270 lines
10 KiB
Python
270 lines
10 KiB
Python
import torch
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import numpy as np
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import requests
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import json
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import uuid
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import io
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import base64
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import time
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from PIL import Image
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class XmilesNanobananaNode:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"text": ("STRING", {"multiline": True, "default": ""}),
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"resolution": (["1K", "2K", "4K"], {"default": "4K"}),
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"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"}),
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},
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"optional": {
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"images": ("IMAGE",),
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"proxy_url": ("STRING", {"default": "", "multiline": False}),
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"verbose": ("BOOLEAN", {"default": True}),
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}
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}
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RETURN_TYPES = ("IMAGE", "STRING")
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RETURN_NAMES = ("images", "log")
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OUTPUT_IS_LIST = (True, False)
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FUNCTION = "generate"
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CATEGORY = "Rui-Node🐶/AI模型🤖"
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def _make_client_id(self):
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return f"{uuid.uuid4()}-test"
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def _tensor_to_png_base64(self, tensor):
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arr = tensor.cpu().numpy()
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arr = np.clip(arr, 0, 1)
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img = Image.fromarray((arr * 255).astype(np.uint8), 'RGB')
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buf = io.BytesIO()
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img.save(buf, format="PNG")
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return base64.b64encode(buf.getvalue()).decode("utf-8")
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def _pil_to_tensor(self, pil_img):
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if pil_img.mode != "RGB":
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pil_img = pil_img.convert("RGB")
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np_img = np.array(pil_img).astype(np.float32) / 255.0
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t = torch.from_numpy(np_img).unsqueeze(0)
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return t
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def _download_image_tensor(self, url, proxies=None):
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r = requests.get(url, proxies=proxies, timeout=60)
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r.raise_for_status()
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img = Image.open(io.BytesIO(r.content))
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return self._pil_to_tensor(img)
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def generate(self, text, resolution, aspect_ratio, images=None, proxy_url="", verbose=True):
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t0 = time.perf_counter()
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logs = []
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if verbose:
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print("Xmiles-nanobanana:start", {"ts": t0, "resolution": resolution, "aspect_ratio": aspect_ratio}, flush=True)
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logs.append(f"start_ts={t0}")
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client_id = self._make_client_id()
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if verbose:
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print("Xmiles-nanobanana:client_id", client_id, flush=True)
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logs.append(f"client_id={client_id}")
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proxies = None
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if proxy_url and proxy_url.strip():
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proxies = {"http": proxy_url, "https": proxy_url}
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if verbose:
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print("Xmiles-nanobanana:proxies", proxies, flush=True)
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logs.append(f"proxies={proxy_url}")
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else:
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if verbose:
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print("Xmiles-nanobanana:proxies=none", flush=True)
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logs.append("proxies=none")
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parts = []
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if text and text.strip():
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parts.append({"text": text})
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if verbose:
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print("Xmiles-nanobanana:text_len", len(text), flush=True)
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logs.append(f"text_len={len(text)}")
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image_parts = []
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if images is not None:
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if isinstance(images, list):
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tensors = [img[0] for img in images]
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else:
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tensors = [images[0]]
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if verbose:
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print("Xmiles-nanobanana:image_count", len(tensors), flush=True)
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logs.append(f"image_count={len(tensors)}")
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for t in tensors:
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b64 = self._tensor_to_png_base64(t)
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image_parts.append({"inlineData": {"data": "data:image/png;base64," + b64, "mimeType": "image/png"}})
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for p in image_parts:
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parts.append(p)
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t1 = time.perf_counter()
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if verbose:
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print("Xmiles-nanobanana:parts_ready_ms", int((t1 - t0) * 1000), flush=True)
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logs.append(f"parts_ready_ms={(t1-t0)*1000:.2f}")
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body_obj = {
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"contents": [
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{
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"parts": parts,
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"role": "user"
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}
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],
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"filePath": {
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"inputs": {
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"filePath": ""
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}
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},
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"generationConfig": {
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"candidateCount": 1,
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"imageConfig": {
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"aspectRatio": aspect_ratio,
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"imageSize": resolution
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},
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"responseModalities": ["TEXT", "IMAGE"],
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"temperature": 1.0,
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"topP": 0.95
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},
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"model": "gemini-3.1-flash-image-preview"
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}
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t2 = time.perf_counter()
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if verbose:
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print("Xmiles-nanobanana:body_size", len(json.dumps(body_obj, ensure_ascii=False)), flush=True)
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logs.append(f"body_size={len(json.dumps(body_obj, ensure_ascii=False))}")
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payload = {
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"taskType": "ZENMUX",
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"clientId": client_id,
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"clientType": "image",
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"callBackService": "remoteApi",
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"extraData": {
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"faceDetailer": 0,
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"filePath": "",
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"loraNum": 0,
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"memberType": "PLUS",
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"moduleName": "全能编辑 V2",
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"resolution": "",
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"taskType": "ZENMUX",
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"uniqueId": str(uuid.uuid4().int)[:19],
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"workflowName": ""
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},
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"imgIdList": [],
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"memberType": "PLUS",
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"body": json.dumps(body_obj, ensure_ascii=False)
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}
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url = "https://test.holopix.cn/ai-holopix-queue/api/prompt"
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try:
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t3 = time.perf_counter()
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if verbose:
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print("Xmiles-nanobanana:post_begin", {"ts": t3, "url": url}, flush=True)
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logs.append(f"post_begin_ts={t3}")
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resp = requests.post(url, headers={"Content-Type": "application/json"}, json=payload, proxies=proxies, timeout=60)
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t4 = time.perf_counter()
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resp.raise_for_status()
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data = resp.json()
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if verbose:
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print("Xmiles-nanobanana:post_done", {"status_code": resp.status_code, "elapsed_ms": int((t4 - t3) * 1000)}, flush=True)
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logs.append(f"post_elapsed_ms={(t4-t3)*1000:.2f}")
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status = data.get("status")
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gen_status = data.get("generateStatus")
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logs = {"status": status, "generateStatus": gen_status, "clientId": data.get("clientId"), "timestamp": data.get("timestamp")}
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tensors = []
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if status == 0 and gen_status == 1:
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items = data.get("data") or []
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if verbose:
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print("Xmiles-nanobanana:result_items", len(items), flush=True)
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for item in items:
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url_item = item.get("url")
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if url_item:
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d0 = time.perf_counter()
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if verbose:
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print("Xmiles-nanobanana:download_begin", url_item, flush=True)
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t = self._download_image_tensor(url_item, proxies=proxies)
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tensors.append(t)
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d1 = time.perf_counter()
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if verbose:
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print("Xmiles-nanobanana:download_done_ms", int((d1 - d0) * 1000), flush=True)
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return (tensors if tensors else [], json.dumps(logs, ensure_ascii=False))
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else:
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if verbose:
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print("Xmiles-nanobanana:task_failed", {"status": status, "generateStatus": gen_status}, flush=True)
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return ([], json.dumps(data, ensure_ascii=False))
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except Exception as e:
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if verbose:
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print("Xmiles-nanobanana:error", str(e), flush=True)
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return ([], str(e))
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class XmilesNanobananaResultParser:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"json_text": ("STRING", {"multiline": True, "default": ""}),
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"proxy_url": ("STRING", {"default": "", "multiline": False}),
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"verbose": ("BOOLEAN", {"default": True}),
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}
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}
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RETURN_TYPES = ("IMAGE", "STRING")
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RETURN_NAMES = ("images", "log")
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OUTPUT_IS_LIST = (True, False)
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FUNCTION = "parse"
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CATEGORY = "Rui-Node🐶/AI模型🤖"
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def _download_image_tensor(self, url, proxies=None):
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r = requests.get(url, proxies=proxies, timeout=60)
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r.raise_for_status()
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img = Image.open(io.BytesIO(r.content))
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if img.mode != "RGB":
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img = img.convert("RGB")
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np_img = np.array(img).astype(np.float32) / 255.0
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return torch.from_numpy(np_img).unsqueeze(0)
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def parse(self, json_text, proxy_url="", verbose=True):
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p0 = time.perf_counter()
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if verbose:
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print("Xmiles-nanobanana:parse_begin", {"ts": p0}, flush=True)
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proxies = None
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if proxy_url and proxy_url.strip():
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proxies = {"http": proxy_url, "https": proxy_url}
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if verbose:
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print("Xmiles-nanobanana:parse_proxies", proxies, flush=True)
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try:
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obj = json.loads(json_text)
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status = obj.get("status")
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gen_status = obj.get("generateStatus")
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if verbose:
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print("Xmiles-nanobanana:parse_status", {"status": status, "generateStatus": gen_status}, flush=True)
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tensors = []
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if status == 0 and gen_status == 1:
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items = obj.get("data") or []
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if verbose:
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print("Xmiles-nanobanana:parse_items", len(items), flush=True)
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for item in items:
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url_item = item.get("url")
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if url_item:
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z0 = time.perf_counter()
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if verbose:
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print("Xmiles-nanobanana:parse_download_begin", url_item, flush=True)
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tensors.append(self._download_image_tensor(url_item, proxies=proxies))
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z1 = time.perf_counter()
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if verbose:
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print("Xmiles-nanobanana:parse_download_ms", int((z1 - z0) * 1000), flush=True)
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return (tensors, json.dumps({"status": status, "generateStatus": gen_status}, ensure_ascii=False))
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except Exception as e:
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if verbose:
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print("Xmiles-nanobanana:parse_error", str(e), flush=True)
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return ([], str(e))
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NODE_CLASS_MAPPINGS = {
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"XmilesNanobanana": XmilesNanobananaNode,
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"XmilesNanobananaResultParser": XmilesNanobananaResultParser,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"XmilesNanobanana": "Xmiles-nanobanana",
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"XmilesNanobananaResultParser": "Xmiles-nanobanana 结果解析",
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}
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