\n\u4e0a\u9762\u7684\u94fe\u63a5\u5305\u542b\u6211\u7684\u9080\u8bf7\u53c2\u6570\u3002\u901a\u8fc7\u8be5\u94fe\u63a5\u6ce8\u518c\u6216\u5145\u503c\uff0c\u4f1a\u7ed9\u6211\u5e26\u6765\u5e73\u53f0\u5956\u52b1\u3002\u662f\u5426\u4f7f\u7528\u9080\u8bf7\u94fe\u63a5\u4e0d\u5f71\u54cd\u672c\u6587\u7684\u64cd\u4f5c\u6b65\u9aa4\uff0c\u4e5f\u5e0c\u671b\u5927\u5bb6\u5206\u4eab\u597d\u7528\u7684\u4e2d\u8f6c\u7ad9\u3002<\/p>\n<\/blockquote>\n\n\n\n
\u8bfb\u61c2\u6a21\u578b\u5e7f\u573a\u7684\u4fe1\u606f<\/h2>\n\n\n\n \u4e2d\u8f6c\u7ad9\u627e\u5230\u4e86\uff0c\u63a5\u4e0b\u6765\u5c31\u662f\u600e\u4e48\u7528\u3002\u6211\u770b\u4e86\u4e0d\u5c11\u89c6\u9891\uff0c\u4ecd\u7136\u6ca1\u6709\u627e\u5230\u5b8c\u5168\u5bf9\u5e94\u6559\u7a0b\uff0c\u6700\u540e\u53ea\u80fd\u81ea\u5df1\u4e00\u70b9\u70b9\u6478\u7d22\u3002<\/p>\n\n\n\n
\u9996\u5148\u5728\u6a21\u578b\u5e7f\u573a\u627e\u5230\u51c6\u5907\u4f7f\u7528\u7684\u6a21\u578b\u3002\u4e0b\u9762\u4ee5\u5e73\u53f0\u5728 2026 \u5e74 7 \u6708 17 \u65e5\u5c55\u793a\u7684 gpt-5.6-sol<\/code> \u4e3a\u4f8b\u3002\u8fd9\u662f\u8be5\u4e2d\u8f6c\u7ad9\u5f53\u524d\u63d0\u4f9b\u7684\u6a21\u578b\u6216\u8def\u7531\u540d\u79f0\uff0c\u4ef7\u683c\u548c\u6307\u6807\u968f\u65f6\u53ef\u80fd\u53d8\u5316\u3002<\/p>\n\n\n\n<\/div><\/figure>\n\n\n\n
<\/div><\/figure>\n\n\n\n
TPS 44.4 t\/s\uff1a\u8f93\u51fa\u901f\u5ea6\u5927\u7ea6\u6bcf\u79d2 44.4 \u4e2a token\uff0c\u8f93\u51fa\u5185\u5bb9\u8d8a\u591a\uff0c\u65f6\u95f4\u8d8a\u957f
\u8f93\u5165\u548c\u8f93\u51fa\u4ef7\u683c<\/h3>\n\n\n\n \u9875\u9762\u5f53\u65f6\u663e\u793a\uff1a<\/p>\n\n\n\n
\n\u8f93\u5165\uff1a\u00a50.9 \/ 1M tokens<\/code><\/li>\n\n\n\n\u8f93\u51fa\uff1a\u00a55.4 \/ 1M tokens<\/code><\/li>\n<\/ul>\n\n\n\n\u8f93\u5165 token \u662f\u6211\u4eec\u53d1\u7ed9\u6a21\u578b\u7684\u63d0\u793a\u8bcd\u3001\u6587\u4ef6\u5185\u5bb9\u548c\u4e0a\u4e0b\u6587\uff1b\u8f93\u51fa token \u662f\u6a21\u578b\u8fd4\u56de\u7684\u5185\u5bb9\u3002\u90e8\u5206\u670d\u52a1\u8fd8\u4f1a\u628a\u63a8\u7406 token \u8ba1\u5165\u8f93\u51fa\uff0c\u5177\u4f53\u8981\u4ee5\u5e73\u53f0\u8d26\u5355\u65e5\u5fd7\u4e3a\u51c6\u3002<\/p>\n\n\n\n
\u5047\u8bbe\u4e00\u6b21\u8bf7\u6c42\u4f7f\u7528 10,000 \u4e2a\u8f93\u5165 token \u548c 2,000 \u4e2a\u8f93\u51fa token\uff0c\u7406\u8bba\u8d39\u7528\u4e3a\uff1a<\/p>\n\n\n\n
\u8f93\u5165\uff1a10,000 \u00f7 1,000,000 \u00d7 0.9 = \u00a50.009\n\u8f93\u51fa\uff1a2,000 \u00f7 1,000,000 \u00d7 5.4 = \u00a50.0108\n\u5408\u8ba1\uff1a\u00a50.0198\uff0c\u7ea6 1.98 \u5206<\/code><\/pre>\n\n\n\n\u4e0d\u8fc7\uff0c\u8fd9\u53ea\u662f\u6309\u9875\u9762\u5355\u4ef7\u8fdb\u884c\u7684\u7406\u8bba\u8ba1\u7b97\u3002\u5e73\u53f0\u53ef\u80fd\u8fd8\u6709\u5206\u7ec4\u500d\u7387\u3001\u7f13\u5b58\u8ba1\u8d39\u3001\u4f59\u989d\u6362\u7b97\u3001\u670d\u52a1\u8d39\u548c\u56db\u820d\u4e94\u5165\u89c4\u5219\uff0c\u6700\u7ec8\u5e94\u8be5\u4ee5\u5b9e\u9645\u8d26\u5355\u4e3a\u51c6\u3002<\/p>\n\n\n\n
\u5206\u7ec4\u500d\u7387\u600e\u4e48\u9009<\/h3>\n\n\n\n GPT-plus \u5f53\u524d\u6bd4 GPT-pro \u548c default \u4fbf\u5b9c\uff0c\u4f46\u8d28\u91cf\u3001\u7ebf\u8def\u548c\u7a33\u5b9a\u6027\u53ef\u80fd\u4f1a\u5dee\u4e00\u70b9\u3002<\/p>\n\n\n\n
\u5982\u679c\u9ed8\u8ba4\u5c31\u4f1a\u5148\u8d70 GPT-pro\uff0c\u4e0d\u884c\u518d\u8d70 default\uff0c\u518d\u5230 GPT-plus\u3002
\u521b\u5efa API Key<\/h2>\n\n\n\n \u9009\u597d\u6a21\u578b\u548c\u5206\u7ec4\u540e\uff0c\u5728\u63a7\u5236\u53f0\u627e\u5230 API \u5bc6\u94a5\uff0c\u70b9\u51fb\u521b\u5efa\u3002<\/p>\n\n\n\n<\/div><\/figure>\n\n\n\n
\u540d\u79f0\u5c31\u6839\u636e\u7528\u9014\u547d\u540d\u5c31\u884c\uff0c\u5176\u4ed6\u53ef\u4ee5\u9ed8\u8ba4\u6216\u8005\u6309\u7167\u81ea\u5df1\u9700\u6c42\u6765<\/p>\n\n\n\n
\u521b\u5efa\u540e\u7acb\u5373\u590d\u5236\u5e76\u59a5\u5584\u4fdd\u7ba1\u3002API Key \u901a\u5e38\u53ea\u4f1a\u5b8c\u6574\u663e\u793a\u4e00\u6b21\uff0c\u4e0d\u8981\u653e\u8fdb\u6587\u7ae0\u622a\u56fe\u3001\u804a\u5929\u8bb0\u5f55\u6216\u516c\u5f00\u4ed3\u5e93\u3002<\/p>\n\n\n\n
\u4e00\u952e\u5bfc\u5165 CC Switch<\/h2>\n\n\n\n CC Switch \u7684\u5b89\u88c5\u548c\u57fa\u7840\u7528\u6cd5\uff0c\u6211\u5728\u4e0a\u4e00\u7bc7\u6587\u7ae0\u4e2d\u5df2\u7ecf\u8bb2\u8fc7\uff0c\u8fd9\u91cc\u4e0d\u518d\u91cd\u590d\u3002<\/p>\n\n\n\n
\u5728\u4e2d\u8f6c\u7ad9\u6a21\u578b\u9875\u9762\u53f3\u4fa7\u7684\u83dc\u5355\u4e2d\u9009\u62e9 CC Switch\uff0c\u5373\u53ef\u8fdb\u5165\u4e00\u952e\u5bfc\u5165\u6d41\u7a0b\uff1a<\/p>\n\n\n\n
\n\u9009\u62e9\u8981\u5bfc\u5165\u7684 AI \u5e94\u7528\uff0c\u4f8b\u5982 Codex\uff1b<\/li>\n\n\n\n \u7ed9\u670d\u52a1\u5546\u914d\u7f6e\u53d6\u4e00\u4e2a\u5bb9\u6613\u8fa8\u8ba4\u7684\u540d\u79f0\uff1b<\/li>\n\n\n\n \u9009\u62e9\u51c6\u5907\u4f7f\u7528\u7684\u6a21\u578b\u548c\u5206\u7ec4\uff1b<\/li>\n\n\n\n \u70b9\u51fb\u6253\u5f00 CC Switch\uff1b<\/li>\n\n\n\n \u5728 CC Switch \u4e2d\u786e\u8ba4\u5bfc\u5165\u5e76\u542f\u7528\u914d\u7f6e\u3002<\/li>\n<\/ol>\n\n\n\n<\/div><\/figure>\n\n\n\n
<\/div><\/figure>\n\n\n\n
<\/div><\/figure>\n\n\n\n
\u5bfc\u5165\u540e\u4e0d\u8981\u6025\u7740\u6b63\u5f0f\u4f7f\u7528\u3002\u5148\u786e\u8ba4\u65c1\u8fb9\u7684\u8054\u901a\u6d4b\u8bd5\u80fd\u591f\u901a\u8fc7\uff0c\u5e76\u89c2\u5bdf\u5ef6\u8fdf\u662f\u5426\u5904\u4e8e\u53ef\u4ee5\u63a5\u53d7\u7684\u8303\u56f4\u3002\u5982\u679c\u6210\u529f\u7387\u4f4e\u6216\u5ef6\u8fdf\u6ce2\u52a8\u660e\u663e\uff0c\u5373\u4f7f\u6a21\u578b\u5355\u4ef7\u5f88\u4fbf\u5b9c\uff0c\u4e5f\u4e0d\u9002\u5408\u4f5c\u4e3a\u9879\u76ee\u7684\u4e3b\u8981\u7ebf\u8def\u3002<\/p>\n\n\n\n
\u4f46\u8fd9\u6837\u8fd8\u662f\u6ca1\u6709\u56fe\u50cf\u80fd\u529b<\/h2>\n\n\n\n \u901a\u8fc7\u7b2c\u4e09\u65b9 API \u63a5\u5165\u4e00\u4e2a\u804a\u5929\u6216\u7f16\u7a0b\u6a21\u578b\u540e\uff0c\u4e0d\u4f1a\u81ea\u52a8\u51fa\u73b0\u5bf9\u5e94\u7684\u56fe\u7247\u751f\u6210\u5de5\u5177\u3002\u8981\u8ba9 Codex \u5e2e\u6211\u51fa\u56fe\uff0c\u8fd8\u9700\u8981\u5355\u72ec\u51c6\u5907\u56fe\u7247\u6a21\u578b\u3001\u56fe\u7247\u4e13\u7528 API Key \u548c\u4e00\u4e2a\u8c03\u7528 Images API \u7684\u547d\u4ee4\u811a\u672c\u3002<\/p>\n\n\n\n
\u521b\u5efa\u56fe\u7247\u4e13\u7528 API Key<\/h2>\n\n\n\n \u56de\u5230\u4e2d\u8f6c\u7ad9\u63a7\u5236\u53f0\uff0c\u518d\u521b\u5efa\u4e00\u4e2a\u5355\u72ec\u7684 API Key\u3002\u540d\u79f0\u53ef\u4ee5\u586b\u5199 Codex-image<\/code>\uff0c\u65b9\u4fbf\u4e0e\u65e5\u5e38\u5bf9\u8bdd\u5bc6\u94a5\u533a\u5206\u3002<\/p>\n\n\n\n\u5728\u9ad8\u7ea7\u8bbe\u7f6e\u4e2d\uff0c\u628a\u53ef\u7528\u6a21\u578b\u9650\u5236\u4e3a\uff1a<\/p>\n\n\n\n
gpt-image-2<\/code><\/pre>\n\n\n\n<\/div><\/figure>\n\n\n\n
\u4e09\u4e2a\u6838\u5fc3\u6587\u4ef6<\/h2>\n\n\n\n \u6574\u4e2a\u56fe\u7247\u751f\u6210\u914d\u7f6e\u7531\u4e09\u90e8\u5206\u7ec4\u6210\uff1a<\/p>\n\n\n\n
\nimagegen.env<\/code>\uff1a\u4fdd\u5b58\u56fe\u7247 API \u5730\u5740\u3001\u6a21\u578b\u548c\u5bc6\u94a5\uff1b<\/li>\n\n\n\ncodex-gpt-image<\/code>\uff1a\u628a\u63d0\u793a\u8bcd\u53d1\u9001\u5230 Images API\uff0c\u5e76\u4fdd\u5b58\u8fd4\u56de\u56fe\u7247\uff1b<\/li>\n\n\n\ngpt-image-proxy\/SKILL.md<\/code>\uff1a\u544a\u8bc9 Codex \u4ec0\u4e48\u65f6\u5019\u3001\u600e\u6837\u8c03\u7528\u8fd9\u4e2a\u547d\u4ee4\u3002<\/li>\n<\/ol>\n\n\n\n\u5176\u4e2d Python \u7a0b\u5e8f\u540c\u65f6\u517c\u5bb9\u4e24\u79cd\u5e38\u89c1\u54cd\u5e94\uff1aAPI \u76f4\u63a5\u8fd4\u56de b64_json<\/code>\uff0c\u6216\u8005\u8fd4\u56de\u4e00\u4e2a\u4e34\u65f6\u56fe\u7247 URL\u3002<\/p>\n\n\n\n\u516c\u5171 Python \u7a0b\u5e8f<\/h2>\n\n\n\n#!\/usr\/bin\/env python3\n\"\"\"Generate an image through an OpenAI-compatible image proxy.\"\"\"\n\nfrom __future__ import annotations\n\nimport argparse\nimport base64\nimport binascii\nimport json\nimport os\nfrom pathlib import Path\nimport sys\nimport urllib.error\nimport urllib.parse\nimport urllib.request\n\n\nENV_PATH = Path.home() \/ \".codex\" \/ \"imagegen.env\"\n\n\ndef load_env_file(path: Path) -> None:\n \"\"\"Load simple KEY=VALUE pairs without overwriting existing variables.\"\"\"\n if not path.exists():\n return\n\n for raw_line in path.read_text(encoding=\"utf-8\").splitlines():\n line = raw_line.strip()\n if not line or line.startswith(\"#\") or \"=\" not in line:\n continue\n\n key, value = line.split(\"=\", 1)\n key = key.strip()\n value = value.strip()\n if len(value) >= 2 and value[0] == value[-1] and value[0] in {'\"', \"'\"}:\n value = value[1:-1]\n\n if key and key not in os.environ:\n os.environ[key] = value\n\n\ndef post_json(url: str, payload: dict, api_key: str, timeout: int) -> dict:\n request = urllib.request.Request(\n url,\n data=json.dumps(payload).encode(\"utf-8\"),\n headers={\n \"Authorization\": f\"Bearer {api_key}\",\n \"Content-Type\": \"application\/json\",\n \"Accept\": \"application\/json\",\n },\n method=\"POST\",\n )\n with urllib.request.urlopen(request, timeout=timeout) as response:\n return json.loads(response.read().decode(\"utf-8\", errors=\"replace\"))\n\n\ndef download(url: str, referer: str, timeout: int) -> bytes:\n parsed = urllib.parse.urlparse(url)\n if parsed.scheme not in {\"http\", \"https\"}:\n raise ValueError(\"Image URL must use http or https.\")\n\n request = urllib.request.Request(\n url,\n headers={\n \"User-Agent\": \"Mozilla\/5.0 CodexImageProxy\/1.0\",\n \"Referer\": referer,\n },\n )\n with urllib.request.urlopen(request, timeout=timeout) as response:\n return response.read()\n\n\ndef api_origin(base_url: str) -> str:\n parsed = urllib.parse.urlparse(base_url)\n if parsed.scheme not in {\"http\", \"https\"} or not parsed.netloc:\n raise ValueError(\"OPENAI_IMAGE_BASE_URL must be an http(s) URL.\")\n return f\"{parsed.scheme}:\/\/{parsed.netloc}\/\"\n\n\ndef main() -> int:\n load_env_file(ENV_PATH)\n\n parser = argparse.ArgumentParser(\n description=\"Generate an image through an OpenAI-compatible image proxy.\"\n )\n parser.add_argument(\"--prompt\", required=True)\n parser.add_argument(\"--out\", required=True)\n parser.add_argument(\n \"--model\",\n default=os.getenv(\"OPENAI_IMAGE_MODEL\", \"gpt-image-2\"),\n )\n parser.add_argument(\"--size\", default=\"2048x1152\")\n parser.add_argument(\"--quality\", default=\"high\")\n parser.add_argument(\"--n\", type=int, default=1)\n parser.add_argument(\"--timeout\", type=int, default=180)\n args = parser.parse_args()\n\n api_key = os.getenv(\"OPENAI_IMAGE_API_KEY\") or os.getenv(\"OPENAI_API_KEY\")\n base_url = (\n os.getenv(\"OPENAI_IMAGE_BASE_URL\")\n or os.getenv(\"OPENAI_BASE_URL\")\n or \"\"\n ).rstrip(\"\/\")\n\n if not api_key:\n print(\n \"Missing OPENAI_IMAGE_API_KEY or OPENAI_API_KEY.\",\n file=sys.stderr,\n )\n return 2\n if not base_url:\n print(\n \"Missing OPENAI_IMAGE_BASE_URL or OPENAI_BASE_URL.\",\n file=sys.stderr,\n )\n return 2\n\n payload = {\n \"model\": args.model,\n \"prompt\": args.prompt,\n \"size\": args.size,\n \"quality\": args.quality,\n \"n\": args.n,\n }\n\n try:\n result = post_json(\n f\"{base_url}\/images\/generations\",\n payload,\n api_key,\n args.timeout,\n )\n data = result.get(\"data\")\n if not isinstance(data, list) or not data or not isinstance(data[0], dict):\n raise ValueError(\"Image response did not contain data[0].\")\n\n item = data[0]\n if item.get(\"b64_json\"):\n raw = base64.b64decode(item[\"b64_json\"], validate=True)\n elif item.get(\"url\"):\n raw = download(\n item[\"url\"],\n referer=api_origin(base_url),\n timeout=args.timeout,\n )\n else:\n raise ValueError(\"Image response had neither b64_json nor url.\")\n except urllib.error.HTTPError as exc:\n body = exc.read().decode(\"utf-8\", errors=\"replace\")\n print(f\"HTTP {exc.code}: {body}\", file=sys.stderr)\n return 1\n except urllib.error.URLError as exc:\n print(f\"Network error: {exc.reason}\", file=sys.stderr)\n return 1\n except (ValueError, KeyError, TypeError, json.JSONDecodeError, binascii.Error) as exc:\n print(f\"Invalid image response or configuration: {exc}\", file=sys.stderr)\n return 1\n\n output = Path(args.out).expanduser().resolve()\n output.parent.mkdir(parents=True, exist_ok=True)\n output.write_bytes(raw)\n print(output)\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())<\/code><\/pre>\n\n\n\nmacOS \u914d\u7f6e\u65b9\u6cd5<\/h2>\n\n\n\n1. \u521b\u5efa\u76ee\u5f55<\/h3>\n\n\n\n \u6253\u5f00\u7ec8\u7aef\uff1a<\/p>\n\n\n\n
mkdir -p ~\/.codex\/bin ~\/.codex\/skills\/gpt-image-proxy<\/code><\/pre>\n\n\n\n2. \u521b\u5efa\u73af\u5883\u53d8\u91cf\u6587\u4ef6<\/h3>\n\n\n\nnano ~\/.codex\/imagegen.env<\/code><\/pre>\n\n\n\n\u5199\u5165\uff1a<\/p>\n\n\n\n
OPENAI_IMAGE_API_KEY=\u66ff\u6362\u4e3a\u4f60\u7684\u5bc6\u94a5\nOPENAI_IMAGE_BASE_URL=https:\/\/xn--vduyey89e.com\/v1\nOPENAI_IMAGE_MODEL=gpt-image-2<\/code><\/pre>\n\n\n\n\u4fdd\u5b58\u540e\u9650\u5236\u6587\u4ef6\u6743\u9650\uff1a<\/p>\n\n\n\n
chmod 600 ~\/.codex\/imagegen.env<\/code><\/pre>\n\n\n\n3. \u521b\u5efa\u56fe\u7247\u751f\u6210\u547d\u4ee4<\/h3>\n\n\n\nnano ~\/.codex\/bin\/codex-gpt-image<\/code><\/pre>\n\n\n\n\u628a\u4e0a\u9762\u7684\u516c\u5171 Python \u7a0b\u5e8f\u5b8c\u6574\u7c98\u8d34\u8fdb\u53bb\uff0c\u7136\u540e\u6dfb\u52a0\u6267\u884c\u6743\u9650\uff1a<\/p>\n\n\n\n
chmod +x ~\/.codex\/bin\/codex-gpt-image<\/code><\/pre>\n\n\n\n4. \u624b\u52a8\u6d4b\u8bd5<\/h3>\n\n\n\n~\/.codex\/bin\/codex-gpt-image \\\n --prompt \"\u4e00\u53ea\u5750\u5728\u7a97\u8fb9\u5199\u4ee3\u7801\u7684\u6a58\u732b\uff0c\u6e29\u6696\u63d2\u753b\u98ce\u683c\uff0c\u65e0\u6587\u5b57\" \\\n --size 2048x1152 \\\n --quality high \\\n --out \"$HOME\/Desktop\/image-test.png\"<\/code><\/pre>\n\n\n\n\u547d\u4ee4\u6210\u529f\u540e\uff0c\u684c\u9762\u5e94\u8be5\u51fa\u73b0 image-test.png<\/code>\u3002<\/p>\n\n\n\nWindows \u914d\u7f6e\u65b9\u6cd5<\/h2>\n\n\n\n1. \u51c6\u5907 Python \u548c\u76ee\u5f55<\/h3>\n\n\n\n \u5148\u786e\u8ba4\u5df2\u7ecf\u5b89\u88c5 Python 3\uff1a<\/p>\n\n\n\n
py -3 --version<\/code><\/pre>\n\n\n\n\u5982\u679c\u7cfb\u7edf\u6ca1\u6709 py<\/code> \u547d\u4ee4\uff0c\u4e5f\u53ef\u4ee5\u5c1d\u8bd5\uff1a<\/p>\n\n\n\npython --version<\/code><\/pre>\n\n\n\n\u5728 PowerShell \u4e2d\u521b\u5efa\u76ee\u5f55\uff1a<\/p>\n\n\n\n
New-Item -ItemType Directory -Force \"$HOME\\.codex\\bin\"\nNew-Item -ItemType Directory -Force \"$HOME\\.codex\\skills\\gpt-image-proxy\"<\/code><\/pre>\n\n\n\n2. \u521b\u5efa\u73af\u5883\u53d8\u91cf\u6587\u4ef6<\/h3>\n\n\n\nnotepad \"$HOME\\.codex\\imagegen.env\"<\/code><\/pre>\n\n\n\n\u5199\u5165\u4e0e macOS \u76f8\u540c\u7684\u4e09\u884c\uff1a<\/p>\n\n\n\n
OPENAI_IMAGE_API_KEY=\u66ff\u6362\u4e3a\u4f60\u7684\u5bc6\u94a5\nOPENAI_IMAGE_BASE_URL=https:\/\/xn--vduyey89e.com\/v1\nOPENAI_IMAGE_MODEL=gpt-image-2<\/code><\/pre>\n\n\n\n3. \u4fdd\u5b58 Python \u7a0b\u5e8f<\/h3>\n\n\n\nnotepad \"$HOME\\.codex\\bin\\codex-gpt-image.py\"<\/code><\/pre>\n\n\n\n\u5c06\u4e0a\u9762\u7684\u516c\u5171 Python \u7a0b\u5e8f\u5b8c\u6574\u7c98\u8d34\u5e76\u4fdd\u5b58\u3002<\/p>\n\n\n\n
4. \u521b\u5efa CMD \u542f\u52a8\u5668<\/h3>\n\n\n\nnotepad \"$HOME\\.codex\\bin\\codex-gpt-image.cmd\"<\/code><\/pre>\n\n\n\n\u5199\u5165\uff1a<\/p>\n\n\n\n
@echo off\nwhere py >nul 2>nul\nif errorlevel 1 (\n python \"%USERPROFILE%\\.codex\\bin\\codex-gpt-image.py\" %*\n) else (\n py -3 \"%USERPROFILE%\\.codex\\bin\\codex-gpt-image.py\" %*\n)\nexit \/b %errorlevel%<\/code><\/pre>\n\n\n\n\u8fd9\u4e2a\u542f\u52a8\u5668\u4f1a\u4f18\u5148\u4f7f\u7528 Windows \u7684 py -3<\/code>\uff0c\u627e\u4e0d\u5230\u65f6\u518d\u5c1d\u8bd5 python<\/code>\u3002<\/p>\n\n\n\n5. \u624b\u52a8\u6d4b\u8bd5<\/h3>\n\n\n\n \u5728 PowerShell \u4e2d\u8fd0\u884c\uff1a<\/p>\n\n\n\n
& \"$HOME\\.codex\\bin\\codex-gpt-image.cmd\" `\n --prompt \"\u4e00\u53ea\u5750\u5728\u7a97\u8fb9\u5199\u4ee3\u7801\u7684\u6a58\u732b\uff0c\u6e29\u6696\u63d2\u753b\u98ce\u683c\uff0c\u65e0\u6587\u5b57\" `\n --size 2048x1152 `\n --quality high `\n --out \"$HOME\\Desktop\\image-test.png\"<\/code><\/pre>\n\n\n\n\u6210\u529f\u540e\uff0c\u684c\u9762\u5e94\u8be5\u51fa\u73b0 image-test.png<\/code>\u3002<\/p>\n\n\n\n\u8ba9 Codex \u81ea\u52a8\u4f7f\u7528\u56fe\u7247\u751f\u6210\u547d\u4ee4<\/h2>\n\n\n\n \u505a\u5230\u524d\u9762\u4e00\u6b65\u540e\uff0c\u6211\u4eec\u5df2\u7ecf\u53ef\u4ee5\u624b\u52a8\u8fd0\u884c\u56fe\u7247\u751f\u6210\u7a0b\u5e8f\u3002\u4f46 Codex \u4e0d\u4f1a\u4ec5\u4ec5\u56e0\u4e3a\u67d0\u4e2a\u6587\u4ef6\u5b58\u5728\uff0c\u5c31\u81ea\u52a8\u77e5\u9053\u4ec0\u4e48\u65f6\u5019\u5e94\u8be5\u8c03\u7528\u5b83\u3002<\/p>\n\n\n\n
\u5982\u679c\u5e0c\u671b\u5728\u5bf9\u8bdd\u4e2d\u8bf4\u201c\u751f\u6210\u4e00\u5f20\u56fe\u7247\u201d\u65f6\uff0cCodex \u80fd\u4e3b\u52a8\u9009\u62e9\u8fd9\u4e2a\u547d\u4ee4\uff0c\u8fd8\u9700\u8981\u521b\u5efa\u4e00\u4e2a Skill\u3002<\/p>\n\n\n\n
macOS\/Linux \u8def\u5f84\uff1a<\/p>\n\n\n\n
~\/.codex\/skills\/gpt-image-proxy\/SKILL.md<\/code><\/pre>\n\n\n\nWindows \u8def\u5f84\uff1a<\/p>\n\n\n\n
%USERPROFILE%\\.codex\\skills\\gpt-image-proxy\\SKILL.md<\/code><\/pre>\n\n\n\nSkill \u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n\n\n\n
---\nname: gpt-image-proxy\ndescription: Use when the user asks to generate, draw, or create an image, wallpaper, avatar, poster, or other raster image through the configured image proxy.\n---\n\n# GPT Image Proxy\n\nUse the configured image proxy only for image generation. Existing image-reading capabilities are separate and do not need this wrapper.\n\nOn macOS or Linux, run:\n\n```bash\n~\/.codex\/bin\/codex-gpt-image \\\n --prompt \"USER_PROMPT\" \\\n --size 2048x1152 \\\n --quality high \\\n --out \"\/absolute\/path\/to\/output.png\"\n```\n\nOn Windows, run:\n\n```bat\n\"%USERPROFILE%\\.codex\\bin\\codex-gpt-image.cmd\" --prompt \"USER_PROMPT\" --size 2048x1152 --quality high --out \"C:\\absolute\\path\\to\\output.png\"\n```\n\nAlways use an absolute output path. After generation, return or display the saved image. Never print, expose, or copy the API key into prompts, logs, or article content.<\/code><\/pre>\n\n\n\n\u4fdd\u5b58\u540e\u91cd\u542f Codex\uff0c\u6216\u8005\u65b0\u5efa\u4e00\u4e2a\u4efb\u52a1\uff0c\u8ba9 Codex \u91cd\u65b0\u52a0\u8f7d\u6280\u80fd\u3002\u7136\u540e\u53ef\u4ee5\u5c1d\u8bd5\uff1a<\/p>\n\n\n\n
\n\u751f\u6210\u4e00\u5f20 16:9 \u7684\u535a\u5ba2\u5c01\u9762\u56fe\uff1a\u6df1\u8272\u4e66\u684c\u4e0a\u653e\u7740\u4e00\u53f0\u6b63\u5728\u8fd0\u884c Codex \u7684\u7b14\u8bb0\u672c\u7535\u8111\uff0c\u79d1\u6280\u611f\u63d2\u753b\u98ce\u683c\uff0c\u4e0d\u8981\u6587\u5b57\u3002<\/p>\n<\/blockquote>\n\n\n\n
\u5199\u5728\u6700\u540e<\/h2>\n\n\n\n \u4e2d\u8f6c\u7ad9\u786e\u5b9e\u964d\u4f4e\u4e86\u5c1d\u8bd5\u4e0d\u540c\u6a21\u578b\u7684\u95e8\u69db\uff0c\u4f46\u5b83\u4e0d\u662f\u514d\u8d39\u7684\u5348\u9910\uff0c\u4e5f\u4e0d\u662f\u5b98\u65b9\u670d\u52a1\u7684\u5b8c\u5168\u66ff\u4ee3\u54c1\u3002\u4ef7\u683c\u66f4\u4f4e\u7684\u540c\u65f6\uff0c\u6211\u4eec\u4e5f\u627f\u62c5\u4e86\u66f4\u591a\u9690\u79c1\u3001\u7a33\u5b9a\u6027\u548c\u552e\u540e\u98ce\u9669\u3002<\/p>\n\n\n\n
\u8fd9\u7bc7\u6587\u7ae0\u8bb0\u5f55\u7684\u662f\u6211\u76ee\u524d\u6478\u7d22\u51fa\u6765\u7684\u4e00\u5957\u65b9\u6cd5\u3002\u5e73\u53f0\u754c\u9762\u3001\u4ef7\u683c\u3001\u500d\u7387\u548c\u6a21\u578b\u540d\u79f0\u90fd\u53ef\u80fd\u53d8\u5316\uff0c\u5927\u5bb6\u5728\u5b9e\u9645\u64cd\u4f5c\u65f6\u4ecd\u8981\u4ee5\u5f53\u65f6\u9875\u9762\u4e3a\u51c6\uff0c\u4e5f\u6b22\u8fce\u5206\u4eab\u81ea\u5df1\u7528\u8fc7\u7684\u6a21\u578b\u548c\u4e2d\u8f6c\u670d\u52a1\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
\u4ece DeepSeek \u6362\u5230\u4e2d\u8f6c\u7ad9\uff0c\u4e3a Codex \u914d\u4e0a\u56fe\u7247\u751f\u6210\u80fd\u529b\u3002<\/p>\n","protected":false},"author":1,"featured_media":101,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[5,6,9],"class_list":["post-142","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-article","tag-ai","tag-codex","tag-9"],"_links":{"self":[{"href":"https:\/\/www.kmori.xyz\/index.php\/wp-json\/wp\/v2\/posts\/142","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kmori.xyz\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kmori.xyz\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kmori.xyz\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kmori.xyz\/index.php\/wp-json\/wp\/v2\/comments?post=142"}],"version-history":[{"count":9,"href":"https:\/\/www.kmori.xyz\/index.php\/wp-json\/wp\/v2\/posts\/142\/revisions"}],"predecessor-version":[{"id":167,"href":"https:\/\/www.kmori.xyz\/index.php\/wp-json\/wp\/v2\/posts\/142\/revisions\/167"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.kmori.xyz\/index.php\/wp-json\/wp\/v2\/media\/101"}],"wp:attachment":[{"href":"https:\/\/www.kmori.xyz\/index.php\/wp-json\/wp\/v2\/media?parent=142"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kmori.xyz\/index.php\/wp-json\/wp\/v2\/categories?post=142"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kmori.xyz\/index.php\/wp-json\/wp\/v2\/tags?post=142"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"\u6a21\u578b\u5e7f\u573a\u4e2d\u7684\u6a21\u578b\u4fe1\u606f\"](\"https:\/\/www.kmori.xyz\/wp-content\/uploads\/2026\/07\/image-scaled.png\")
<\/div><\/figure>\n\n\n\n![\"\u6a21\u578b\u4ef7\u683c\u4e0e\u6027\u80fd\u4fe1\u606f\"](\"https:\/\/www.kmori.xyz\/wp-content\/uploads\/2026\/07\/image-1.png\")
<\/div><\/figure>\n\n\n\n![\"\u521b\u5efa](\"https:\/\/www.kmori.xyz\/wp-content\/uploads\/2026\/07\/image-2-scaled.png\")
<\/div><\/figure>\n\n\n\n![\"CC](\"https:\/\/www.kmori.xyz\/wp-content\/uploads\/2026\/07\/image-3-edited-scaled.png\")
<\/div><\/figure>\n\n\n\n![\"CC](\"https:\/\/www.kmori.xyz\/wp-content\/uploads\/2026\/07\/image-4-edited-scaled.png\")
<\/div><\/figure>\n\n\n\n![\"CC](\"https:\/\/www.kmori.xyz\/wp-content\/uploads\/2026\/07\/image-5.png\")
<\/div><\/figure>\n\n\n\n![\"\u56fe\u7247\u4e13\u7528](\"https:\/\/www.kmori.xyz\/wp-content\/uploads\/2026\/07\/image-6.png\")
<\/div><\/figure>\n\n\n\n