Add files via upload
This commit is contained in:
Luis Fernando Martinelli
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MIT License
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Copyright (c) 2026 fmartinellidev
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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# 🌊 ComfyUI-WaveSpeed
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[](LICENSE)
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[](https://github.com/comfyanonymous/ComfyUI)
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Nó **genérico e flexível** para integrar **qualquer modelo do [WaveSpeed](https://wavespeed.ai)** no ComfyUI — incluindo Nano Banana Pro, Wan 2.1, Flux, Kling, Veo e mais.
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✅ Upload direto no WaveSpeed CDN
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✅ Barra de progresso no terminal (igual ao KSampler)
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✅ Suporte a imagem e vídeo
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✅ `endpoint_url` customizável — basta colar a URL do playground!
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---
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## 📦 Instalação
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No seu terminal (dentro da pasta `ComfyUI/custom_nodes`):
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```bash
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git clone https://github.com/fmartinellidev/ComfyUI-WaveSpeed.git
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# __init__.py — wavespeed
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from .node_wavespeed_api import WaveSpeedAPINode
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from .node_apikey_loader import WaveSpeedAPIKeyLoader
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from .node_image_uploader import WaveSpeedImageUploader
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NODE_CLASS_MAPPINGS = {
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"WaveSpeedAPI": WaveSpeedAPINode, # ← ID genérico
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"WaveSpeedAPIKeyLoader": WaveSpeedAPIKeyLoader,
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"WaveSpeedImageUploader": WaveSpeedImageUploader,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"WaveSpeedAPI": "WaveSpeed API (Generic)", # ← nome claro e reutilizável
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"WaveSpeedAPIKeyLoader": "WaveSpeed API Key Loader",
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"WaveSpeedImageUploader": "WaveSpeed Image Uploader (Official)",
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}
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
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# node_apikey_loader.py
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class WaveSpeedAPIKeyLoader:
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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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"api_key": ("STRING", {"multiline": False, "default": "", "placeholder": "ex: ws_sk_xxxxxxxx"}),
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}
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("api_key",)
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FUNCTION = "load"
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CATEGORY = "api node/utility/WaveSpeed"
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DISPLAY_NAME = "WaveSpeed API Key Loader"
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NODE_ID = "WaveSpeedAPIKeyLoader"
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def load(self, api_key):
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key = api_key.strip()
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if not key:
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raise ValueError("API key não pode estar vazia.")
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#if not key.startswith("ws_sk_"):
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# Apenas aviso — não impede (algumas chaves podem não seguir padrão)
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#print("⚠️ Aviso: sua chave não começa com 'ws_sk_' — verifique se está correta.")
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return (key,)
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# node_image_uploader.py
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import requests
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import torch
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import numpy as np
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from PIL import Image
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from io import BytesIO
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import os
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class WaveSpeedImageUploader:
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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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"image": ("IMAGE",),
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"api_key": ("STRING", {
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"multiline": False,
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"default": "",
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"placeholder": "ou defina WAVESPEED_API_KEY"
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}),
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"output_format": (["png", "jpeg"], {"default": "png"})
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}
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("image_url",)
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FUNCTION = "upload"
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CATEGORY = "api node/image/WaveSpeed"
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DISPLAY_NAME = "WaveSpeed Image Uploader (Official)"
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NODE_ID = "WaveSpeedImageUploader"
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def upload(self, image: torch.Tensor, api_key: str, output_format: str):
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final_api_key = api_key.strip() or os.getenv("WAVESPEED_API_KEY", "").strip()
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if not final_api_key:
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raise ValueError("API key não fornecida.")
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if image.ndim == 4:
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image = image[0]
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img_np = (image.cpu().numpy() * 255).astype("uint8")
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pil_img = Image.fromarray(img_np, mode="RGB")
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buffer = BytesIO()
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if output_format == "png":
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pil_img.save(buffer, format="PNG")
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else:
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pil_img.save(buffer, format="JPEG", quality=95)
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buffer.seek(0)
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upload_url = "https://api.wavespeed.ai/api/v3/media/upload/binary"
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headers = {"Authorization": f"Bearer {final_api_key}"}
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files = {"file": (f"image.{output_format}", buffer, f"image/{output_format}")}
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try:
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print("📤 Upload oficial no WaveSpeed...")
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response = requests.post(upload_url, headers=headers, files=files, timeout=30)
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response.raise_for_status()
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data = response.json()
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# ✅ Extrai download_url de data e remove espaços
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image_url = data.get("data", {}).get("download_url", "").strip()
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if not image_url.startswith("http"):
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raise ValueError(f"download_url não encontrada. Resposta: {data}")
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print(f"✅ URL oficial do WaveSpeed: {image_url}")
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return (image_url,)
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except Exception as e:
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raise RuntimeError(f"Falha no upload: {e}")
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# node_wavespeed_api.py
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import requests
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import time
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import os
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import sys
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import torch
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import numpy as np
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from PIL import Image
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from io import BytesIO
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def _print_progress(percent, status="processing", width=40):
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filled = int(width * percent / 100)
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bar = "█" * filled + "░" * (width - filled)
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sys.stdout.write(f"\r⏳ [{bar}] {int(percent)}% | {status}")
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sys.stdout.flush()
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def _clear_progress():
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sys.stdout.write("\r" + " " * 80 + "\r")
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sys.stdout.flush()
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class WaveSpeedAPINode:
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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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"prompt": ("STRING", {"multiline": True, "default": "make her smile, studio lighting"}),
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"endpoint_url": ("STRING", {
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"multiline": False,
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"default": "https://api.wavespeed.ai/api/v3/google/nano-banana-pro/edit"
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}),
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"poll_interval": ("INT", {"default": 3, "min": 1, "max": 10}),
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},
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"optional": {
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"image_url": ("STRING", {"multiline": False, "default": ""}),
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"image": ("IMAGE",),
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"api_key": ("STRING", {"multiline": False, "default": ""}),
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"extra_payload": ("STRING", {
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"multiline": True,
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"default": "{}",
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"placeholder": "JSON adicional (ex: {\"resolution\": \"2k\"})"
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}),
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}
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}
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RETURN_TYPES = ("IMAGE", "STRING", "STRING")
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RETURN_NAMES = ("image", "request_id", "raw_response")
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FUNCTION = "generate"
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NODE_ID = "WaveSpeedAPI"
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DISPLAY_NAME = "WaveSpeed API (Generic)"
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CATEGORY = "api node/image/WaveSpeed"
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def generate(self, prompt, endpoint_url, poll_interval, image_url="", image=None, api_key="", extra_payload="{}"):
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# 1. Resolve API key
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final_api_key = api_key.strip() or os.getenv("WAVESPEED_API_KEY", "").strip()
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if not final_api_key:
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raise ValueError("API key não fornecida.")
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# 2. Resolve imagem
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if image is not None:
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print("📤 Upload da imagem no WaveSpeed CDN...")
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image_url = self._upload_to_wavespeed(image, final_api_key)
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print(f"✅ URL oficial: {image_url}")
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elif not image_url.strip():
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# ❗ Alguns endpoints não exigem imagem (ex: text-to-video)
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print("⚠️ Nenhuma imagem fornecida — verifique se o endpoint a suporta.")
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# 3. Monta payload base
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payload = {"prompt": prompt}
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if image_url:
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payload["images"] = [image_url]
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# 4. Adiciona extra_payload (JSON seguro)
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import json
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try:
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extra = json.loads(extra_payload) if extra_payload.strip() else {}
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payload.update(extra)
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except Exception as e:
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raise ValueError(f"extra_payload inválido (deve ser JSON): {e}")
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# 5. Headers
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headers = {
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"Authorization": f"Bearer {final_api_key}",
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"Content-Type": "application/json"
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}
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# 6. POST
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print(f"📤 Enviando para: {endpoint_url}")
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response = requests.post(endpoint_url, json=payload, headers=headers, timeout=30)
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response.raise_for_status()
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resp_json = response.json()
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raw_response_str = json.dumps(resp_json, indent=2)
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data = resp_json.get("data", {})
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request_id = data.get("id")
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get_url = data.get("urls", {}).get("get", "").strip()
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if not request_id or not get_url:
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raise RuntimeError(f"Resposta inválida. Campos ausentes: id={bool(request_id)}, get_url={bool(get_url)}")
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print(f"\n🚀 Job iniciado: {request_id}")
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# 7. Polling com progresso
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current_percent = 0
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ESTIMATED_TOTAL_SECONDS = 20 # adaptável
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steps = max(1, int(ESTIMATED_TOTAL_SECONDS / poll_interval))
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increment = 99.0 / steps
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try:
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while True:
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time.sleep(poll_interval)
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result = requests.get(get_url, headers=headers, timeout=30)
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result.raise_for_status()
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poll_data = result.json()
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poll_result = poll_data.get("data", {})
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status = poll_result.get("status")
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if status == "completed":
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_print_progress(100.0, "done")
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print()
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outputs = poll_result.get("outputs", [])
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if not outputs:
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_clear_progress()
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raise RuntimeError("Nenhum output retornado.")
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# Suporta image ou video
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output_url = outputs[0]
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break
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elif status == "failed":
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_clear_progress()
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error = poll_result.get("error", "Erro desconhecido")
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raise RuntimeError(f"Falha: {error}")
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elif status in ("pending", "processing", "created"):
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current_percent = min(99, current_percent + increment)
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_print_progress(current_percent, status)
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else:
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_clear_progress()
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raise RuntimeError(f"Status inesperado: {status}")
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except Exception as e:
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_clear_progress()
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raise
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# 8. Download (suporte a imagem apenas — para vídeo, retornar URL)
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if output_url.lower().endswith(('.png', '.jpg', '.jpeg')):
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print("📥 Baixando imagem...")
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img_response = requests.get(output_url, timeout=30)
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img_response.raise_for_status()
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pil_img = Image.open(BytesIO(img_response.content)).convert("RGB")
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image_np = np.array(pil_img).astype(np.float32) / 255.0
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image_tensor = torch.from_numpy(image_np)[None,]
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return (image_tensor, request_id, raw_response_str)
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else:
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# Para vídeos, retornar placeholder preto + URL no raw_response
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print(f"🎬 Saída é um vídeo: {output_url}")
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placeholder = torch.zeros((1, 512, 512, 3), dtype=torch.float32)
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return (placeholder, request_id, raw_response_str + f"\n\nVIDEO_URL: {output_url}")
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def _upload_to_wavespeed(self, image_tensor: torch.Tensor, api_key: str, output_format="png"):
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if image_tensor.ndim == 4:
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image_tensor = image_tensor[0]
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img_np = (image_tensor.cpu().numpy() * 255).astype("uint8")
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pil_img = Image.fromarray(img_np, mode="RGB")
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buffer = BytesIO()
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pil_img.save(buffer, format=output_format.upper())
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buffer.seek(0)
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upload_url = "https://api.wavespeed.ai/api/v3/media/upload/binary"
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headers = {"Authorization": f"Bearer {api_key}"}
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files = {"file": (f"img.{output_format}", buffer, f"image/{output_format}")}
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response = requests.post(upload_url, headers=headers, files=files, timeout=30)
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response.raise_for_status()
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data = response.json()
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image_url = data.get("data", {}).get("download_url", "").strip()
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if not image_url.startswith("http"):
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raise ValueError(f"download_url inválida: {data}")
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return image_url
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@@ -0,0 +1,32 @@
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# utils.py — versão final e funcional (comprovada por você)
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import requests
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from PIL import Image
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from io import BytesIO
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def image_to_temp_url(image_tensor, output_format="jpeg"):
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if image_tensor.ndim == 4:
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image_tensor = image_tensor[0]
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img_np = (image_tensor.cpu().numpy() * 255).astype("uint8")
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pil_img = Image.fromarray(img_np, mode="RGB")
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buffer = BytesIO()
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pil_img.save(buffer, format=output_format.upper())
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buffer.seek(0)
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try:
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# ✅ Funcionando: temp.sh + User-Agent válido
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response = requests.post(
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"https://temp.sh/upload",
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files={"file": (f"img.{output_format}", buffer, f"image/{output_format}")},
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headers={
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
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}
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)
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response.raise_for_status()
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url = response.text.strip()
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if not url.startswith("http"):
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raise ValueError(f"URL inválida: {url}")
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return url
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except Exception as e:
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raise RuntimeError(f"Falha ao fazer upload da imagem: {e}")
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