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README.md
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# ComfyUI-ExternalAPI-Helpers
A collection of powerful custom nodes for ComfyUI that connect your local workflows to closed-source AI models via their APIs. Use Google's Gemini, OpenAI's GPT-Image-1, and Black Forest Labs' FLUX models directly within ComfyUI.
A collection of powerful custom nodes for ComfyUI that connect your local workflows to closed-source AI models via their APIs. Use Google's Gemini, Imagen, Veo, OpenAI's GPT-Image-1, and Black Forest Labs' FLUX models directly within ComfyUI.
@@ -8,7 +8,11 @@ A collection of powerful custom nodes for ComfyUI that connect your local workfl
* **FLUX Kontext Pro & Max:** Image-to-image transformations using the FLUX models via the Replicate API.
* **Gemini Chat:** Google's powerful multimodal AI. Ask questions about an image, generate detailed descriptions or create prompts for other models. Supports thinking budget controls for applicable models.
* **Gemini Segmentation:** Generate segmentation masks for objects in an image using Gemini.
* **GPT Image Edit:** OpenAI's `gpt-image-1` for prompt-based image editing and inpainting. Simply mask an area and describe the change you want to see.
* **Google Imagen Generator & Edit:** Create and edit images with Google's Imagen models, with support for Vertex AI.
* **Nano Banana:** A creative image generation node using a specialized Gemini model.
* **Veo Text-to-Video:** Generate high-quality video clips from text prompts using Google's Veo model via Vertex AI.
* **Seamless Integration:** All nodes are designed to work seamlessly with standard ComfyUI inputs (IMAGE, MASK, STRING) and outputs, allowing you to chain them into complex and creative workflows.
* **Secure & Simple:** Simply provide your API key in the node's input field to get started.
@@ -40,10 +44,11 @@ A collection of powerful custom nodes for ComfyUI that connect your local workfl
All nodes in this collection require API keys to function.
* **FLUX Nodes (Replicate):** You will need a [Replicate API Token](https://replicate.com/account/api-tokens).
* **Gemini Chat Node:** You will need a [Google AI Studio API Key](https://aistudio.google.com/app/api-keys).
* **Gemini, Imagen, and Nano Banana Nodes:** You will need a [Google AI Studio API Key](https://aistudio.google.com/app/api-keys).
* **GPT Image Edit Node:** You will need an [OpenAI API Key](https://platform.openai.com/api-keys).
* **Vertex AI Nodes (Imagen Edit, Veo):** You will need a Google Cloud Project ID, a service account with appropriate permissions, and the location for the resources.
You can paste your key directly into the `api_key` or `replicate_api_token` field on the corresponding node.
You can paste your key directly into the `api_key` field on the corresponding node. For Vertex AI nodes, you will need to provide the project ID, location, and path to your service account JSON file.
---
@@ -53,7 +58,7 @@ You can paste your key directly into the `api_key` or `replicate_api_token` fiel
These nodes allow you to transform an input image based on a text prompt. They are ideal for applying artistic styles or making significant conceptual changes to an existing image.
* **Category:** `image/generation`
* **Category:** `image/edit`
* **Inputs:**
* `image`: The source image to transform.
* `prompt`: A text description of the desired output (e.g., "A vibrant Van Gogh painting", "Make this a 90s cartoon").
@@ -68,7 +73,7 @@ These nodes allow you to transform an input image based on a text prompt. They a
A versatile node for text generation and image analysis. Use it to understand an image's content or to generate creative text for other nodes.
* **Category:** `AI/Gemini`
* **Category:** `text/generation`
* **Inputs:**
* `prompt`: The text prompt or question you want to ask the model.
* `image` (Optional): An input image for the model to analyze.
@@ -80,12 +85,25 @@ A versatile node for text generation and image analysis. Use it to understand an
* **Output:**
* `response`: The text generated by the Gemini model.
### Gemini Segmentation
This node uses a Gemini model to generate segmentation masks for specified objects within an image.
* **Category:** `image/generation`
* **Inputs:**
* `image`: The source image for segmentation.
* `segment_prompt`: A text description of the objects to segment (e.g., "the car", "all people").
* `api_key`: Your API key from Google AI Studio.
* `model`: The Gemini model to use.
* `...other_params`: Controls for temperature, thinking, and seed.
* **Output:**
* `mask`: A black and white mask of the segmented objects.
### GPT Image Edit
This node uses OpenAI's API to perform powerful, prompt-based inpainting and editing.
* **Category:** `image/ai`
* **Category:** `image/edit`
* **Inputs:**
* `image`: The source image to edit.
* `mask` (Optional): A black and white mask. The model will edit the **white area** of the mask.
@@ -97,6 +115,63 @@ This node uses OpenAI's API to perform powerful, prompt-based inpainting and edi
**Note:** If a mask is provided, the edits will be constrained to the masked region. If no mask is provided, the model will attempt to edit the entire image based on the prompt.
### Google Imagen Generator
Generate images from a text prompt using Google's Imagen models.
* **Category:** `image/generation`
* **Inputs:**
* `prompt`: A text description of the image to generate.
* `api_key`: Your API key from Google AI Studio.
* `model`: The Imagen model to use.
* `...other_params`: Options for number of images, aspect ratio, and image size.
* **Output:**
* `images`: The generated image(s).
### Google Imagen Edit (Vertex AI only)
Perform advanced image editing, inpainting, outpainting, and background swapping using Imagen on Google's Vertex AI platform.
* **Category:** `image/edit`
* **Inputs:**
* `image`: The source image to edit.
* `mask`: A mask defining the area to edit.
* `prompt`: A description of the desired edit.
* `project_id`: Your Google Cloud Project ID.
* `location`: The Google Cloud location for the model.
* `service_account`: Path to your Google Cloud service account JSON file.
* `edit_mode`: The type of edit to perform (e.g., inpainting, outpainting).
* `...other_params`: Controls for negative prompt, seed, and steps.
* **Output:**
* `edited_images`: The edited image(s).
### Nano Banana
A creative image generation node that can take a combination of text and up to five images as input.
* **Category:** `image/generation`
* **Inputs:**
* `api_key`: Your API key from Google AI Studio.
* `prompt` (Optional): A text prompt.
* `image_1` to `image_5` (Optional): Up to five source images.
* `...other_params`: Controls for aspect ratio, temperature, top_p, and seed.
* **Output:**
* `image`: The generated image.
### Veo Text-to-Video (Vertex AI)
Generate short, high-quality video clips from a text description using Google's Veo model on Vertex AI.
* **Category:** `video/generation`
* **Inputs:**
* `prompt`: A text description of the video to generate.
* `project_id`: Your Google Cloud Project ID.
* `location`: The Google Cloud location for the model.
* `service_account`: Path to your Google Cloud service account JSON file.
* `...other_params`: Controls for negative prompt, aspect ratio, audio generation, and seed.
* **Output:**
* `frames`: The generated video frames, output as an image batch.
---
__init__.py
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@@ -6,9 +6,10 @@ from .imagen import NODE_CLASS_MAPPINGS as IMAGEN_IMAGE_MAPPINGS, NODE_DISPLAY_N
from .imagen_edit import NODE_CLASS_MAPPINGS as IMAGEN_EDIT_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as IMAGEN_EDIT_DISPLAY
from .veo import NODE_CLASS_MAPPINGS as VEO_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as VEO_DISPLAY
from .gemini_segment import NODE_CLASS_MAPPINGS as GEMINI_SEGMENT_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as GEMINI_SEGMENT_DISPLAY
from .nano_banana import NODE_CLASS_MAPPINGS as NANO_BANANA_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as NANO_BANANA_DISPLAY
# Combine both mappings
NODE_CLASS_MAPPINGS = {**PRO_MAPPINGS, **MAX_MAPPINGS, **GEMINI_MAPPINGS, **GPT_IMAGE_MAPPINGS, **IMAGEN_IMAGE_MAPPINGS, **IMAGEN_EDIT_MAPPINGS, **VEO_MAPPINGS, **GEMINI_SEGMENT_MAPPINGS}
NODE_DISPLAY_NAME_MAPPINGS = {**PRO_DISPLAY, **MAX_DISPLAY, **GEMINI_DISPLAY, **GPT_IMAGE_DISPLAY, **IMAGEN_IMAGE_DISPLAY, **IMAGEN_EDIT_DISPLAY, **VEO_DISPLAY, **GEMINI_SEGMENT_DISPLAY}
NODE_CLASS_MAPPINGS = {**PRO_MAPPINGS, **MAX_MAPPINGS, **GEMINI_MAPPINGS, **GPT_IMAGE_MAPPINGS, **IMAGEN_IMAGE_MAPPINGS, **IMAGEN_EDIT_MAPPINGS, **VEO_MAPPINGS, **GEMINI_SEGMENT_MAPPINGS, **NANO_BANANA_MAPPINGS}
NODE_DISPLAY_NAME_MAPPINGS = {**PRO_DISPLAY, **MAX_DISPLAY, **GEMINI_DISPLAY, **GPT_IMAGE_DISPLAY, **IMAGEN_IMAGE_DISPLAY, **IMAGEN_EDIT_DISPLAY, **VEO_DISPLAY, **GEMINI_SEGMENT_DISPLAY, **NANO_BANANA_DISPLAY}
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
flux_kontext_max_node.py
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@@ -16,7 +16,7 @@ class FluxKontextMaxNode:
"multiline": True,
"default": "Make this a 90s cartoon"
}),
"replicate_api_token": ("STRING", {
"api_key": ("STRING", {
"default": ""
}),
"aspect_ratio": (["1:1", "16:9", "9:16", "4:3", "3:4", "3:2", "2:3", "5:4", "4:5", "21:9", "9:21", "2:1", "1:2", "match_input_image"], {
@@ -37,11 +37,11 @@ class FluxKontextMaxNode:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "generate_image"
CATEGORY = "image/generation"
CATEGORY = "image/edit"
def generate_image(self, image, prompt, replicate_api_token, aspect_ratio, output_format, safety_tolerance):
def generate_image(self, image, prompt, api_key, aspect_ratio, output_format, safety_tolerance):
try:
os.environ["REPLICATE_API_TOKEN"] = replicate_api_token
os.environ["REPLICATE_API_TOKEN"] = api_key
# Convert tensor to PIL and save to buffer
tensor = image.squeeze(0) if len(image.shape) == 4 else image
flux_kontext_pro_node.py
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@@ -16,7 +16,7 @@ class FluxKontextProNode:
"multiline": True,
"default": "Make this a 90s cartoon"
}),
"replicate_api_token": ("STRING", {
"api_key": ("STRING", {
"default": ""
}),
"aspect_ratio": (["1:1", "16:9", "9:16", "4:3", "3:4", "3:2", "2:3", "5:4", "4:5", "21:9", "9:21", "2:1", "1:2", "match_input_image"], {
@@ -37,11 +37,11 @@ class FluxKontextProNode:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "generate_image"
CATEGORY = "image/generation"
CATEGORY = "image/edit"
def generate_image(self, image, prompt, replicate_api_token, aspect_ratio, output_format, safety_tolerance):
def generate_image(self, image, prompt, api_key, aspect_ratio, output_format, safety_tolerance):
try:
os.environ["REPLICATE_API_TOKEN"] = replicate_api_token
os.environ["REPLICATE_API_TOKEN"] = api_key
# Convert tensor to PIL and save to buffer
tensor = image.squeeze(0) if len(image.shape) == 4 else image
gemini_node.py
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@@ -32,7 +32,7 @@ class GeminiChatNode:
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("response",)
FUNCTION = "generate"
CATEGORY = "AI/Gemini"
CATEGORY = "text/generation"
def generate(self, prompt: str, model: str, temperature: float, thinking: bool, seed: int, api_key: str,
system_instruction: Optional[str] = None, thinking_budget: int = -1,
gemini_segment.py
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@@ -31,7 +31,7 @@ class GeminiSegmentationNode:
RETURN_TYPES = ("MASK",)
RETURN_NAMES = ("mask",)
FUNCTION = "generate_segmentation"
CATEGORY = "AI/Gemini"
CATEGORY = "image/generation"
def generate_segmentation(self, image: torch.Tensor, segment_prompt: str, model: str,
temperature: float, thinking: bool, seed: int, api_key: str,
gpt_image1.py
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@@ -53,7 +53,7 @@ class GPTImageEditNode:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "edit_image"
CATEGORY = "image/ai"
CATEGORY = "image/edit"
def tensor_to_pil(self, tensor):
if len(tensor.shape) == 3:
imagen.py
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@@ -23,7 +23,7 @@ class GoogleImagenNode:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
FUNCTION = "generate_images"
CATEGORY = "image/ai"
CATEGORY = "image/generation"
def pil_to_tensor(self, images):
if not isinstance(images, list):
imagen_edit.py
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@@ -31,7 +31,7 @@ class GoogleImagenEditNode:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("edited_images",)
FUNCTION = "edit_image"
CATEGORY = "image/ai"
CATEGORY = "image/edit"
def tensor_to_pil(self, tensor):
array = (tensor.cpu().numpy() * 255).astype(np.uint8)
nano_banana.py
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@@ -0,0 +1,119 @@
import os
import io
import torch
import numpy as np
from PIL import Image
from google import genai
from google.genai import types
class NanoBananaNode:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"api_key": ("STRING", {"multiline": False, "default": ""}),
"aspect_ratio": (["1:1", "2:3", "3:2", "3:4", "4:3", "9:16", "16:9", "21:9"],),
"temperature": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"top_p": ("FLOAT", {"default": 0.85, "min": 0.0, "max": 1.0, "step": 0.01}),
"seed": ("INT", {"default": 69, "min": -1, "max": 2147483646, "step": 1}),
},
"optional": {
"prompt": ("STRING", {"multiline": True, "default": ""}),
"system_instruction": ("STRING", {"multiline": True, "default": ""}),
"image_1": ("IMAGE",),
"image_2": ("IMAGE",),
"image_3": ("IMAGE",),
"image_4": ("IMAGE",),
"image_5": ("IMAGE",),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "generate"
CATEGORY = "image/generation"
def tensor_to_pil(self, tensor):
if tensor.dim() == 4:
tensor = tensor[0]
array = (tensor.cpu().numpy() * 255).astype(np.uint8)
return Image.fromarray(array)
def pil_to_tensor(self, image):
if image.mode != 'RGB':
image = image.convert('RGB')
array = np.array(image).astype(np.float32) / 255.0
tensor = torch.from_numpy(array)
return tensor.unsqueeze(0)
def generate(self, api_key, aspect_ratio, temperature, top_p, seed,
prompt="", system_instruction="",
image_1=None, image_2=None, image_3=None, image_4=None, image_5=None):
key = api_key.strip() or os.environ.get("GEMINI_API_KEY")
if not key:
raise ValueError("No API key provided.")
client = genai.Client(api_key=key)
# Build parts list
parts = []
# Add images
for img_tensor in [image_1, image_2, image_3, image_4, image_5]:
if img_tensor is not None:
pil_img = self.tensor_to_pil(img_tensor)
buffer = io.BytesIO()
pil_img.save(buffer, format='PNG')
parts.append(types.Part.from_bytes(
mime_type="image/png",
data=buffer.getvalue()
))
# Add prompt if provided
if prompt.strip():
parts.append(types.Part.from_text(text=prompt))
if not parts:
raise ValueError("At least one image or prompt must be provided.")
contents = [types.Content(role="user", parts=parts)]
# Build config
config = types.GenerateContentConfig(
temperature=temperature,
seed=seed,
top_p=top_p,
response_modalities=["IMAGE"],
image_config=types.ImageConfig(aspect_ratio=aspect_ratio),
)
if system_instruction.strip():
config.system_instruction = [types.Part.from_text(text=system_instruction)]
# Generate
response = client.models.generate_content(
model="gemini-2.5-flash-image",
contents=contents,
config=config,
)
result_image = None
if response.candidates and response.candidates[0].content and response.candidates[0].content.parts:
for part in response.candidates[0].content.parts:
if part.inline_data and part.inline_data.data:
result_image = Image.open(io.BytesIO(part.inline_data.data))
break
if result_image is None:
raise ValueError("No image generated by the API.")
return (self.pil_to_tensor(result_image),)
@classmethod
def IS_CHANGED(cls, **kwargs):
return f"{kwargs.get('prompt', '')}-{kwargs.get('temperature', 0.5)}-{kwargs.get('top_p', 0.85)}-{kwargs.get('seed', 69)}-{kwargs.get('aspect_ratio', '1:1')}-{kwargs.get('image_1')}-{kwargs.get('image_2')}-{kwargs.get('image_3')}-{kwargs.get('image_4')}-{kwargs.get('image_5')}"
NODE_CLASS_MAPPINGS = {"NanoBananaNode": NanoBananaNode}
NODE_DISPLAY_NAME_MAPPINGS = {"NanoBananaNode": "Nano Banana"}