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Publish current ComfyUI O1Key code baseline

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Replace the prior release tree with the current plugin, frontend, tests, and documentation. Document retired node IDs and the public Gitea update source.
This commit is contained in:
Jony
2026-09-24 19:56:48 +08:00
parent 3e337722ab
commit ba920f2b66
183 changed files with 49496 additions and 9934 deletions
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nodes/gpt_image.py
+220 -160
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@@ -1,6 +1,6 @@
"""
o1key GPT Image 节点
支持 gpt-image-1 / gpt-image-1.5 模型的文生图、图生图、图像编辑（带蒙版）
支持 GPT Image 2 / 2.5 系列的文生图、图生图和带蒙版图像编辑
"""
import os
@@ -8,10 +8,24 @@ import time
from typing import List, Optional, Tuple
from PIL import Image
from comfy_api.latest import io
from ..clients.gpt_image_client import GptImageClient
from ..clients.gpt_image_client import (
GPT_IMAGE_MODEL_OPTIONS,
GPT_IMAGE_ROUTE_OPTIONS,
GptImageClient,
resolve_gpt_image_model,
)
from ..utils.image_utils import parse_batch_prompts, pil_to_tensor, tensor_to_pil
from ..utils.config import NETWORK_ROUTE_OPTIONS, get_base_url_by_route
from ..utils.config import get_base_url_by_route
from ..utils.o1key_image_catalog import (
GPT_IMAGE_BACKGROUND_OPTIONS,
GPT_IMAGE_EXACT_SIZE_OPTIONS,
GPT_IMAGE_OUTPUT_FORMAT_OPTIONS,
GPT_IMAGE_25_QUALITY_OPTIONS,
resolve_gpt_image_quality,
resolve_gpt_image_size,
)
from ..utils.file_utils import (
ImageInfo,
generate_timestamp_filename,
@@ -68,31 +82,20 @@ def _make_node_progress_callback(progress_bar, task_index: int, total_tasks: int
def _resolve_async_size(value: str) -> str:
value = (value or "").strip()
if not value or value == "智能" or value.lower() == "auto":
return "auto"
first_part = value.split("（")[0].strip()
normalized_size = first_part.lower().replace("*", "x").replace("×", "x")
size_parts = [part.strip() for part in normalized_size.split("x")]
if len(size_parts) == 2 and all(part.isdigit() for part in size_parts):
return f"{int(size_parts[0])}x{int(size_parts[1])}"
allowed = {"auto", "1024x1024", "1K", "2K", "4K"}
if first_part in allowed:
return first_part
if "4K" in value:
return "4K"
if "2K" in value:
return "2K"
if "1K" in value:
return "1K"
return "auto"
return resolve_gpt_image_size(value)
class O1keyGPTImage:
MAX_GPT_IMAGE_REFERENCES = 9
def _collect_autogrow_inputs(value) -> list:
"""收集已连接的 Autogrow 输入，并兼容单个旧值。"""
if value is None:
return []
if isinstance(value, dict):
return [item for item in value.values() if item is not None]
return [value]
class O1keyGPTImage(io.ComfyNode):
"""
o1key GPT Image 节点
@@ -104,7 +107,8 @@ class O1keyGPTImage:
参数：
- prompt : 文本提示词（多行；用 --- 独占一行分隔批量提示词）
- 模型 : 模型选择
- 模型 : GPT Image 主模型
- 模型线路 : 畅速、直连或专线
- 分辨率 : 图像尺寸（auto 让 API 自动决定）
- 生图数量 : 每条提示词生成数量 1-8
- 质量 : 生成质量
@@ -114,110 +118,144 @@ class O1keyGPTImage:
"""
@classmethod
def INPUT_TYPES(cls):
# 创建9个独立的参考图输入
optional_inputs = {}
for i in range(1, 10):
optional_inputs[f"参考图{i}"] = ("IMAGE", {
"tooltip": f"Optional reference image {i} for image editing.",
})
def define_schema(cls):
reference_images = io.Autogrow.Input(
"参考图组",
template=io.Autogrow.TemplateNames(
input=io.Image.Input("参考图"),
names=[f"参考图{i}" for i in range(1, MAX_GPT_IMAGE_REFERENCES + 1)],
min=0,
),
tooltip=f"连接后自动增加输入端口，合计最多 {MAX_GPT_IMAGE_REFERENCES} 张参考图。",
)
return io.Schema(
node_id="O1keyGPTImage",
display_name="gpt image",
category="o1key/image",
inputs=[
io.String.Input(
"prompt",
default="",
multiline=True,
tooltip="Text prompt for GPT Image. Use --- on its own line to separate batch prompts.",
),
io.Combo.Input(
"模型",
options=GPT_IMAGE_MODEL_OPTIONS,
default="gpt-image-2.5-sunburst",
),
io.Combo.Input(
"模型线路",
options=GPT_IMAGE_ROUTE_OPTIONS,
default="畅速",
),
io.Combo.Input(
"分辨率",
options=GPT_IMAGE_EXACT_SIZE_OPTIONS,
default="智能",
tooltip="Image size (智能 = API decides)",
),
io.Int.Input(
"生图数量",
default=1,
min=1,
max=8,
step=1,
display_mode=io.NumberDisplay.number,
tooltip="How many images to generate per prompt",
),
io.Combo.Input(
"质量",
options=GPT_IMAGE_25_QUALITY_OPTIONS,
default="自动",
tooltip="GPT Image 2 支持高/中/低/自动；GPT Image 2.5 另支持超高=xhigh、最高=max。",
),
io.Combo.Input(
"输出格式",
options=["png", "jpeg", "webp"],
default="png",
tooltip="Generated image output format",
),
io.Combo.Input(
"背景",
options=list(GPT_IMAGE_BACKGROUND_OPTIONS),
default="auto",
tooltip="透明背景仅支持 PNG 或 WebP 输出格式。",
),
io.Mask.Input(
"遮罩",
optional=True,
tooltip="Optional mask for inpainting (white areas will be replaced)",
),
reference_images,
io.Combo.Input(
"缩放图片",
options=["不缩放", "智能缩放"],
default="智能缩放",
tooltip="请求体超过 18 MiB 时，智能缩放会等比缩小占用最大的参考图。",
),
io.Int.Input(
"seed",
default=0,
min=0,
max=2**31 - 1,
step=1,
display_mode=io.NumberDisplay.number,
control_after_generate=io.ControlAfterGenerate.randomize,
tooltip="Random seed (0 = not specified)",
),
],
outputs=[io.Image.Output(display_name="IMAGE")],
# 兼容 Autogrow 改造前保存的参考图1～参考图9端口。
accept_all_inputs=True,
)
optional_inputs["模型"] = ([
"gpt-image-2-按量",
"gpt-image-2-次卡",
], {
"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）",
"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）",
"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 ──
"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）",
], {
"default": "智能",
"tooltip": "Image size (智能 = API decides)",
})
optional_inputs["生图数量"] = ("INT", {
"default": 1,
"min": 1,
"max": 8,
"step": 1,
"display": "number",
"tooltip": "How many images to generate per prompt",
})
optional_inputs["质量"] = (["高", "中", "低", "自动"], {
"default": "自动",
"tooltip": "Image quality: 高=high, 中=medium, 低=low, 自动=auto",
})
optional_inputs["输出格式"] = (["png", "jpeg", "webp"], {
"default": "jpeg",
"tooltip": "Generated image output format",
})
optional_inputs["seed"] = ("INT", {
"default": 0,
"min": 0,
"max": 2**31 - 1,
"step": 1,
"display": "number",
"control_after_generate": True,
"tooltip": "Random seed (0 = not specified)",
})
optional_inputs["遮罩"] = ("MASK", {
"tooltip": "Optional mask for inpainting (white areas will be replaced)",
})
return {
"required": {
"prompt": ("STRING", {
"default": "",
"multiline": True,
"tooltip": "Text prompt for GPT Image. Use --- on its own line to separate batch prompts.",
}),
},
"optional": optional_inputs,
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "generate"
CATEGORY = "o1key/image"
OUTPUT_NODE = False
def generate(
self,
@classmethod
def execute(
cls,
prompt: str,
模型: str = "gpt-image-2-次卡",
网络: str = "全球加速",
分辨率: str = "auto",
模型: str = "gpt-image-2.5-sunburst",
模型线路: str = "畅速",
分辨率: str = "智能",
质量: str = "自动",
输出格式: str = "jpeg",
输出格式: str = "png",
生图数量: int = 1,
seed: int = 0,
遮罩=None,
缩放图片: str = "智能缩放",
背景: str = "auto",
**kwargs,
) -> io.NodeOutput:
result = cls.generate(
prompt=prompt,
模型=模型,
模型线路=模型线路,
分辨率=分辨率,
质量=质量,
输出格式=输出格式,
生图数量=生图数量,
seed=seed,
遮罩=遮罩,
缩放图片=缩放图片,
背景=背景,
**kwargs,
)
return io.NodeOutput(*result)
@classmethod
def generate(
cls,
prompt: str,
模型: str = "gpt-image-2.5-sunburst",
模型线路: str = "畅速",
分辨率: str = "智能",
质量: str = "自动",
输出格式: str = "png",
生图数量: int = 1,
seed: int = 0,
遮罩=None,
缩放图片: str = "智能缩放",
背景: str = "auto",
**kwargs,
):
"""
@@ -230,35 +268,45 @@ class O1keyGPTImage:
- prompt 含 --- → 批量模式，逐条调用上述接口
"""
start_time = time.time()
# ── 0. 收集多参考图输入 ────────────────────────────────────────────────
reference_tensors = []
for i in range(1, 10):
key = f"参考图{i}"
if key in kwargs and kwargs[key] is not None:
reference_tensors.append(kwargs[key])
reference_tensors = _collect_autogrow_inputs(kwargs.get("参考图组"))
if not reference_tensors:
# 兼容 Autogrow 改造前保存的固定参考图端口。
reference_tensors = [
kwargs[f"参考图{i}"]
for i in range(1, MAX_GPT_IMAGE_REFERENCES + 1)
if kwargs.get(f"参考图{i}") is not None
]
图片 = reference_tensors if reference_tensors else None
# ── 1. 参数校验 ───────────────────────────────────────────────────────
if 遮罩 is not None and 图片 is None:
raise ValueError("提供了遮罩但未提供图片，请同时提供图片和遮罩")
if 缩放图片 not in {"不缩放", "智能缩放"}:
raise ValueError("缩放图片参数无效")
if 输出格式 not in GPT_IMAGE_OUTPUT_FORMAT_OPTIONS:
raise ValueError("GPT Image 输出格式无效")
if 背景 not in GPT_IMAGE_BACKGROUND_OPTIONS:
raise ValueError("GPT Image 背景参数无效")
if 背景 == "transparent" and 输出格式 == "jpeg":
raise ValueError("GPT Image 透明背景仅支持 PNG 或 WebP 输出格式")
# ── 2. 解析分辨率显示值 → API 参数值 ──────────────────────────────────
size = _resolve_async_size(分辨率)
# ── 2b. 解析模型显示值 → API 参数值 ───────────────────────────────────
_model_map = {"gpt-image-2-次卡": "gpt-image-2-c", "gpt-image-2-按量": "gpt-image-2"}
model = _model_map.get(模型, 模型)
# ── 2b. 主模型与线路共同解析为 API 模型名 ───────────────────────────
model = resolve_gpt_image_model(模型, 模型线路)
# ── 2c. 解析质量显示值 → API 参数值 ───────────────────────────────────
_quality_map = {"高": "high", "中": "medium", "低": "low", "自动": "auto"}
quality = _quality_map.get(质量, "auto")
quality = resolve_gpt_image_quality(模型, 质量)
# ── 3. 创建客户端 ─────────────────────────────────────────────────────
try:
client = GptImageClient()
client.base_url = get_base_url_by_route(网络)
client.base_url = get_base_url_by_route()
client.response_log_enabled = False
client.poll_log_enabled = False
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！")
@@ -271,6 +319,12 @@ class O1keyGPTImage:
# ── 5. 调用 API ───────────────────────────────────────────────────
all_pil_images = []
def _submitted(task_id, status, elapsed):
print(f"[o1key GPT Image] 已提交 | task_id={task_id} | 状态={status} | 耗时={elapsed:.1f}s")
def _completed(task_id, image_count, elapsed, urls):
del urls
print(f"[o1key GPT Image] 完成 ✓ | task_id={task_id} | 生成={image_count} 张 | 耗时={elapsed:.1f}s")
progress_total = len(batch_prompts) if batch_prompts else 1
progress_bar = ProgressBar(progress_total * 100) if _PROGRESS_BAR_AVAILABLE else None
@@ -293,17 +347,22 @@ class O1keyGPTImage:
image_tensor=图片,
mask_tensor=遮罩,
output_format=输出格式,
background=背景,
progress_callback=_make_node_progress_callback(progress_bar, idx, total),
special_price_parallel=True,
task_submitted_callback=_submitted,
task_completed_callback=_completed,
log_request_start=False,
log_downloads=True,
log_prefix=f"[o1key GPT Image] [{idx}/{total}]",
resize_mode=缩放图片,
)
all_pil_images.extend(pil_images)
snippet = p[:30] + ("..." if len(p) >= 30 else "")
print(f"[o1key GPT Image] [{idx}/{total}] ✓ {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 GPT Image] [{idx}/{total}] ❌ {snippet} → {error_msg}")
print(f"[o1key GPT Image] [{idx}/{total}] ❌ {error_msg}")
if progress_bar is not None:
progress_bar.update_absolute(idx * 100, total * 100)
else:
@@ -321,7 +380,14 @@ class O1keyGPTImage:
image_tensor=图片,
mask_tensor=遮罩,
output_format=输出格式,
background=背景,
progress_callback=_make_node_progress_callback(progress_bar, 1, 1),
special_price_parallel=True,
task_submitted_callback=_submitted,
task_completed_callback=_completed,
log_request_start=False,
log_downloads=True,
resize_mode=缩放图片,
)
all_pil_images.extend(pil_images)
except InterruptProcessingException:
@@ -349,9 +415,10 @@ class O1keyGPTImage:
return (output_tensor,)
finally:
self._print_balance(client)
cls._print_balance(client)
def _print_balance(self, client):
@staticmethod
def _print_balance(client):
try:
balance_data = client.query_balance_sync()
balance_info = client.format_balance_info(balance_data)
@@ -360,7 +427,7 @@ class O1keyGPTImage:
pass
class O1keyGPTImageBatch:
class _LegacyO1keyGPTImageBatch:
"""
o1key GPT Image 批量节点
@@ -374,7 +441,7 @@ class O1keyGPTImageBatch:
PAIRING_MODES = ["按相同图片命名", "1*N", "不配对"]
IMAGE_FORMATS = ["原始", "JPEG", "PNG", "WebP"]
MODEL_OPTIONS = ["gpt-image-2-按量", "gpt-image-2-次卡"]
MODEL_OPTIONS = GPT_IMAGE_ROUTE_OPTIONS
QUALITY_OPTIONS = ["高", "中", "低", "自动"]
RESOLUTION_OPTIONS = [
"智能",
@@ -424,11 +491,8 @@ class O1keyGPTImageBatch:
"multiline": True,
"tooltip": "提示词；可用独占一行的 --- 分隔多条批量提示词。",
}),
"模型": (cls.MODEL_OPTIONS, {
"default": "gpt-image-2-次卡",
}),
"网络": (NETWORK_ROUTE_OPTIONS, {
"default": "全球加速",
"模型线路": (cls.MODEL_OPTIONS, {
"default": "畅速",
}),
"分辨率": (cls.RESOLUTION_OPTIONS, {
"default": "智能",
@@ -561,12 +625,9 @@ class O1keyGPTImageBatch:
return _resolve_async_size(分辨率)
@staticmethod
def _resolve_model(模型: str) -> str:
model_map = {
"gpt-image-2-次卡": "gpt-image-2-c",
"gpt-image-2-按量": "gpt-image-2",
}
return model_map.get(模型, 模型)
def _resolve_model(模型线路: str) -> str:
# 直接返回，客户端会映射到 API 值
return 模型线路
@staticmethod
def _resolve_quality(质量: str) -> str:
@@ -642,8 +703,7 @@ class O1keyGPTImageBatch:
def process_batch(
self,
prompt: str,
模型: str,
网络: str,
模型线路: str,
分辨率: str,
生图数量: int,
质量: str,
@@ -707,12 +767,12 @@ class O1keyGPTImageBatch:
output_folder = self._ensure_output_folder(保存路径)
size = self._resolve_size(分辨率)
model = self._resolve_model(模型)
model = self._resolve_model(模型线路)
quality = self._resolve_quality(质量)
output_format = self._resolve_output_format(图片格式)
client = GptImageClient()
client.base_url = get_base_url_by_route(网络)
client.base_url = get_base_url_by_route()
progress_bar = ProgressBar(total_tasks * 100) if _PROGRESS_BAR_AVAILABLE else None
results = []