
+
+ @classmethod
+ def define_schema(cls):
+ return io.Schema(
+ node_id="MyNode", # 必须与 NODE_CLASS_MAPPINGS 的 key 一致
+ display_name="我的节点",
+ category="image/generation",
+ inputs=[...],
+ outputs=[io.Image.Output(display_name="输出")],
+ )
+
+ @classmethod
+ def execute(cls, input1, input2, ...) -> io.NodeOutput:
+ # 业务逻辑
+ return io.NodeOutput(result)
+
+=== V1 → V3 对照表 ===
+
+ V1 V3
+ ─────────────────────────────────────────────────────
+ INPUT_TYPES() classmethod define_schema() → io.Schema
+ RETURN_TYPES = ("IMAGE",) outputs=[io.Image.Output()]
+ RETURN_NAMES = ("输出",) io.Image.Output(display_name="输出")
+ FUNCTION = "generate" 固定为 execute
+ CATEGORY = "xxx" Schema(category="xxx")
+ generate(self, ...) execute(cls, ...) classmethod
+ self.xxx 实例状态 模块级单例函数
+
+=== DynamicCombo（动态联动下拉框）===
+
+场景：一个 combo 的选项决定其他 combo 显示哪些值。
+
+ io.DynamicCombo.Input("模型", options=[
+ io.DynamicCombo.Option("选项A", [
+ io.Combo.Input("子参数1", options=["x", "y"]),
+ io.Combo.Input("子参数2", options=["1K", "2K"]),
+ ]),
+ io.DynamicCombo.Option("选项B", [
+ io.Combo.Input("子参数1", options=["x", "y", "z", "w"]),
+ io.Combo.Input("子参数2", options=["512px", "1K", "2K", "4K"]),
+ ]),
+ ])
+
+execute 中接收为 dict：
+ def execute(cls, 模型, ...):
+ selected = 模型["模型"] # "选项A" 或 "选项B"
+ sub1 = 模型["子参数1"] # 对应选项下的子输入值
+ sub2 = 模型["子参数2"]
+
+注意：dict 的 key 是 DynamicCombo.Input 的 id（"模型"），
+子输入的 key 是各 Combo.Input 的 id。
+
+=== Autogrow（自动增长输入槽）===
+
+场景：用户连接一个槽后自动出现下一个，最多 N 个。
+
+ io.Autogrow.Input("参考图",
+ template=io.Autogrow.TemplatePrefix(
+ input=io.Image.Input("img"),
+ prefix="参考图", # 生成 参考图0, 参考图1, ...
+ min=0, # 最少显示几个槽
+ max=9, # 最多几个槽
+ ),
+ )
+
+execute 中接收为 dict（或 io.Autogrow.Type）：
+ def execute(cls, 参考图=None, ...):
+ if 参考图:
+ for key, tensor in 参考图.items():
+ # key = "参考图0", "参考图1", ...
+ # tensor = IMAGE tensor 或 None
+
+=== 实例状态处理 ===
+
+V3 的 execute 是 classmethod，无法用 self。
+用模块级单例替代：
+
+ _client = None
+
+ def _get_client():
+ global _client
+ if _client is None:
+ _client = MyAPIClient()
+ return _client
+
+=== 注册方式（与 V1 共存）===
+
+在 __init__.py 中照常注册，无需任何特殊处理：
+
+ NODE_CLASS_MAPPINGS = {
+ "MyV1Node": MyV1Node, # V1 节点
+ "MyV3Node": MyV3Node, # V3 节点，自动识别
+ }
+
+ NODE_DISPLAY_NAME_MAPPINGS = {
+ "MyV1Node": "V1 节点",
+ "MyV3Node": "V3 节点", # 也可省略，V3 用 Schema.display_name
+ }
+
+=== 注意事项 ===
+
+1. node_id 必须与 NODE_CLASS_MAPPINGS 的 key 完全一致
+2. V3 execute 返回 io.NodeOutput(tensor)，不是 tuple
+3. _wrap_generate_for_error_display 等 V1 包装器对 V3 无效
+ （找不到 generate 方法会安全跳过）
+4. V3 支持 async execute（直接加 async 即可）
+5. 输入参数名必须与 Schema inputs 的 id 一致
+6. DynamicCombo 的子输入在前端会随选项切换动态显示/隐藏
+7. Autogrow 的 widget 输入会被强制为 force_input（仅连接，无控件）
+
+=== 可用输入类型速查 ===
+
+ io.String.Input(id, default="", multiline=False)
+ io.Int.Input(id, default=0, min=0, max=N, step=1)
+ io.Float.Input(id, default=0.0, min=0.0, max=N, step=0.01)
+ io.Combo.Input(id, options=[...], default="...")
+ io.Boolean.Input(id, default=False)
+ io.Image.Input(id)
+ io.Mask.Input(id)
+ io.Latent.Input(id)
+ io.DynamicCombo.Input(id, options=[DynamicCombo.Option(...)])
+ io.Autogrow.Input(id, template=TemplatePrefix/TemplateNames)
+
+=== 可用输出类型速查 ===
+
+ io.Image.Output(display_name="...")
+ io.String.Output(display_name="...")
+ io.Int.Output()
+ io.Float.Output()
+ io.Latent.Output()
+ io.Mask.Output()
+"""
diff --git a/__init__.py b/__init__.py
index 4997e73..9de7617 100644
--- a/__init__.py
+++ b/__init__.py
@@ -11,9 +11,17 @@ Comfyui_o1key - ComfyUI 自定义节点集合
import ssl
+import logging
-from .nodes import NanoBananaPro, BatchNanoBananaPro, GoogleGemini, LoadFile, ImageStitchPro, SaveCleanImage, BatchCleanMetadata, VideoPreview, GoogleVeo, FluxImageEdit, UniversalLLMChat, KlingVideo, KlingFirstLastFrame, KlingMotionControlTest, AspectRatioPreset, MultiResPreview, BatchImagesO1key, Seedance, SeedanceMultiModal, StreamPreview, DoubaoImage, O1keyGPTImage, KVideoFirstLast, KVideoImage2Video
-from .nodes import K3Video, K3VideoFirstLast, K3MotionControl, K3MotionVideoCheck, NanoBananaV2, NanoBananaV2Batch
+# 屏蔽 ComfyUI 资产扫描的终端日志输出
+_seeder_filter = lambda record: not any(
+ kw in record.getMessage()
+ for kw in ("Seeder start", "Asset scan", "Scan(", "Fast scan")
+)
+logging.getLogger().addFilter(_seeder_filter)
+
+from .nodes import NanoBananaPro, BatchNanoBananaPro, GoogleGemini, LoadFile, ImageStitchPro, BatchCleanMetadata, VideoPreview, GoogleVeo, FluxImageEdit, UniversalLLMChat, KlingVideo, KlingFirstLastFrame, KlingMotionControlTest, AspectRatioPreset, BatchImagesO1key, Seedance, SeedanceMultiModal, StreamPreview, DoubaoImage, O1keyGPTImage, O1keyGrokImage, KVideoFirstLast, KVideoImage2Video
+from .nodes import K3Video, K3VideoFirstLast, K3MotionControl, K3MotionVideoCheck, NanoBananaV2, NanoBananaV2Batch, SaveImageFormat
# 报错弹框友好文案（不修改原节点代码，仅在外层统一处理）
_MSG_TIMEOUT = "API 请求超时，请稍后重试或检查网络。"
@@ -52,12 +60,12 @@ _wrap_generate_for_error_display(NanoBananaV2Batch)
# ComfyUI 节点注册
NODE_CLASS_MAPPINGS = {
- "NanoBananaPro": NanoBananaPro,
+ "NanoBanana": NanoBananaPro,
"BatchNanoBananaPro": BatchNanoBananaPro,
"GoogleGemini": GoogleGemini,
"LoadFile": LoadFile,
"ImageStitchPro": ImageStitchPro,
- "SaveCleanImage": SaveCleanImage,
+
"BatchCleanMetadata": BatchCleanMetadata,
"VideoPreview": VideoPreview,
"GoogleVeo": GoogleVeo,
@@ -67,13 +75,14 @@ NODE_CLASS_MAPPINGS = {
"KlingFirstLastFrame": KlingFirstLastFrame,
"KlingMotionControlTest": KlingMotionControlTest,
"AspectRatioPreset": AspectRatioPreset,
- "MultiResPreview": MultiResPreview,
+
"BatchImagesO1key": BatchImagesO1key,
"Seedance": Seedance,
"SeedanceMultiModal": SeedanceMultiModal,
"StreamPreview": StreamPreview,
"DoubaoImage": DoubaoImage,
"O1keyGPTImage": O1keyGPTImage,
+ "O1keyGrokImage": O1keyGrokImage,
"KVideoFirstLast": KVideoFirstLast,
"KVideoImage2Video": KVideoImage2Video,
"K3Video": K3Video,
@@ -82,15 +91,16 @@ NODE_CLASS_MAPPINGS = {
"K3MotionVideoCheck": K3MotionVideoCheck,
"NanoBananaV2": NanoBananaV2,
"NanoBananaV2Batch": NanoBananaV2Batch,
+ "SaveImageFormat": SaveImageFormat,
}
NODE_DISPLAY_NAME_MAPPINGS = {
- "NanoBananaPro": "Nano Banana",
+ "NanoBanana": "Nano Banana",
"BatchNanoBananaPro": "批量 Nano Banana",
"GoogleGemini": "Google Gemini",
"LoadFile": "加载文件",
"ImageStitchPro": "图像拼接 Pro",
- "SaveCleanImage": "保存图像（防AI识别）",
+
"BatchCleanMetadata": "批量任务（防AI识别）",
"VideoPreview": "预览视频",
"GoogleVeo": "Google Veo - ab",
@@ -100,13 +110,14 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"KlingFirstLastFrame": "首尾帧生视频 自研模型",
"KlingMotionControlTest": "动作控制 自研模型",
"AspectRatioPreset": "图片宽高比预设",
- "MultiResPreview": "预览图像（v2）",
+
"BatchImagesO1key": "加载图像（批量）",
"Seedance": "Seedance 视频生成",
"SeedanceMultiModal": "Seedance 多模态参考生视频",
"StreamPreview": "流式文本预览",
"DoubaoImage": "豆包生图",
"O1keyGPTImage": "o1key GPT Image",
+ "O1keyGrokImage": "Grok Image",
"KVideoFirstLast": "K26 图生视频（首尾帧）",
"KVideoImage2Video": "K26 图生视频",
"K3Video": "K3 图生视频 自研",
@@ -115,6 +126,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"K3MotionVideoCheck": "视频时长检测 K3",
"NanoBananaV2": "Nano Banana V2",
"NanoBananaV2Batch": "Nano Banana V2（批量）",
+ "SaveImageFormat": "保存图像（格式转换）",
}
WEB_DIRECTORY = "./web"
@@ -126,11 +138,351 @@ try:
from aiohttp import web
from server import PromptServer
import folder_paths
+ from .utils.config import CONFIG_FILE, load_config, NETWORK_ROUTES
@PromptServer.instance.routes.get("/o1key/input_dir")
async def get_input_dir(request):
import os
path = os.path.abspath(folder_paths.get_input_directory())
return web.json_response({"path": path})
+
+ @PromptServer.instance.routes.get("/o1key/api_key")
+ async def get_api_key_route(request):
+ config = load_config()
+ key = config.get("O1KEY_API_KEY", "")
+ masked = ""
+ if key:
+ if len(key) > 8:
+ masked = key[:3] + "****" + key[-4:]
+ else:
+ masked = "****"
+ return web.json_response({"has_key": bool(key), "masked": masked})
+
+ @PromptServer.instance.routes.post("/o1key/api_key")
+ async def set_api_key_route(request):
+ import os
+ data = await request.json()
+ new_key = data.get("api_key", "").strip()
+ if not new_key:
+ return web.json_response({"error": "API Key 不能为空"}, status=400)
+ config = load_config()
+ config["O1KEY_API_KEY"] = new_key
+ lines = []
+ for k, v in config.items():
+ lines.append(f"{k}={v}")
+ with open(CONFIG_FILE, 'w', encoding='utf-8') as f:
+ f.write("\n".join(lines) + "\n")
+ return web.json_response({"success": True})
+
+ @PromptServer.instance.routes.post("/o1key/test_key")
+ async def test_api_key_route(request):
+ import aiohttp as _aiohttp
+ data = await request.json()
+ test_key = data.get("api_key", "").strip()
+ if not test_key:
+ return web.json_response({"valid": False, "error": "密钥不能为空"})
+ base_url = NETWORK_ROUTES.get("全球加速", "https://api.o1key.cn")
+ url = f"{base_url}/v1/models"
+ headers = {"Authorization": f"Bearer {test_key}"}
+ try:
+ async with _aiohttp.ClientSession() as session:
+ async with session.get(url, headers=headers, timeout=_aiohttp.ClientTimeout(total=10)) as resp:
+ if resp.status == 200:
+ return web.json_response({"valid": True})
+ elif resp.status == 401:
+ return web.json_response({"valid": False, "error": "密钥无效或已过期"})
+ else:
+ text = await resp.text()
+ return web.json_response({"valid": False, "error": f"验证失败 ({resp.status})"})
+ except Exception as e:
+ return web.json_response({"valid": False, "error": f"网络错误: {str(e)}"})
+
+ @PromptServer.instance.routes.delete("/o1key/api_key")
+ async def delete_api_key_route(request):
+ config = load_config()
+ config.pop("O1KEY_API_KEY", None)
+ lines = []
+ for k, v in config.items():
+ lines.append(f"{k}={v}")
+ with open(CONFIG_FILE, 'w', encoding='utf-8') as f:
+ f.write("\n".join(lines) + "\n")
+ return web.json_response({"success": True})
+
+ @PromptServer.instance.routes.get("/o1key/output_history")
+ async def get_output_history(request):
+ """读取 output 目录文件，按执行分组返回 /api/jobs 兼容格式"""
+ import os, uuid, json as _json
+ limit = int(request.query.get("limit", "200"))
+ offset = int(request.query.get("offset", "0"))
+ output_dir = os.path.abspath(folder_paths.get_output_directory())
+ meta_file = os.path.join(output_dir, ".o1key_history.json")
+ meta = {}
+ if os.path.isfile(meta_file):
+ try:
+ with open(meta_file, "r", encoding="utf-8") as mf:
+ meta = _json.load(mf)
+ except Exception:
+ pass
+ supported_ext = {'.png', '.jpg', '.jpeg', '.webp', '.gif', '.mp4', '.webm'}
+ # 收集所有文件并按 workflow_id 分组
+ all_files = []
+ for fname in os.listdir(output_dir):
+ ext = os.path.splitext(fname)[1].lower()
+ if ext not in supported_ext:
+ continue
+ fpath = os.path.join(output_dir, fname)
+ if not os.path.isfile(fpath):
+ continue
+ mtime = os.path.getmtime(fpath)
+ media = "images" if ext in {'.png','.jpg','.jpeg','.webp','.gif'} else "video"
+ all_files.append({"name": fname, "mtime": mtime, "media": media})
+ # 按 workflow_id 分组（同一次执行合并为一个 job）
+ groups = {}
+ ungrouped = []
+ for f in all_files:
+ m = meta.get(f["name"], {})
+ wid = m.get("workflow_id")
+ if wid:
+ groups.setdefault(wid, []).append((f, m))
+ else:
+ ungrouped.append((f, {}))
+ # 构建 job 列表
+ jobs = []
+ for wid, items in groups.items():
+ items.sort(key=lambda x: x[0]["mtime"], reverse=True)
+ latest = items[0]
+ f, m = latest
+ start_ms = int(m.get("start_time", f["mtime"]) * 1000)
+ end_ms = int(m.get("end_time", f["mtime"]) * 1000)
+ jobs.append({
+ "id": wid,
+ "status": "completed",
+ "create_time": start_ms,
+ "execution_start_time": start_ms,
+ "execution_end_time": end_ms,
+ "preview_output": {
+ "filename": f["name"],
+ "subfolder": "",
+ "type": "output",
+ "nodeId": "0",
+ "mediaType": f["media"],
+ },
+ "outputs_count": len(items),
+ "execution_error": None,
+ "workflow_id": wid,
+ })
+ # 无元数据的文件各自作为独立 job
+ for f, m in ungrouped:
+ mtime_ms = int(f["mtime"] * 1000)
+ job_id = str(uuid.uuid5(uuid.NAMESPACE_URL, f["name"]))
+ jobs.append({
+ "id": job_id,
+ "status": "completed",
+ "create_time": mtime_ms,
+ "execution_start_time": mtime_ms,
+ "execution_end_time": mtime_ms,
+ "preview_output": {
+ "filename": f["name"],
+ "subfolder": "",
+ "type": "output",
+ "nodeId": "0",
+ "mediaType": f["media"],
+ },
+ "outputs_count": 1,
+ "execution_error": None,
+ "workflow_id": None,
+ })
+ # 按时间倒序排列，分页
+ jobs.sort(key=lambda x: x["create_time"], reverse=True)
+ total = len(jobs)
+ page = jobs[offset:offset+limit]
+ return web.json_response({
+ "jobs": page,
+ "pagination": {"offset": offset, "limit": limit, "total": total, "has_more": offset + limit < total}
+ })
+
+ @PromptServer.instance.routes.get("/o1key/output_workflow")
+ async def get_output_workflow(request):
+ """从 PNG 元数据中读取工作流，供前端恢复使用"""
+ import os, struct, json as _json
+ filename = request.query.get("filename", "")
+ if not filename:
+ return web.json_response({"error": "missing filename"}, status=400)
+ output_dir = os.path.abspath(folder_paths.get_output_directory())
+ fpath = os.path.join(output_dir, filename)
+ if not os.path.isfile(fpath) or not fpath.lower().endswith(".png"):
+ return web.json_response({"error": "file not found"}, status=404)
+ workflow = None
+ prompt_data = None
+ try:
+ with open(fpath, "rb") as pf:
+ pf.read(8) # PNG signature
+ while True:
+ raw = pf.read(8)
+ if len(raw) < 8:
+ break
+ length = struct.unpack(">I", raw[:4])[0]
+ chunk_type = raw[4:8]
+ data = pf.read(length)
+ pf.read(4) # CRC
+ if chunk_type == b"tEXt":
+ key, val = data.split(b"\x00", 1)
+ k = key.decode("ascii", errors="replace")
+ if k == "workflow":
+ workflow = _json.loads(val)
+ elif k == "prompt":
+ prompt_data = _json.loads(val)
+ elif chunk_type == b"IEND":
+ break
+ except Exception:
+ pass
+ return web.json_response({"workflow": workflow, "prompt": prompt_data})
+
+ @PromptServer.instance.routes.get("/o1key/job_detail/{job_id}")
+ async def get_job_detail(request):
+ """根据 job_id (workflow_id 或 uuid5) 返回含工作流的 job 详情"""
+ import os, uuid, struct, json as _json
+ job_id = request.match_info["job_id"]
+ output_dir = os.path.abspath(folder_paths.get_output_directory())
+ meta_file = os.path.join(output_dir, ".o1key_history.json")
+ meta = {}
+ if os.path.isfile(meta_file):
+ try:
+ with open(meta_file, "r", encoding="utf-8") as mf:
+ meta = _json.load(mf)
+ except Exception:
+ pass
+ supported_ext = {'.png', '.jpg', '.jpeg', '.webp', '.gif', '.mp4', '.webm'}
+ # 查找属于该 job 的所有文件（按 workflow_id 或 uuid5 匹配）
+ matched_files = []
+ for fname in os.listdir(output_dir):
+ ext = os.path.splitext(fname)[1].lower()
+ if ext not in supported_ext:
+ continue
+ m = meta.get(fname, {})
+ if m.get("workflow_id") == job_id:
+ matched_files.append(fname)
+ elif str(uuid.uuid5(uuid.NAMESPACE_URL, fname)) == job_id:
+ matched_files.append(fname)
+ if not matched_files:
+ return web.json_response({"error": "not found"}, status=404)
+ # 用最新文件作为代表
+ matched_files.sort(key=lambda f: os.path.getmtime(os.path.join(output_dir, f)), reverse=True)
+ target_file = matched_files[0]
+ fpath = os.path.join(output_dir, target_file)
+ m = meta.get(target_file, {})
+ mtime = os.path.getmtime(fpath)
+ start_ms = int(m.get("start_time", mtime) * 1000)
+ end_ms = int(m.get("end_time", mtime) * 1000)
+ ext = os.path.splitext(target_file)[1].lower()
+ media = "images" if ext in {'.png','.jpg','.jpeg','.webp','.gif'} else "video"
+ # 读取 PNG 工作流元数据
+ workflow = None
+ if ext == ".png":
+ try:
+ with open(fpath, "rb") as pf:
+ pf.read(8)
+ while True:
+ raw = pf.read(8)
+ if len(raw) < 8:
+ break
+ length = struct.unpack(">I", raw[:4])[0]
+ chunk_type = raw[4:8]
+ data = pf.read(length)
+ pf.read(4)
+ if chunk_type == b"tEXt":
+ key, val = data.split(b"\x00", 1)
+ k = key.decode("ascii", errors="replace")
+ if k == "workflow":
+ workflow = _json.loads(val)
+ elif chunk_type == b"IEND":
+ break
+ except Exception:
+ pass
+ # 构建 outputs：包含该执行的所有文件
+ outputs = {}
+ for i, fname in enumerate(matched_files):
+ e = os.path.splitext(fname)[1].lower()
+ mt = "images" if e in {'.png','.jpg','.jpeg','.webp','.gif'} else "gifs"
+ outputs.setdefault(str(i), {}).setdefault(mt, []).append(
+ {"filename": fname, "subfolder": "", "type": "output"}
+ )
+ job_detail = {
+ "id": job_id,
+ "status": "completed",
+ "create_time": start_ms,
+ "execution_start_time": start_ms,
+ "execution_end_time": end_ms,
+ "preview_output": {
+ "filename": target_file,
+ "subfolder": "",
+ "type": "output",
+ "nodeId": "0",
+ "mediaType": media,
+ },
+ "outputs_count": len(matched_files),
+ "execution_error": None,
+ "workflow_id": job_id,
+ "workflow": {
+ "extra_data": {
+ "extra_pnginfo": {"workflow": workflow}
+ }
+ } if workflow else None,
+ "outputs": outputs,
+ }
+ return web.json_response(job_detail)
+
+ # === 执行事件 Hook：持久化耗时元数据 ===
+ import time as _time, json as _json2, os as _os
+ _execution_tracker = {}
+
+ _orig_send_sync = PromptServer.instance.send_sync
+
+ def _patched_send_sync(event, data, *args, **kwargs):
+ try:
+ if event == "execution_start":
+ pid = data.get("prompt_id", "")
+ if pid:
+ _execution_tracker[pid] = {"start": _time.time(), "outputs": []}
+ elif event == "executed":
+ pid = data.get("prompt_id", "")
+ output = data.get("output") or {}
+ if pid and pid in _execution_tracker:
+ for img in output.get("images", []) + output.get("gifs", []):
+ if img.get("type") == "output" and img.get("filename"):
+ _execution_tracker[pid]["outputs"].append(img["filename"])
+ elif event == "executing" and data.get("node") is None:
+ pid = data.get("prompt_id", "")
+ tracker = _execution_tracker.pop(pid, None)
+ if tracker and tracker["outputs"]:
+ end_time = _time.time()
+ start_time = tracker["start"]
+ output_dir = _os.path.abspath(folder_paths.get_output_directory())
+ meta_file = _os.path.join(output_dir, ".o1key_history.json")
+ meta = {}
+ if _os.path.isfile(meta_file):
+ try:
+ with open(meta_file, "r", encoding="utf-8") as mf:
+ meta = _json2.load(mf)
+ except Exception:
+ pass
+ for fname in tracker["outputs"]:
+ meta[fname] = {
+ "start_time": start_time,
+ "end_time": end_time,
+ "outputs_count": len(tracker["outputs"]),
+ "workflow_id": pid,
+ }
+ try:
+ with open(meta_file, "w", encoding="utf-8") as mf:
+ _json2.dump(meta, mf, ensure_ascii=False)
+ except Exception:
+ pass
+ except Exception:
+ pass
+ return _orig_send_sync(event, data, *args, **kwargs)
+
+ PromptServer.instance.send_sync = _patched_send_sync
+
except Exception:
pass
diff --git a/clients/base_client.py b/clients/base_client.py
index ffd4fc8..be709c2 100644
--- a/clients/base_client.py
+++ b/clients/base_client.py
@@ -14,6 +14,8 @@ import time
import aiohttp
+from ..utils.http_error import HTTP_ERROR_MESSAGES, RETRYABLE_STATUS_CODES, _compute_delay, DEFAULT_MAX_RETRIES, DEFAULT_BASE_DELAY, DEFAULT_MAX_DELAY, DEFAULT_BACKOFF_FACTOR
+
class BaseAPIClient(ABC):
@@ -206,7 +208,8 @@ class BaseAPIClient(ABC):
if response.status != 200:
error_text = await response.text()
- raise RuntimeError(error_text)
+ # 返回状态码和错误文本，由外层处理重试
+ return {"_error": True, "_status": response.status, "_text": error_text}
wait_start = time.time()
response_data = await response.json()
@@ -231,31 +234,55 @@ class BaseAPIClient(ABC):
return
try:
- if _interrupt_available:
- request_task = asyncio.ensure_future(_do_request())
- interrupt_task = asyncio.ensure_future(_poll_interrupt())
+ last_error_status = None
+ last_error_text = ""
- done, pending = await asyncio.wait(
- [request_task, interrupt_task],
- return_when=asyncio.FIRST_COMPLETED
- )
+ for attempt in range(DEFAULT_MAX_RETRIES + 1):
+ if _interrupt_available:
+ request_task = asyncio.ensure_future(_do_request())
+ interrupt_task = asyncio.ensure_future(_poll_interrupt())
- # 取消未完成的任务
- for t in pending:
- t.cancel()
- try:
- await t
- except (asyncio.CancelledError, Exception):
- pass
+ done, pending = await asyncio.wait(
+ [request_task, interrupt_task],
+ return_when=asyncio.FIRST_COMPLETED
+ )
- # 判断是哪个先完成
- if interrupt_task in done and request_task not in done:
- raise InterruptProcessingException()
+ for t in pending:
+ t.cancel()
+ try:
+ await t
+ except (asyncio.CancelledError, Exception):
+ pass
- # 请求完成，取出结果（可能含异常）
- return request_task.result()
- else:
- return await _do_request()
+ if interrupt_task in done and request_task not in done:
+ raise InterruptProcessingException()
+
+ result = request_task.result()
+ else:
+ result = await _do_request()
+
+ if isinstance(result, dict) and result.get("_error"):
+ status = result["_status"]
+ error_text = result["_text"]
+ last_error_status = status
+ last_error_text = error_text
+
+ if status in RETRYABLE_STATUS_CODES and attempt < DEFAULT_MAX_RETRIES:
+ friendly = HTTP_ERROR_MESSAGES.get(status, f"请求失败 ({status})")
+ delay = _compute_delay(attempt, DEFAULT_BASE_DELAY, DEFAULT_MAX_DELAY, DEFAULT_BACKOFF_FACTOR)
+ print(f"{friendly} {delay:.1f}s 后重试 ({attempt+1}/{DEFAULT_MAX_RETRIES})...")
+ await asyncio.sleep(delay)
+ continue
+
+ if status in HTTP_ERROR_MESSAGES:
+ raise RuntimeError(HTTP_ERROR_MESSAGES[status])
+ raise RuntimeError(error_text)
+
+ return result
+
+ if last_error_status and last_error_status in HTTP_ERROR_MESSAGES:
+ raise RuntimeError(HTTP_ERROR_MESSAGES[last_error_status])
+ raise RuntimeError(last_error_text)
except InterruptProcessingException:
raise
@@ -352,30 +379,16 @@ class BaseAPIClient(ABC):
custom = self.get_http_error_message(429, error_message)
if custom is not None:
raise RuntimeError(custom)
- raise RuntimeError(
- f"请求频率超限 (429 Too Many Requests)\n"
- f"API 返回错误：{error_message}\n"
- f"建议：等待一段时间后重试"
- )
+ raise RuntimeError(HTTP_ERROR_MESSAGES[429])
elif response.status == 503:
custom = self.get_http_error_message(503, error_message)
if custom is not None:
raise RuntimeError(custom)
- raise RuntimeError(
- f"服务暂时不可用 (503 Service Unavailable)\n"
- f"API 返回错误：{error_message}\n"
- f"建议：稍后重试"
- )
+ raise RuntimeError(HTTP_ERROR_MESSAGES[503])
elif response.status == 504:
- raise RuntimeError(
- f"API 请求超时 (504 Gateway Timeout)\n"
- f"API 返回错误：{error_message}\n"
- f"建议：稍后重试"
- )
+ raise RuntimeError(HTTP_ERROR_MESSAGES[504])
elif response.status == 502:
- raise RuntimeError(
- "糟糕！请求到上游时遇到超时或过载！别担心，过会儿再次点击运行即可！"
- )
+ raise RuntimeError(HTTP_ERROR_MESSAGES[502])
else:
raise RuntimeError(
f"API 请求失败 (状态码: {response.status})\n"
diff --git a/clients/doubao_image_client.py b/clients/doubao_image_client.py
index 0f870a1..4887aae 100644
--- a/clients/doubao_image_client.py
+++ b/clients/doubao_image_client.py
@@ -21,6 +21,7 @@ from PIL import Image
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
+from ..utils.http_error import RETRYABLE_STATUS_CODES, HTTP_ERROR_MESSAGES, _compute_delay, DEFAULT_MAX_RETRIES, DEFAULT_BASE_DELAY, DEFAULT_MAX_DELAY, DEFAULT_BACKOFF_FACTOR
# ── 固定端点 ──────────────────────────────────────────────────────────────────
@@ -245,39 +246,55 @@ class DoubaoImageClient:
async with aiohttp.ClientSession(connector=connector, timeout=timeout) as session:
- # 3. 发送 POST 请求
- t0 = time.time()
- async with session.post(
- url,
- json=body,
- headers=self._headers(),
- ) as resp:
- elapsed_req = time.time() - t0
- text = await resp.text()
+ # 3. 发送 POST 请求（带退避重试）
+ last_status = None
+ for attempt in range(DEFAULT_MAX_RETRIES + 1):
+ t0 = time.time()
+ async with session.post(
+ url,
+ json=body,
+ headers=self._headers(),
+ ) as resp:
+ elapsed_req = time.time() - t0
+ text = await resp.text()
+
+ if resp.status != 200:
+ last_status = resp.status
+ if resp.status in RETRYABLE_STATUS_CODES and attempt < DEFAULT_MAX_RETRIES:
+ friendly = HTTP_ERROR_MESSAGES.get(resp.status)
+ delay = _compute_delay(attempt, DEFAULT_BASE_DELAY, DEFAULT_MAX_DELAY, DEFAULT_BACKOFF_FACTOR)
+ print(f"[豆包生图] {friendly} {delay:.1f}s 后重试 ({attempt+1}/{DEFAULT_MAX_RETRIES})...")
+ await asyncio.sleep(delay)
+ continue
+ if resp.status in HTTP_ERROR_MESSAGES:
+ raise RuntimeError(HTTP_ERROR_MESSAGES[resp.status])
+ try:
+ err_json = json.loads(text)
+ err_obj = err_json.get("error", {})
+ if isinstance(err_obj, dict):
+ msg = (
+ err_obj.get("message")
+ or err_obj.get("msg")
+ or text
+ )
+ else:
+ msg = str(err_obj) or text
+ except Exception:
+ msg = text
+ raise RuntimeError(
+ f"请求失败 HTTP {resp.status}: {msg}"
+ )
- if resp.status != 200:
- # 尝试解析错误信息
try:
- err_json = json.loads(text)
- err_obj = err_json.get("error", {})
- if isinstance(err_obj, dict):
- msg = (
- err_obj.get("message")
- or err_obj.get("msg")
- or text
- )
- else:
- msg = str(err_obj) or text
+ resp_json = json.loads(text)
except Exception:
- msg = text
- raise RuntimeError(
- f"请求失败 HTTP {resp.status}: {msg}"
- )
+ raise RuntimeError(f"响应 JSON 解析失败，原始内容：{text[:500]}")
- try:
- resp_json = json.loads(text)
- except Exception:
- raise RuntimeError(f"响应 JSON 解析失败，原始内容：{text[:500]}")
+ break
+ else:
+ if last_status and last_status in HTTP_ERROR_MESSAGES:
+ raise RuntimeError(HTTP_ERROR_MESSAGES[last_status])
+ raise RuntimeError(f"请求失败: 重试 {DEFAULT_MAX_RETRIES} 次后仍然失败")
print(f"[豆包生图] API 响应耗时 {elapsed_req:.1f}s，开始下载图像...")
diff --git a/clients/flux_edit_client.py b/clients/flux_edit_client.py
index 9e3ff3a..f4ba1f7 100644
--- a/clients/flux_edit_client.py
+++ b/clients/flux_edit_client.py
@@ -14,6 +14,7 @@ from typing import Optional
import requests
from ..utils.config import get_api_key_or_raise, get_api_base_url
+from ..utils.http_error import HTTP_ERROR_MESSAGES
# 显示名 → 实际请求值的映射
@@ -112,6 +113,8 @@ class FluxEditClient:
raise RuntimeError("无法连接到服务器，请检查网络或服务器地址")
if resp.status_code != 200:
+ if resp.status_code in HTTP_ERROR_MESSAGES:
+ raise RuntimeError(HTTP_ERROR_MESSAGES[resp.status_code])
raise RuntimeError(
f"提交任务失败 (HTTP {resp.status_code})\n"
f"响应: {resp.text[:500]}"
diff --git a/clients/gemini_async_provider.py b/clients/gemini_async_provider.py
index 28f59ef..64cb070 100644
--- a/clients/gemini_async_provider.py
+++ b/clients/gemini_async_provider.py
@@ -44,7 +44,7 @@ class GeminiAsyncImageProvider(BaseAsyncImageProvider):
@property
def api_base_url(self) -> str:
- return get_async_api_base_url()
+ return getattr(self, '_route_base_url', None) or get_async_api_base_url()
def get_submit_endpoint(self, model: str, resolution: str) -> str:
gemini_endpoint = self._client.get_endpoint(
diff --git a/clients/gemini_client.py b/clients/gemini_client.py
index bbb5f6a..a76cc8f 100644
--- a/clients/gemini_client.py
+++ b/clients/gemini_client.py
@@ -187,6 +187,7 @@ class GeminiAPIClient(BaseAPIClient):
enable_grounding: bool = False,
enable_image_search: bool = False,
image_compression: str = None,
+ thinking_level: str = None,
**kwargs
) -> Dict[str, Any]:
"""
@@ -227,15 +228,15 @@ class GeminiAPIClient(BaseAPIClient):
})
# 估算完整 body 大小（不含工具字段，工具字段很小可忽略）
+ est_image_config = {"imageSize": resolution}
+ if aspect_ratio and aspect_ratio != "智能":
+ est_image_config["aspectRatio"] = aspect_ratio
estimated = self._estimate_body_size(
parts + img_parts,
{
"generationConfig": {
"responseModalities": ["IMAGE"],
- "imageConfig": {
- "aspectRatio": aspect_ratio,
- "imageSize": resolution
- }
+ "imageConfig": est_image_config
}
}
)
@@ -267,6 +268,10 @@ class GeminiAPIClient(BaseAPIClient):
parts.extend(img_parts)
# 构建请求体
+ image_config = {"imageSize": resolution}
+ if aspect_ratio and aspect_ratio != "智能":
+ image_config["aspectRatio"] = aspect_ratio
+
request_body = {
"contents": [
{
@@ -276,13 +281,17 @@ class GeminiAPIClient(BaseAPIClient):
],
"generationConfig": {
"responseModalities": ["IMAGE"],
- "imageConfig": {
- "aspectRatio": aspect_ratio,
- "imageSize": resolution
- }
+ "imageConfig": image_config
}
}
+ # 添加思考深度配置
+ if thinking_level:
+ request_body["generationConfig"]["thinkingConfig"] = {
+ "thinkingLevel": thinking_level,
+ "includeThoughts": True
+ }
+
# 添加图片压缩参数
if image_compression:
request_body["image_compression"] = image_compression
@@ -564,6 +573,7 @@ class GeminiAPIClient(BaseAPIClient):
enable_grounding: bool = False,
enable_image_search: bool = False,
image_format: str = "base64",
+ thinking_level: str = None,
) -> tuple[List[Image.Image], Dict[str, Any]]:
"""
单次异步生成请求（极简单行日志）
@@ -602,6 +612,7 @@ class GeminiAPIClient(BaseAPIClient):
resolution=resolution,
enable_grounding=enable_grounding,
enable_image_search=enable_image_search,
+ thinking_level=thinking_level,
)
build_time = time.time() - build_start
@@ -737,6 +748,7 @@ class GeminiAPIClient(BaseAPIClient):
enable_grounding: bool = False,
enable_image_search: bool = False,
image_format: str = "base64",
+ thinking_level: str = None,
) -> List[Image.Image]:
"""
批量全并发生成 - 改进版：支持分批处理和内存管理
@@ -801,6 +813,7 @@ class GeminiAPIClient(BaseAPIClient):
enable_grounding=enable_grounding,
enable_image_search=enable_image_search,
image_format=image_format,
+ thinking_level=thinking_level,
),
name=f"task_{task_index}"
)
@@ -873,6 +886,7 @@ class GeminiAPIClient(BaseAPIClient):
enable_grounding: bool = False,
enable_image_search: bool = False,
image_format: str = "base64",
+ thinking_level: str = None,
) -> List[Image.Image]:
"""
同步生成接口（用于 ComfyUI）
@@ -906,6 +920,7 @@ class GeminiAPIClient(BaseAPIClient):
enable_grounding=enable_grounding,
enable_image_search=enable_image_search,
image_format=image_format,
+ thinking_level=thinking_level,
)
return self.run_async_in_thread(coro)
diff --git a/clients/gpt_image_client.py b/clients/gpt_image_client.py
index c18d351..e1852bb 100644
--- a/clients/gpt_image_client.py
+++ b/clients/gpt_image_client.py
@@ -26,6 +26,7 @@ from PIL import Image
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
+from ..utils.http_error import RETRYABLE_STATUS_CODES, HTTP_ERROR_MESSAGES, _compute_delay, DEFAULT_MAX_RETRIES, DEFAULT_BASE_DELAY, DEFAULT_MAX_DELAY, DEFAULT_BACKOFF_FACTOR
try:
from comfy.model_management import processing_interrupted, InterruptProcessingException
@@ -330,31 +331,46 @@ class GptImageClient:
async def _do_request():
async with aiohttp.ClientSession(connector=connector, timeout=timeout) as session:
- t0 = time.time()
- async with session.post(url, json=body, headers=self._json_headers()) as resp:
- elapsed = time.time() - t0
- text = await resp.text()
+ last_status = None
+ for attempt in range(DEFAULT_MAX_RETRIES + 1):
+ t0 = time.time()
+ async with session.post(url, json=body, headers=self._json_headers()) as resp:
+ elapsed = time.time() - t0
+ text = await resp.text()
+
+ if resp.status != 200:
+ last_status = resp.status
+ if resp.status in RETRYABLE_STATUS_CODES and attempt < DEFAULT_MAX_RETRIES:
+ friendly = HTTP_ERROR_MESSAGES.get(resp.status)
+ delay = _compute_delay(attempt, DEFAULT_BASE_DELAY, DEFAULT_MAX_DELAY, DEFAULT_BACKOFF_FACTOR)
+ print(f"[o1key GPT Image] {friendly} {delay:.1f}s 后重试 ({attempt+1}/{DEFAULT_MAX_RETRIES})...")
+ await asyncio.sleep(delay)
+ continue
+ if resp.status in HTTP_ERROR_MESSAGES:
+ raise RuntimeError(HTTP_ERROR_MESSAGES[resp.status])
+ try:
+ err_json = json.loads(text)
+ err_obj = err_json.get("error", {})
+ msg = (
+ err_obj.get("message") or err_obj.get("msg") or text
+ if isinstance(err_obj, dict)
+ else str(err_obj) or text
+ )
+ except Exception:
+ msg = text
+ raise RuntimeError(f"请求失败 HTTP {resp.status}: {msg}")
- if resp.status != 200:
try:
- err_json = json.loads(text)
- err_obj = err_json.get("error", {})
- msg = (
- err_obj.get("message") or err_obj.get("msg") or text
- if isinstance(err_obj, dict)
- else str(err_obj) or text
- )
+ resp_json = json.loads(text)
except Exception:
- msg = text
- raise RuntimeError(f"请求失败 HTTP {resp.status}: {msg}")
+ raise RuntimeError(f"响应 JSON 解析失败，原始内容：{text[:500]}")
- try:
- resp_json = json.loads(text)
- except Exception:
- raise RuntimeError(f"响应 JSON 解析失败，原始内容：{text[:500]}")
+ print(f"[o1key GPT Image] API 响应耗时 {elapsed:.1f}s")
+ return await self._parse_response(resp_json, session)
- print(f"[o1key GPT Image] API 响应耗时 {elapsed:.1f}s")
- return await self._parse_response(resp_json, session)
+ if last_status and last_status in HTTP_ERROR_MESSAGES:
+ raise RuntimeError(HTTP_ERROR_MESSAGES[last_status])
+ raise RuntimeError(f"请求失败: 重试 {DEFAULT_MAX_RETRIES} 次后仍然失败")
return await self._run_with_interrupt(_do_request())
@@ -446,6 +462,8 @@ class GptImageClient:
text = await resp.text()
if resp.status != 200:
+ if resp.status in HTTP_ERROR_MESSAGES:
+ raise RuntimeError(HTTP_ERROR_MESSAGES[resp.status])
try:
err_json = json.loads(text)
err_obj = err_json.get("error", {})
diff --git a/clients/grok_image_client.py b/clients/grok_image_client.py
new file mode 100644
index 0000000..4fdefd5
--- /dev/null
+++ b/clients/grok_image_client.py
@@ -0,0 +1,355 @@
+"""
+Grok Image API 客户端
+支持两个接口：
+ - POST /v1/images/generations 文生图
+ - POST /v1/images/edits 图生图（带参考图）
+
+上游 API 格式与 OpenAI Images API 兼容。
+"""
+
+import asyncio
+import base64
+import json
+import time
+from concurrent.futures import ThreadPoolExecutor
+from io import BytesIO
+from typing import List, Optional
+
+import aiohttp
+import numpy as np
+import torch
+from PIL import Image
+
+from ..utils.config import get_api_key_or_raise, get_base_url_by_route
+from ..utils.image_utils import tensor_to_pil
+
+try:
+ from comfy.model_management import processing_interrupted, InterruptProcessingException
+ _INTERRUPT_AVAILABLE = True
+except ImportError:
+ _INTERRUPT_AVAILABLE = False
+ InterruptProcessingException = RuntimeError
+ processing_interrupted = lambda: False
+
+_ENDPOINT_GENERATIONS = "/v1/images/generations"
+_ENDPOINT_EDITS = "/v1/images/edits"
+
+_MODEL_NAME_MAP = {
+ "Grok Image": "grok-imagine-image",
+ "Grok Image Pro": "grok-imagine-image-quality",
+}
+
+_REQUEST_TIMEOUT = 900
+_MAX_BODY_BYTES = 20 * 1024 * 1024
+
+_MAX_RETRIES = 3
+_RETRY_DELAY = 5
+
+
+class GrokImageClient:
+
+ def __init__(self, route: str = "全球加速"):
+ self.api_key = get_api_key_or_raise("O1KEY_API_KEY")
+ self.base_url = get_base_url_by_route(route)
+
+ def _json_headers(self) -> dict:
+ return {
+ "Authorization": f"Bearer {self.api_key}",
+ "Content-Type": "application/json",
+ }
+
+ def _auth_headers(self) -> dict:
+ return {"Authorization": f"Bearer {self.api_key}"}
+
+ # ── 图像工具 ──────────────────────────────────────────────────────────────
+
+ @staticmethod
+ def _shrink_png_to_limit(png_bytes: bytes, max_bytes: int, label: str = "") -> bytes:
+ if len(png_bytes) <= max_bytes:
+ return png_bytes
+ img = Image.open(BytesIO(png_bytes))
+ w, h = img.size
+ original_size = len(png_bytes)
+ step = 0
+ while len(png_bytes) > max_bytes:
+ scale = 0.894
+ w = max(1, int(w * scale))
+ h = max(1, int(h * scale))
+ img = img.resize((w, h), Image.LANCZOS)
+ buf = BytesIO()
+ img.save(buf, format="PNG")
+ png_bytes = buf.getvalue()
+ step += 1
+ tag = f" ({label})" if label else ""
+ print(
+ f"[o1key Grok Image] 图像{tag}超出 {max_bytes // (1024*1024)}MB 限制，"
+ f"已等比缩放 {step} 次：{original_size // 1024}KB → {len(png_bytes) // 1024}KB "
+ f"（{w}×{h}）"
+ )
+ return png_bytes
+
+ @staticmethod
+ def _pil_list_to_tensor(images: List[Image.Image]) -> torch.Tensor:
+ if not images:
+ placeholder = Image.new("RGB", (512, 512), (128, 128, 128))
+ images = [placeholder]
+ tensors = []
+ for img in images:
+ arr = np.array(img.convert("RGB")).astype(np.float32) / 255.0
+ tensors.append(torch.from_numpy(arr))
+ return torch.stack(tensors, dim=0)
+
+ # ── 中断轮询 ──────────────────────────────────────────────────────────────
+
+ @staticmethod
+ async def _poll_interrupt():
+ while True:
+ await asyncio.sleep(0.5)
+ if _INTERRUPT_AVAILABLE and processing_interrupted():
+ return
+
+ @staticmethod
+ async def _run_with_interrupt(coro):
+ if not _INTERRUPT_AVAILABLE:
+ return await coro
+ request_task = asyncio.ensure_future(coro)
+ interrupt_task = asyncio.ensure_future(GrokImageClient._poll_interrupt())
+ done, pending = await asyncio.wait(
+ [request_task, interrupt_task],
+ return_when=asyncio.FIRST_COMPLETED,
+ )
+ for t in pending:
+ t.cancel()
+ try:
+ await t
+ except (asyncio.CancelledError, Exception):
+ pass
+ if interrupt_task in done and request_task not in done:
+ raise InterruptProcessingException()
+ return request_task.result()
+
+ # ── 响应解析 ──────────────────────────────────────────────────────────────
+
+ async def _parse_response(self, resp_json: dict, session: aiohttp.ClientSession) -> List[Image.Image]:
+ if "error" in resp_json:
+ err = resp_json["error"]
+ msg = (
+ err.get("message") or err.get("msg") or json.dumps(err, ensure_ascii=False)
+ if isinstance(err, dict) else str(err)
+ )
+ raise RuntimeError(f"API 返回错误: {msg}")
+ data_list = resp_json.get("data")
+ if not data_list:
+ raise RuntimeError(f"API 响应中未找到 data 字段")
+ images: List[Image.Image] = []
+ for idx, item in enumerate(data_list):
+ b64 = item.get("b64_json", "")
+ url = item.get("url", "")
+ if b64:
+ img_bytes = base64.b64decode(b64)
+ img = Image.open(BytesIO(img_bytes))
+ images.append(img)
+ elif url and url.startswith("http"):
+ async with session.get(url, allow_redirects=True) as r:
+ if r.status != 200:
+ raise RuntimeError(f"图像下载失败 HTTP {r.status}")
+ img_bytes = await r.read()
+ images.append(Image.open(BytesIO(img_bytes)))
+ else:
+ print(f"[o1key Grok Image] 警告：第 {idx + 1} 条数据无有效图像，已跳过")
+ return images
+
+ # ── 文生图（generations 接口）─────────────────────────────────────────────
+
+ async def _generate_async(
+ self,
+ prompt: str,
+ model: str,
+ aspect_ratio: str,
+ resolution: str,
+ n: int,
+ ) -> List[Image.Image]:
+ api_model = _MODEL_NAME_MAP.get(model, model)
+ body: dict = {
+ "model": api_model,
+ "prompt": prompt,
+ "aspect_ratio": aspect_ratio if aspect_ratio else "auto",
+ "resolution": resolution if resolution else "1k",
+ "response_format": "b64_json",
+ }
+
+ url = f"{self.base_url}{_ENDPOINT_GENERATIONS}"
+ log_body = {k: v for k, v in body.items()}
+ print(f"[o1key Grok Image] 请求 URL: {url}")
+ print(f"[o1key Grok Image] 请求体: {json.dumps(log_body, ensure_ascii=False)}")
+
+ results = []
+ for i in range(n):
+ images = await self._do_request_with_retry(url, body)
+ results.extend(images)
+ if n > 1:
+ print(f"[o1key Grok Image] 第 {i+1}/{n} 张完成")
+ return results
+
+ # ── 图生图（edits 接口）───────────────────────────────────────────────────
+
+ async def _edit_async(
+ self,
+ prompt: str,
+ model: str,
+ aspect_ratio: str,
+ resolution: str,
+ n: int,
+ image_list: List[torch.Tensor],
+ ) -> List[Image.Image]:
+ api_model = _MODEL_NAME_MAP.get(model, model)
+ body: dict = {
+ "model": api_model,
+ "prompt": prompt,
+ "response_format": "b64_json",
+ }
+ if aspect_ratio and aspect_ratio != "auto":
+ body["aspect_ratio"] = aspect_ratio
+ if resolution:
+ body["resolution"] = resolution
+
+ # 参考图转 base64 字符串
+ pil_images = tensor_to_pil(image_list[0])
+ img = pil_images[0]
+ buf = BytesIO()
+ img.save(buf, format="PNG")
+ png_bytes = buf.getvalue()
+ png_bytes = self._shrink_png_to_limit(png_bytes, _MAX_BODY_BYTES // 2)
+ body["image"] = base64.b64encode(png_bytes).decode("utf-8")
+
+ url = f"{self.base_url}{_ENDPOINT_EDITS}"
+ log_body = {k: (v[:50] + "..." if k == "image" and len(v) > 50 else v) for k, v in body.items()}
+ print(f"[o1key Grok Image] 请求 URL: {url}")
+ print(f"[o1key Grok Image] 请求体: {json.dumps(log_body, ensure_ascii=False)}")
+
+ results = []
+ for i in range(n):
+ images = await self._do_request_with_retry(url, body)
+ results.extend(images)
+ if n > 1:
+ print(f"[o1key Grok Image] 第 {i+1}/{n} 张完成")
+ return results
+
+ # ── 带重试的请求 ────────────────────────────────────────────────────────
+
+ async def _do_request_with_retry(self, url: str, body: dict) -> List[Image.Image]:
+ connector = aiohttp.TCPConnector(ssl=False, force_close=True)
+ timeout = aiohttp.ClientTimeout(total=_REQUEST_TIMEOUT)
+
+ async def _do_request():
+ async with aiohttp.ClientSession(connector=connector, timeout=timeout) as session:
+ last_error = None
+ for attempt in range(1, _MAX_RETRIES + 1):
+ t0 = time.time()
+ async with session.post(url, json=body, headers=self._json_headers()) as resp:
+ elapsed = time.time() - t0
+ text = await resp.text()
+
+ if resp.status == 429 or resp.status in (502, 503, 504):
+ last_error = f"HTTP {resp.status}"
+ print(f"[o1key Grok Image] 重试 {attempt}/{_MAX_RETRIES}（{last_error}）")
+ await asyncio.sleep(_RETRY_DELAY * attempt)
+ continue
+
+ if resp.status == 400 and "high load" in text.lower():
+ last_error = "high load"
+ print(f"[o1key Grok Image] 重试 {attempt}/{_MAX_RETRIES}（服务繁忙）")
+ await asyncio.sleep(_RETRY_DELAY * attempt)
+ continue
+
+ if resp.status != 200:
+ try:
+ err_json = json.loads(text)
+ err_obj = err_json.get("error", {})
+ msg = (
+ err_obj.get("message") or err_obj.get("msg") or text
+ if isinstance(err_obj, dict) else str(err_obj) or text
+ )
+ except Exception:
+ msg = text
+ raise RuntimeError(f"请求失败 HTTP {resp.status}: {msg}")
+
+ try:
+ resp_json = json.loads(text)
+ except Exception:
+ raise RuntimeError(f"响应 JSON 解析失败，原始内容：{text[:500]}")
+
+ print(f"[o1key Grok Image] API 响应耗时 {elapsed:.1f}s")
+ return await self._parse_response(resp_json, session)
+
+ raise RuntimeError(f"重试 {_MAX_RETRIES} 次后仍失败: {last_error}")
+
+ return await self._run_with_interrupt(_do_request())
+
+ # ── 同步入口 ──────────────────────────────────────────────────────────────
+
+ def run_sync(
+ self,
+ prompt: str,
+ model: str,
+ aspect_ratio: str,
+ resolution: str,
+ n: int,
+ image_list: Optional[List[torch.Tensor]] = None,
+ ) -> List[Image.Image]:
+ if image_list:
+ coro = self._edit_async(
+ prompt=prompt, model=model, aspect_ratio=aspect_ratio,
+ resolution=resolution, n=n, image_list=image_list,
+ )
+ else:
+ coro = self._generate_async(
+ prompt=prompt, model=model, aspect_ratio=aspect_ratio,
+ resolution=resolution, n=n,
+ )
+
+ def _run():
+ loop = asyncio.new_event_loop()
+ asyncio.set_event_loop(loop)
+ try:
+ return loop.run_until_complete(coro)
+ finally:
+ loop.close()
+
+ with ThreadPoolExecutor(max_workers=1) as executor:
+ future = executor.submit(_run)
+ try:
+ return future.result(timeout=_REQUEST_TIMEOUT + 30)
+ except TimeoutError:
+ raise RuntimeError("Grok Image 请求超时，请检查网络或稍后重试")
+
+ # ── 余额查询 ──────────────────────────────────────────────────────────────
+
+ async def _query_balance_async(self) -> dict:
+ url = f"{self.base_url}/api/usage/token"
+ connector = aiohttp.TCPConnector(ssl=False, force_close=True)
+ timeout = aiohttp.ClientTimeout(total=10)
+ async with aiohttp.ClientSession(connector=connector, timeout=timeout) as session:
+ async with session.get(url, headers=self._auth_headers()) as resp:
+ if resp.status != 200:
+ raise RuntimeError(f"余额查询失败 HTTP {resp.status}")
+ return await resp.json()
+
+ def query_balance_sync(self) -> dict:
+ def _run():
+ loop = asyncio.new_event_loop()
+ asyncio.set_event_loop(loop)
+ try:
+ return loop.run_until_complete(self._query_balance_async())
+ finally:
+ loop.close()
+ with ThreadPoolExecutor(max_workers=1) as executor:
+ return executor.submit(_run).result(timeout=15)
+
+ @staticmethod
+ def format_balance_info(balance_data: dict) -> str:
+ data = balance_data.get("data", {})
+ api_name = data.get("name", "未知")
+ total_available = data.get("total_available", 0)
+ balance_in_dollars = total_available / 500000
+ return f"当前余额：{balance_in_dollars:.2f} | API：{api_name}"
diff --git a/clients/kling_client.py b/clients/kling_client.py
index a965a01..c091c87 100644
--- a/clients/kling_client.py
+++ b/clients/kling_client.py
@@ -10,6 +10,7 @@ from typing import Any, Callable, Dict, Optional
import aiohttp
from ..utils.config import get_api_key_or_raise, get_api_base_url
+from ..utils.http_error import async_request_with_retry
class KlingClient:
@@ -49,11 +50,11 @@ class KlingClient:
) -> Dict[str, Any]:
url = f"{self.base_url}{self.ENDPOINTS[endpoint_type]}"
- async with session.post(url, json=body, headers=self._headers()) as resp:
- text = await resp.text()
- if resp.status != 200:
- raise RuntimeError(f"提交失败 ({resp.status}): {text}")
- return json.loads(text)
+ resp = await async_request_with_retry(
+ session, "POST", url, json=body, headers=self._headers(), prefix="Kling 提交: "
+ )
+ text = await resp.text()
+ return json.loads(text)
# ── 轮询状态 ──────────────────────────────────────────────────────
@@ -203,17 +204,11 @@ class KlingClient:
if on_stage:
on_stage("submitting")
create_url = f"{self.base_url}{self.NEW_API_CREATE}"
- async with session.post(create_url, json=body, headers=headers) as resp:
- text = await resp.text()
- if resp.status != 200:
- # 尝试提取友好错误信息
- try:
- err = json.loads(text)
- msg = err.get("error", {}).get("message") or err.get("message") or text
- except Exception:
- msg = text
- raise RuntimeError(f"动作控制提交失败 ({resp.status}): {msg}")
- create_resp = json.loads(text)
+ resp = await async_request_with_retry(
+ session, "POST", create_url, json=body, headers=headers, prefix="Kling 动作控制提交: "
+ )
+ text = await resp.text()
+ create_resp = json.loads(text)
video_id = create_resp.get("id")
if not video_id:
diff --git a/clients/openai_client.py b/clients/openai_client.py
index 56e7639..5815e9e 100644
--- a/clients/openai_client.py
+++ b/clients/openai_client.py
@@ -204,13 +204,15 @@ class OpenAIAPIClient(BaseAPIClient):
"extra_body": {
"google": {
"image_config": {
- "aspect_ratio": aspect_ratio,
"image_size": api_image_size
}
}
}
}
+ if aspect_ratio and aspect_ratio != "智能":
+ request_body["extra_body"]["google"]["image_config"]["aspect_ratio"] = aspect_ratio
+
return request_body
def parse_response(self, response: Dict[str, Any]) -> List[Image.Image]:
diff --git a/clients/seedance_client.py b/clients/seedance_client.py
index bce0fbe..b763d86 100644
--- a/clients/seedance_client.py
+++ b/clients/seedance_client.py
@@ -11,6 +11,7 @@ from typing import Any, Callable, Dict, Optional
import aiohttp
from ..utils.config import get_api_key_or_raise
+from ..utils.http_error import async_request_with_retry
class SeedanceClient:
@@ -47,18 +48,11 @@ class SeedanceClient:
) -> str:
"""提交视频生成任务，返回 task_id"""
url = f"{self.base_url}{self.CREATE_ENDPOINT}"
- async with session.post(url, json=body, headers=self._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"提交失败 ({resp.status}): {msg}")
- data = json.loads(text)
+ resp = await async_request_with_retry(
+ session, "POST", url, json=body, headers=self._headers(), prefix="Seedance 提交: "
+ )
+ text = await resp.text()
+ data = json.loads(text)
# new-api 返回字段：id / task_id
task_id = data.get("id") or data.get("task_id")
diff --git a/nodes/K3_motion_control.py b/nodes/K3_motion_control.py
index 14ea3f4..5584e91 100644
--- a/nodes/K3_motion_control.py
+++ b/nodes/K3_motion_control.py
@@ -13,9 +13,10 @@ import tempfile
import aiohttp
-from ..utils.config import get_api_key_or_raise, get_async_api_base_url
+from ..utils.config import get_api_key_or_raise, get_async_api_base_url, NETWORK_ROUTE_OPTIONS, get_base_url_by_route
from ..utils.r2_uploader import upload_video, upload_image
from ..utils.image_utils import tensor_to_pil
+from ..utils.http_error import async_request_with_retry
try:
from comfy_api.latest import InputImpl
@@ -106,6 +107,7 @@ class K3MotionControl:
"参考图片": ("IMAGE",),
"参考视频": ("VIDEO",),
"提示词": ("STRING", {"multiline": True, "default": ""}),
+ "网络线路": (NETWORK_ROUTE_OPTIONS, {"default": "全球加速"}),
"模型": (["v3", "v2-6"], {"default": "v3"}),
"模式": (["720p", "1080p"], {"default": "1080p"}),
"时长": ([5, 10, 15, 20, 25, 30], {"default": 5}),
@@ -123,9 +125,9 @@ class K3MotionControl:
FUNCTION = "generate"
CATEGORY = "comfyui_o1key/KVideo"
- 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):
+ 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, 网络线路, seed, **kwargs):
api_key = get_api_key_or_raise()
- base_url = get_async_api_base_url()
+ base_url = get_base_url_by_route(网络线路)
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
@@ -207,20 +209,13 @@ class K3MotionControl:
# 1. 提交任务
_stage("submitting")
create_url = f"{base_url}{_ENDPOINT_CREATE}"
- async with session.post(
- create_url,
+ resp = await async_request_with_retry(
+ session, "POST", create_url,
data=json.dumps(body, ensure_ascii=False).encode("utf-8"),
- headers=headers,
- ) as resp:
- text = await resp.text()
- if resp.status != 200:
- try:
- err = json.loads(text)
- msg = err.get("message") or text
- except Exception:
- msg = text
- raise RuntimeError(f"K3 动作控制提交失败 ({resp.status}): {msg}")
- create_resp = json.loads(text)
+ headers=headers, prefix="K3 动作控制提交: "
+ )
+ text = await resp.text()
+ create_resp = json.loads(text)
# task_id 兼容扁平结构和 data 嵌套结构
task_id = (
diff --git a/nodes/K3_video.py b/nodes/K3_video.py
index 72a2615..9970a0b 100644
--- a/nodes/K3_video.py
+++ b/nodes/K3_video.py
@@ -11,8 +11,9 @@ import tempfile
import aiohttp
-from ..utils.config import get_api_key_or_raise, get_async_api_base_url
+from ..utils.config import get_api_key_or_raise, get_async_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
try:
from comfy_api.latest import InputImpl
@@ -113,6 +114,7 @@ class K3Video:
"时长": ([5, 10, 15], {"default": 5}),
"生成音频": (["关闭", "打开"], {"default": "关闭"}),
"模式": (_MODES, {"default": "720p"}),
+ "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。",
@@ -137,9 +139,9 @@ class K3Video:
FUNCTION = "generate"
CATEGORY = "comfyui_o1key/KVideo"
- 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):
+ 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, 网络线路, seed, **kwargs):
api_key = get_api_key_or_raise()
- base_url = get_async_api_base_url()
+ base_url = get_base_url_by_route(网络线路)
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
@@ -246,16 +248,11 @@ class K3Video:
# 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)
+ resp = await async_request_with_retry(
+ session, "POST", create_url, json=body, headers=headers, prefix="K3 提交: "
+ )
+ text = await resp.text()
+ create_resp = json.loads(text)
task_id = (
create_resp.get("task_id")
diff --git a/nodes/K3_video_firstlast.py b/nodes/K3_video_firstlast.py
index be14307..c0b33f2 100644
--- a/nodes/K3_video_firstlast.py
+++ b/nodes/K3_video_firstlast.py
@@ -10,8 +10,9 @@ import tempfile
import aiohttp
-from ..utils.config import get_api_key_or_raise, get_async_api_base_url
+from ..utils.config import get_api_key_or_raise, get_async_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
try:
from comfy_api.latest import InputImpl
@@ -94,6 +95,7 @@ class K3VideoFirstLast:
"时长": ([5, 10, 15], {"default": 5}),
"生成音频": (["关闭", "打开"], {"default": "关闭"}),
"模式": (_MODES, {"default": "720p"}),
+ "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。",
@@ -109,9 +111,9 @@ class K3VideoFirstLast:
FUNCTION = "generate"
CATEGORY = "comfyui_o1key/KVideo"
- 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):
+ async def generate(self, 起始帧, 提示词, 负向提示词, 时长, 生成音频, 模式, 网络线路, seed, 尾帧=None):
api_key = get_api_key_or_raise()
- base_url = get_async_api_base_url()
+ base_url = get_base_url_by_route(网络线路)
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
@@ -185,16 +187,11 @@ class K3VideoFirstLast:
# 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 首尾帧提交失败 ({resp.status}): {msg}")
- create_resp = json.loads(text)
+ resp = await async_request_with_retry(
+ session, "POST", create_url, json=body, headers=headers, prefix="K3 首尾帧提交: "
+ )
+ text = await resp.text()
+ create_resp = json.loads(text)
task_id = (
create_resp.get("task_id")
diff --git a/nodes/K_video_firstlast.py b/nodes/K_video_firstlast.py
index aabebaf..261b402 100644
--- a/nodes/K_video_firstlast.py
+++ b/nodes/K_video_firstlast.py
@@ -10,8 +10,9 @@ 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_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
try:
from comfy_api.latest import InputImpl
@@ -55,6 +56,7 @@ class KVideoFirstLast:
"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。",
@@ -70,9 +72,9 @@ class KVideoFirstLast:
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, seed=0):
+ 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_api_base_url()
+ base_url = get_base_url_by_route(网络线路)
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
@@ -154,16 +156,11 @@ class KVideoFirstLast:
# 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"K26 提交失败 ({resp.status}): {msg}")
- create_resp = json.loads(text)
+ resp = await async_request_with_retry(
+ session, "POST", create_url, json=body, headers=headers, prefix="K26 提交: "
+ )
+ text = await resp.text()
+ create_resp = json.loads(text)
task_id = (
create_resp.get("task_id")
diff --git a/nodes/K_video_image2video.py b/nodes/K_video_image2video.py
index 5485f20..d784cda 100644
--- a/nodes/K_video_image2video.py
+++ b/nodes/K_video_image2video.py
@@ -11,8 +11,9 @@ 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_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
try:
from comfy_api.latest import InputImpl
@@ -56,6 +57,7 @@ class KVideoImage2Video:
"模式": (["720p", "1080p"], {"default": "720p"}),
"时长": ([5, 10], {"default": 5}),
"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。",
@@ -68,9 +70,9 @@ class KVideoImage2Video:
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):
+ 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", seed=0):
api_key = get_api_key_or_raise()
- base_url = get_api_base_url()
+ base_url = get_base_url_by_route(网络线路)
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
@@ -150,11 +152,10 @@ class KVideoImage2Video:
# 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()
+ resp = await async_request_with_retry(
+ session, "POST", create_url, json=body, headers=headers, prefix="K26 图生视频提交: "
+ )
+ sr = await resp.json()
task_id = sr.get("task_id") or sr.get("id")
if not task_id:
diff --git a/nodes/__init__.py b/nodes/__init__.py
index 2ff6d87..453e265 100644
--- a/nodes/__init__.py
+++ b/nodes/__init__.py
@@ -4,27 +4,29 @@
"""
from .stream_preview import StreamPreview
-from .nano_banana_pro import NanoBananaPro
-from .batch_nano_banana_pro import BatchNanoBananaPro
+from .nano_banana import NanoBanana
+NanoBananaPro = NanoBanana
+from .batch_nano_banana import BatchNanoBananaPro
from .google_gemini import GoogleGemini
from .load_file import LoadFile
from .image_stitch_pro import ImageStitchPro
-from .remove_metadata import SaveCleanImage, BatchCleanMetadata
+from .remove_metadata import BatchCleanMetadata
from .video_preview import VideoPreview
from .kling_video import KlingVideo, KlingFirstLastFrame, KlingMotionControlTest, AspectRatioPreset
from .veo_video import GoogleVeo
from .flux_edit import FluxImageEdit
from .universal_llm import UniversalLLMChat
-from .multi_res_preview import MultiResPreview
from .batch_images_o1key import BatchImagesO1key
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 .grok_image import O1keyGrokImage
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
+from .save_image_format import SaveImageFormat
-__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']
+__all__ = ['NanoBananaV2', 'NanoBananaV2Batch', 'NanoBanana', 'BatchNanoBananaPro', 'GoogleGemini', 'LoadFile', 'ImageStitchPro', 'BatchCleanMetadata', 'VideoPreview', 'KlingVideo', 'KlingFirstLastFrame', 'KlingMotionControlTest', 'AspectRatioPreset', 'GoogleVeo', 'FluxImageEdit', 'UniversalLLMChat', 'BatchImagesO1key', 'Seedance', 'SeedanceMultiModal', 'StreamPreview', 'DoubaoImage', 'O1keyGPTImage', 'O1keyGrokImage', 'KVideoFirstLast', 'KVideoImage2Video', 'K3Video', 'K3VideoFirstLast', 'K3MotionControl', 'K3MotionVideoCheck', 'AsyncImageGenerator', 'BatchAsyncImageGenerator', 'SaveImageFormat']
diff --git a/nodes/batch_nano_banana_pro.py b/nodes/batch_nano_banana.py
similarity index 76%
rename from nodes/batch_nano_banana_pro.py
rename to nodes/batch_nano_banana.py
index 4a9399c..c7c90b7 100644
--- a/nodes/batch_nano_banana_pro.py
+++ b/nodes/batch_nano_banana.py
@@ -1,11 +1,15 @@
"""
-批量 Nano Banana Pro 节点
+批量 Nano Banana 节点
ComfyUI 自定义节点，用于批量处理图像生成任务
支持多文件夹加载、1:1/笛卡尔积配对、智能命名保存
"""
+import io as _io
+import re
+import json
import time
import math
+import base64
import random
import asyncio
import aiohttp
@@ -16,7 +20,7 @@ from PIL import Image
import torch
import numpy as np
-from ..utils.image_utils import tensor_to_pil, pil_to_tensor, parse_batch_prompts
+from ..utils.image_utils import tensor_to_pil, pil_to_tensor, parse_batch_prompts, encode_image_to_base64, encode_image_to_base64_limited
from ..utils.file_utils import (
ImageInfo,
load_images_from_folder,
@@ -25,9 +29,10 @@ from ..utils.file_utils import (
generate_timestamp_filename,
save_image,
)
+from ..utils.config import NETWORK_ROUTE_OPTIONS, get_base_url_by_route, get_api_key_or_raise
+from ..utils.http_error import RETRYABLE_STATUS_CODES, HTTP_ERROR_MESSAGES, _compute_delay, DEFAULT_MAX_RETRIES, DEFAULT_BASE_DELAY, DEFAULT_MAX_DELAY, DEFAULT_BACKOFF_FACTOR
from ..clients.gemini_client import GeminiAPIClient
from ..models_config import (
- get_enabled_models,
get_model_supported_aspect_ratios, get_all_supported_aspect_ratios,
get_model_supported_resolutions, get_all_supported_resolutions
)
@@ -67,7 +72,146 @@ DEBUG_LOG_ENABLED = False
REQUEST_LOG_ENABLED = False
# ============================================================================
-_NODE = "Nano Banana Pro"
+_NODE = "Nano Banana"
+_ENDPOINT = "/v1/chat/completions"
+
+_IMAGE_RE = re.compile(r"!\[.*?\]\(data:image/(\w+);base64,([A-Za-z0-9+/=]+)\)")
+
+
+def _get_headers(api_key: str) -> dict:
+ return {
+ "Authorization": f"Bearer {api_key}",
+ "Content-Type": "application/json",
+ "X-Accel-Buffering": "no",
+ "Cache-Control": "no-cache, no-transform",
+ }
+
+
+def _build_request_body(
+ prompt: str,
+ model: str,
+ aspect_ratio: str,
+ resolution: str,
+ images: Optional[List[Image.Image]] = None,
+ enable_grounding: bool = False,
+) -> dict:
+ content_parts = [{"type": "text", "text": prompt}]
+
+ if images:
+ for img in images:
+ b64 = encode_image_to_base64_limited(img, format="PNG")
+ content_parts.append({
+ "type": "image_url",
+ "image_url": {"url": f"data:image/png;base64,{b64}"}
+ })
+
+ body = {
+ "model": model,
+ "stream": True,
+ "messages": [{"role": "user", "content": content_parts}],
+ }
+
+ google_config = {
+ "image_config": {
+ "image_size": resolution,
+ }
+ }
+ if aspect_ratio and aspect_ratio != "智能":
+ google_config["image_config"]["aspect_ratio"] = aspect_ratio
+ body["extra_body"] = {"google": google_config}
+
+ if enable_grounding:
+ body["extra_body"]["google_search"] = True
+
+ return body
+
+
+async def _generate_single_openai(
+ session: aiohttp.ClientSession,
+ base_url: str,
+ api_key: str,
+ prompt: str,
+ model: str,
+ resolution: str,
+ aspect_ratio: str,
+ images: Optional[List[Image.Image]] = None,
+ enable_grounding: bool = False,
+) -> List[Image.Image]:
+ url = f"{base_url}{_ENDPOINT}"
+ headers = _get_headers(api_key)
+ body = _build_request_body(
+ prompt=prompt,
+ model=model,
+ aspect_ratio=aspect_ratio,
+ resolution=resolution,
+ images=images,
+ enable_grounding=enable_grounding,
+ )
+
+ if REQUEST_LOG_ENABLED:
+ extra = json.dumps(body.get("extra_body", {}), ensure_ascii=False)
+ print(f"[请求] POST {url} | model={model} | extra_body={extra}")
+
+ last_status = None
+ for attempt in range(DEFAULT_MAX_RETRIES + 1):
+ resp = await session.post(url, headers=headers, json=body)
+ if resp.status == 200:
+ break
+ last_status = resp.status
+ if resp.status in RETRYABLE_STATUS_CODES and attempt < DEFAULT_MAX_RETRIES:
+ friendly = HTTP_ERROR_MESSAGES.get(resp.status, f"请求失败 ({resp.status})")
+ delay = _compute_delay(attempt, DEFAULT_BASE_DELAY, DEFAULT_MAX_DELAY, DEFAULT_BACKOFF_FACTOR)
+ resp.close()
+ await asyncio.sleep(delay)
+ continue
+ error_text = await resp.text()
+ resp.close()
+ if resp.status in HTTP_ERROR_MESSAGES:
+ raise RuntimeError(HTTP_ERROR_MESSAGES[resp.status])
+ try:
+ err_json = json.loads(error_text)
+ msg = err_json.get("error", {}).get("message", error_text[:200])
+ except Exception:
+ msg = error_text[:200]
+ raise RuntimeError(f"API 错误 ({resp.status}): {msg}")
+ else:
+ if last_status and last_status in HTTP_ERROR_MESSAGES:
+ raise RuntimeError(HTTP_ERROR_MESSAGES[last_status])
+ raise RuntimeError(f"API 错误: 重试 {DEFAULT_MAX_RETRIES} 次后仍然失败")
+
+ full_content = ""
+ buffer = ""
+ async for raw_chunk in resp.content.iter_any():
+ buffer += raw_chunk.decode("utf-8")
+ while "\n" in buffer:
+ line_str, buffer = buffer.split("\n", 1)
+ line_str = line_str.strip()
+ if not line_str or not line_str.startswith("data:"):
+ continue
+ data_str = line_str[5:].strip()
+ if data_str == "[DONE]":
+ break
+ try:
+ chunk = json.loads(data_str)
+ delta = chunk.get("choices", [{}])[0].get("delta", {})
+ if "content" in delta:
+ full_content += delta["content"]
+ except (json.JSONDecodeError, IndexError):
+ continue
+ resp.close()
+
+ if not full_content:
+ raise RuntimeError("API 未返回有效内容")
+
+ matches = list(_IMAGE_RE.finditer(full_content))
+ if not matches:
+ raise RuntimeError(f"响应中未找到图片: {full_content[:100]}")
+
+ last_match = matches[-1]
+ img_data = base64.b64decode(last_match.group(2))
+ final_image = Image.open(_io.BytesIO(img_data)).convert("RGB")
+
+ return [final_image]
def _images_to_tensor_safe(images: List[Image.Image], node_label: str) -> torch.Tensor:
@@ -98,7 +242,7 @@ def _images_to_tensor_safe(images: List[Image.Image], node_label: str) -> torch.
class BatchNanoBananaPro:
"""
- 批量 Nano Banana Pro 节点
+ 批量 Nano Banana 节点
功能：
- 从多个文件夹加载图片
@@ -116,26 +260,32 @@ class BatchNanoBananaPro:
- 要添加/禁用模型，请编辑 models_config.py 文件
"""
- # 支持的模型列表（从配置文件动态加载）
- MODELS = None # 将在 INPUT_TYPES 中动态获取
-
- # 支持的宽高比列表（全量：所有启用模型的并集，动态加载）
- # 实际渲染时通过 get_all_supported_aspect_ratios() 获取
- ASPECT_RATIOS = [
- "1:1", "4:3", "3:4", "16:9", "9:16",
- "2:3", "3:2", "4:5", "5:4", "21:9",
- "1:4", "4:1", "1:8", "8:1"
- ]
-
- # 支持的分辨率列表（全量兜底，实际由 get_all_supported_resolutions() 动态生成）
- RESOLUTIONS = ["512px", "1K", "2K", "4K"]
-
+ # 模型展示名到基础 ID 的映射
+ MODEL_DISPLAY_NAMES = ["Nano Banana Pro", "Nano Banana 2", "Nano Banana"]
+ MODEL_ID_MAP = {
+ "Nano Banana Pro": "nano-banana-pro",
+ "Nano Banana 2": "nano-banana-2",
+ "Nano Banana": "nano-banana",
+ }
+ # 计费后缀映射
+ BILLING_SUFFIX = {
+ "特价": "-次卡",
+ "官方": "-官方计费",
+ }
+ RESOLUTION_KEY_MAP = {
+ "512px": "0.5k",
+ "1K": "1k",
+ "2K": "2k",
+ "4K": "4k",
+ }
+ # 仅支持特价的模型
+ BILLING_SPECIAL_ONLY = {"nano-banana"}
+
# 配对模式
PAIRING_MODES = ["按相同图片命名", "1*N", "不配对"]
-
+
def __init__(self):
- """初始化节点"""
- self.client = None
+ pass
def resize_to_megapixels(
self,
@@ -181,33 +331,17 @@ class BatchNanoBananaPro:
@classmethod
def INPUT_TYPES(cls):
- """
- 定义输入参数
-
- ComfyUI 节点规范：
- - required: 必选参数
- - optional: 可选参数
- """
- # 从配置文件动态获取启用的模型列表
- enabled_models = get_enabled_models()
-
- # 如果没有启用的模型，使用空列表（会导致节点不可用，提示用户配置）
- if not enabled_models:
- enabled_models = ["请在 models_config.py 中启用至少一个模型"]
-
- # 动态获取所有启用模型支持的宽高比（去重合并）
all_aspect_ratios = get_all_supported_aspect_ratios()
if not all_aspect_ratios:
- all_aspect_ratios = cls.ASPECT_RATIOS
-
- # 动态获取所有启用模型支持的分辨率（去重合并）
+ all_aspect_ratios = ["1:1", "4:3", "3:4", "16:9", "9:16", "2:3", "3:2", "4:5", "5:4", "21:9", "1:4", "4:1", "1:8", "8:1"]
+
all_resolutions = get_all_supported_resolutions()
if not all_resolutions:
- all_resolutions = cls.RESOLUTIONS
+ all_resolutions = ["512px", "1K", "2K", "4K"]
- # 创建9个独立的图像输入
+ # 创建5个独立的图像输入
optional_inputs = {}
- for i in range(1, 10): # 1-9
+ for i in range(1, 6): # 1-5
optional_inputs[f"参考图{i}"] = ("IMAGE",)
# 图片配对模式移到可选参数
@@ -215,35 +349,29 @@ class BatchNanoBananaPro:
"default": "不配对"
})
- optional_inputs["代理端口（如7897）"] = ("STRING", {
- "default": "",
- "multiline": False,
- "placeholder": "本地代理端口，如 7897（Clash Verge）或 10808（v2rayN），留空不使用"
- })
-
return {
"required": {
"prompt": ("STRING", {
"default": "一个中国女子的OOTD",
"multiline": True
}),
- "模型": (enabled_models, {
- "default": enabled_models[0]
+ "模型": (cls.MODEL_DISPLAY_NAMES, {
+ "default": cls.MODEL_DISPLAY_NAMES[0]
}),
- "宽高比": (all_aspect_ratios, {
- "default": "1:1"
+ "宽高比": (["智能"] + all_aspect_ratios, {
+ "default": "智能"
}),
"分辨率": (all_resolutions, {
"default": "2K"
}),
- "谷歌搜索（联网）": (["关闭", "打开"], {
- "default": "关闭"
+ "图片格式": (["原始", "JPEG", "PNG", "WebP"], {
+ "default": "原始"
}),
- "图片搜索（联网）": (["关闭", "打开"], {
- "default": "关闭"
+ "计费": (["特价", "官方"], {
+ "default": "特价"
}),
- "返回格式": (["url", "base64"], {
- "default": "url"
+ "网络": (NETWORK_ROUTE_OPTIONS, {
+ "default": "全球加速"
}),
"seed": ("INT", {
"default": 0,
@@ -270,22 +398,6 @@ class BatchNanoBananaPro:
"default": "",
"multiline": False
}),
- "文件夹6": ("STRING", {
- "default": "",
- "multiline": False
- }),
- "文件夹7": ("STRING", {
- "default": "",
- "multiline": False
- }),
- "文件夹8": ("STRING", {
- "default": "",
- "multiline": False
- }),
- "文件夹9": ("STRING", {
- "default": "",
- "multiline": False
- }),
"保存路径": ("STRING", {
"default": "",
"multiline": False
@@ -403,8 +515,9 @@ class BatchNanoBananaPro:
async def _generate_single_task(
self,
- client: GeminiAPIClient,
session: aiohttp.ClientSession,
+ base_url: str,
+ api_key: str,
prompt: str,
model: str,
resolution: str,
@@ -412,27 +525,12 @@ class BatchNanoBananaPro:
images: List[ImageInfo],
output_folder: str,
task_index: int,
- enable_grounding: bool = True,
- enable_image_search: bool = False,
+ enable_grounding: bool = False,
base_filename: str = None,
- image_format: str = "url",
+ image_format: str = "原始",
) -> dict:
"""
- 执行单个生成任务
-
- Args:
- client: API 客户端
- session: aiohttp 会话
- prompt: 提示词
- model: 模型名称
- resolution: 分辨率
- aspect_ratio: 宽高比
- images: 输入图片列表
- output_folder: 输出文件夹
- task_index: 任务索引
-
- Returns:
- 包含结果信息的字典
+ 执行单个生成任务（OpenAI 兼容接口）
"""
result = {
"task_index": task_index,
@@ -440,34 +538,29 @@ class BatchNanoBananaPro:
"success": False,
"generated_count": 0,
"saved_files": [],
- "output_images": [], # 无保存路径时存储内存图片
+ "output_images": [],
"error": None
}
-
+
try:
# 准备输入图片
input_pil_images = [info.image for info in images]
-
- # 调用 API 生成图片（固定生成1次）
+
+ # 调用 OpenAI 兼容接口生成图片
generated_images = []
try:
- gen_result = await client.generate_single_async(
+ gen_images = await _generate_single_openai(
+ session=session,
+ base_url=base_url,
+ api_key=api_key,
prompt=prompt,
model=model,
resolution=resolution,
aspect_ratio=aspect_ratio,
- images=input_pil_images,
- session=session,
- debug=DEBUG_LOG_ENABLED,
- debug_request=REQUEST_LOG_ENABLED,
+ images=input_pil_images if input_pil_images else None,
enable_grounding=enable_grounding,
- enable_image_search=enable_image_search,
- image_format=image_format,
)
- if gen_result:
- # 正确解包元组：第一个元素是图像列表，第二个是计时信息
- images_list, timing_info = gen_result
- generated_images.extend(images_list)
+ generated_images.extend(gen_images)
except Exception as e:
import traceback
error_msg = str(e)
@@ -490,29 +583,54 @@ class BatchNanoBananaPro:
# 保存生成的图片到磁盘（始终保存）
import os
+
+ # 确定保存扩展名
+ _FORMAT_EXT_MAP = {"JPEG": ".jpg", "PNG": ".png", "WebP": ".webp"}
+ save_ext = _FORMAT_EXT_MAP.get(image_format, ".png")
+
for i, gen_img in enumerate(generated_images):
+ # 格式转换：非"原始"时检测并转换
+ if image_format != "原始":
+ src_format = (gen_img.format or "").upper()
+ target_upper = image_format.upper()
+ # JPEG 格式名在 PIL 中为 "JPEG"
+ if src_format == "JPG":
+ src_format = "JPEG"
+ need_convert = (src_format != target_upper)
+ if need_convert:
+ if target_upper in ("JPEG", "WEBP") and gen_img.mode in ("RGBA", "LA", "P"):
+ gen_img = gen_img.convert("RGB")
+
# 使用文件夹1图片的名称，如果重名则+1
if base_filename:
base_name = base_filename
counter = 0
while True:
if counter == 0:
- filename = f"{base_name}.png"
+ filename = f"{base_name}{save_ext}"
else:
- filename = f"{base_name}+{counter}.png"
+ filename = f"{base_name}+{counter}{save_ext}"
output_path = os.path.join(output_folder, filename)
if not os.path.exists(output_path):
break
counter += 1
else:
- # 如果没有base_filename，使用时间戳
output_path = generate_timestamp_filename(
output_folder=output_folder,
- extension=".png"
+ extension=save_ext
)
- save_image(gen_img, output_path)
+
+ # 保存时不压缩
+ if image_format == "JPEG":
+ if gen_img.mode != "RGB":
+ gen_img = gen_img.convert("RGB")
+ gen_img.save(output_path, quality=100)
+ elif image_format == "WebP":
+ gen_img.save(output_path, lossless=True)
+ else:
+ save_image(gen_img, output_path)
+
result["saved_files"].append(output_path)
- # 立即释放内存
gen_img = None
# 只有生成了图片才标记为成功
@@ -533,38 +651,20 @@ class BatchNanoBananaPro:
resolution: str,
aspect_ratio: str,
output_folder: str,
+ base_url: str,
+ api_key: str,
pbar=None,
prompts_per_task: Optional[List[str]] = None,
- enable_grounding: bool = True,
- enable_image_search: bool = False,
- image_format: str = "url",
+ enable_grounding: bool = False,
+ image_format: str = "原始",
) -> List[dict]:
"""
- 异步批量处理所有任务 - 改进版：支持分批保存
-
- Args:
- pairs: 配对后的图片组合
- prompt: 提示词（单提示词模式时使用）
- model: 模型名称
- resolution: 分辨率
- aspect_ratio: 宽高比
- output_folder: 输出文件夹
- pbar: ComfyUI 进度条
- prompts_per_task: 每个任务对应的提示词列表（批量提示词模式时使用）
-
- Returns:
- 所有任务的结果列表
+ 异步批量处理所有任务（OpenAI 兼容接口）
"""
- if self.client is None:
- self.client = GeminiAPIClient()
-
total_tasks = len(pairs)
max_concurrent = 50
- # 分批保存的批次大小（与并发数一致）
- save_batch_size = 10
-
print(f"BatchNanoBananaPro: 检测到 {total_tasks} 个任务")
all_results = []
@@ -617,8 +717,9 @@ class BatchNanoBananaPro:
task = asyncio.create_task(
self._generate_single_task(
- client=self.client,
session=session,
+ base_url=base_url,
+ api_key=api_key,
prompt=task_prompt,
model=model,
resolution=resolution,
@@ -627,7 +728,6 @@ class BatchNanoBananaPro:
output_folder=output_folder,
task_index=start_idx + i,
enable_grounding=enable_grounding,
- enable_image_search=enable_image_search,
base_filename=base_filename,
image_format=image_format,
)
@@ -721,15 +821,14 @@ class BatchNanoBananaPro:
文件夹3: str,
文件夹4: str,
文件夹5: str,
- 文件夹6: str,
- 文件夹7: str,
- 文件夹8: str,
- 文件夹9: str,
seed: int,
图片配对模式: str,
模型: str,
+ 计费: str,
宽高比: str,
分辨率: str,
+ 图片格式: str,
+ 网络: str,
保存路径: str = "",
**kwargs
) -> Tuple[torch.Tensor]:
@@ -738,14 +837,14 @@ class BatchNanoBananaPro:
Args:
prompt: 提示词
- 文件夹1-9: 图片文件夹路径
+ 文件夹1-5: 图片文件夹路径
seed: 随机种子
保存路径: 输出保存路径
图片配对模式: 1:1 或 1*N
模型: 模型名称
宽高比: 输出宽高比
分辨率: 输出分辨率
- **kwargs: 动态参考图输入 (参考图1-9)
+ **kwargs: 动态参考图输入 (参考图1-5)
Returns:
输出图像张量
@@ -753,10 +852,27 @@ class BatchNanoBananaPro:
start_time = time.time()
# 从 kwargs 提取搜索参数（界面显示为「关闭/打开」，转为 bool 供调用）
- enable_grounding: bool = (kwargs.pop("谷歌搜索（联网）", "关闭") == "打开")
- enable_image_search: bool = (kwargs.pop("图片搜索（联网）", "关闭") == "打开")
- proxy_port: str = kwargs.pop("代理端口（如7897）", "")
- image_format: str = kwargs.pop("返回格式", "url")
+ enable_grounding: bool = False
+
+ # 拼接实际模型 ID
+ base_model_id = self.MODEL_ID_MAP.get(模型, "nano-banana-pro")
+ if base_model_id == "nano-banana":
+ if 计费 == "官方":
+ raise ValueError(f"模型 \"{模型}\" 仅支持特价计费")
+ 模型 = "nano-banana"
+ else:
+ res_key = self.RESOLUTION_KEY_MAP.get(分辨率, "2k")
+ is_official = (计费 == "官方")
+ if base_model_id == "nano-banana-pro" and res_key == "1k" and not is_official:
+ 模型 = "nano-banana-pro"
+ elif base_model_id == "nano-banana-2" and res_key == "0.5k":
+ if is_official:
+ raise ValueError("Nano Banana 2 的 512px 分辨率仅支持特价计费")
+ 模型 = "nano-banana-2-0.5k"
+ else:
+ 模型 = f"{base_model_id}-{res_key}"
+ if is_official:
+ 模型 += "-official"
try:
@@ -767,7 +883,7 @@ class BatchNanoBananaPro:
# 验证：至少需要填写一个文件夹路径
has_any_folder = any(
f and f.strip()
- for f in [文件夹1, 文件夹2, 文件夹3, 文件夹4, 文件夹5, 文件夹6, 文件夹7, 文件夹8, 文件夹9]
+ for f in [文件夹1, 文件夹2, 文件夹3, 文件夹4, 文件夹5]
)
if not has_any_folder:
raise ValueError("请至少填写一个文件夹路径，该节点专为批量文件夹处理设计")
@@ -782,26 +898,16 @@ class BatchNanoBananaPro:
# 校验宽高比与模型的兼容性
supported_ratios = get_model_supported_aspect_ratios(模型)
- if supported_ratios and 宽高比 not in supported_ratios:
+ if 宽高比 != "智能" and supported_ratios and 宽高比 not in supported_ratios:
raise ValueError(
f"宽高比 \"{宽高比}\" 与模型 \"{模型}\" 不兼容！\n"
f"该模型支持的宽高比：{', '.join(supported_ratios)}"
)
- # 校验图片搜索（联网）与模型的兼容性
- # 仅 nano-banana-2-限时特价 和 gemini-3.1-flash-image-preview 支持图片搜索
- IMAGE_SEARCH_UNSUPPORTED_MODELS = ["nano-banana-pro-次卡", "nano-banana-pro-官方计费", "gemini-3-pro-image-preview"]
- if enable_image_search and 模型 in IMAGE_SEARCH_UNSUPPORTED_MODELS:
- raise ValueError(
- f"模型 \"{模型}\" 不支持【图片搜索（联网）】功能！"
- f"请切换到 nano-banana-2-限时特价 或 gemini-3.1-flash-image-preview 后再使用"
- )
-
# 加载文件夹图片
print("BatchNanoBananaPro: 开始加载图片...")
image_lists = self._load_folders(
- 文件夹1, 文件夹2, 文件夹3, 文件夹4,
- 文件夹5, 文件夹6, 文件夹7, 文件夹8, 文件夹9
+ 文件夹1, 文件夹2, 文件夹3, 文件夹4, 文件夹5
)
# 验证文件夹是否有可用图片
@@ -811,7 +917,7 @@ class BatchNanoBananaPro:
# 处理独立的参考图输入
manual_images = []
- for i in range(1, 10): # 1-9
+ for i in range(1, 6): # 1-5
key = f"参考图{i}"
if key in kwargs and kwargs[key] is not None:
pil_images = tensor_to_pil(kwargs[key])
@@ -848,11 +954,8 @@ class BatchNanoBananaPro:
total_tasks = len(pairs)
# 打印首行概览
- # 图片搜索（联网）开启时隐含谷歌搜索接地，与客户端请求逻辑保持一致
grounding_str = ""
- if enable_image_search:
- grounding_str = " | 谷歌图片搜索接地"
- elif enable_grounding:
+ if enable_grounding:
grounding_str = " | 谷歌搜索接地"
if batch_prompts:
@@ -890,18 +993,10 @@ class BatchNanoBananaPro:
except Exception as e:
raise ValueError(f"保存路径无效或无写入权限: {保存路径} - {str(e)}")
- # 初始化 API 客户端
- if self.client is None:
- try:
- self.client = GeminiAPIClient()
- except ValueError as e:
- raise ValueError(f"初始化 API 客户端失败: {str(e)}")
+ # 获取 API 密钥和基础 URL
+ api_key = get_api_key_or_raise("O1KEY_API_KEY")
+ base_url = get_base_url_by_route(网络)
- # 注入代理设置（每次执行都刷新，支持用户中途修改端口）
- self.client.proxy_url = GeminiAPIClient.build_proxy_url(proxy_port)
- if self.client.proxy_url:
- print(f"BatchNanoBananaPro: 已启用代理加速 → {self.client.proxy_url}")
-
# 判断是否使用默认 output 目录
original_save_path = kwargs.get('保存路径', '')
user_set_save_path = bool(original_save_path and original_save_path.strip())
@@ -920,11 +1015,12 @@ class BatchNanoBananaPro:
resolution=分辨率,
aspect_ratio=宽高比,
output_folder=保存路径,
+ base_url=base_url,
+ api_key=api_key,
pbar=pbar,
prompts_per_task=prompts_per_task,
enable_grounding=enable_grounding,
- enable_image_search=enable_image_search,
- image_format=image_format,
+ image_format=图片格式,
)
)
except Exception as e:
@@ -1050,14 +1146,15 @@ class BatchNanoBananaPro:
finally:
# 查询余额
- 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"BatchNanaBananaPro: {balance_info}")
- print("=" * 60)
- except Exception:
- pass
+ try:
+ client = GeminiAPIClient()
+ client.base_url = get_base_url_by_route(网络)
+ balance_data = client.query_balance_sync()
+ balance_info = client.format_balance_info(balance_data)
+ print(f"BatchNanoBananaPro: {balance_info}")
+ print("=" * 60)
+ except Exception:
+ pass
# 最终内存清理
import gc
diff --git a/nodes/gpt_image.py b/nodes/gpt_image.py
index 73aef41..35f74e1 100644
--- a/nodes/gpt_image.py
+++ b/nodes/gpt_image.py
@@ -6,6 +6,7 @@ o1key GPT Image 节点
import time
from ..clients.gpt_image_client import GptImageClient
from ..utils.image_utils import parse_batch_prompts
+from ..utils.config import NETWORK_ROUTE_OPTIONS, get_base_url_by_route
try:
from comfy.model_management import processing_interrupted, InterruptProcessingException
@@ -52,30 +53,33 @@ class O1keyGPTImage:
], {
"default": "gpt-image-2-次卡",
})
+ optional_inputs["网络"] = (NETWORK_ROUTE_OPTIONS, {
+ "default": "全球加速",
+ })
optional_inputs["分辨率"] = ([
"智能",
# ── 1K ──
"1024x1024（1K 正方形 1:1）",
"1536x1024（1K 横版 3:2）",
"1024x1536（1K 竖版 2:3）",
- "1365x1024（1K 横版 4:3）",
- "1024x1365（1K 竖版 3:4）",
- "1820x1024（1K 横版 16:9）",
- "1024x1820（1K 竖版 9:16）",
+ "1360x1024（1K 横版 4:3）",
+ "1024x1360（1K 竖版 3:4）",
+ "1824x1024（1K 横版 16:9）",
+ "1024x1824（1K 竖版 9:16）",
# ── 2K ──
"2048x2048（2K 正方形 1:1）",
"3072x2048（2K 横版 3:2）",
"2048x3072（2K 竖版 2:3）",
- "2732x2048（2K 横版 4:3）",
- "2048x2732（2K 竖版 3:4）",
- "3640x2048（2K 横版 16:9）",
- "2048x3640（2K 竖版 9:16）",
+ "2736x2048（2K 横版 4:3）",
+ "2048x2736（2K 竖版 3:4）",
+ "3648x2048（2K 横版 16:9）",
+ "2048x3648（2K 竖版 9:16）",
# ── 4K ──
- "3840x3840（4K 正方形 1:1）",
- "3840x2560（4K 横版 3:2）",
- "2560x3840（4K 竖版 2:3）",
- "3840x2880（4K 横版 4:3）",
- "2880x3840（4K 竖版 3:4）",
+ "2880x2880（4K 正方形 1:1）",
+ "3504x2336（4K 横版 3:2）",
+ "2336x3504（4K 竖版 2:3）",
+ "3264x2448（4K 横版 4:3）",
+ "2448x3264（4K 竖版 3:4）",
"3840x2160（4K 横版 16:9）",
"2160x3840（4K 竖版 9:16）",
], {
@@ -128,6 +132,7 @@ class O1keyGPTImage:
self,
prompt: str,
模型: str = "gpt-image-2-次卡",
+ 网络: str = "全球加速",
分辨率: str = "auto",
质量: str = "自动",
生图数量: int = 1,
@@ -169,6 +174,7 @@ class O1keyGPTImage:
# ── 3. 创建客户端 ─────────────────────────────────────────────────────
try:
client = GptImageClient()
+ client.base_url = get_base_url_by_route(网络)
except ValueError as e:
if str(e) == "未授权！":
print("[o1key GPT Image] 请联系作者授权后方可使用！")
diff --git a/nodes/grok_image.py b/nodes/grok_image.py
new file mode 100644
index 0000000..a890cfe
--- /dev/null
+++ b/nodes/grok_image.py
@@ -0,0 +1,165 @@
+"""
+o1key Grok Image 节点
+支持 Grok Image / Grok Image Pro 模型的文生图和图生图
+"""
+
+import time
+from ..clients.grok_image_client import GrokImageClient
+from ..utils.image_utils import parse_batch_prompts
+from ..utils.config import NETWORK_ROUTE_OPTIONS
+
+try:
+ from comfy.model_management import processing_interrupted, InterruptProcessingException
+ _INTERRUPT_AVAILABLE = True
+except ImportError:
+ _INTERRUPT_AVAILABLE = False
+ processing_interrupted = lambda: False
+ InterruptProcessingException = RuntimeError
+
+_ASPECT_RATIOS = [
+ "auto", "1:1", "16:9", "9:16", "4:3", "3:4",
+ "3:2", "2:3", "2:1", "1:2", "19.5:9", "9:19.5", "20:9", "9:20",
+]
+
+
+class O1keyGrokImage:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ optional_inputs = {}
+ for i in range(1, 4):
+ optional_inputs[f"参考图{i}"] = ("IMAGE", {
+ "tooltip": f"Optional reference image {i}",
+ })
+
+ return {
+ "required": {
+ "prompt": ("STRING", {
+ "default": "",
+ "multiline": True,
+ "tooltip": "文本提示词，用 --- 独占一行分隔批量提示词",
+ }),
+ },
+ "optional": {
+ "模型": (["Grok Image", "Grok Image Pro"], {
+ "default": "Grok Image Pro",
+ }),
+ "宽高比": (_ASPECT_RATIOS, {
+ "default": "auto",
+ }),
+ "分辨率": (["1k", "2k"], {
+ "default": "1k",
+ }),
+ "生图数量": ("INT", {
+ "default": 1,
+ "min": 1,
+ "max": 4,
+ "step": 1,
+ "display": "number",
+ }),
+ "网络线路": (NETWORK_ROUTE_OPTIONS, {
+ "default": NETWORK_ROUTE_OPTIONS[0],
+ }),
+ "seed": ("INT", {
+ "default": 0,
+ "min": 0,
+ "max": 2**31 - 1,
+ "step": 1,
+ "display": "number",
+ "control_after_generate": True,
+ }),
+ **optional_inputs,
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("IMAGE",)
+ FUNCTION = "generate"
+ CATEGORY = "o1key/image"
+ OUTPUT_NODE = False
+
+ def generate(
+ self,
+ prompt: str,
+ 模型: str = "Grok Image Pro",
+ 宽高比: str = "auto",
+ 分辨率: str = "1k",
+ 生图数量: int = 1,
+ 网络线路: str = "全球加速",
+ seed: int = 0,
+ **kwargs,
+ ):
+ start_time = time.time()
+
+ reference_tensors = []
+ for i in range(1, 4):
+ key = f"参考图{i}"
+ if key in kwargs and kwargs[key] is not None:
+ reference_tensors.append(kwargs[key])
+ image_list = reference_tensors if reference_tensors else None
+
+ try:
+ client = GrokImageClient(route=网络线路)
+ except ValueError as e:
+ if str(e) == "未授权！":
+ print("[o1key Grok Image] 请联系作者授权后方可使用！")
+ raise ValueError("未授权！") from None
+ raise
+
+ try:
+ batch_prompts = parse_batch_prompts(prompt)
+ all_pil_images = []
+
+ if batch_prompts:
+ total = len(batch_prompts)
+ print(f"[o1key Grok Image] 批量模式 | {total} 条提示词 | 每条生成 {生图数量} 张")
+ for idx, p in enumerate(batch_prompts, 1):
+ if _INTERRUPT_AVAILABLE and processing_interrupted():
+ print("[o1key Grok Image] 用户取消")
+ raise InterruptProcessingException()
+ try:
+ pil_images = client.run_sync(
+ prompt=p, model=模型, aspect_ratio=宽高比,
+ resolution=分辨率, n=生图数量, image_list=image_list,
+ )
+ all_pil_images.extend(pil_images)
+ snippet = p[:30] + ("..." if len(p) >= 30 else "")
+ print(f"[o1key Grok Image] [{idx}/{total}] done: {snippet}")
+ except InterruptProcessingException:
+ raise
+ except Exception as e:
+ error_msg = str(e).split('\n')[0]
+ snippet = p[:30] + ("..." if len(p) >= 30 else "")
+ print(f"[o1key Grok Image] [{idx}/{total}] fail: {snippet} → {error_msg}")
+ else:
+ if not prompt or not prompt.strip():
+ raise ValueError("提示词不能为空")
+ pil_images = client.run_sync(
+ prompt=prompt, model=模型, aspect_ratio=宽高比,
+ resolution=分辨率, n=生图数量, image_list=image_list,
+ )
+ all_pil_images.extend(pil_images)
+
+ if not all_pil_images:
+ raise RuntimeError("所有提示词均生成失败，无可用图像输出")
+
+ output_tensor = GrokImageClient._pil_list_to_tensor(all_pil_images)
+
+ elapsed = time.time() - start_time
+ print(
+ f"[o1key Grok Image] 完成！耗时 {elapsed:.1f}s，"
+ f"输出 {output_tensor.shape[0]} 张 "
+ f"{output_tensor.shape[2]}x{output_tensor.shape[1]}"
+ )
+ return (output_tensor,)
+
+ finally:
+ self._print_balance(client)
+
+ def _print_balance(self, client):
+ try:
+ balance_data = client.query_balance_sync()
+ balance_info = client.format_balance_info(balance_data)
+ print(f"[o1key Grok Image] {balance_info}")
+ except Exception:
+ pass
diff --git a/nodes/kling_video.py b/nodes/kling_video.py
index f37a3e0..8e1091e 100644
--- a/nodes/kling_video.py
+++ b/nodes/kling_video.py
@@ -8,6 +8,7 @@ import tempfile
from ..clients.kling_client import KlingClient
from ..clients.gemini_client import GeminiAPIClient
from ..utils.image_utils import tensor_to_pil, encode_image_to_base64
+from ..utils.config import NETWORK_ROUTE_OPTIONS, get_base_url_by_route
from comfy_api.latest import InputImpl
@@ -121,6 +122,7 @@ class KlingVideo:
"required": {
"提示词": ("STRING", {"multiline": True, "default": ""}),
"反向提示词": ("STRING", {"multiline": True, "default": ""}),
+ "网络线路": (NETWORK_ROUTE_OPTIONS, {"default": "全球加速"}),
"模型版本": (["v3", "v2-6"], {"default": "v3"}),
"时长": ([5, 10, 15],),
"分辨率": (["1080p", "720p"],),
@@ -244,13 +246,15 @@ class KlingVideo:
body["image"] = _tensor_to_base64(start_frame)
endpoint_type = "image2video"
else:
- body["metadata"] = {"aspect_ratio": aspect_ratio}
+ if aspect_ratio and aspect_ratio != "智能":
+ body["metadata"] = {"aspect_ratio": aspect_ratio}
endpoint_type = "text2video"
# ── 保存路径（临时文件，避免与下游保存节点重复落盘）──────────────────
_, save_path = tempfile.mkstemp(suffix=".mp4", prefix="kling_")
client = KlingClient()
+ client.base_url = get_base_url_by_route(kwargs.get("网络线路", "全球加速"))
# ── 进度条 ────────────────────────────────────────────────────
try:
@@ -307,6 +311,7 @@ class KlingFirstLastFrame:
"首帧": ("IMAGE",),
"尾帧": ("IMAGE",),
"提示词": ("STRING", {"multiline": True, "default": ""}),
+ "网络线路": (NETWORK_ROUTE_OPTIONS, {"default": "全球加速"}),
"模型": (["v3", "v2-6"], {"default": "v3"}),
"分辨率": (["1080p", "720p"],),
"时长": ([5, 10, 15],),
@@ -391,6 +396,7 @@ class KlingFirstLastFrame:
_, save_path = tempfile.mkstemp(suffix=".mp4", prefix="kling_")
client = KlingClient()
+ client.base_url = get_base_url_by_route(kwargs.get("网络线路", "全球加速"))
# 进度条：0~100 步
try:
@@ -454,6 +460,7 @@ class KlingMotionControlTest:
"提示词": ("STRING", {"multiline": True, "default": ""}),
"参考图片": ("IMAGE",),
"参考视频": ("VIDEO",),
+ "网络线路": (NETWORK_ROUTE_OPTIONS, {"default": "全球加速"}),
},
"optional": {
"模型": (["v3", "v2-6"], {"default": "v3"}),
@@ -560,6 +567,7 @@ class KlingMotionControlTest:
_, save_path = tempfile.mkstemp(suffix=".mp4", prefix="kling_motion_")
client = KlingClient()
+ client.base_url = get_base_url_by_route(kwargs.get("网络线路", "全球加速"))
# ── 进度条 ────────────────────────────────────────────────────
try:
diff --git a/nodes/multi_res_preview.py b/nodes/multi_res_preview.py
deleted file mode 100644
index 17fb2f1..0000000
--- a/nodes/multi_res_preview.py
+++ /dev/null
@@ -1,165 +0,0 @@
-"""
-多分辨率图像预览节点
-ComfyUI 自定义节点，支持同时预览多张不同分辨率的图像
-
-背景：
- ComfyUI 原生「预览图像」节点要求 batch 内所有图片分辨率相同（因为它们被
- stack 成一个 [B, H, W, C] tensor）。当 API 返回多张不同尺寸的图片时
- （例如 nano-banana-2 同时返回 1K + 2K），原生节点会报错。
-
-解决方案：
- 声明 INPUT_IS_LIST = True，ComfyUI 会将连入的所有图像作为
- Python list[Tensor] 传入，而不是强行 stack 成单个 tensor。
- 节点逐张单独保存为临时 PNG，再通过 ui.images 列表返回给前端并列展示，
- 完全不受分辨率一致性的限制。
-"""
-
-import os
-import uuid
-import json
-import numpy as np
-from PIL import Image
-from PIL.PngImagePlugin import PngInfo
-
-try:
- import folder_paths
- FOLDER_PATHS_AVAILABLE = True
-except ImportError:
- FOLDER_PATHS_AVAILABLE = False
-
-
-def _get_temp_dir() -> str:
- """获取 ComfyUI temp 目录，不可用时回退到系统临时目录"""
- if FOLDER_PATHS_AVAILABLE:
- return folder_paths.get_temp_directory()
- import tempfile
- return tempfile.gettempdir()
-
-
-def _tensor_to_pil(tensor) -> list:
- """
- 将单个 IMAGE tensor 转换为 PIL Image 列表。
-
- ComfyUI IMAGE tensor 格式：[B, H, W, C]，float32，值域 [0, 1]
- 支持：
- - 单张图 tensor: shape [H, W, C] 或 [1, H, W, C]
- - batch tensor: shape [B, H, W, C]（B 张相同尺寸图）
- """
- import torch
- if not isinstance(tensor, torch.Tensor):
- return []
-
- if tensor.ndim == 3:
- tensor = tensor.unsqueeze(0)
-
- results = []
- for i in range(tensor.shape[0]):
- img_np = tensor[i].cpu().numpy()
- img_np = np.clip(img_np * 255.0, 0, 255).astype(np.uint8)
- results.append(Image.fromarray(img_np))
- return results
-
-
-class MultiResPreview:
- """
- 多分辨率图像预览节点
-
- 功能：
- - 单个「图像」输入端口，支持接入批次图像
- - INPUT_IS_LIST = True：ComfyUI 将每张图作为独立 tensor 传入，
- 不强制要求尺寸相同，彻底解决不同分辨率无法共存的问题
- - 每张图像独立保存为临时 PNG，在节点上并列展示所有图像
-
- 用法：
- 将 Nano Banana 节点的输出直接连入「图像」端口即可，
- 无论返回几张、分辨率是否相同，都能正确展示。
- """
-
- @classmethod
- def INPUT_TYPES(cls):
- return {
- "required": {
- "图像": ("IMAGE",),
- },
- "hidden": {
- "prompt": "PROMPT",
- "extra_pnginfo": "EXTRA_PNGINFO",
- },
- }
-
- # 关键：告知 ComfyUI 以 list[Tensor] 而非 stacked Tensor 传入图像
- # 这样不同分辨率的图片可以共存于同一个输入中
- INPUT_IS_LIST = True
-
- RETURN_TYPES = ()
- OUTPUT_NODE = True
- FUNCTION = "preview"
- CATEGORY = "image"
-
- DESCRIPTION = (
- "多分辨率图像预览节点。\n"
- "单个图像输入端口，支持任意数量、任意分辨率的批次图像。\n"
- "解决了原生「预览图像」节点要求 batch 内图片尺寸相同的限制。\n"
- "常用场景：nano-banana-2 同时返回 1K + 2K 图时，直接连入本节点即可。"
- )
-
- def preview(self, 图像, prompt=None, extra_pnginfo=None) -> dict:
- """
- 逐张将图像保存到 temp 目录，返回 ui.images 供前端展示。
-
- Args:
- 图像: list[Tensor]，每个元素是一张或一批图（INPUT_IS_LIST）
- prompt: ComfyUI 注入的 prompt 元数据（可选）
- extra_pnginfo: ComfyUI 注入的额外 PNG 信息（可选）
-
- Returns:
- {"ui": {"images": [...]}} 格式，每项对应一张图
- """
- temp_dir = _get_temp_dir()
- os.makedirs(temp_dir, exist_ok=True)
-
- # 构建 PNG 元数据（与原生预览节点行为一致）
- metadata = PngInfo()
- # INPUT_IS_LIST 时 hidden 值也会被包装成 list，取第一个元素
- _prompt = prompt[0] if isinstance(prompt, list) else prompt
- _extra = extra_pnginfo[0] if isinstance(extra_pnginfo, list) else extra_pnginfo
- if _prompt is not None:
- try:
- metadata.add_text("prompt", json.dumps(_prompt))
- except Exception:
- pass
- if _extra is not None:
- try:
- for k, v in _extra.items():
- metadata.add_text(k, json.dumps(v))
- except Exception:
- pass
-
- saved = []
- total_input = 0
- total_saved = 0
-
- # 图像 是 list[Tensor]，逐个处理（每个 Tensor 可能自身是个 batch）
- for tensor in 图像:
- pil_images = _tensor_to_pil(tensor)
- total_input += len(pil_images)
-
- for pil_img in pil_images:
- try:
- filename = f"multi_res_preview_{uuid.uuid4().hex[:12]}.png"
- filepath = os.path.join(temp_dir, filename)
- pil_img.save(filepath, pnginfo=metadata, compress_level=1)
-
- saved.append({
- "filename": filename,
- "subfolder": "",
- "type": "temp",
- })
- total_saved += 1
- except Exception as e:
- print(f"多分辨率预览: ⚠️ 保存图像失败 - {e}")
-
- if total_input == 0:
- print("多分辨率预览: ⚠️ 没有接收到任何图像")
-
- return {"ui": {"images": saved}}
diff --git a/nodes/nano_banana.py b/nodes/nano_banana.py
new file mode 100644
index 0000000..1668ea1
--- /dev/null
+++ b/nodes/nano_banana.py
@@ -0,0 +1,636 @@
+"""
+Nano Banana 节点 (V3)
+ComfyUI 自定义节点，用于调用生图模型（OpenAI 兼容接口）
+使用 V3 DynamicCombo 实现模型-宽高比-分辨率动态联动
+"""
+
+import io as _io
+import re
+import json
+import time
+import math
+import base64
+import random
+import asyncio
+import aiohttp
+from concurrent.futures import ThreadPoolExecutor
+from typing import List, Optional
+
+import torch
+import numpy as np
+from PIL import Image
+
+from comfy_api.latest import io
+
+from ..utils.image_utils import tensor_to_pil, pil_to_tensor, parse_batch_prompts, encode_image_to_base64, encode_image_to_base64_limited
+from ..utils.config import (
+ NETWORK_ROUTE_OPTIONS,
+ get_base_url_by_route,
+ get_api_key_or_raise,
+)
+from ..utils.http_error import RETRYABLE_STATUS_CODES, HTTP_ERROR_MESSAGES, _compute_delay, DEFAULT_MAX_RETRIES, DEFAULT_BASE_DELAY, DEFAULT_MAX_DELAY, DEFAULT_BACKOFF_FACTOR
+from ..clients.gemini_client import GeminiAPIClient
+
+try:
+ import folder_paths
+ FOLDER_PATHS_AVAILABLE = True
+except ImportError:
+ FOLDER_PATHS_AVAILABLE = False
+
+try:
+ from comfy.utils import ProgressBar
+ PROGRESS_BAR_AVAILABLE = True
+except ImportError:
+ PROGRESS_BAR_AVAILABLE = False
+
+try:
+ import psutil
+ MEMORY_MONITOR_AVAILABLE = True
+except ImportError:
+ MEMORY_MONITOR_AVAILABLE = False
+
+DEBUG_LOG_ENABLED = True
+REQUEST_LOG_ENABLED = False
+
+_NODE = "Nano Banana"
+_ENDPOINT = "/v1/chat/completions"
+
+_client_instance = None
+
+
+def _get_client():
+ global _client_instance
+ if _client_instance is None:
+ _client_instance = GeminiAPIClient()
+ return _client_instance
+
+
+def _images_to_tensor_safe(images: List[Image.Image], node_label: str) -> torch.Tensor:
+ if not images:
+ placeholder = Image.new('RGB', (512, 512), color=(128, 128, 128))
+ return pil_to_tensor([placeholder])
+
+ base_size = max(images, key=lambda img: img.size[0] * img.size[1]).size
+ matched = [img for img in images if img.size == base_size]
+ skipped = [img for img in images if img.size != base_size]
+
+ if skipped:
+ sizes_str = ", ".join(f"{img.size[0]}x{img.size[1]}" for img in skipped)
+ print(
+ f"{node_label}: 丢弃 {len(skipped)} 张较小尺寸的图 ({sizes_str})，"
+ f"仅输出最大尺寸 {base_size[0]}x{base_size[1]} 的 {len(matched)} 张"
+ )
+
+ return pil_to_tensor(matched)
+
+
+MODEL_ID_MAP = {
+ "Nano Banana Pro": "nano-banana-pro",
+ "Nano Banana 2": "nano-banana-2",
+ "Nano Banana": "nano-banana",
+}
+RESOLUTION_KEY_MAP = {
+ "512px": "0.5k",
+ "1K": "1k",
+ "2K": "2k",
+ "4K": "4k",
+}
+BILLING_SPECIAL_ONLY = {"nano-banana"}
+
+
+def _build_model_id(model_name: str, resolution: str, billing: str) -> str:
+ base = MODEL_ID_MAP.get(model_name, "nano-banana-pro")
+
+ if base == "nano-banana":
+ if billing == "官方":
+ raise ValueError(f"模型 \"{model_name}\" 仅支持特价计费")
+ return "nano-banana"
+
+ res_key = RESOLUTION_KEY_MAP.get(resolution, "2k")
+ is_official = (billing == "官方")
+
+ if base == "nano-banana-pro" and res_key == "1k" and not is_official:
+ return "nano-banana-pro"
+
+ if base == "nano-banana-2" and res_key == "0.5k":
+ if is_official:
+ raise ValueError("Nano Banana 2 的 512px 分辨率仅支持特价计费")
+ return "nano-banana-2-0.5k"
+
+ model_id = f"{base}-{res_key}"
+ if is_official:
+ model_id += "-official"
+ return model_id
+
+_IMAGE_RE = re.compile(r"!\[.*?\]\(data:image/(\w+);base64,([A-Za-z0-9+/=]+)\)")
+
+
+def _get_headers(api_key: str) -> dict:
+ return {
+ "Authorization": f"Bearer {api_key}",
+ "Content-Type": "application/json",
+ "X-Accel-Buffering": "no",
+ "Cache-Control": "no-cache, no-transform",
+ }
+
+
+def _build_request_body(
+ prompt: str,
+ model: str,
+ aspect_ratio: str,
+ resolution: str,
+ images: Optional[List[Image.Image]] = None,
+ enable_grounding: bool = False,
+ thinking_level: Optional[str] = None,
+) -> dict:
+ content_parts = [{"type": "text", "text": prompt}]
+
+ if images:
+ for img in images:
+ b64 = encode_image_to_base64_limited(img, format="PNG")
+ content_parts.append({
+ "type": "image_url",
+ "image_url": {"url": f"data:image/png;base64,{b64}"}
+ })
+
+ body = {
+ "model": model,
+ "stream": True,
+ "messages": [{"role": "user", "content": content_parts}],
+ }
+
+ google_config = {
+ "image_config": {
+ "image_size": resolution,
+ }
+ }
+ if aspect_ratio and aspect_ratio != "智能":
+ google_config["image_config"]["aspect_ratio"] = aspect_ratio
+ if thinking_level:
+ google_config["thinking_config"] = {
+ "thinking_level": thinking_level.lower(),
+ "include_thoughts": True,
+ }
+ body["extra_body"] = {"google": google_config}
+
+ if enable_grounding:
+ body["extra_body"]["google_search"] = True
+
+ return body
+
+
+async def _generate_single(
+ session: aiohttp.ClientSession,
+ base_url: str,
+ api_key: str,
+ prompt: str,
+ model: str,
+ resolution: str,
+ aspect_ratio: str,
+ images: Optional[List[Image.Image]] = None,
+ enable_grounding: bool = False,
+ thinking_level: Optional[str] = None,
+) -> List[Image.Image]:
+ url = f"{base_url}{_ENDPOINT}"
+ headers = _get_headers(api_key)
+ body = _build_request_body(
+ prompt=prompt,
+ model=model,
+ aspect_ratio=aspect_ratio,
+ resolution=resolution,
+ images=images,
+ enable_grounding=enable_grounding,
+ thinking_level=thinking_level,
+ )
+
+ if REQUEST_LOG_ENABLED:
+ extra = json.dumps(body.get("extra_body", {}), ensure_ascii=False)
+ print(f"[请求] POST {url} | model={model} | extra_body={extra}")
+
+ last_status = None
+ for attempt in range(DEFAULT_MAX_RETRIES + 1):
+ resp = await session.post(url, headers=headers, json=body)
+ if resp.status == 200:
+ break
+ last_status = resp.status
+ if resp.status in RETRYABLE_STATUS_CODES and attempt < DEFAULT_MAX_RETRIES:
+ friendly = HTTP_ERROR_MESSAGES.get(resp.status, f"请求失败 ({resp.status})")
+ delay = _compute_delay(attempt, DEFAULT_BASE_DELAY, DEFAULT_MAX_DELAY, DEFAULT_BACKOFF_FACTOR)
+ print(f"Nano Banana: {friendly} {delay:.1f}s 后重试 ({attempt+1}/{DEFAULT_MAX_RETRIES})...")
+ resp.close()
+ await asyncio.sleep(delay)
+ continue
+ error_text = await resp.text()
+ resp.close()
+ if resp.status in HTTP_ERROR_MESSAGES:
+ raise RuntimeError(HTTP_ERROR_MESSAGES[resp.status])
+ try:
+ err_json = json.loads(error_text)
+ msg = err_json.get("error", {}).get("message", error_text[:200])
+ except Exception:
+ msg = error_text[:200]
+ raise RuntimeError(f"API 错误 ({resp.status}): {msg}")
+ else:
+ if last_status and last_status in HTTP_ERROR_MESSAGES:
+ raise RuntimeError(HTTP_ERROR_MESSAGES[last_status])
+ raise RuntimeError(f"API 错误: 重试 {DEFAULT_MAX_RETRIES} 次后仍然失败")
+
+ full_content = ""
+ buffer = ""
+ t_request = time.time()
+ t_first_token = None
+ async for raw_chunk in resp.content.iter_any():
+ if t_first_token is None:
+ t_first_token = time.time()
+ buffer += raw_chunk.decode("utf-8")
+ while "\n" in buffer:
+ line_str, buffer = buffer.split("\n", 1)
+ line_str = line_str.strip()
+ if not line_str or not line_str.startswith("data:"):
+ continue
+ data_str = line_str[5:].strip()
+ if data_str == "[DONE]":
+ break
+ try:
+ chunk = json.loads(data_str)
+ delta = chunk.get("choices", [{}])[0].get("delta", {})
+ if "content" in delta:
+ full_content += delta["content"]
+ except (json.JSONDecodeError, IndexError):
+ continue
+ t_done = time.time()
+ resp.close()
+
+ if not full_content:
+ raise RuntimeError("API 未返回有效内容")
+
+ # 思考模型可能输出多张临时图片，最终图片始终是最后一张
+ matches = list(_IMAGE_RE.finditer(full_content))
+ if not matches:
+ raise RuntimeError(f"响应中未找到图片: {full_content[:100]}")
+
+ last_match = matches[-1]
+ img_data = base64.b64decode(last_match.group(2))
+ final_image = Image.open(_io.BytesIO(img_data)).convert("RGB")
+
+ first_token_ms = (t_first_token - t_request) * 1000 if t_first_token else 0
+ download_ms = (t_done - t_first_token) * 1000 if t_first_token else 0
+
+ return [final_image], first_token_ms, download_ms
+
+
+async def _generate_single_task(
+ session: aiohttp.ClientSession,
+ base_url: str,
+ api_key: str,
+ prompt: str,
+ model: str,
+ resolution: str,
+ aspect_ratio: str,
+ images: Optional[List[Image.Image]],
+ global_task_index: int,
+ enable_grounding: bool = False,
+ thinking_level: Optional[str] = None,
+) -> dict:
+ result = {
+ "global_task_index": global_task_index,
+ "prompt": prompt,
+ "success": False,
+ "generated_count": 0,
+ "output_images": [],
+ "error": None,
+ }
+ try:
+ gen_images, first_token_ms, download_ms = await _generate_single(
+ session=session,
+ base_url=base_url,
+ api_key=api_key,
+ prompt=prompt,
+ model=model,
+ resolution=resolution,
+ aspect_ratio=aspect_ratio,
+ images=images if images else None,
+ enable_grounding=enable_grounding,
+ thinking_level=thinking_level,
+ )
+ result["output_images"] = gen_images
+ result["success"] = True
+ result["generated_count"] = len(gen_images)
+ except Exception as e:
+ result["error"] = str(e)
+ return result
+
+
+async def _process_batch_async(
+ base_url: str,
+ api_key: str,
+ prompts: List[str],
+ model: str,
+ resolution: str,
+ aspect_ratio: str,
+ images_per_prompt: int,
+ input_images: Optional[List[Image.Image]],
+ pbar=None,
+ enable_grounding: bool = False,
+ thinking_level: Optional[str] = None,
+) -> List[dict]:
+ tasks_def = []
+ for p_idx, prompt in enumerate(prompts):
+ for sub_idx in range(images_per_prompt):
+ tasks_def.append((p_idx, sub_idx, prompt))
+
+ total_tasks = len(tasks_def)
+ max_concurrent = 50
+ num_batches = math.ceil(total_tasks / max_concurrent)
+
+ all_results = []
+ completed = 0
+ success_count = 0
+ fail_count = 0
+
+ connector = aiohttp.TCPConnector(ssl=False, limit=0, limit_per_host=0)
+
+ async with aiohttp.ClientSession(connector=connector) as session:
+ for batch_idx in range(num_batches):
+ start_idx = batch_idx * max_concurrent
+ end_idx = min(start_idx + max_concurrent, total_tasks)
+
+ tasks = []
+ for i in range(start_idx, end_idx):
+ _, _, prompt = tasks_def[i]
+ task = asyncio.create_task(
+ _generate_single_task(
+ session=session,
+ base_url=base_url,
+ api_key=api_key,
+ prompt=prompt,
+ model=model,
+ resolution=resolution,
+ aspect_ratio=aspect_ratio,
+ images=input_images,
+ global_task_index=i,
+ enable_grounding=enable_grounding,
+ thinking_level=thinking_level,
+ )
+ )
+ tasks.append(task)
+
+ batch_results = []
+ for coro in asyncio.as_completed(tasks):
+ result_data = None
+ try:
+ result = await coro
+ if isinstance(result, Exception):
+ result_data = {"success": False, "error": str(result), "generated_count": 0, "output_images": [], "prompt": ""}
+ else:
+ result_data = result
+ except Exception as e:
+ result_data = {"success": False, "error": str(e), "generated_count": 0, "output_images": [], "prompt": ""}
+
+ batch_results.append(result_data)
+ completed += 1
+ prompt_snippet = (result_data.get("prompt", "") or "")[:30]
+
+ if result_data and result_data.get("success", False):
+ success_count += 1
+ count = result_data.get("generated_count", 1)
+ print(f"Nano Banana: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} → ✓成功({count}张)")
+ else:
+ fail_count += 1
+ error_msg = result_data.get("error", "未知错误") if result_data else "未知错误"
+ print(f"Nano Banana: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} → ✗失败: {error_msg}")
+
+ if pbar is not None:
+ pbar.update(1)
+
+ all_results.extend(batch_results)
+ import gc; gc.collect()
+ await asyncio.sleep(0.1)
+
+ return all_results
+
+
+class NanoBanana(io.ComfyNode):
+
+ @classmethod
+ def define_schema(cls):
+ return io.Schema(
+ node_id="NanoBanana",
+ display_name="Nano Banana",
+ category="image/generation",
+ inputs=[
+ io.String.Input(
+ "prompt",
+ default="一个中国女子的OOTD",
+ multiline=True,
+ ),
+ io.DynamicCombo.Input("模型", options=[
+ io.DynamicCombo.Option("Nano Banana Pro", [
+ io.Combo.Input("宽高比", options=[
+ "智能", "1:1", "2:3", "3:2", "3:4", "4:3",
+ "4:5", "5:4", "9:16", "16:9", "21:9",
+ ], default="智能"),
+ io.Combo.Input("分辨率", options=["1K", "2K", "4K"], default="2K"),
+ ]),
+ io.DynamicCombo.Option("Nano Banana 2", [
+ io.Combo.Input("宽高比", options=[
+ "智能", "1:1", "1:4", "1:8", "2:3", "3:2", "3:4",
+ "4:1", "4:3", "4:5", "5:4", "8:1",
+ "9:16", "16:9", "21:9",
+ ], default="智能"),
+ io.Combo.Input("分辨率", options=["512px", "1K", "2K", "4K"], default="2K"),
+ io.Combo.Input("思考深度", options=["高", "低"], default="高"),
+ ]),
+ io.DynamicCombo.Option("Nano Banana", [
+ io.Combo.Input("宽高比", options=[
+ "智能", "1:1", "2:3", "3:2", "3:4", "4:3",
+ "4:5", "5:4", "9:16", "16:9", "21:9",
+ ], default="智能"),
+ io.Combo.Input("分辨率", options=["1K"], default="1K"),
+ ]),
+ ]),
+ io.Int.Input("生图数量", default=1, min=1, max=1000, step=1),
+ io.Combo.Input("网络", options=NETWORK_ROUTE_OPTIONS, default="全球加速"),
+ io.Combo.Input("计费", options=["特价", "官方"], default="特价"),
+ io.Combo.Input("谷歌搜索", options=["关闭", "打开"], default="关闭"),
+ io.Int.Input("seed", default=0, min=0, max=0xFFFFFFFFFFFFFFFF),
+ io.Image.Input("参考图1", optional=True),
+ io.Image.Input("参考图2", optional=True),
+ io.Image.Input("参考图3", optional=True),
+ io.Image.Input("参考图4", optional=True),
+ io.Image.Input("参考图5", optional=True),
+ io.Image.Input("参考图6", optional=True),
+ io.Image.Input("参考图7", optional=True),
+ io.Image.Input("参考图8", optional=True),
+ io.Image.Input("参考图9", optional=True),
+ ],
+ outputs=[
+ io.Image.Output(display_name="输出图像"),
+ ],
+ )
+
+ @classmethod
+ def execute(cls, prompt, 模型, 生图数量, 计费, 网络, 谷歌搜索, seed, **kwargs) -> io.NodeOutput:
+ start_time = time.time()
+
+ model_name = 模型["模型"]
+ 宽高比 = 模型["宽高比"]
+ 分辨率 = 模型["分辨率"]
+ 思考深度 = 模型.get("思考深度")
+
+ enable_grounding = (谷歌搜索 == "打开")
+
+ thinking_level = None
+ if model_name == "Nano Banana 2" and 思考深度:
+ thinking_level = "High" if 思考深度 == "高" else "Low"
+
+ actual_model = _build_model_id(model_name, 分辨率, 计费)
+
+ api_key = get_api_key_or_raise("O1KEY_API_KEY")
+ base_url = get_base_url_by_route(网络)
+
+ pbar = ProgressBar(生图数量) if PROGRESS_BAR_AVAILABLE else None
+
+ try:
+ random.seed(seed)
+ np.random.seed(seed % (2**32))
+
+ input_images = []
+ for i in range(1, 10):
+ key = f"参考图{i}"
+ if key in kwargs and kwargs[key] is not None:
+ pil_imgs = tensor_to_pil(kwargs[key])
+ input_images.extend(pil_imgs)
+
+ if input_images and len(input_images) > 14:
+ raise ValueError(f"输入图像数量 {len(input_images)} 超过限制 14 张")
+
+ batch_prompts = parse_batch_prompts(prompt)
+
+ grounding_str = " | 谷歌搜索接地" if enable_grounding else ""
+ thinking_str = f" | 思考:{thinking_level}" if thinking_level else ""
+ if batch_prompts:
+ num_prompts = len(batch_prompts)
+ total_images = num_prompts * 生图数量
+ mode_str = f"批量提示词模式 ({num_prompts}个提示词)"
+ if input_images:
+ mode_str += f" (输入{len(input_images)}张)"
+ print(f"Nano Banana: {mode_str} | {分辨率} {宽高比} | 共{total_images}张{grounding_str}{thinking_str}")
+ else:
+ mode_str = f"图生图模式 (输入{len(input_images)}张)" if input_images else "文生图模式"
+ print(f"Nano Banana: {mode_str} | {分辨率} {宽高比} | {生图数量}张{grounding_str}{thinking_str}")
+
+ if batch_prompts or 生图数量 > 1:
+ prompts = batch_prompts if batch_prompts else [prompt]
+ images_per_prompt = 生图数量
+ total_tasks = len(prompts) * images_per_prompt
+
+ if pbar is not None:
+ pbar = ProgressBar(total_tasks)
+
+ def run_async_in_thread():
+ loop = asyncio.new_event_loop()
+ asyncio.set_event_loop(loop)
+ try:
+ return loop.run_until_complete(
+ _process_batch_async(
+ base_url=base_url,
+ api_key=api_key,
+ prompts=prompts,
+ model=actual_model,
+ resolution=分辨率,
+ aspect_ratio=宽高比,
+ images_per_prompt=images_per_prompt,
+ input_images=input_images,
+ pbar=pbar,
+ enable_grounding=enable_grounding,
+ thinking_level=thinking_level,
+ )
+ )
+ finally:
+ loop.close()
+
+ with ThreadPoolExecutor(max_workers=1) as executor:
+ future = executor.submit(run_async_in_thread)
+ try:
+ results = future.result(timeout=900)
+ except TimeoutError:
+ raise RuntimeError("任务执行超时（900秒）")
+
+ success_count = sum(1 for r in results if r.get("success", False))
+ fail_count = len(results) - success_count
+ elapsed = time.time() - start_time
+ time_str = f"{elapsed:.3f}s" if elapsed < 1 else f"{elapsed:.2f}s"
+ print(f"完成！总耗时 {time_str} | 成功: {success_count}/{total_tasks} | 失败: {fail_count}")
+
+ output_images = []
+ for r in results:
+ output_images.extend(r.get("output_images", []))
+ if not output_images:
+ placeholder = Image.new('RGB', (512, 512), color=(128, 128, 128))
+ output_images = [placeholder]
+
+ output_tensor = _images_to_tensor_safe(output_images, _NODE)
+ import gc; gc.collect()
+ return io.NodeOutput(output_tensor)
+
+ else:
+ def run_single():
+ loop = asyncio.new_event_loop()
+ asyncio.set_event_loop(loop)
+ try:
+ async def _do():
+ connector = aiohttp.TCPConnector(ssl=False)
+ async with aiohttp.ClientSession(connector=connector) as session:
+ return await _generate_single(
+ session=session,
+ base_url=base_url,
+ api_key=api_key,
+ prompt=prompt,
+ model=actual_model,
+ resolution=分辨率,
+ aspect_ratio=宽高比,
+ images=input_images if input_images else None,
+ enable_grounding=enable_grounding,
+ thinking_level=thinking_level,
+ )
+ return loop.run_until_complete(_do())
+ finally:
+ loop.close()
+
+ with ThreadPoolExecutor(max_workers=1) as executor:
+ future = executor.submit(run_single)
+ generated_images, first_token_ms, download_ms = future.result(timeout=900)
+
+ if pbar is not None:
+ pbar.update(1)
+
+ output_tensor = _images_to_tensor_safe(generated_images, _NODE)
+ elapsed = time.time() - start_time
+ time_str = f"{elapsed:.3f}s" if elapsed < 1 else f"{elapsed:.2f}s"
+ ft_str = f"{first_token_ms/1000:.2f}s"
+ dl_str = f"{download_ms/1000:.2f}s"
+ print(f"完成！总耗时 {time_str} | 首字 {ft_str} | 下载 {dl_str} | 成功 {len(generated_images)}张")
+
+ import gc; gc.collect()
+ return io.NodeOutput(output_tensor)
+
+ except ValueError as e:
+ if str(e) == "未授权！":
+ print("请联系作者授权后方可使用！")
+ raise ValueError("未授权！") from None
+ raise ValueError(str(e)) from None
+ except RuntimeError as e:
+ raise RuntimeError(str(e)) from None
+ except Exception as e:
+ raise type(e)(str(e)) from None
+ finally:
+ try:
+ client = _get_client()
+ client.base_url = base_url
+ balance_data = client.query_balance_sync()
+ balance_info = client.format_balance_info(balance_data)
+ print(f"Nano Banana: {balance_info}")
+ except Exception:
+ pass
+ import gc; gc.collect()
\ No newline at end of file
diff --git a/nodes/nano_banana_pro.py b/nodes/nano_banana_pro.py
deleted file mode 100644
index 026cbef..0000000
--- a/nodes/nano_banana_pro.py
+++ /dev/null
@@ -1,831 +0,0 @@
-"""
-Nano Banana Pro 节点
-ComfyUI 自定义节点，用于调用 Gemini 模型生成图像
-"""
-
-import time
-import math
-import random
-import asyncio
-import aiohttp
-from concurrent.futures import ThreadPoolExecutor
-from typing import Optional, Tuple, List
-
-import torch
-import numpy as np
-from PIL import Image
-
-from ..utils.image_utils import tensor_to_pil, pil_to_tensor, parse_batch_prompts
-from ..utils.file_utils import ImageInfo, generate_timestamp_filename, save_image
-from ..clients.gemini_client import GeminiAPIClient
-from ..models_config import (
- get_enabled_models, get_model_description,
- get_model_supported_aspect_ratios, get_all_supported_aspect_ratios,
- get_model_supported_resolutions, get_all_supported_resolutions
-)
-
-# 检查 folder_paths 是否可用
-try:
- import folder_paths
- FOLDER_PATHS_AVAILABLE = True
-except ImportError:
- FOLDER_PATHS_AVAILABLE = False
-
-# 导入 ComfyUI 原生进度条
-try:
- from comfy.utils import ProgressBar
- PROGRESS_BAR_AVAILABLE = True
-except ImportError:
- PROGRESS_BAR_AVAILABLE = False
- print("⚠️ NanoBananaPro: comfy.utils.ProgressBar 不可用，将只使用终端进度显示")
-
-# 内存监控（可选）
-try:
- import psutil
- MEMORY_MONITOR_AVAILABLE = True
-except ImportError:
- MEMORY_MONITOR_AVAILABLE = False
- print("⚠️ NanoBananaPro: psutil 不可用，内存监控功能禁用")
-
-# ============================================================================
-# 调试日志配置
-# ============================================================================
-# 是否启用调试日志（打印完整的 API 响应内容）
-# 设置为 True 以启用调试日志，False 以禁用
-DEBUG_LOG_ENABLED = True
-# 是否启用请求体日志（打印发送给 API 的请求体，base64 图片数据将自动截断）
-# 设置为 True 以启用请求体日志，False 以禁用
-REQUEST_LOG_ENABLED = True
-# ============================================================================
-
-_NODE = "Nano Banana Pro"
-
-
-def _images_to_tensor_safe(images: List[Image.Image], node_label: str) -> torch.Tensor:
- """
- 将 PIL Image 列表转换为 ComfyUI tensor，安全处理多张不同尺寸的情况。
-
- 策略：
- - 以像素数最大的图尺寸为基准
- - 只输出与最大尺寸相同的图，其余较小的图丢弃
- """
- if not images:
- placeholder = Image.new('RGB', (512, 512), color=(128, 128, 128))
- return pil_to_tensor([placeholder])
-
- base_size = max(images, key=lambda img: img.size[0] * img.size[1]).size
- matched = [img for img in images if img.size == base_size]
- skipped = [img for img in images if img.size != base_size]
-
- if skipped:
- sizes_str = ", ".join(f"{img.size[0]}×{img.size[1]}" for img in skipped)
- print(
- f"{node_label}: 丢弃 {len(skipped)} 张较小尺寸的图 ({sizes_str})，"
- f"仅输出最大尺寸 {base_size[0]}×{base_size[1]} 的 {len(matched)} 张"
- )
-
- return pil_to_tensor(matched)
-
-
-class NanoBananaPro:
- """
- Nano Banana Pro 节点
-
- 功能：
- - 文生图：基于提示词生成图像
- - 图生图：基于输入图像和提示词生成新图像
- - 批量生成：支持并发生成多张图像
-
- 注意：
- - 支持的模型列表从 models_config.py 动态加载
- - 要添加/禁用模型，请编辑 models_config.py 文件
- """
-
- # 支持的模型列表（从配置文件动态加载）
- MODELS = None # 将在 INPUT_TYPES 中动态获取
-
- # 支持的宽高比列表（全量：所有启用模型的并集，动态加载）
- # 实际渲染时通过 get_all_supported_aspect_ratios() 获取
- ASPECT_RATIOS = [
- "1:1", "4:3", "3:4", "16:9", "9:16",
- "2:3", "3:2", "4:5", "5:4", "21:9",
- "1:4", "4:1", "1:8", "8:1"
- ]
-
- # 支持的分辨率列表（全量兜底，实际由 get_all_supported_resolutions() 动态生成）
- RESOLUTIONS = ["512px", "1K", "2K", "4K"]
-
- def __init__(self):
- """初始化节点"""
- self.client = None
-
- @classmethod
- def INPUT_TYPES(cls):
- """
- 定义输入参数
-
- ComfyUI 节点规范：
- - required: 必选参数
- - optional: 可选参数
- """
- # 从配置文件动态获取启用的模型列表
- enabled_models = get_enabled_models()
-
- # 如果没有启用的模型，使用空列表（会导致节点不可用，提示用户配置）
- if not enabled_models:
- enabled_models = ["请在 models_config.py 中启用至少一个模型"]
-
- # 动态获取所有启用模型支持的宽高比（去重合并）
- all_aspect_ratios = get_all_supported_aspect_ratios()
- if not all_aspect_ratios:
- all_aspect_ratios = cls.ASPECT_RATIOS
-
- # 动态获取所有启用模型支持的分辨率（去重合并）
- all_resolutions = get_all_supported_resolutions()
- if not all_resolutions:
- all_resolutions = cls.RESOLUTIONS
-
- # 创建9个独立的图像输入
- optional_inputs = {}
- for i in range(1, 10): # 1-9
- optional_inputs[f"参考图{i}"] = ("IMAGE",)
-
- optional_inputs["代理端口（如7897）"] = ("STRING", {
- "default": "",
- "multiline": False,
- "placeholder": "本地代理端口，如 7897（Clash Verge）或 10808（v2rayN），留空不使用"
- })
-
- return {
- "required": {
- "prompt": ("STRING", {
- "default": "一个中国女子的OOTD",
- "multiline": True
- }),
- "模型": (enabled_models, {
- "default": enabled_models[0]
- }),
- "宽高比": (all_aspect_ratios, {
- "default": "1:1"
- }),
- "分辨率": (all_resolutions, {
- "default": "2K"
- }),
- "生图数量": ("INT", {
- "default": 1,
- "min": 1,
- "max": 1000,
- "step": 1
- }),
- "谷歌搜索（联网）": (["关闭", "打开"], {
- "default": "关闭"
- }),
- "图片搜索（联网）": (["关闭", "打开"], {
- "default": "关闭"
- }),
- "返回格式": (["url", "base64"], {
- "default": "url"
- }),
- "seed": ("INT", {
- "default": 0,
- "min": 0,
- "max": 0xffffffffffffffff
- })
- },
- "optional": optional_inputs
- }
-
- # 返回值类型
- RETURN_TYPES = ("IMAGE",)
- RETURN_NAMES = ("输出图像",)
-
- # 导入 ComfyUI 的文件夹路径管理
- try:
- import folder_paths
- FOLDER_PATHS_AVAILABLE = True
- except ImportError:
- FOLDER_PATHS_AVAILABLE = False
-
- # 执行函数名
- FUNCTION = "generate"
-
- # 节点分类
- CATEGORY = "image/generation"
-
- def resize_to_megapixels(
- self,
- image: Image.Image,
- target_megapixels: float
- ) -> Image.Image:
- """
- 将图像缩放到指定的总像素数，保持纵横比
-
- Args:
- image: PIL Image 对象
- target_megapixels: 目标像素数（百万像素）
-
- Returns:
- 缩放后的 PIL Image
-
- Example:
- >>> resized = self.resize_to_megapixels(img, 2.0) # 缩放到2百万像素
- """
- # 计算当前像素数
- current_pixels = image.width * image.height
- target_pixels = int(target_megapixels * 1_000_000)
-
- # 如果当前像素数已经接近目标，则不缩放
- if abs(current_pixels - target_pixels) / target_pixels < 0.05:
- return image
-
- # 计算缩放比例
- scale = (target_pixels / current_pixels) ** 0.5
-
- # 计算新尺寸
- new_width = int(image.width * scale)
- new_height = int(image.height * scale)
-
- # 确保至少为1像素
- new_width = max(1, new_width)
- new_height = max(1, new_height)
-
- # 使用 Lanczos 重采样
- resized_image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
-
- return resized_image
-
- def validate_inputs(
- self,
- images: Optional[torch.Tensor],
- batch_size: int
- ) -> None:
- """
- 验证输入参数
-
- Args:
- images: 输入图像张量（可选）
- batch_size: 批次大小
-
- Raises:
- ValueError: 如果输入参数不合法
- """
- # 检查图像数量
- if images is not None:
- num_images = images.shape[0]
- if num_images > 14:
- raise ValueError(
- f"输入图像数量 {num_images} 超过限制 14 张，请减少输入图像数量"
- )
-
- # 检查批次大小
- if batch_size < 1 or batch_size > 1000:
- raise ValueError(
- f"批次大小 {batch_size} 超出范围 [1, 1000]"
- )
-
- async def _generate_single_task(
- self,
- session: aiohttp.ClientSession,
- prompt: str,
- model: str,
- resolution: str,
- aspect_ratio: str,
- images: List[Image.Image],
- output_folder: str,
- global_task_index: int,
- enable_grounding: bool = False,
- enable_image_search: bool = False,
- save_to_disk: bool = True,
- image_format: str = "url",
- ) -> dict:
- """执行单个生成任务"""
- result = {
- "global_task_index": global_task_index,
- "prompt": prompt,
- "success": False,
- "generated_count": 0,
- "saved_files": [],
- "output_images": [],
- "error": None
- }
-
- try:
- gen_result = await self.client.generate_single_async(
- prompt=prompt,
- model=model,
- resolution=resolution,
- aspect_ratio=aspect_ratio,
- images=images if images else None,
- session=session,
- debug=DEBUG_LOG_ENABLED,
- debug_request=REQUEST_LOG_ENABLED,
- enable_grounding=enable_grounding,
- enable_image_search=enable_image_search,
- image_format=image_format,
- )
- if gen_result:
- images_list, _ = gen_result
- if save_to_disk:
- for gen_img in images_list:
- output_path = generate_timestamp_filename(
- output_folder=output_folder,
- extension=".png"
- )
- save_image(gen_img, output_path)
- result["saved_files"].append(output_path)
- gen_img = None
- else:
- result["output_images"] = images_list
-
- result["success"] = True
- result["generated_count"] = len(images_list)
- except Exception as e:
- result["error"] = str(e)
-
- return result
-
- async def _process_batch_async(
- self,
- prompts: List[str],
- model: str,
- resolution: str,
- aspect_ratio: str,
- images_per_prompt: int,
- input_images: List[Image.Image],
- output_folder: str,
- pbar=None,
- enable_grounding: bool = False,
- enable_image_search: bool = False,
- save_to_disk: bool = True,
- image_format: str = "url",
- ) -> List[dict]:
- """异步批量处理：每个提示词独立调用 API"""
- # 构建任务列表：(prompt, sub_index) 用于 images_per_prompt > 1 的情况
- tasks_def = []
- for p_idx, prompt in enumerate(prompts):
- for sub_idx in range(images_per_prompt):
- tasks_def.append((p_idx, sub_idx, prompt))
-
- total_tasks = len(tasks_def)
- num_prompts = len(prompts)
-
- max_concurrent = 50
- num_batches = math.ceil(total_tasks / max_concurrent)
-
- all_results = []
- completed = 0
- success_count = 0
- fail_count = 0
-
- connector = aiohttp.TCPConnector(ssl=False, limit=0, limit_per_host=0)
-
- async with aiohttp.ClientSession(connector=connector) as session:
- for batch_idx in range(num_batches):
- start_idx = batch_idx * max_concurrent
- end_idx = min(start_idx + max_concurrent, total_tasks)
-
- tasks = []
- for i in range(start_idx, end_idx):
- _, _, prompt = tasks_def[i]
- task = asyncio.create_task(
- self._generate_single_task(
- session=session,
- prompt=prompt,
- model=model,
- resolution=resolution,
- aspect_ratio=aspect_ratio,
- images=input_images,
- output_folder=output_folder,
- global_task_index=i,
- enable_grounding=enable_grounding,
- enable_image_search=enable_image_search,
- save_to_disk=save_to_disk,
- image_format=image_format,
- )
- )
- tasks.append(task)
-
- batch_results = []
- for coro in asyncio.as_completed(tasks):
- result_data = None
- try:
- result = await coro
- if isinstance(result, Exception):
- result_data = {"success": False, "error": str(result), "generated_count": 0, "saved_files": [], "prompt": ""}
- else:
- result_data = result
- batch_results.append(result_data)
- except Exception as e:
- result_data = {"success": False, "error": str(e), "generated_count": 0, "saved_files": [], "prompt": ""}
- batch_results.append(result_data)
-
- completed += 1
- prompt_snippet = (result_data.get("prompt", "") or "")[:30]
-
- if result_data and result_data.get("success", False):
- success_count += 1
- count = result_data.get("generated_count", 1)
- print(f"Nano Banana Pro: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} → ✓成功({count}张)")
- else:
- fail_count += 1
- error_msg = result_data.get("error", "未知错误") if result_data else "未知错误"
- print(f"Nano Banana Pro: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} → ✗失败: {error_msg}")
-
- if pbar is not None:
- pbar.update(1)
-
- all_results.extend(batch_results)
-
- import gc
- gc.collect()
-
- await asyncio.sleep(0.1)
-
- return all_results
-
- def generate(
- self,
- prompt: str,
- 模型: str,
- 宽高比: str,
- 分辨率: str,
- 生图数量: int,
- seed: int,
- **kwargs
- ) -> Tuple[torch.Tensor]:
- """
- 生成图像
-
- Args:
- prompt: 提示词
- 模型: 模型名称
- 宽高比: 宽高比
- 分辨率: 分辨率
- 生图数量: 批次大小
- seed: 随机种子
- **kwargs: 搜索开关（谷歌搜索（联网）/ 图片搜索（联网））及动态参考图输入 (参考图1-9)
- 注：两个搜索参数名含全角括号，不能作为 Python 形参，从 kwargs 中提取
-
- 注意：
- 调试日志功能已移至文件顶部配置，通过修改 DEBUG_LOG_ENABLED 常量控制
-
- Returns:
- 生成的图像张量 (IMAGE,)
- """
- start_time = time.time()
-
- # 从 kwargs 提取搜索参数（界面显示为「关闭/打开」，转为 bool 供调用）
- enable_grounding: bool = (kwargs.pop("谷歌搜索（联网）", "关闭") == "打开")
- enable_image_search: bool = (kwargs.pop("图片搜索（联网）", "关闭") == "打开")
- proxy_port: str = kwargs.pop("代理端口（如7897）", "")
- image_format: str = kwargs.pop("返回格式", "url")
-
- # 创建 ComfyUI 原生进度条
- pbar = None
- if PROGRESS_BAR_AVAILABLE:
- pbar = ProgressBar(生图数量)
-
- try:
- # 设置随机种子（用于本地随机操作）
- random.seed(seed)
- np.random.seed(seed % (2**32))
-
- # 内存监控初始化
- if MEMORY_MONITOR_AVAILABLE and 生图数量 > 50:
- import psutil
- process = psutil.Process()
- initial_memory = process.memory_info().rss / 1024 / 1024
- print(f"Nano Banana Pro: 初始内存使用: {initial_memory:.1f} MB")
-
- # 初始化 API 客户端
- if self.client is None:
- try:
- self.client = GeminiAPIClient()
- except ValueError as e:
- raise ValueError(f"初始化失败: {str(e)}")
-
- # 注入代理设置（每次执行都刷新，支持用户中途修改端口）
- self.client.proxy_url = GeminiAPIClient.build_proxy_url(proxy_port)
- if self.client.proxy_url:
- print(f"Nano Banana Pro: 已启用代理加速 → {self.client.proxy_url}")
-
- # 校验分辨率与模型的兼容性
- supported_resolutions = get_model_supported_resolutions(模型)
- if supported_resolutions and 分辨率 not in supported_resolutions:
- raise ValueError(
- f"分辨率 \"{分辨率}\" 与模型 \"{模型}\" 不兼容！\n"
- f"该模型支持的分辨率：{', '.join(supported_resolutions)}"
- )
-
- # 校验宽高比与模型的兼容性
- supported_ratios = get_model_supported_aspect_ratios(模型)
- if supported_ratios and 宽高比 not in supported_ratios:
- raise ValueError(
- f"宽高比 \"{宽高比}\" 与模型 \"{模型}\" 不兼容！\n"
- f"该模型支持的宽高比：{', '.join(supported_ratios)}"
- )
-
- # 校验图片搜索（联网）与模型的兼容性
- # 仅 nano-banana-2-限时特价 和 gemini-3.1-flash-image-preview 支持图片搜索
- IMAGE_SEARCH_UNSUPPORTED_MODELS = ["nano-banana-pro-次卡", "nano-banana-pro-官方计费", "gemini-3-pro-image-preview"]
- if enable_image_search and 模型 in IMAGE_SEARCH_UNSUPPORTED_MODELS:
- raise ValueError(
- f"模型 \"{模型}\" 不支持【图片搜索（联网）】功能！"
- f"请切换到 nano-banana-2-限时特价 或 gemini-3.1-flash-image-preview 后再使用"
- )
-
- # 收集独立输入的参考图
- input_images = []
- for i in range(1, 10): # 1-9
- key = f"参考图{i}"
- if key in kwargs and kwargs[key] is not None:
- pil_imgs = tensor_to_pil(kwargs[key])
- input_images.extend(pil_imgs)
-
- # 验证输入图像数量
- if input_images:
- if len(input_images) > 14:
- raise ValueError(
- f"输入图像数量 {len(input_images)} 超过限制 14 张，请减少输入图像数量"
- )
-
- # 解析批量提示词
- batch_prompts = parse_batch_prompts(prompt)
-
- # 打印首行概览
- # 图片搜索（联网）开启时隐含谷歌搜索接地，与客户端请求逻辑保持一致
- grounding_str = ""
- if enable_image_search:
- grounding_str = " | 谷歌图片搜索接地"
- elif enable_grounding:
- grounding_str = " | 谷歌搜索接地"
-
- if batch_prompts:
- # 批量提示词模式
- num_prompts = len(batch_prompts)
- total_images = num_prompts * 生图数量
- mode_str = f"批量提示词模式 ({num_prompts}个提示词)"
- if input_images:
- mode_str += f" (输入{len(input_images)}张)"
- print(f"Nano Banana Pro: {mode_str} | {分辨率} {宽高比} | 共{total_images}张{grounding_str}")
-
- # 大批量警告
- if total_images > 100:
- print(f"⚠️ Nano Banana Pro: 警告！批量生成 {total_images} 张图片，内存占用可能较高")
- print(f"⚠️ 建议：分批执行或减少生图数量")
- else:
- # 单提示词模式
- mode_str = f"图生图模式 (输入{len(input_images)}张)" if input_images else "文生图模式"
- print(f"Nano Banana Pro: {mode_str} | {分辨率} {宽高比} | {生图数量}张{grounding_str}")
-
- # 大批量警告
- if 生图数量 > 100:
- print(f"⚠️ Nano Banana Pro: 警告！批量生成 {生图数量} 张图片，内存占用可能较高")
- print(f"⚠️ 建议：分批执行或减少生图数量")
-
- # 统计变量
- success_count = 0
- fail_count = 0
-
- # 进度回调 - 打印错误信息并更新进度条，添加内存监控
- def progress_callback(current, total, success, error_msg=None):
- nonlocal success_count, fail_count
- if success:
- success_count += 1
- else:
- fail_count += 1
-
- # 更新 ComfyUI 原生进度条
- if pbar is not None:
- pbar.update(1)
-
- # 内存监控（每完成10个任务检查一次）
- if MEMORY_MONITOR_AVAILABLE and total > 50 and current % 10 == 0:
- import gc
- gc.collect() # 强制垃圾回收
- current_memory = process.memory_info().rss / 1024 / 1024
- memory_increase = current_memory - initial_memory
- print(f"Nano Banana Pro: 内存使用: {current_memory:.1f} MB (+{memory_increase:.1f} MB)")
-
- # 内存警告阈值（2GB）
- if current_memory > 2000:
- print(f"⚠️ Nano Banana Pro: 内存使用过高！建议减少生图数量或分批执行")
-
- # 根据是否有批量提示词选择生成模式
- if batch_prompts:
- num_prompts = len(batch_prompts)
- total_images = num_prompts * 生图数量
-
- # ===== 批量提示词模式：异步并发，内存输出 =====
- if pbar is not None:
- pbar = ProgressBar(total_images)
-
- def run_async_in_thread():
- loop = asyncio.new_event_loop()
- asyncio.set_event_loop(loop)
- try:
- return loop.run_until_complete(
- self._process_batch_async(
- prompts=batch_prompts,
- model=模型,
- resolution=分辨率,
- aspect_ratio=宽高比,
- images_per_prompt=生图数量,
- input_images=input_images,
- output_folder="",
- pbar=pbar,
- enable_grounding=enable_grounding,
- enable_image_search=enable_image_search,
- save_to_disk=False,
- image_format=image_format,
- )
- )
- finally:
- loop.close()
-
- with ThreadPoolExecutor(max_workers=1) as executor:
- future = executor.submit(run_async_in_thread)
- try:
- results = future.result(timeout=900)
- except TimeoutError:
- raise RuntimeError("任务执行超时（900秒），请减少提示词数量或检查网络连接")
-
- # 统计结果
- success_count = sum(1 for r in results if r.get("success", False))
- fail_count = len(results) - success_count
- total_generated = sum(r.get("generated_count", 0) for r in results)
-
- elapsed = time.time() - start_time
- time_str = f"{elapsed:.3f}s" if elapsed < 1 else f"{elapsed:.2f}s"
-
- print(f"完成！总耗时 {time_str} | 成功: {success_count}/{total_images} | 失败: {fail_count}")
-
- # 失败详情
- failed_results = [r for r in results if not r.get("success", False)]
- if failed_results:
- for fr in failed_results:
- idx = fr.get("global_task_index", -1) + 1
- prompt_snippet = (fr.get("prompt", "") or "")[:30]
- error_msg = fr.get("error", "未知错误")
- print(f" 失败 #{idx}: {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} → {error_msg}")
-
- # 收集内存中的图像
- output_images = []
- for r in results:
- output_images.extend(r.get("output_images", []))
-
- if not output_images:
- placeholder = Image.new('RGB', (512, 512), color=(128, 128, 128))
- output_images = [placeholder]
-
- output_tensor = _images_to_tensor_safe(output_images, _NODE)
-
- import gc
- gc.collect()
- return (output_tensor,)
- else:
- # 单提示词模式
- if 生图数量 == 1:
- # 单张：同步生成
- generated_images = self.client.generate_sync(
- prompt=prompt,
- model=模型,
- resolution=分辨率,
- aspect_ratio=宽高比,
- batch_size=1,
- images=input_images,
- progress_callback=progress_callback,
- debug=DEBUG_LOG_ENABLED,
- debug_request=REQUEST_LOG_ENABLED,
- enable_grounding=enable_grounding,
- enable_image_search=enable_image_search,
- image_format=image_format,
- )
- else:
- # 多张：异步并发，内存输出
-
- if pbar is not None:
- pbar = ProgressBar(生图数量)
-
- def run_async_in_thread():
- loop = asyncio.new_event_loop()
- asyncio.set_event_loop(loop)
- try:
- return loop.run_until_complete(
- self._process_batch_async(
- prompts=[prompt],
- model=模型,
- resolution=分辨率,
- aspect_ratio=宽高比,
- images_per_prompt=生图数量,
- input_images=input_images,
- output_folder="",
- pbar=pbar,
- enable_grounding=enable_grounding,
- enable_image_search=enable_image_search,
- save_to_disk=False,
- image_format=image_format,
- )
- )
- finally:
- loop.close()
-
- with ThreadPoolExecutor(max_workers=1) as executor:
- future = executor.submit(run_async_in_thread)
- try:
- results = future.result(timeout=900)
- except TimeoutError:
- raise RuntimeError("任务执行超时（900秒），请减少生图数量或检查网络连接")
-
- success_count = sum(1 for r in results if r.get("success", False))
- fail_count = len(results) - success_count
- total_generated = sum(r.get("generated_count", 0) for r in results)
-
- elapsed = time.time() - start_time
- time_str = f"{elapsed:.3f}s" if elapsed < 1 else f"{elapsed:.2f}s"
- print(f"完成！总耗时 {time_str} | 成功: {success_count}/{生图数量} | 失败: {fail_count}")
-
- # 失败详情
- failed_results = [r for r in results if not r.get("success", False)]
- if failed_results:
- for fr in failed_results:
- idx = fr.get("global_task_index", -1) + 1
- error_msg = fr.get("error", "未知错误")
- print(f" 失败 #{idx}: {prompt[:30]}{'...' if len(prompt) >= 30 else ''} → {error_msg}")
-
- # 收集内存中的图像
- output_images = []
- for r in results:
- output_images.extend(r.get("output_images", []))
-
- if not output_images:
- placeholder = Image.new('RGB', (512, 512), color=(128, 128, 128))
- output_images = [placeholder]
-
- output_tensor = _images_to_tensor_safe(output_images, _NODE)
-
- import gc
- gc.collect()
- return (output_tensor,)
-
-
- # 优化：限制输出图片数量，避免内存爆炸
- max_output_images = 20 # 最多输出20张图片到ComfyUI
-
- if len(generated_images) > max_output_images:
- print(f"Nano Banana Pro: 生成 {len(generated_images)} 张图片，限制输出前 {max_output_images} 张到ComfyUI")
- output_images = generated_images[:max_output_images]
- else:
- output_images = generated_images
-
- # 转换输出图像
- output_tensor = _images_to_tensor_safe(output_images, _NODE)
-
- # 计算耗时并打印最终统计
- elapsed = time.time() - start_time
- if elapsed < 1:
- time_str = f"{elapsed:.3f}s"
- else:
- time_str = f"{elapsed:.2f}s"
-
- # 打印最终汇总
- if fail_count > 0:
- print(f"完成！总耗时 {time_str} | 成功 {success_count}张 | 失败 {fail_count}张")
- else:
- print(f"完成！总耗时 {time_str} | 成功 {len(generated_images)}张")
-
- # 最终内存清理
- import gc
- gc.collect()
- if MEMORY_MONITOR_AVAILABLE and 生图数量 > 50:
- final_memory = process.memory_info().rss / 1024 / 1024
- print(f"Nano Banana Pro: 最终内存使用: {final_memory:.1f} MB")
-
- return (output_tensor,)
-
- except ValueError as e:
- # 检测是否为授权错误
- if str(e) == "未授权！":
- print("请联系作者授权后方可使用！")
- raise ValueError("未授权！") from None
- raise ValueError(str(e)) from None
-
- except RuntimeError as e:
- raise RuntimeError(str(e)) from None
-
- except Exception as e:
- raise type(e)(str(e)) from None
-
- finally:
- # 查询余额
- 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"Nano Banana Pro: {balance_info}")
- except Exception:
- pass
-
- # 最终内存清理
- import gc
- gc.collect()
\ No newline at end of file
diff --git a/nodes/nano_banana_v2.py b/nodes/nano_banana_v2.py
index 29b8f55..91a6027 100644
--- a/nodes/nano_banana_v2.py
+++ b/nodes/nano_banana_v2.py
@@ -30,7 +30,8 @@ from PIL import Image
from ..utils.image_utils import tensor_to_pil, pil_to_tensor, parse_batch_prompts
from ..utils.file_utils import load_images_from_folder, pair_images_by_name, pair_images_cartesian
-from ..utils.config import get_api_key_or_raise
+from ..utils.config import get_api_key_or_raise, NETWORK_ROUTE_OPTIONS, get_base_url_by_route
+from ..utils.http_error import async_request_with_retry
from ..models_config import (
get_enabled_async_models,
get_model_provider,
@@ -169,15 +170,16 @@ class NanoBananaV2:
"default": "一个中国女子的OOTD",
"multiline": True
}),
+ "网络线路": (NETWORK_ROUTE_OPTIONS, {"default": "全球加速"}),
"模型": (models, {"default": models[0]}),
- "宽高比": (all_aspect_ratios, {"default": "1:1"}),
+ "宽高比": (["智能"] + all_aspect_ratios, {"default": "智能"}),
"分辨率": (all_resolutions, {"default": "2K"}),
"生图数量": ("INT", {
"default": 1,
"min": 1,
"max": 9,
"step": 1
- })
+ }),
},
"optional": optional
}
@@ -299,13 +301,11 @@ class NanoBananaV2:
print(f"[异步提交] URL: {url}")
print(f"[异步提交] 请求体: {json.dumps(_log_body, ensure_ascii=False)[:500]}")
- async with session.post(url, json=request_body, headers=headers, proxy=provider.proxy_url) as response:
- if response.status != 200:
- error_text = await response.text()
- if not error_text.strip():
- error_text = "(服务器未返回错误详情)"
- raise RuntimeError(f"提交任务失败 ({response.status}): {error_text}")
- data = await response.json()
+ resp = await async_request_with_retry(
+ session, "POST", url, json=request_body, headers=headers,
+ proxy=provider.proxy_url, prefix="异步提交: "
+ )
+ data = await resp.json()
if DEBUG_LOG_ENABLED:
import json
@@ -541,6 +541,7 @@ class NanoBananaV2:
宽高比: str,
分辨率: str,
生图数量: int,
+ 网络线路: str = "全球加速",
**kwargs
) -> Tuple[torch.Tensor]:
"""生成图像（异步模式）"""
@@ -557,6 +558,7 @@ class NanoBananaV2:
proxy_url = BaseAsyncImageProvider.build_proxy_url(proxy_port)
provider = self._get_provider(模型, proxy_url=proxy_url, api_key_override=api_key_override)
provider.image_compression = "webp" if 图片质量 == "日常" else None
+ provider._route_base_url = get_base_url_by_route(网络线路)
if proxy_url:
print(f"{self.NODE_LABEL}: 已启用代理加速 -> {proxy_url}")
@@ -590,7 +592,7 @@ class NanoBananaV2:
# 运行时验证宽高比
supported_ratios = provider.get_model_aspect_ratios(模型)
- if supported_ratios and 宽高比 not in supported_ratios:
+ if 宽高比 != "智能" and supported_ratios and 宽高比 not in supported_ratios:
raise ValueError(
f"宽高比 \"{宽高比}\" 与模型 \"{模型}\" 不兼容！\n"
f"该模型支持的宽高比：{', '.join(supported_ratios)}"
@@ -977,6 +979,7 @@ class NanoBananaV2Batch(NanoBananaV2):
宽高比: str,
分辨率: str,
生图数量: int = 1,
+ 网络线路: str = "全球加速",
**kwargs
) -> Tuple[torch.Tensor]:
"""生成图像（异步模式 - 批量版：全并发 + 即时落盘）"""
@@ -997,6 +1000,7 @@ class NanoBananaV2Batch(NanoBananaV2):
proxy_url = BaseAsyncImageProvider.build_proxy_url(proxy_port)
provider = self._get_provider(模型, proxy_url=proxy_url, api_key_override=api_key_override)
provider.image_compression = "webp" if 图片质量 == "日常" else None
+ provider._route_base_url = get_base_url_by_route(网络线路)
if proxy_url:
print(f"{self.NODE_LABEL}: 已启用代理加速 -> {proxy_url}")
diff --git a/nodes/remove_metadata.py b/nodes/remove_metadata.py
index 568daf4..7cf2ec3 100644
--- a/nodes/remove_metadata.py
+++ b/nodes/remove_metadata.py
@@ -1,79 +1,18 @@
"""
图像元数据去除节点
-替代 ComfyUI 原生"保存图像"节点，保存时不写入提示词、工作流等 AI 元数据
-提供两种节点：
-1. SaveCleanImage - 接收 IMAGE 张量，去除元数据后直接保存到 output 目录
-2. BatchCleanMetadata - 指定文件夹路径，批量去除已有图片中的元数据
+提供批量去除已有图片中元数据的功能
"""
import os
-from datetime import datetime
-import random
-from typing import List
-import numpy as np
-import torch
from PIL import Image
from PIL.PngImagePlugin import PngInfo
-from ..utils.image_utils import tensor_to_pil
-from ..utils.file_utils import _get_port_suffix
-
-# 尝试导入 ComfyUI 的 folder_paths
-try:
- import folder_paths
- FOLDER_PATHS_AVAILABLE = True
-except ImportError:
- FOLDER_PATHS_AVAILABLE = False
-
# 支持的图片格式
SUPPORTED_EXTENSIONS = {'.png', '.jpg', '.jpeg', '.webp', '.bmp', '.tiff', '.tif'}
-def _get_output_dir() -> str:
- """
- 获取 ComfyUI output 目录
-
- Returns:
- output 目录的绝对路径
- """
- if FOLDER_PATHS_AVAILABLE:
- return folder_paths.get_output_directory()
- # fallback: 相对于插件目录推断
- plugin_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
- return os.path.join(os.path.dirname(os.path.dirname(plugin_dir)), "output")
-
-
-def _get_next_counter(directory: str, prefix: str) -> int:
- """
- 扫描目录，获取下一个可用的文件计数器
-
- Args:
- directory: 目标目录
- prefix: 文件名前缀
-
- Returns:
- 下一个计数器值
- """
- if not os.path.exists(directory):
- return 1
-
- if prefix:
- pattern = re.compile(rf'^{re.escape(prefix)}_(\d+)')
- else:
- pattern = re.compile(rf'^(\d+)\.')
- max_counter = 0
-
- for f in os.listdir(directory):
- m = pattern.match(f)
- if m:
- counter = int(m.group(1))
- max_counter = max(max_counter, counter)
-
- return max_counter + 1
-
-
def _save_image_clean(image: Image.Image, path: str, fmt: str = None, quality: int = 95) -> None:
"""
保存图像，不包含任何元数据
@@ -127,129 +66,7 @@ def _save_image_clean(image: Image.Image, path: str, fmt: str = None, quality: i
# ============================================================================
-# 节点 1：保存干净图像
-# ============================================================================
-
-class SaveCleanImage:
- """
- 保存干净图像节点（不含元数据）
-
- 功能：
- - 接收 IMAGE 张量（支持单图和批次）
- - 去除所有元数据后保存到 ComfyUI/output 目录
- - 文件名自动添加 nometa 标识，方便辨认
- - 支持 PNG/JPEG/WEBP 格式
- - 作为终端节点，替代 ComfyUI 原生"保存图像"节点
-
- 使用场景：
- - 生图完成后，直接保存不含 AI 元数据的干净图像
- - 分享图像时不暴露提示词和工作流
- """
-
- SAVE_FORMATS = ["PNG", "JPEG", "WEBP"]
-
- @classmethod
- def INPUT_TYPES(cls):
- """
- 定义输入参数
-
- Returns:
- 输入参数配置字典
- """
- return {
- "required": {
- "图像": ("IMAGE",),
- "文件名前缀": ("STRING", {"default": "ComfyUI_nometa"}),
- "保存格式": (cls.SAVE_FORMATS, {"default": "PNG"}),
- },
- "optional": {
- "JPEG/WEBP质量": ("INT", {
- "default": 95,
- "min": 1,
- "max": 100,
- "step": 1
- }),
- }
- }
-
- RETURN_TYPES = ()
- OUTPUT_NODE = True
- FUNCTION = "save_clean"
- CATEGORY = "image"
-
- DESCRIPTION = (
- "保存干净图像（不含元数据）。\n"
- "替代 ComfyUI 原生'保存图像'节点，保存时不写入提示词、工作流等 AI 元数据。\n"
- "文件保存到 ComfyUI/output 目录。"
- )
-
- def save_clean(
- self,
- 图像: torch.Tensor,
- 文件名前缀: str = "ComfyUI_nometa",
- 保存格式: str = "PNG",
- **kwargs
- ) -> dict:
- """
- 去除元数据并保存图像
-
- Args:
- 图像: ComfyUI 图像张量 [B, H, W, C]
- 文件名前缀: 保存文件名前缀
- 保存格式: 图像格式（PNG/JPEG/WEBP）
- **kwargs: 可选参数（JPEG/WEBP质量）
-
- Returns:
- UI 结果字典，包含保存的图像信息用于前端预览
- """
- quality = kwargs.get("JPEG/WEBP质量", 95)
-
- output_dir = _get_output_dir()
- port_suffix = _get_port_suffix()
- os.makedirs(output_dir, exist_ok=True)
-
- # 格式与扩展名映射
- ext_map = {"PNG": ".png", "JPEG": ".jpg", "WEBP": ".webp"}
- ext = ext_map.get(保存格式, ".png")
-
- # 转换为 PIL 图像
- pil_images = tensor_to_pil(图像)
-
- results = []
- saved_paths = []
- for img in pil_images:
- ts = datetime.now().strftime("%Y%m%d_%H%M%S")
- ms = random.randint(0, 999)
-
- while True:
- if 文件名前缀:
- filename = f"{文件名前缀}_{ts}_{ms:03d}{port_suffix}{ext}"
- else:
- filename = f"{ts}_{ms:03d}{port_suffix}{ext}"
- filepath = os.path.join(output_dir, filename)
- if not os.path.exists(filepath):
- break
- ms = (ms + 1) % 1000
-
- _save_image_clean(img, filepath, fmt=保存格式, quality=quality)
-
- results.append({
- "filename": filename,
- "subfolder": "",
- "type": "output"
- })
- saved_paths.append(filepath)
-
- # 打印详细日志，方便用户定位保存的文件
- print(f"保存干净图像: 已保存 {len(pil_images)} 张无元数据图像 (格式: {保存格式})")
- for p in saved_paths:
- print(f" → {p}")
-
- return {"ui": {"images": results}}
-
-
-# ============================================================================
-# 节点 2：批量去除元数据
+# 批量去除元数据
# ============================================================================
class BatchCleanMetadata:
diff --git a/nodes/save_image_format.py b/nodes/save_image_format.py
new file mode 100644
index 0000000..f83dd9b
--- /dev/null
+++ b/nodes/save_image_format.py
@@ -0,0 +1,91 @@
+"""保存图像节点 - 支持 PNG/JPEG/WebP 格式输出"""
+
+import os
+import json
+import numpy as np
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+
+import folder_paths
+from comfy.cli_args import args
+
+
+class SaveImageFormat:
+ """保存图像，支持 PNG / JPEG / WebP 三种格式"""
+
+ FORMATS = ["PNG", "JPEG", "WebP"]
+
+ def __init__(self):
+ self.output_dir = folder_paths.get_output_directory()
+ self.type = "output"
+ self.compress_level = 4
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "images": ("IMAGE",),
+ "filename_prefix": ("STRING", {"default": "ComfyUI"}),
+ "format": (cls.FORMATS, {"default": "PNG"}),
+ },
+ "optional": {},
+ "hidden": {
+ "prompt": "PROMPT",
+ "extra_pnginfo": "EXTRA_PNGINFO",
+ },
+ }
+
+ RETURN_TYPES = ()
+ FUNCTION = "save_images"
+ OUTPUT_NODE = True
+ CATEGORY = "image"
+ DESCRIPTION = "保存图像，支持 PNG / JPEG / WebP 格式输出。"
+
+ _EXT_MAP = {"PNG": ".png", "JPEG": ".jpg", "WebP": ".webp"}
+
+ def save_images(self, images, filename_prefix="ComfyUI", format="PNG",
+ prompt=None, extra_pnginfo=None):
+ full_output_folder, filename, counter, subfolder, filename_prefix = \
+ folder_paths.get_save_image_path(
+ filename_prefix, self.output_dir,
+ images[0].shape[1], images[0].shape[0]
+ )
+
+ ext = self._EXT_MAP.get(format, ".png")
+ results = []
+
+ for batch_number, image in enumerate(images):
+ i = 255.0 * image.cpu().numpy()
+ img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+
+ filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+ file = f"{filename_with_batch_num}_{counter:05}_{ext}"
+
+ filepath = os.path.join(full_output_folder, file)
+
+ if format == "PNG":
+ metadata = None
+ if not args.disable_metadata:
+ metadata = PngInfo()
+ if prompt is not None:
+ metadata.add_text("prompt", json.dumps(prompt))
+ if extra_pnginfo is not None:
+ for x in extra_pnginfo:
+ metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+ img.save(filepath, pnginfo=metadata,
+ compress_level=self.compress_level)
+ elif format == "JPEG":
+ if img.mode == "RGBA":
+ img = img.convert("RGB")
+ img.save(filepath, quality=100, optimize=True)
+ elif format == "WebP":
+ img.save(filepath, lossless=True)
+
+ results.append({
+ "filename": file,
+ "subfolder": subfolder,
+ "type": self.type,
+ })
+ counter += 1
+
+ return {"ui": {"images": results}}
diff --git a/nodes/seedance_video.py b/nodes/seedance_video.py
index 13ba73f..420987e 100644
--- a/nodes/seedance_video.py
+++ b/nodes/seedance_video.py
@@ -15,6 +15,7 @@ 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 ..utils.config import NETWORK_ROUTE_OPTIONS, get_base_url_by_route
from comfy_api.latest import InputImpl
@@ -116,6 +117,7 @@ class Seedance:
return {
"required": {
"提示词": ("STRING", {"multiline": True, "default": ""}),
+ "网络线路": (NETWORK_ROUTE_OPTIONS, {"default": "全球加速"}),
"模型": (_MODELS, {"default": "doubao-seedance-2-0-260128"}),
"分辨率": (_RESOLUTIONS, {"default": "720p"}),
"宽高比": (["16:9", "adaptive", "9:16", "1:1", "4:3", "3:4", "21:9"],
@@ -242,6 +244,7 @@ class Seedance:
_, save_path = tempfile.mkstemp(suffix=".mp4", prefix=f"{file_prefix}_")
client = SeedanceClient()
+ client.base_url = get_base_url_by_route(kwargs.get("网络线路", "全球加速"))
pbar = _make_pbar()
on_stage, on_prog = _make_callbacks(tag, pbar)
@@ -268,6 +271,7 @@ class SeedanceMultiModal:
return {
"required": {
"提示词": ("STRING", {"multiline": True, "default": ""}),
+ "网络线路": (NETWORK_ROUTE_OPTIONS, {"default": "全球加速"}),
"模型": (_MODELS, {"default": "doubao-seedance-2-0-260128"}),
"分辨率": (_RESOLUTIONS, {"default": "720p"}),
"宽高比": (["adaptive", "16:9", "9:16", "1:1", "4:3", "3:4", "21:9"],
@@ -414,6 +418,7 @@ class SeedanceMultiModal:
_, save_path = tempfile.mkstemp(suffix=".mp4", prefix="seedance_mm_")
client = SeedanceClient()
+ client.base_url = get_base_url_by_route(kwargs.get("网络线路", "全球加速"))
pbar = _make_pbar()
on_stage, on_prog = _make_callbacks("Seedance多模态", pbar)
diff --git a/nodes/universal_llm.py b/nodes/universal_llm.py
index 2cec821..015a636 100644
--- a/nodes/universal_llm.py
+++ b/nodes/universal_llm.py
@@ -17,7 +17,7 @@ import torch
from PIL import Image
from ..utils.image_utils import tensor_to_pil
-from ..utils.config import get_api_key_or_raise, get_api_base_url
+from ..utils.config import get_api_key_or_raise, get_api_base_url, NETWORK_ROUTE_OPTIONS, get_base_url_by_route
from ..utils.file_types import FileList
# ============================================================================
@@ -66,6 +66,9 @@ class UniversalLLMChat:
def INPUT_TYPES(cls):
return {
"required": {
+ "网络线路": (NETWORK_ROUTE_OPTIONS, {
+ "default": "全球加速"
+ }),
"模型": (SUPPORTED_MODELS, {
"default": SUPPORTED_MODELS[0]
}),
@@ -370,6 +373,7 @@ class UniversalLLMChat:
self,
模型: str,
提示词: str,
+ 网络线路: str = "全球加速",
图片: Optional[torch.Tensor] = None,
视频=None,
文件: Optional[FileList] = None,
@@ -380,6 +384,7 @@ class UniversalLLMChat:
try:
self._ensure_config()
+ self._base_url = get_base_url_by_route(网络线路)
# 如果用户传入了自定义令牌，则覆盖默认 API Key
effective_api_key = 令牌.strip() if 令牌 and 令牌.strip() else self._api_key
diff --git a/utils/config.py b/utils/config.py
index c9c9e60..f370efa 100644
--- a/utils/config.py
+++ b/utils/config.py
@@ -21,6 +21,14 @@ DEFAULT_API_BASE_URL = "https://api.o1key.com"
# 可通过环境变量 O1KEY_ASYNC_API_BASE_URL 覆盖
DEFAULT_ASYNC_API_BASE_URL = "https://cf-api.o1key.com"
+# ============ 网络线路配置 ============
+NETWORK_ROUTES = {
+ "全球加速": "https://api.o1key.cn",
+ "CF加速": "https://cf-api.o1key.com",
+ "美国直连": "https://api.o1key.com",
+}
+NETWORK_ROUTE_OPTIONS = ["全球加速", "CF加速", "美国直连"]
+
def load_config(config_path: Optional[str] = None) -> Dict[str, str]:
"""
@@ -136,3 +144,8 @@ def get_async_api_base_url() -> str:
return base_url.rstrip('/')
return DEFAULT_ASYNC_API_BASE_URL
+
+
+def get_base_url_by_route(route: str) -> str:
+ """根据网络线路选项返回对应域名，未匹配则走 config 垫底"""
+ return NETWORK_ROUTES.get(route, get_api_base_url())
diff --git a/utils/http_error.py b/utils/http_error.py
new file mode 100644
index 0000000..23d7810
--- /dev/null
+++ b/utils/http_error.py
@@ -0,0 +1,125 @@
+"""
+统一 HTTP 错误处理 & 退避重试模块
+
+使用方式：
+ 1. 对于 aiohttp 请求，用 async_request_with_retry() 包裹 POST/GET 调用
+ 2. 对于已拿到 status code 的场景，调用 raise_for_status() 抛出友好错误
+
+新增生图/视频节点时，请统一使用本模块处理 HTTP 错误。
+"""
+
+import asyncio
+import random
+from typing import Optional
+
+import aiohttp
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 状态码 → 用户友好文案
+# ═══════════════════════════════════════════════════════════════════════════════
+
+HTTP_ERROR_MESSAGES = {
+ 429: "模型速率超限或额度不足！",
+ 502: "网关超时。请重试或将网络切换为美国直连",
+ 503: "模型超载。请稍后重试！",
+ 504: "网关超时。请稍后重试。",
+}
+
+# 可退避重试的状态码
+RETRYABLE_STATUS_CODES = {429, 502, 503, 504}
+
+# 退避重试默认参数
+DEFAULT_MAX_RETRIES = 3
+DEFAULT_BASE_DELAY = 2.0 # 首次重试等待秒数
+DEFAULT_MAX_DELAY = 30.0 # 最大等待秒数
+DEFAULT_BACKOFF_FACTOR = 2.0 # 指数退避因子
+
+
+def get_friendly_message(status_code: int, raw_message: str = "") -> str:
+ """根据状态码返回友好文案，未匹配则返回原始信息"""
+ friendly = HTTP_ERROR_MESSAGES.get(status_code)
+ if friendly:
+ return friendly
+ return raw_message or f"请求失败 ({status_code})"
+
+
+def raise_for_status(status_code: int, raw_message: str = "", prefix: str = ""):
+ """根据状态码抛出带友好文案的 RuntimeError"""
+ friendly = get_friendly_message(status_code, raw_message)
+ full_msg = f"{prefix}{friendly}" if prefix else friendly
+ raise RuntimeError(full_msg)
+
+
+def is_retryable(status_code: int) -> bool:
+ return status_code in RETRYABLE_STATUS_CODES
+
+
+def _compute_delay(attempt: int, base_delay: float, max_delay: float, backoff_factor: float) -> float:
+ """计算第 attempt 次重试的等待时间（含 jitter）"""
+ delay = base_delay * (backoff_factor ** attempt)
+ delay = min(delay, max_delay)
+ jitter = random.uniform(0, delay * 0.3)
+ return delay + jitter
+
+
+async def async_request_with_retry(
+ session: aiohttp.ClientSession,
+ method: str,
+ url: str,
+ *,
+ max_retries: int = DEFAULT_MAX_RETRIES,
+ base_delay: float = DEFAULT_BASE_DELAY,
+ max_delay: float = DEFAULT_MAX_DELAY,
+ backoff_factor: float = DEFAULT_BACKOFF_FACTOR,
+ prefix: str = "",
+ **request_kwargs,
+) -> aiohttp.ClientResponse:
+ """
+ 带退避重试的 aiohttp 请求。
+
+ 仅对 RETRYABLE_STATUS_CODES (429/503/504) 进行重试。
+ 超过最大重试次数后抛出友好 RuntimeError。
+ 成功时返回 response 对象（调用者需在 async with 外自行处理 body）。
+
+ 用法示例：
+ resp = await async_request_with_retry(session, "POST", url, json=body, headers=headers)
+ data = await resp.json()
+ """
+ last_status: Optional[int] = None
+ last_message = ""
+
+ for attempt in range(max_retries + 1):
+ try:
+ resp = await session.request(method, url, **request_kwargs)
+ except (aiohttp.ClientError, asyncio.TimeoutError) as e:
+ if attempt < max_retries:
+ delay = _compute_delay(attempt, base_delay, max_delay, backoff_factor)
+ print(f"{prefix}网络错误，{delay:.1f}s 后重试 ({attempt+1}/{max_retries})...")
+ await asyncio.sleep(delay)
+ continue
+ raise RuntimeError(f"{prefix}网络错误: {e}") from None
+
+ if resp.status == 200:
+ return resp
+
+ last_status = resp.status
+ try:
+ last_message = await resp.text()
+ except Exception:
+ last_message = ""
+
+ if is_retryable(resp.status) and attempt < max_retries:
+ delay = _compute_delay(attempt, base_delay, max_delay, backoff_factor)
+ friendly = get_friendly_message(resp.status)
+ print(f"{prefix}{friendly} {delay:.1f}s 后重试 ({attempt+1}/{max_retries})...")
+ await asyncio.sleep(delay)
+ continue
+
+ break
+
+ if last_status and last_status in HTTP_ERROR_MESSAGES:
+ raise_for_status(last_status, prefix=prefix)
+
+ raw_msg = last_message[:200] if last_message else ""
+ raise RuntimeError(f"{prefix}请求失败 ({last_status}): {raw_msg}")
diff --git a/utils/image_utils.py b/utils/image_utils.py
index 18005a1..5b7f8e7 100644
--- a/utils/image_utils.py
+++ b/utils/image_utils.py
@@ -110,6 +110,57 @@ def encode_image_to_base64(image: Image.Image, format: str = "PNG") -> str:
return base64.b64encode(img_bytes).decode('utf-8')
+_MAX_IMAGE_BYTES = 10 * 1024 * 1024 # 10MB base64 上限
+
+
+def encode_image_to_base64_limited(
+ image: Image.Image,
+ format: str = "PNG",
+ max_bytes: int = _MAX_IMAGE_BYTES,
+) -> str:
+ """
+ 将 PIL Image 编码为 base64，若超过 max_bytes 则自动缩放直到满足限制。
+
+ 策略：等比缩放，每轮缩小到上一轮的 80%，最多 10 轮。
+
+ Args:
+ image: PIL Image 对象
+ format: 图像格式，默认 PNG
+ max_bytes: base64 字符串最大字节数，默认 10MB
+
+ Returns:
+ base64 编码的字符串（保证 <= max_bytes）
+ """
+ working = image
+ if working.mode == 'RGBA':
+ working = working.convert('RGB')
+
+ for attempt in range(10):
+ buffered = BytesIO()
+ working.save(buffered, format=format)
+ b64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
+
+ if len(b64) <= max_bytes:
+ if attempt > 0:
+ print(
+ f"图片已自动缩放: {image.width}x{image.height} → "
+ f"{working.width}x{working.height} "
+ f"({len(b64) / 1024 / 1024:.2f}MB)"
+ )
+ return b64
+
+ # 缩放到 80%
+ scale = 0.8
+ new_w = max(1, int(working.width * scale))
+ new_h = max(1, int(working.height * scale))
+ working = working.resize((new_w, new_h), Image.Resampling.LANCZOS)
+
+ # 兜底：返回最后一次编码结果
+ buffered = BytesIO()
+ working.save(buffered, format=format)
+ return base64.b64encode(buffered.getvalue()).decode('utf-8')
+
+
def decode_base64_to_pil(base64_string: str) -> Image.Image:
"""
将 base64 字符串解码为 PIL Image
diff --git a/web/js/assetToggle.js b/web/js/assetToggle.js
new file mode 100644
index 0000000..5dd5f96
--- /dev/null
+++ b/web/js/assetToggle.js
@@ -0,0 +1,92 @@
+import { app } from "../../../scripts/app.js";
+import { api } from "../../../scripts/api.js";
+
+app.registerExtension({
+ name: "o1key.assetToggle",
+ settings: [
+ {
+ id: "o1key.AssetSave",
+ name: "资产保存",
+ tooltip: "持久性保存生图记录",
+ type: "boolean",
+ defaultValue: true,
+ },
+ ],
+ async init() {
+ const enabled = () => {
+ try {
+ return app.ui.settings.getSettingValue("o1key.AssetSave", true);
+ } catch {
+ return true;
+ }
+ };
+
+ // --- Patch getHistory: 合并真实 jobs 与持久化历史 ---
+ const _origGetHistory = api.getHistory.bind(api);
+ api.getHistory = async function (maxItems = 200, opts = {}) {
+ const real = await _origGetHistory(maxItems, opts);
+ if (!enabled()) return real;
+ try {
+ const offset = opts?.offset || 0;
+ const resp = await fetch(
+ `/o1key/output_history?limit=${maxItems}&offset=${offset}`
+ );
+ if (!resp.ok) return real;
+ const data = await resp.json();
+ const persisted = data.jobs || [];
+ if (!persisted.length) return real;
+ if (!real || !Array.isArray(real) || !real.length) {
+ // 仅持久化数据时也补充 priority
+ const t = data.pagination?.total || persisted.length;
+ return persisted.map((j, i) => ({
+ ...j,
+ priority: j.priority ?? t - i,
+ }));
+ }
+ // 合并去重：以 id 为 key，真实 jobs 优先
+ const seen = new Set(real.map((j) => j.id));
+ const merged = [...real];
+ for (const job of persisted) {
+ if (!seen.has(job.id)) {
+ merged.push(job);
+ }
+ }
+ // 按时间倒序
+ merged.sort(
+ (a, b) => (b.create_time || 0) - (a.create_time || 0)
+ );
+ const result = merged.slice(0, maxItems);
+ // 补充 priority 字段（队列面板依赖此字段排序）
+ const total = data.pagination?.total || result.length;
+ for (let idx = 0; idx < result.length; idx++) {
+ if (result[idx].priority == null) {
+ result[idx] = {
+ ...result[idx],
+ priority: total - idx,
+ };
+ }
+ }
+ return result;
+ } catch (e) {
+ return real;
+ }
+ };
+
+ // --- Patch getJobDetail: 真实 API 失败时回退到本地路由 ---
+ const _origGetJobDetail = api.getJobDetail.bind(api);
+ api.getJobDetail = async function (jobId) {
+ const real = await _origGetJobDetail(jobId);
+ if (real) return real;
+ if (!enabled()) return undefined;
+ try {
+ const resp = await fetch(
+ `/o1key/job_detail/${encodeURIComponent(jobId)}`
+ );
+ if (!resp.ok) return undefined;
+ return await resp.json();
+ } catch (e) {
+ return undefined;
+ }
+ };
+ },
+});
diff --git a/web/js/dotGrid.js b/web/js/dotGrid.js
new file mode 100644
index 0000000..ad1a959
--- /dev/null
+++ b/web/js/dotGrid.js
@@ -0,0 +1,41 @@
+import { app } from "../../../scripts/app.js";
+
+app.registerExtension({
+ name: "o1key.dotGrid",
+ async setup() {
+ function createBlackTile() {
+ const size = 64;
+ const c = document.createElement("canvas");
+ c.width = size;
+ c.height = size;
+ const ctx = c.getContext("2d");
+ ctx.fillStyle = "#1a1a1a";
+ ctx.fillRect(0, 0, size, size);
+ return c;
+ }
+
+ // Hook immediately so the first draw already uses our tile
+ const orig = LGraphCanvas.prototype.drawBackCanvas;
+ LGraphCanvas.prototype.drawBackCanvas = function () {
+ if (!this._pattern || !this._pattern_img) {
+ const ctx = this.bgcanvas?.getContext("2d");
+ if (ctx) {
+ const t = createBlackTile();
+ this._pattern = ctx.createPattern(t, "repeat");
+ this._pattern_img = t;
+ }
+ }
+ return orig.apply(this, arguments);
+ };
+
+ // Also apply to current canvas instance if already exists
+ const canvas = app.canvas;
+ if (canvas?.bgcanvas) {
+ const bgCtx = canvas.bgcanvas.getContext("2d");
+ const tile = createBlackTile();
+ canvas._pattern = bgCtx.createPattern(tile, "repeat");
+ canvas._pattern_img = tile;
+ canvas.draw(true, true);
+ }
+ },
+});
diff --git a/web/js/hideSidebar.js b/web/js/hideSidebar.js
new file mode 100644
index 0000000..8049f44
--- /dev/null
+++ b/web/js/hideSidebar.js
@@ -0,0 +1,215 @@
+import { app } from "../../../scripts/app.js";
+
+app.registerExtension({
+ name: "o1key.hideSidebarItems",
+ async setup() {
+ const hide = () => {
+ // 隐藏侧边栏的"说明"、"应用"、"模型"、"节点"按钮
+ const hiddenLabels = ["说明", "帮助", "Help", "应用", "Apps", "模型", "Models", "节点", "Nodes"];
+ document.querySelectorAll(".side-bar-button, [class*='sidebar'] button, .p-togglebutton").forEach(btn => {
+ const label = btn.getAttribute("aria-label") || btn.textContent || "";
+ if (hiddenLabels.some(k => label.includes(k))) {
+ btn.style.display = "none";
+ }
+ });
+ // 隐藏左上角下拉菜单中的"帮助"项
+ document.querySelectorAll(".p-menuitem, .p-menu-item, [class*='menu'] li, [class*='Menu'] li").forEach(item => {
+ const text = item.textContent || "";
+ if (text.trim() === "帮助" || text.trim() === "Help") {
+ item.style.display = "none";
+ }
+ });
+ // 隐藏登录/注册弹框（Google/Github 登录对话框）
+ document.querySelectorAll("[class*='dialog'], [class*='Dialog'], [class*='modal'], [class*='Modal']").forEach(dialog => {
+ const text = dialog.textContent || "";
+ if ((text.includes("Google") || text.includes("Github")) &&
+ (text.includes("登录") || text.includes("注册"))) {
+ dialog.style.display = "none";
+ const mask = dialog.previousElementSibling;
+ if (mask && mask.className && mask.className.includes("mask")) {
+ mask.style.display = "none";
+ }
+ }
+ });
+ // 在右侧内容区隐藏"登录/注册"按钮并注入 API Key
+ injectApiKeyPanel();
+ };
+
+ async function injectApiKeyPanel() {
+ // 找到右侧内容区中包含"我的用户设置"的区域
+ let contentArea = null;
+ document.querySelectorAll("h1, h2, h3, h4, span, div").forEach(el => {
+ const t = (el.textContent || "").trim();
+ if (t === "我的用户设置" || t === "My User Settings") {
+ contentArea = el.closest("div");
+ }
+ });
+ if (!contentArea) return;
+
+ // 隐藏"登录/注册"按钮和"登录您的账户"文字
+ contentArea.querySelectorAll("button, a, span, p, div").forEach(el => {
+ const t = (el.textContent || "").trim();
+ if (t.includes("登录") || t.includes("注册") || t === "Sign In" || t === "Sign Up" || t.includes("登录您的账户") || t.includes("Log in")) {
+ if (el.tagName === "BUTTON" || el.tagName === "A" || t.includes("登录您的账户")) {
+ el.style.display = "none";
+ }
+ }
+ });
+
+ // 检查是否已注入（DOM 中已存在则跳过）
+ if (document.querySelector("#o1key-apikey-box")) return;
+
+ // 创建 API Key 输入区域
+ const box = document.createElement("div");
+ box.id = "o1key-apikey-box";
+ box.style.cssText = "margin-top:24px;padding:20px;border:1px solid #444;border-radius:8px;background:#1e1e1e;";
+ box.innerHTML = `
+ O1Key API 密钥
+ 输入您的 API 密钥，测试通过后方可保存
+
+
+
+
+
+
+
+ `;
+ contentArea.appendChild(box);
+
+ // 加载当前状态
+ try {
+ const resp = await fetch("/o1key/api_key");
+ const data = await resp.json();
+ const status = box.querySelector("#o1key-apikey-status");
+ if (data.has_key) {
+ status.textContent = "当前密钥: " + data.masked;
+ status.style.color = "#3b8";
+ } else {
+ status.textContent = "尚未配置 API 密钥";
+ status.style.color = "#a84";
+ }
+ } catch (e) {}
+
+ const saveBtn = box.querySelector("#o1key-apikey-save");
+ const testBtn = box.querySelector("#o1key-apikey-test");
+ let testPassed = false;
+
+ // 输入变化时重置测试状态
+ box.querySelector("#o1key-apikey-input").addEventListener("input", () => {
+ testPassed = false;
+ saveBtn.disabled = true;
+ saveBtn.style.background = "#555";
+ saveBtn.style.color = "#999";
+ saveBtn.style.cursor = "not-allowed";
+ });
+
+ // 测试令牌按钮
+ testBtn.addEventListener("click", async () => {
+ const input = box.querySelector("#o1key-apikey-input");
+ const status = box.querySelector("#o1key-apikey-status");
+ const key = input.value.trim();
+ if (!key) { status.textContent = "请输入密钥"; status.style.color = "#a44"; return; }
+ testBtn.disabled = true;
+ testBtn.textContent = "验证中...";
+ status.textContent = "正在验证密钥...";
+ status.style.color = "#999";
+ try {
+ const resp = await fetch("/o1key/test_key", {
+ method: "POST",
+ headers: {"Content-Type": "application/json"},
+ body: JSON.stringify({api_key: key})
+ });
+ const data = await resp.json();
+ if (data.valid) {
+ testPassed = true;
+ status.textContent = "验证通过，可以保存";
+ status.style.color = "#3b8";
+ saveBtn.disabled = false;
+ saveBtn.style.background = "#3b8";
+ saveBtn.style.color = "#fff";
+ saveBtn.style.cursor = "pointer";
+ } else {
+ testPassed = false;
+ status.textContent = data.error || "验证失败";
+ status.style.color = "#a44";
+ }
+ } catch (e) {
+ status.textContent = "网络错误";
+ status.style.color = "#a44";
+ }
+ testBtn.disabled = false;
+ testBtn.textContent = "测试令牌";
+ });
+
+ // 保存按钮（仅测试通过后可用）
+ saveBtn.addEventListener("click", async () => {
+ if (!testPassed) return;
+ const input = box.querySelector("#o1key-apikey-input");
+ const status = box.querySelector("#o1key-apikey-status");
+ const key = input.value.trim();
+ try {
+ const resp = await fetch("/o1key/api_key", {
+ method: "POST",
+ headers: {"Content-Type": "application/json"},
+ body: JSON.stringify({api_key: key})
+ });
+ const data = await resp.json();
+ if (data.success) {
+ status.textContent = "密钥已保存";
+ status.style.color = "#3b8";
+ input.value = "";
+ testPassed = false;
+ saveBtn.disabled = true;
+ saveBtn.style.background = "#555";
+ saveBtn.style.color = "#999";
+ saveBtn.style.cursor = "not-allowed";
+ } else {
+ status.textContent = data.error || "保存失败";
+ status.style.color = "#a44";
+ }
+ } catch (e) {
+ status.textContent = "网络错误";
+ status.style.color = "#a44";
+ }
+ });
+
+ // 清空密钥按钮
+ const clearBtn = box.querySelector("#o1key-apikey-clear");
+ clearBtn.addEventListener("click", async () => {
+ if (!confirm("确定要清空 API 密钥吗？")) return;
+ const status = box.querySelector("#o1key-apikey-status");
+ try {
+ const resp = await fetch("/o1key/api_key", { method: "DELETE" });
+ const data = await resp.json();
+ if (data.success) {
+ status.textContent = "API 密钥已清空";
+ status.style.color = "#a84";
+ box.querySelector("#o1key-apikey-input").value = "";
+ testPassed = false;
+ saveBtn.disabled = true;
+ saveBtn.style.background = "#555";
+ saveBtn.style.color = "#999";
+ saveBtn.style.cursor = "not-allowed";
+ } else {
+ status.textContent = data.error || "清空失败";
+ status.style.color = "#a44";
+ }
+ } catch (e) {
+ status.textContent = "网络错误";
+ status.style.color = "#a44";
+ }
+ });
+ }
+
+ const observer = new MutationObserver(hide);
+ observer.observe(document.body, { childList: true, subtree: true });
+ setTimeout(hide, 1000);
+ setTimeout(hide, 3000);
+ },
+});
\ No newline at end of file
diff --git a/web/js/lib/fabric.min.js b/web/js/lib/fabric.min.js
new file mode 100644
index 0000000..f679aff
--- /dev/null
+++ b/web/js/lib/fabric.min.js
@@ -0,0 +1 @@
+var fabric=fabric||{version:"5.3.0"};if("undefined"!=typeof exports?exports.fabric=fabric:"function"==typeof define&&define.amd&&define([],function(){return fabric}),"undefined"!=typeof document&&"undefined"!=typeof window)document instanceof("undefined"!=typeof HTMLDocument?HTMLDocument:Document)?fabric.document=document:fabric.document=document.implementation.createHTMLDocument(""),fabric.window=window;else{var jsdom=require("jsdom"),virtualWindow=new jsdom.JSDOM(decodeURIComponent("%3C!DOCTYPE%20html%3E%3Chtml%3E%3Chead%3E%3C%2Fhead%3E%3Cbody%3E%3C%2Fbody%3E%3C%2Fhtml%3E"),{features:{FetchExternalResources:["img"]},resources:"usable"}).window;fabric.document=virtualWindow.document,fabric.jsdomImplForWrapper=require("jsdom/lib/jsdom/living/generated/utils").implForWrapper,fabric.nodeCanvas=require("jsdom/lib/jsdom/utils").Canvas,fabric.window=virtualWindow,DOMParser=fabric.window.DOMParser}function resizeCanvasIfNeeded(t){var e=t.targetCanvas,i=e.width,r=e.height,n=t.destinationWidth,s=t.destinationHeight;i===n&&r===s||(e.width=n,e.height=s)}function copyGLTo2DDrawImage(t,e){var i=t.canvas,r=e.targetCanvas,n=r.getContext("2d");n.translate(0,r.height),n.scale(1,-1);var s=i.height-r.height;n.drawImage(i,0,s,r.width,r.height,0,0,r.width,r.height)}function copyGLTo2DPutImageData(t,e){var i=e.targetCanvas.getContext("2d"),r=e.destinationWidth,n=e.destinationHeight,s=r*n*4,o=new Uint8Array(this.imageBuffer,0,s),a=new Uint8ClampedArray(this.imageBuffer,0,s);t.readPixels(0,0,r,n,t.RGBA,t.UNSIGNED_BYTE,o);var c=new ImageData(a,r,n);i.putImageData(c,0,0)}fabric.isTouchSupported="ontouchstart"in fabric.window||"ontouchstart"in fabric.document||fabric.window&&fabric.window.navigator&&0_)for(var C=1,S=d.length;Ct[i-2].x?1:n.x===t[i-2].x?0:-1,c=n.y>t[i-2].y?1:n.y===t[i-2].y?0:-1),r.push(["L",n.x+a*e,n.y+c*e]),r},fabric.util.getPathSegmentsInfo=l,fabric.util.getBoundsOfCurve=function(t,e,i,r,n,s,o,a){var c;if(fabric.cachesBoundsOfCurve&&(c=A.call(arguments),fabric.boundsOfCurveCache[c]))return fabric.boundsOfCurveCache[c];var h,l,u,f,d,g,p,v,m=Math.sqrt,b=Math.min,y=Math.max,_=Math.abs,x=[],C=[[],[]];l=6*t-12*i+6*n,h=-3*t+9*i-9*n+3*o,u=3*i-3*t;for(var S=0;S<2;++S)if(0/g,">")},graphemeSplit:function(t){var e,i=0,r=[];for(i=0;it.x&&this.y>t.y},gte:function(t){return this.x>=t.x&&this.y>=t.y},lerp:function(t,e){return void 0===e&&(e=.5),e=Math.max(Math.min(1,e),0),new i(this.x+(t.x-this.x)*e,this.y+(t.y-this.y)*e)},distanceFrom:function(t){var e=this.x-t.x,i=this.y-t.y;return Math.sqrt(e*e+i*i)},midPointFrom:function(t){return this.lerp(t)},min:function(t){return new i(Math.min(this.x,t.x),Math.min(this.y,t.y))},max:function(t){return new i(Math.max(this.x,t.x),Math.max(this.y,t.y))},toString:function(){return this.x+","+this.y},setXY:function(t,e){return this.x=t,this.y=e,this},setX:function(t){return this.x=t,this},setY:function(t){return this.y=t,this},setFromPoint:function(t){return this.x=t.x,this.y=t.y,this},swap:function(t){var e=this.x,i=this.y;this.x=t.x,this.y=t.y,t.x=e,t.y=i},clone:function(){return new i(this.x,this.y)}}}("undefined"!=typeof exports?exports:this),function(t){"use strict";var f=t.fabric||(t.fabric={});function d(t){this.status=t,this.points=[]}f.Intersection?f.warn("fabric.Intersection is already defined"):(f.Intersection=d,f.Intersection.prototype={constructor:d,appendPoint:function(t){return this.points.push(t),this},appendPoints:function(t){return this.points=this.points.concat(t),this}},f.Intersection.intersectLineLine=function(t,e,i,r){var n,s=(r.x-i.x)*(t.y-i.y)-(r.y-i.y)*(t.x-i.x),o=(e.x-t.x)*(t.y-i.y)-(e.y-t.y)*(t.x-i.x),a=(r.y-i.y)*(e.x-t.x)-(r.x-i.x)*(e.y-t.y);if(0!==a){var c=s/a,h=o/a;0<=c&&c<=1&&0<=h&&h<=1?(n=new d("Intersection")).appendPoint(new f.Point(t.x+c*(e.x-t.x),t.y+c*(e.y-t.y))):n=new d}else n=new d(0===s||0===o?"Coincident":"Parallel");return n},f.Intersection.intersectLinePolygon=function(t,e,i){var r,n,s,o,a=new d,c=i.length;for(o=0;o=c&&(h.x-=c),h.x<=-c&&(h.x+=c),h.y>=c&&(h.y-=c),h.y<=c&&(h.y+=c),h.x-=o.offsetX,h.y-=o.offsetY,h}function y(t){return t.flipX!==t.flipY}function _(t,e,i,r,n){if(0!==t[e]){var s=n/t._getTransformedDimensions()[r]*t[i];t.set(i,s)}}function x(t,e,i,r){var n,s=e.target,o=s._getTransformedDimensions(0,s.skewY),a=P(e,e.originX,e.originY,i,r),c=Math.abs(2*a.x)-o.x,h=s.skewX;c<2?n=0:(n=v(Math.atan2(c/s.scaleX,o.y/s.scaleY)),e.originX===f&&e.originY===p&&(n=-n),e.originX===g&&e.originY===d&&(n=-n),y(s)&&(n=-n));var l=h!==n;if(l){var u=s._getTransformedDimensions().y;s.set("skewX",n),_(s,"skewY","scaleY","y",u)}return l}function C(t,e,i,r){var n,s=e.target,o=s._getTransformedDimensions(s.skewX,0),a=P(e,e.originX,e.originY,i,r),c=Math.abs(2*a.y)-o.y,h=s.skewY;c<2?n=0:(n=v(Math.atan2(c/s.scaleY,o.x/s.scaleX)),e.originX===f&&e.originY===p&&(n=-n),e.originX===g&&e.originY===d&&(n=-n),y(s)&&(n=-n));var l=h!==n;if(l){var u=s._getTransformedDimensions().x;s.set("skewY",n),_(s,"skewX","scaleX","x",u)}return l}function E(t,e,i,r,n){n=n||{};var s,o,a,c,h,l,u=e.target,f=u.lockScalingX,d=u.lockScalingY,g=n.by,p=w(t,u),v=k(u,g,p),m=e.gestureScale;if(v)return!1;if(m)o=e.scaleX*m,a=e.scaleY*m;else{if(s=P(e,e.originX,e.originY,i,r),h="y"!==g?T(s.x):1,l="x"!==g?T(s.y):1,e.signX||(e.signX=h),e.signY||(e.signY=l),u.lockScalingFlip&&(e.signX!==h||e.signY!==l))return!1;if(c=u._getTransformedDimensions(),p&&!g){var b=Math.abs(s.x)+Math.abs(s.y),y=e.original,_=b/(Math.abs(c.x*y.scaleX/u.scaleX)+Math.abs(c.y*y.scaleY/u.scaleY));o=y.scaleX*_,a=y.scaleY*_}else o=Math.abs(s.x*u.scaleX/c.x),a=Math.abs(s.y*u.scaleY/c.y);O(e)&&(o*=2,a*=2),e.signX!==h&&"y"!==g&&(e.originX=S[e.originX],o*=-1,e.signX=h),e.signY!==l&&"x"!==g&&(e.originY=S[e.originY],a*=-1,e.signY=l)}var x=u.scaleX,C=u.scaleY;return g?("x"===g&&u.set("scaleX",o),"y"===g&&u.set("scaleY",a)):(!f&&u.set("scaleX",o),!d&&u.set("scaleY",a)),x!==u.scaleX||C!==u.scaleY}n.scaleCursorStyleHandler=function(t,e,i){var r=w(t,i),n="";if(0!==e.x&&0===e.y?n="x":0===e.x&&0!==e.y&&(n="y"),k(i,n,r))return"not-allowed";var s=a(i,e);return o[s]+"-resize"},n.skewCursorStyleHandler=function(t,e,i){var r="not-allowed";if(0!==e.x&&i.lockSkewingY)return r;if(0!==e.y&&i.lockSkewingX)return r;var n=a(i,e)%4;return s[n]+"-resize"},n.scaleSkewCursorStyleHandler=function(t,e,i){return t[i.canvas.altActionKey]?n.skewCursorStyleHandler(t,e,i):n.scaleCursorStyleHandler(t,e,i)},n.rotationWithSnapping=b("rotating",m(function(t,e,i,r){var n=e,s=n.target,o=s.translateToOriginPoint(s.getCenterPoint(),n.originX,n.originY);if(s.lockRotation)return!1;var a,c=Math.atan2(n.ey-o.y,n.ex-o.x),h=Math.atan2(r-o.y,i-o.x),l=v(h-c+n.theta);if(0o.r2,h=this.gradientTransform?this.gradientTransform.concat():fabric.iMatrix.concat(),l=-this.offsetX,u=-this.offsetY,f=!!e.additionalTransform,d="pixels"===this.gradientUnits?"userSpaceOnUse":"objectBoundingBox";if(a.sort(function(t,e){return t.offset-e.offset}),"objectBoundingBox"===d?(l/=t.width,u/=t.height):(l+=t.width/2,u+=t.height/2),"path"===t.type&&"percentage"!==this.gradientUnits&&(l-=t.pathOffset.x,u-=t.pathOffset.y),h[4]-=l,h[5]-=u,s='id="SVGID_'+this.id+'" gradientUnits="'+d+'"',s+=' gradientTransform="'+(f?e.additionalTransform+" ":"")+fabric.util.matrixToSVG(h)+'" ',"linear"===this.type?n=["\n']:"radial"===this.type&&(n=["\n']),"radial"===this.type){if(c)for((a=a.concat()).reverse(),i=0,r=a.length;i\n')}return n.push("linear"===this.type?"\n":"\n"),n.join("")},toLive:function(t){var e,i,r,n=fabric.util.object.clone(this.coords);if(this.type){for("linear"===this.type?e=t.createLinearGradient(n.x1,n.y1,n.x2,n.y2):"radial"===this.type&&(e=t.createRadialGradient(n.x1,n.y1,n.r1,n.x2,n.y2,n.r2)),i=0,r=this.colorStops.length;i\n\n\n'},setOptions:function(t){for(var e in t)this[e]=t[e]},toLive:function(t){var e=this.source;if(!e)return"";if(void 0!==e.src){if(!e.complete)return"";if(0===e.naturalWidth||0===e.naturalHeight)return""}return t.createPattern(e,this.repeat)}})}(),function(t){"use strict";var o=t.fabric||(t.fabric={}),a=o.util.toFixed;o.Shadow?o.warn("fabric.Shadow is already defined."):(o.Shadow=o.util.createClass({color:"rgb(0,0,0)",blur:0,offsetX:0,offsetY:0,affectStroke:!1,includeDefaultValues:!0,nonScaling:!1,initialize:function(t){for(var e in"string"==typeof t&&(t=this._parseShadow(t)),t)this[e]=t[e];this.id=o.Object.__uid++},_parseShadow:function(t){var e=t.trim(),i=o.Shadow.reOffsetsAndBlur.exec(e)||[];return{color:(e.replace(o.Shadow.reOffsetsAndBlur,"")||"rgb(0,0,0)").trim(),offsetX:parseFloat(i[1],10)||0,offsetY:parseFloat(i[2],10)||0,blur:parseFloat(i[3],10)||0}},toString:function(){return[this.offsetX,this.offsetY,this.blur,this.color].join("px ")},toSVG:function(t){var e=40,i=40,r=o.Object.NUM_FRACTION_DIGITS,n=o.util.rotateVector({x:this.offsetX,y:this.offsetY},o.util.degreesToRadians(-t.angle)),s=new o.Color(this.color);return t.width&&t.height&&(e=100*a((Math.abs(n.x)+this.blur)/t.width,r)+20,i=100*a((Math.abs(n.y)+this.blur)/t.height,r)+20),t.flipX&&(n.x*=-1),t.flipY&&(n.y*=-1),'\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\n\n'},toObject:function(){if(this.includeDefaultValues)return{color:this.color,blur:this.blur,offsetX:this.offsetX,offsetY:this.offsetY,affectStroke:this.affectStroke,nonScaling:this.nonScaling};var e={},i=o.Shadow.prototype;return["color","blur","offsetX","offsetY","affectStroke","nonScaling"].forEach(function(t){this[t]!==i[t]&&(e[t]=this[t])},this),e}}),o.Shadow.reOffsetsAndBlur=/(?:\s|^)(-?\d+(?:\.\d*)?(?:px)?(?:\s?|$))?(-?\d+(?:\.\d*)?(?:px)?(?:\s?|$))?(\d+(?:\.\d*)?(?:px)?)?(?:\s?|$)(?:$|\s)/)}("undefined"!=typeof exports?exports:this),function(){"use strict";if(fabric.StaticCanvas)fabric.warn("fabric.StaticCanvas is already defined.");else{var n=fabric.util.object.extend,t=fabric.util.getElementOffset,h=fabric.util.removeFromArray,a=fabric.util.toFixed,s=fabric.util.transformPoint,o=fabric.util.invertTransform,i=fabric.util.getNodeCanvas,r=fabric.util.createCanvasElement,e=new Error("Could not initialize `canvas` element");fabric.StaticCanvas=fabric.util.createClass(fabric.CommonMethods,{initialize:function(t,e){e||(e={}),this.renderAndResetBound=this.renderAndReset.bind(this),this.requestRenderAllBound=this.requestRenderAll.bind(this),this._initStatic(t,e)},backgroundColor:"",backgroundImage:null,overlayColor:"",overlayImage:null,includeDefaultValues:!0,stateful:!1,renderOnAddRemove:!0,controlsAboveOverlay:!1,allowTouchScrolling:!1,imageSmoothingEnabled:!0,viewportTransform:fabric.iMatrix.concat(),backgroundVpt:!0,overlayVpt:!0,enableRetinaScaling:!0,vptCoords:{},skipOffscreen:!0,clipPath:void 0,_initStatic:function(t,e){var i=this.requestRenderAllBound;this._objects=[],this._createLowerCanvas(t),this._initOptions(e),this.interactive||this._initRetinaScaling(),e.overlayImage&&this.setOverlayImage(e.overlayImage,i),e.backgroundImage&&this.setBackgroundImage(e.backgroundImage,i),e.backgroundColor&&this.setBackgroundColor(e.backgroundColor,i),e.overlayColor&&this.setOverlayColor(e.overlayColor,i),this.calcOffset()},_isRetinaScaling:function(){return 1\n'),this._setSVGBgOverlayColor(i,"background"),this._setSVGBgOverlayImage(i,"backgroundImage",e),this._setSVGObjects(i,e),this.clipPath&&i.push("\n"),this._setSVGBgOverlayColor(i,"overlay"),this._setSVGBgOverlayImage(i,"overlayImage",e),i.push(""),i.join("")},_setSVGPreamble:function(t,e){e.suppressPreamble||t.push('\n','\n')},_setSVGHeader:function(t,e){var i,r=e.width||this.width,n=e.height||this.height,s='viewBox="0 0 '+this.width+" "+this.height+'" ',o=fabric.Object.NUM_FRACTION_DIGITS;e.viewBox?s='viewBox="'+e.viewBox.x+" "+e.viewBox.y+" "+e.viewBox.width+" "+e.viewBox.height+'" ':this.svgViewportTransformation&&(i=this.viewportTransform,s='viewBox="'+a(-i[4]/i[0],o)+" "+a(-i[5]/i[3],o)+" "+a(this.width/i[0],o)+" "+a(this.height/i[3],o)+'" '),t.push("