update

__init__.py

@@ -30,6 +30,8 @@ from .color_matcher_node import NODE_DISPLAY_NAME_MAPPINGS as COLORMATCHER_NODE_
 # 新增：素材拆分节点
 from .image_splitter_node import NODE_CLASS_MAPPINGS as IMAGESPLITTER_NODE_CLASS_MAPPINGS
 from .image_splitter_node import NODE_DISPLAY_NAME_MAPPINGS as IMAGESPLITTER_NODE_DISPLAY_NAME_MAPPINGS
+from .xmiles_nanobanana_node import NODE_CLASS_MAPPINGS as XMILES_NODE_CLASS_MAPPINGS
+from .xmiles_nanobanana_node import NODE_DISPLAY_NAME_MAPPINGS as XMILES_NODE_DISPLAY_NAME_MAPPINGS
 
 # 合并节点映射字典
 NODE_CLASS_MAPPINGS = {}
@@ -48,6 +50,7 @@ NODE_CLASS_MAPPINGS.update(UTF8_NODE_CLASS_MAPPINGS)
 NODE_CLASS_MAPPINGS.update(OPENAI_NODE_CLASS_MAPPINGS)
 NODE_CLASS_MAPPINGS.update(COLORMATCHER_NODE_CLASS_MAPPINGS)
 NODE_CLASS_MAPPINGS.update(IMAGESPLITTER_NODE_CLASS_MAPPINGS)
+NODE_CLASS_MAPPINGS.update(XMILES_NODE_CLASS_MAPPINGS)
 
 # 合并节点显示名称映射
 NODE_DISPLAY_NAME_MAPPINGS = {}
@@ -66,5 +69,6 @@ NODE_DISPLAY_NAME_MAPPINGS.update(UTF8_NODE_DISPLAY_NAME_MAPPINGS)
 NODE_DISPLAY_NAME_MAPPINGS.update(OPENAI_NODE_DISPLAY_NAME_MAPPINGS)
 NODE_DISPLAY_NAME_MAPPINGS.update(COLORMATCHER_NODE_DISPLAY_NAME_MAPPINGS)
 NODE_DISPLAY_NAME_MAPPINGS.update(IMAGESPLITTER_NODE_DISPLAY_NAME_MAPPINGS)
+NODE_DISPLAY_NAME_MAPPINGS.update(XMILES_NODE_DISPLAY_NAME_MAPPINGS)
 
 __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

xmiles_nanobanana_node.py

@@ -0,0 +1,198 @@
+import torch
+import numpy as np
+import requests
+import json
+import uuid
+import io
+import base64
+from PIL import Image
+
+class XmilesNanobananaNode:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "text": ("STRING", {"multiline": True, "default": ""}),
+                "resolution": (["1K", "2K", "4K"], {"default": "4K"}),
+                "aspect_ratio": (["1:1","2:3","3:2","3:4","4:3","4:5","5:4","9:16","16:9","21:9"], {"default": "9:16"}),
+            },
+            "optional": {
+                "images": ("IMAGE",),
+                "proxy_url": ("STRING", {"default": "", "multiline": False}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "STRING")
+    RETURN_NAMES = ("images", "log")
+    OUTPUT_IS_LIST = (True, False)
+    FUNCTION = "generate"
+    CATEGORY = "Rui-Node🐶/AI模型🤖"
+
+    def _make_client_id(self):
+        return f"{uuid.uuid4()}-test"
+
+    def _tensor_to_png_base64(self, tensor):
+        arr = tensor.cpu().numpy()
+        arr = np.clip(arr, 0, 1)
+        img = Image.fromarray((arr * 255).astype(np.uint8), 'RGB')
+        buf = io.BytesIO()
+        img.save(buf, format="PNG")
+        return base64.b64encode(buf.getvalue()).decode("utf-8")
+
+    def _pil_to_tensor(self, pil_img):
+        if pil_img.mode != "RGB":
+            pil_img = pil_img.convert("RGB")
+        np_img = np.array(pil_img).astype(np.float32) / 255.0
+        t = torch.from_numpy(np_img).unsqueeze(0)
+        return t
+
+    def _download_image_tensor(self, url, proxies=None):
+        r = requests.get(url, proxies=proxies, timeout=60)
+        r.raise_for_status()
+        img = Image.open(io.BytesIO(r.content))
+        return self._pil_to_tensor(img)
+
+    def generate(self, text, resolution, aspect_ratio, images=None, proxy_url=""):
+        client_id = self._make_client_id()
+        proxies = None
+        if proxy_url and proxy_url.strip():
+            proxies = {"http": proxy_url, "https": proxy_url}
+
+        parts = []
+        if text and text.strip():
+            parts.append({"text": text})
+
+        image_parts = []
+        if images is not None:
+            if isinstance(images, list):
+                tensors = [img[0] for img in images]
+            else:
+                tensors = [images[0]]
+            for t in tensors:
+                b64 = self._tensor_to_png_base64(t)
+                image_parts.append({"inlineData": {"data": "data:image/png;base64," + b64, "mimeType": "image/png"}})
+
+        for p in image_parts:
+            parts.append(p)
+
+        body_obj = {
+            "contents": [
+                {
+                    "parts": parts,
+                    "role": "user"
+                }
+            ],
+            "filePath": {
+                "inputs": {
+                    "filePath": ""
+                }
+            },
+            "generationConfig": {
+                "candidateCount": 1,
+                "imageConfig": {
+                    "aspectRatio": aspect_ratio,
+                    "imageSize": resolution
+                },
+                "responseModalities": ["TEXT", "IMAGE"],
+                "temperature": 1.0,
+                "topP": 0.95
+            },
+            "model": "gemini-3.1-flash-image-preview"
+        }
+
+        payload = {
+            "taskType": "ZENMUX",
+            "clientId": client_id,
+            "clientType": "image",
+            "callBackService": "remoteApi",
+            "extraData": {
+                "faceDetailer": 0,
+                "filePath": "",
+                "loraNum": 0,
+                "memberType": "PLUS",
+                "moduleName": "全能编辑 V2",
+                "resolution": "",
+                "taskType": "ZENMUX",
+                "uniqueId": str(uuid.uuid4().int)[:19],
+                "workflowName": ""
+            },
+            "imgIdList": [],
+            "memberType": "PLUS",
+            "body": json.dumps(body_obj, ensure_ascii=False)
+        }
+
+        url = "https://test.holopix.cn/ai-holopix-queue/api/prompt"
+        try:
+            resp = requests.post(url, headers={"Content-Type": "application/json"}, json=payload, proxies=proxies, timeout=60)
+            resp.raise_for_status()
+            data = resp.json()
+            status = data.get("status")
+            gen_status = data.get("generateStatus")
+            logs = {"status": status, "generateStatus": gen_status, "clientId": data.get("clientId"), "timestamp": data.get("timestamp")}
+            tensors = []
+            if status == 0 and gen_status == 1:
+                items = data.get("data") or []
+                for item in items:
+                    url_item = item.get("url")
+                    if url_item:
+                        t = self._download_image_tensor(url_item, proxies=proxies)
+                        tensors.append(t)
+                return (tensors if tensors else [], json.dumps(logs, ensure_ascii=False))
+            else:
+                return ([], json.dumps(data, ensure_ascii=False))
+        except Exception as e:
+            return ([], str(e))
+
+class XmilesNanobananaResultParser:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "json_text": ("STRING", {"multiline": True, "default": ""}),
+                "proxy_url": ("STRING", {"default": "", "multiline": False}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "STRING")
+    RETURN_NAMES = ("images", "log")
+    OUTPUT_IS_LIST = (True, False)
+    FUNCTION = "parse"
+    CATEGORY = "Rui-Node🐶/AI模型🤖"
+
+    def _download_image_tensor(self, url, proxies=None):
+        r = requests.get(url, proxies=proxies, timeout=60)
+        r.raise_for_status()
+        img = Image.open(io.BytesIO(r.content))
+        if img.mode != "RGB":
+            img = img.convert("RGB")
+        np_img = np.array(img).astype(np.float32) / 255.0
+        return torch.from_numpy(np_img).unsqueeze(0)
+
+    def parse(self, json_text, proxy_url=""):
+        proxies = None
+        if proxy_url and proxy_url.strip():
+            proxies = {"http": proxy_url, "https": proxy_url}
+        try:
+            obj = json.loads(json_text)
+            status = obj.get("status")
+            gen_status = obj.get("generateStatus")
+            tensors = []
+            if status == 0 and gen_status == 1:
+                items = obj.get("data") or []
+                for item in items:
+                    url_item = item.get("url")
+                    if url_item:
+                        tensors.append(self._download_image_tensor(url_item, proxies=proxies))
+            return (tensors, json.dumps({"status": status, "generateStatus": gen_status}, ensure_ascii=False))
+        except Exception as e:
+            return ([], str(e))
+
+NODE_CLASS_MAPPINGS = {
+    "XmilesNanobanana": XmilesNanobananaNode,
+    "XmilesNanobananaResultParser": XmilesNanobananaResultParser,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "XmilesNanobanana": "Xmiles-nanobanana",
+    "XmilesNanobananaResultParser": "Xmiles-nanobanana 结果解析",
+}