Files
Bjornulf_custom_nodes/API_openai.py
justumen e02f54b685 0.80
2025-04-24 15:06:44 +02:00

112 lines
3.6 KiB
Python

import os
import requests
from PIL import Image
import numpy as np
import torch
import base64
class APIGenerateGPT4o:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"api_key": ("STRING", {
"multiline": False,
"default": "" # User provides their OpenAI API key
}),
"prompt": ("STRING", {
"multiline": True,
"default": "A cute baby sea otter"
}),
"size": (["1024x1024", "1536x1024", "1024x1536", "auto"], {
"default": "1536x1024"
}),
},
"optional": {
"background": (["auto", "transparent", "opaque"], {
"default": "auto"
}),
"moderation": (["auto", "low"], {
"default": "auto"
}),
"output_format": (["png", "jpeg", "webp"], {
"default": "png"
}),
"quality": (["auto", "high", "medium", "low"], {
"default": "auto"
}),
"n": ("INT", {
"default": 1,
"min": 1,
"max": 1
}),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "generate"
CATEGORY = "OpenAI API"
def get_next_number(self):
"""Get the next available number for naming saved image files."""
save_dir = "output/API/OpenAI_GPT4o"
os.makedirs(save_dir, exist_ok=True)
files = [f for f in os.listdir(save_dir) if f.endswith('.png')]
if not files:
return 1
numbers = [int(f.split('.')[0]) for f in files]
return max(numbers) + 1
def generate(self, api_key, prompt, size, background="auto", moderation="auto", output_format="png", quality="auto", n=1):
"""Generate an image using the OpenAI gpt-image-1 model and return it as a tensor."""
# API headers
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
# API payload
payload = {
"model": "gpt-image-1",
"prompt": prompt,
"n": n,
"size": size,
"background": background,
"moderation": moderation,
"output_format": output_format,
"quality": quality
}
# Send request to OpenAI API
response = requests.post(
"https://api.openai.com/v1/images/generations",
headers=headers,
json=payload
)
if response.status_code != 200:
raise Exception(f"API request failed: {response.text}")
# Parse response and extract base64 image data
data = response.json()
b64_data = data["data"][0]["b64_json"]
# Decode base64 data into image bytes
image_bytes = base64.b64decode(b64_data)
# Save the image with an incrementing filename
next_num = self.get_next_number()
filename = f"{next_num:03d}.png"
filepath = os.path.join("output/API/OpenAI_GPT4o", filename)
with open(filepath, "wb") as f:
f.write(image_bytes)
# Load and process the image
img = Image.open(filepath)
if img.mode != "RGB":
img = img.convert("RGB")
# Convert to tensor for ComfyUI
img_tensor = torch.from_numpy(np.array(img).astype(np.float32) / 255.0)
img_tensor = img_tensor.unsqueeze(0)
return (img_tensor,)