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feat: V2批量节点新增图片格式/命名规则/保存路径，隐藏生图数量，参数重排

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- V2批量节点：隐藏生图数量（固定为1），新增图片格式(PNG/JPEG)、图片命名规则(和图片同名/1,2,3...)、图片保存路径(可选，留空默认output)
- 图片格式JPEG时自动RGBA→RGB + quality=100保存
- 命名规则支持占位符{src}/{num}/{sub}等，默认与输入图片同名+子序号
- 文件名冲突自动追加_1/_2防覆盖
- V2节点：恢复简洁，仅保留核心参数
- 参数顺序重新整理
This commit is contained in:
Jony
2026-05-04 20:12:27 +08:00
parent 3ca7581080
commit a3541cdcec
1 changed files with 128 additions and 15 deletions
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nodes/nano_banana_v2.py
+126 -13
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@@ -244,6 +244,13 @@ class NanoBananaV2:
)
return error_msg
@staticmethod
def _apply_image_format(img: Image.Image, image_format: str) -> Image.Image:
"""按指定格式处理图像：JPEG 时转 RGB（质量100不压缩），PNG 不做处理"""
if image_format == "JPEG" and img.mode in ("RGBA", "LA", "P"):
return img.convert("RGB")
return img
@staticmethod
def _check_interrupt():
"""检查 ComfyUI 是否点击了取消按钮，是则抛出 InterruptProcessingException"""
@@ -701,20 +708,69 @@ class NanoBananaV2Batch(NanoBananaV2):
def INPUT_TYPES(cls):
types = super().INPUT_TYPES()
# 批量专用：文件夹图片路径
# 按指定顺序重建 optional
new_optional = {}
# 1. 联网功能（Provider 专有参数）
for provider_extra in cls._PROVIDER_EXTRA_INPUTS.values():
for k, v in provider_extra.items():
if k in types["optional"]:
new_optional[k] = types["optional"][k]
# 2. 图片格式
new_optional["图片格式"] = (["PNG", "JPEG"], {"default": "JPEG"})
# 3. seed
if "seed" in types["optional"]:
new_optional["seed"] = types["optional"]["seed"]
# 4. 图片配对模式
new_optional["图片配对模式"] = (["不配对", "按相同图片命名", "1*N"], {
"default": "不配对"
})
# 5. 图片命名规则
new_optional["图片命名规则"] = (["和图片同名", "1,2,3,4..."], {
"default": "和图片同名"
})
# 6. 图片保存路径（可选）
new_optional["图片保存路径（可选）"] = ("STRING", {
"default": "",
"multiline": False,
"placeholder": "留空默认保存到 ComfyUI/output；填写则保存到指定路径"
})
# 7. 图片路径（图1-5）
for i in range(1, 6):
types["optional"][f"图片路径（图{i}）"] = ("STRING", {
new_optional[f"图片路径（图{i}）"] = ("STRING", {
"default": "",
"multiline": False,
"placeholder": f"填写后将加载文件夹中的图片作为第{i}组参考图"
})
types["optional"]["图片配对模式"] = (["不配对", "按相同图片命名", "1*N"], {
"default": "不配对"
})
# 8. 分组令牌
if "分组令牌" in types["optional"]:
new_optional["分组令牌"] = types["optional"]["分组令牌"]
# 批量版恢复较大的生图数量上限
types["required"]["生图数量"] = ("INT", {
# 9. 代理端口
if "代理端口" in types["optional"]:
new_optional["代理端口"] = types["optional"]["代理端口"]
# 保留参考图1-9
for i in range(1, 10):
key = f"参考图{i}"
if key in types["optional"]:
new_optional[key] = types["optional"][key]
types["optional"] = new_optional
# 批量版隐藏生图数量参数，固定为1
if "生图数量" in types["required"]:
del types["required"]["生图数量"]
if "hidden" not in types:
types["hidden"] = {}
types["hidden"]["生图数量"] = ("INT", {
"default": 1,
"min": 1,
"max": 1000,
@@ -748,13 +804,18 @@ class NanoBananaV2Batch(NanoBananaV2):
aspect_ratio: str,
images_per_prompt: int,
input_images: List[Image.Image],
image_format: str = "JPEG",
save_path: str = "",
save_naming: str = "{src}_{sub}.png",
pbar=None,
per_task_images: Optional[List[List[Image.Image]]] = None,
per_task_names: Optional[List[str]] = None,
**extra_kwargs,
) -> List[dict]:
"""全并发处理：所有任务一次性提交，谁先完成谁先落盘
per_task_images: 可选，每个任务专属的图片列表，与 input_images 合并
per_task_names: 可选，每个任务的源文件名列表，用于命名时保持与输入图片一致
"""
# 构建任务定义
tasks_def = []
@@ -768,7 +829,11 @@ class NanoBananaV2Batch(NanoBananaV2):
# 初始化输出目录
self._output_file_paths = []
if save_path:
self._output_dir = save_path
else:
self._output_dir = self._get_output_dir()
os.makedirs(self._output_dir, exist_ok=True)
print(f"{self.NODE_LABEL}: 输出目录: {self._output_dir}")
_on_progress = (lambda delta: pbar.update(delta)) if pbar is not None else None
@@ -816,13 +881,44 @@ class NanoBananaV2Batch(NanoBananaV2):
"prompt": "",
}
# 即时落盘
# 即时落盘（应用图片格式转换 + 自定义命名规则）
if result_data and result_data.get("success"):
ext = "jpg" if image_format == "JPEG" else "png"
# 源文件名（优先使用输入图片名，保持命名一致）
task_idx = result_data.get("global_task_index", completed)
source_name = ""
if per_task_names and task_idx < len(per_task_names):
source_name = per_task_names[task_idx]
prompt_snippet = (result_data.get("prompt", "") or "").replace("\n", " ")[:20]
prompt_sanitized = "".join(c for c in prompt_snippet if c.isalnum() or c in "._- ")
time_str = time.strftime("%Y%m%d_%H%M%S")
for img_idx, img in enumerate(result_data.get("output_images", [])):
filepath = os.path.join(
self._output_dir,
f"task_{completed:04d}_{img_idx:02d}.png"
)
img = self._apply_image_format(img, image_format)
# 构建文件名
filename = save_naming
filename = filename.replace("{src}", source_name)
filename = filename.replace("{index}", str(task_idx))
filename = filename.replace("{i}", str(task_idx))
filename = filename.replace("{num}", str(task_idx + 1))
filename = filename.replace("{n}", str(task_idx + 1))
filename = filename.replace("{sub}", str(img_idx))
filename = filename.replace("{prompt}", prompt_sanitized)
filename = filename.replace("{time}", time_str)
# 确保扩展名正确
if not filename.lower().endswith(f".{ext}"):
base, _ = os.path.splitext(filename)
filename = f"{base}.{ext}"
# 防覆盖
filepath = os.path.join(self._output_dir, filename)
if os.path.exists(filepath):
base, file_ext = os.path.splitext(filename)
counter = 1
while os.path.exists(filepath):
filepath = os.path.join(self._output_dir, f"{base}_{counter}{file_ext}")
counter += 1
if image_format == "JPEG":
img.save(filepath, "JPEG", quality=100)
else:
img.save(filepath)
self._output_file_paths.append(filepath)
# 释放内存中的图像对象
@@ -851,7 +947,7 @@ class NanoBananaV2Batch(NanoBananaV2):
模型: str,
宽高比: str,
分辨率: str,
生图数量: int,
生图数量: int = 1,
**kwargs
) -> Tuple[torch.Tensor]:
"""生成图像（异步模式 - 批量版：全并发 + 即时落盘）"""
@@ -860,6 +956,13 @@ class NanoBananaV2Batch(NanoBananaV2):
seed: int = kwargs.pop("seed", 0)
proxy_port: str = kwargs.pop("代理端口", "")
api_key_override: str = kwargs.pop("分组令牌", "")
image_format: str = kwargs.pop("图片格式", "JPEG")
save_path: str = kwargs.pop("图片保存路径（可选）", "").strip()
命名规则选择: str = kwargs.pop("图片命名规则", "和图片同名")
if 命名规则选择 == "1,2,3,4...":
save_naming = "{num}.png"
else:
save_naming = "{src}_{sub}.png"
proxy_url = BaseAsyncImageProvider.build_proxy_url(proxy_port)
provider = self._get_provider(模型, proxy_url=proxy_url, api_key_override=api_key_override)
@@ -916,6 +1019,7 @@ class NanoBananaV2Batch(NanoBananaV2):
pairing_mode = kwargs.pop("图片配对模式", "不配对")
per_task_images = None
per_task_names = None
if folder_paths:
# 按文件夹分组加载
folder_image_lists = [] # List[List[ImageInfo]]
@@ -941,15 +1045,20 @@ class NanoBananaV2Batch(NanoBananaV2):
if pairs:
batch_prompts = parse_batch_prompts(prompt)
per_task_images = []
per_task_names = []
prompts_list = []
for pair in pairs:
task_imgs = [info.image for info in pair] + list(input_images)
# 取第一张输入图的文件名作为源名，保持命名一致
src_name = pair[0].filename if pair else ""
if batch_prompts:
for bp in batch_prompts:
per_task_images.append(list(task_imgs))
per_task_names.append(src_name)
prompts_list.append(bp)
else:
per_task_images.append(list(task_imgs))
per_task_names.append(src_name)
prompts_list.append(prompt)
images_per_prompt = 1
@@ -1001,8 +1110,12 @@ class NanoBananaV2Batch(NanoBananaV2):
aspect_ratio=宽高比,
images_per_prompt=images_per_prompt,
input_images=input_images,
image_format=image_format,
save_path=save_path,
save_naming=save_naming,
pbar=pbar,
per_task_images=per_task_images,
per_task_names=per_task_names,
**extra_kwargs,
)
)