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Aaron Burton
2026-03-18 16:38:55 -07:00
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LICENSE.MD
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Copyright 2026 Augment Studio - Upscale Pro
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
README.md
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# Augment Upscale Pro
Make your logos and brand assets print-ready. Augment Upscale Pro is a ComfyUI node that takes rasterized images and upscales them 2X while keeping lines crisp and colors clean. No more blurry enlargements. A perfect first step to cleaning up assets before vectorizing them.
Drop in a logo, icon, or any flat artwork and get back a sharp, high-resolution version you can actually use.
## Getting started
1. Grab an API key at [augmentstudio.app/pricing](https://augmentstudio.app/pricing)
2. Install the node from ComfyUI Manager (search "augment Upscale Pro")
3. Add the **Upscale Pro** node to your workflow, paste in your key, and connect an image
## Also included
**Trigger** flow nodes for wiring up multi-step workflows. Use them to chain the upscaler with other nodes so everything runs in the right order.
## Manual install
```
cd ComfyUI/custom_nodes
git clone https://github.com/augment-lib/augment-upscale-pro.git augment-upscale-pro
```
## License
See [LICENSE.md](LICENSE.MD) for details.
__init__.py
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"""
Augment - Upscale Pro
"""
NODE_CLASS_MAPPINGS = {}
NODE_DISPLAY_NAME_MAPPINGS = {}
_failed = []
# ── Flow Control ──
try:
from .trigger import GateNode, make_wait_node, WAIT_TYPES
NODE_CLASS_MAPPINGS["GateNode"] = GateNode
NODE_DISPLAY_NAME_MAPPINGS["GateNode"] = "Trigger"
for _name, _type_str in WAIT_TYPES.items():
_cls_name = f"AugmentWait{_name}"
try:
NODE_CLASS_MAPPINGS[_cls_name] = make_wait_node(_name, _type_str)
NODE_DISPLAY_NAME_MAPPINGS[_cls_name] = f"{_name} (Improved)"
except Exception as e:
_failed.append((_cls_name, e))
print(f"[augment-upscale] ⚠ {_cls_name} unavailable: {e}")
except Exception as e:
_failed.append(("Wait nodes", e))
print(f"[augment-upscale] ⚠ Flow control nodes unavailable: {e}")
# ── Logo Upscale (Paid) ──
try:
from .logo_upscale import AugmentLogoUpscale
NODE_CLASS_MAPPINGS["AugmentLogoUpscale"] = AugmentLogoUpscale
NODE_DISPLAY_NAME_MAPPINGS["AugmentLogoUpscale"] = "Upscale Pro"
except Exception as e:
_failed.append(("AugmentLogoUpscale", e))
print(f"[augment-upscale] ⚠ AugmentLogoUpscale unavailable: {e}")
# ── Summary ──
WEB_DIRECTORY = "./web/js"
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
if _failed:
print(f"[augment-upscale] ✓ Registered {len(NODE_CLASS_MAPPINGS)} nodes ({len(_failed)} failed)")
for node_id, err in _failed:
print(f"[augment-upscale] ✗ {node_id}: {err}")
else:
print(f"[augment-upscale] ✓ Registered {len(NODE_CLASS_MAPPINGS)} nodes")
logo_upscale.py
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# "Design-focused node suite for ComfyUI."
# Copyright 2026 Augment Studio
# Paid Node, credit values may change.
# Augmentstudio.app
import io
import json
import os
import requests
import time
import folder_paths
import numpy as np
import torch
from PIL import Image
NODE_ID = "logo_upscale"
API_URL = "https://augmentstudio.app/api"
UPLOAD_DIR = folder_paths.get_input_directory()
class AugmentLogoUpscale:
@classmethod
def INPUT_TYPES(cls):
files = sorted([
f for f in os.listdir(UPLOAD_DIR)
if f.lower().endswith((".png", ".jpg", ".jpeg", ".webp"))
])
return {
"required": {
"api_key": ("STRING", {"default": ""}),
"scale": (["2x"], {"default": "2x"}),
},
"optional": {
"image": ("IMAGE",),
"image_file": (files, {"image_upload": True}),
"trigger": ("TRIGGER",),
},
}
RETURN_TYPES = ("IMAGE", "AUGMENT_JSON", "TRIGGER")
RETURN_NAMES = ("image", "json_result", "trigger")
FUNCTION = "execute"
CATEGORY = "Augment/Enchance"
OUTPUT_NODE = True
@classmethod
def IS_CHANGED(cls, **kwargs):
return time.time()
@classmethod
def VALIDATE_INPUTS(cls, **kwargs):
return True
def execute(self, api_key, scale="2x", image=None, image_file=None, trigger=None):
api_url = API_URL.rstrip("/")
auth = {"Authorization": f"Bearer {api_key}"}
scale_factor = int(scale.replace("x", ""))
if image is not None:
img_np = (image[0].cpu().numpy() * 255).astype(np.uint8)
pil_img = Image.fromarray(img_np, "RGB")
buf = io.BytesIO()
pil_img.save(buf, format="PNG")
img_bytes = buf.getvalue()
source_name = "image_input.png"
mime = "image/png"
elif image_file:
path = os.path.join(UPLOAD_DIR, image_file)
with open(path, "rb") as f:
img_bytes = f.read()
source_name = image_file
ext = os.path.splitext(image_file)[1].lower()
mime_types = {".png": "image/png", ".jpg": "image/jpeg", ".jpeg": "image/jpeg", ".webp": "image/webp"}
mime = mime_types.get(ext, "image/png")
else:
raise RuntimeError("No image provided — connect an image input or select a file")
print(f"[Augment API] Submitting image for {scale} upscale: {source_name} ({len(img_bytes)} bytes)")
try:
r = requests.post(
f"{api_url}/process",
files={"image": (source_name, img_bytes, mime)},
data={"node_id": NODE_ID, "scale": scale_factor},
headers=auth,
timeout=30,
)
except requests.exceptions.RequestException as e:
print(f"[Augment API] Submit failed: {e}")
raise
if r.status_code != 200:
raise RuntimeError(f"Submit error {r.status_code}: {r.text[:500]}")
request_id = r.json().get("request_id")
if not request_id:
raise RuntimeError(f"No request_id: {r.text[:500]}")
print(f"[Augment API] Job submitted: {request_id}")
max_wait = 120
elapsed = 0
job_status = "unknown"
while elapsed < max_wait:
time.sleep(1)
elapsed += 1
try:
status_r = requests.get(
f"{api_url}/job/{request_id}/status",
headers=auth, timeout=10,
)
job_status = status_r.json().get("status", "unknown")
except Exception as e:
print(f"[Augment API] Poll error: {e}")
continue
if job_status == "done":
break
elif job_status == "error":
raise RuntimeError(f"Job failed: {status_r.json().get('error')}")
if job_status != "done":
raise RuntimeError(f"Timed out after {max_wait}s")
img_r = requests.get(
f"{api_url}/job/{request_id}/image",
headers=auth, timeout=60,
)
if img_r.status_code != 200:
raise RuntimeError(f"Image fetch error: {img_r.status_code}")
img = Image.open(io.BytesIO(img_r.content)).convert("RGB")
arr = np.array(img).astype(np.float32) / 255.0
image_tensor = torch.from_numpy(arr).unsqueeze(0)
json_result = json.dumps({
"node": "LogoUpscale",
"scale": scale,
"width": img.size[0],
"height": img.size[1],
"source": source_name,
}, indent=2)
temp_dir = folder_paths.get_temp_directory()
os.makedirs(temp_dir, exist_ok=True)
preview_file = f"augment_upscale_{time.time_ns()}.png"
img.save(os.path.join(temp_dir, preview_file))
print(f"[Augment API] Done! {img.size[0]}x{img.size[1]} ({scale} upscale)")
return {
"ui": {"images": [{"filename": preview_file, "subfolder": "", "type": "temp"}]},
"result": (image_tensor, json_result, "done"),
}
NODE_CLASS_MAPPINGS = {
"AugmentLogoUpscale": AugmentLogoUpscale,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"AugmentLogoUpscale": "Logo Upscale",
}
pyproject.toml
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[project]
name = "augment-UpscalePro"
description = "✨ Augment Upscale — AI-powered logo and image upscaler for ComfyUI. Upscale logos, icons, and brand assets to high resolution with sharp, clean results. A professional alternative to Magnific AI built directly into your ComfyUI workflow. Supports 2x and 4x upscaling via the Augment Studio API with automatic job polling and tensor output. Ideal for logo design, brand asset pipelines, print-ready artwork, and any workflow that needs crisp detail-preserving enlargement. Includes built-in flow control trigger and wait nodes for chaining multi-step pipelines. Get your API key at [a/augmentstudio.app/pricing](https://augmentstudio.app/pricing)"
version = "1.0.0"
licence = { file = "LICENSE.md" }
dependencies = [
"numpy>=1.19.0",
"torch>=1.9.0",
"Pillow>=8.0.0",
"requests>=2.25.0"
]
[project.urls]
Repository = "https://github.com/augment-lib/augment-upscale-pro"
Website = "https://augmentstudio.app/"
[tool.comfy]
PublisherId = "augmentstudio"
DisplayName = "✨ augment Upscale Pro - AI Image & Logo Upscaler, Enhancer and Enlarger for ComfyUI"
Icon = "https://avatars.githubusercontent.com/u/243209371?s=400&u=db6e0d310dcf8863dd5c735d3ebbdaa5c317530d&v=4"
trigger.py
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# "Design-focused node suite for ComfyUI."
# Copyright 2026 Augment Studio
# Augmentstudio.app
import json
class GateNode:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"input": ("*",),
},
}
RETURN_TYPES = ("TRIGGER", "*", "STRING")
RETURN_NAMES = ("trigger", "value", "json")
OUTPUT_NODE = True
FUNCTION = "execute"
CATEGORY = "Augment/Utils"
@classmethod
def VALIDATE_INPUTS(cls, **kwargs):
return True
def execute(self, input):
json_result = json.dumps({"node": "Convert Any", "type": type(input).__name__, "value": str(input)})
return {"ui": {"text": [str(input)]}, "result": ("done", input, json_result)}
WAIT_DEFAULTS = {
"INT": {"default": 0},
"FLOAT": {"default": 0.0, "step": 0.01},
"STRING": {"default": ""},
"BOOLEAN": {"default": False},
}
def make_wait_node(type_name, type_str):
class WaitNode:
@classmethod
def INPUT_TYPES(cls):
has_widget = type_str in WAIT_DEFAULTS
inputs = {
"required": {},
"optional": {
"trigger": ("TRIGGER",),
},
}
if has_widget:
inputs["required"]["value"] = (type_str, WAIT_DEFAULTS[type_str])
inputs["optional"]["input"] = (type_str, {"forceInput": True})
else:
inputs["required"]["value"] = (type_str,)
return inputs
RETURN_TYPES = (type_str, "AUGMENT_JSON", "TRIGGER")
RETURN_NAMES = ("value", "json", "trigger")
OUTPUT_NODE = True
FUNCTION = "execute"
CATEGORY = "Augment/Utils"
@classmethod
def VALIDATE_INPUTS(cls, **kwargs):
return True
def execute(self, value=None, trigger=None, input=None):
v = input if input is not None else value
json_result = json.dumps({"node": type_str, "type": type_str.lower(), "value": str(v)})
return {"ui": {"text": [str(v)]}, "result": (v, json_result, "done")}
WaitNode.__name__ = f"Wait{type_name}Node"
WaitNode.__qualname__ = f"Wait{type_name}Node"
return WaitNode
WAIT_TYPES = {
"Int": "INT",
"Float": "FLOAT",
"String": "STRING",
"Bool": "BOOLEAN",
"Image": "IMAGE",
"Latent": "LATENT",
"Condition": "CONDITIONING",
"VAE": "VAE",
"Model": "MODEL",
"Clip": "CLIP",
"Mask": "MASK",
}
NODE_CLASS_MAPPINGS = {"GateNode": GateNode}
NODE_DISPLAY_NAME_MAPPINGS = {"GateNode": "Trigger"}
for name, type_str in WAIT_TYPES.items():
cls_name = f"Wait{name}Node"
node_cls = make_wait_node(name, type_str)
NODE_CLASS_MAPPINGS[cls_name] = node_cls
NODE_DISPLAY_NAME_MAPPINGS[cls_name] = f"{name} (Improved)"
web/js/augment_style.js
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/*"Augment Upscale — AI Image & Logo Upscaler for ComfyUI."
# Copyright 2026 Augment Studio
Augmentstudio.app */
import { app } from "../../../scripts/app.js";
const TITLE_BG = "#443322";
const NODE_BG = "#665533";
const PAID_NODES = [
"AugmentLogoUpscale",
];
const BADGE_FONT = "bold 9px sans-serif";
const BADGE_RADIUS = 4;
const BADGE_PAD_X = 6;
app.registerExtension({
name: "augment-upscale.NodeStyle",
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (!PAID_NODES.includes(nodeData.name)) return;
const origCreated = nodeType.prototype.onNodeCreated;
nodeType.prototype.onNodeCreated = function () {
origCreated?.apply(this, arguments);
this.addWidget("button", "Get Tokens", null, () => {
window.open("https://augmentstudio.app/pricing", "_blank");
});
const jsonIdx = this.outputs?.findIndex(o => o.name === "json_result");
if (jsonIdx >= 0) {
this.outputs.splice(jsonIdx, 1);
}
};
const origDraw = nodeType.prototype.onDrawForeground;
nodeType.prototype.onDrawForeground = function (ctx) {
this.color = TITLE_BG;
this.bgcolor = NODE_BG;
origDraw?.apply(this, arguments);
ctx.save();
ctx.font = BADGE_FONT;
const creditsText = "🔸 1 export / run";
const creditsW = ctx.measureText(creditsText).width + BADGE_PAD_X * 2;
const creditsX = this.size[0] - creditsW - 6;
const y = -22;
ctx.beginPath();
ctx.roundRect(creditsX, y, creditsW, 14, BADGE_RADIUS);
ctx.fillStyle = "#8d6932";
ctx.fill();
ctx.fillStyle = "#ffffff";
ctx.textAlign = "left";
ctx.textBaseline = "middle";
ctx.fillText(creditsText, creditsX + BADGE_PAD_X, y + 7);
ctx.restore();
};
},
});