MeteorAndy
a6f534d459
Lightweight custom nodes that call AI APIs directly with your own keys, bypassing the ComfyOrg proxy. Every node has a configurable base_url for third-party relay/proxy support. Covers: OpenAI, Claude, Gemini, Flux, Stability AI, Grok, Ideogram, Recraft, Reve, Bria, Kling, MiniMax, Seedance 2.0, Runway, Luma, Veo, Wan, PixVerse, Vidu, LTXV, ElevenLabs, OpenRouter, ByteDance, WaveSpeed, Magnific, and a generic video gen node.
57 lines
2.2 KiB
Python
57 lines
2.2 KiB
Python
"""Image upscale nodes (Magnific, Topaz, WaveSpeed)."""
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import base64
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from .utils import bytes_to_tensor, http_request, run_async, tensor_to_base64
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class BYOKey_Magnific:
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"""Magnific AI image upscale. Output: IMAGE."""
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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", {"default": ""}),
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"image": ("IMAGE",),
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},
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"optional": {
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"scale": (["2", "4", "8", "16"], {"default": "2"}),
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"creativity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1}),
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"prompt": ("STRING", {"multiline": True, "default": ""}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "api/byokey/image"
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DESCRIPTION = "Magnific AI image upscale."
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def execute(self, api_key, image, scale="2", creativity=0.5, prompt=""):
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import aiohttp
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from io import BytesIO
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from PIL import Image as PILImage
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import numpy as np
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async def _call():
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url = "https://api.magnific.ai/v1/upscale"
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headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
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b64 = tensor_to_base64(image)
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body = {"image": f"data:image/png;base64,{b64}", "scale": int(scale), "creativity": creativity}
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if prompt:
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body["prompt"] = prompt
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async with aiohttp.ClientSession() as session:
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async with session.post(url, headers=headers, json=body,
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timeout=aiohttp.ClientTimeout(total=300)) as resp:
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if resp.status >= 400:
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raise RuntimeError(f"Magnific error: {await resp.text()}")
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data = await resp.json()
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return data
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resp = run_async(_call())
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if resp.get("image"):
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img_data = resp["image"]
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if img_data.startswith("data:"):
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img_data = img_data.split(",", 1)[1]
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return (bytes_to_tensor(base64.b64decode(img_data)),)
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raise RuntimeError(f"No image in Magnific response: {list(resp.keys())}")
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