81 lines
2.8 KiB
Python
81 lines
2.8 KiB
Python
import numpy as np
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import requests
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from PIL import Image
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import io
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import torch
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from .common import image_to_base64, preprocess_image, preprocess_mask
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class GenFillNode():
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@classmethod
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def INPUT_TYPES(self):
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return {
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"required": {
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"image": ("IMAGE",), # Input image from another node
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"mask": ("MASK",), # Binary mask input
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"prompt": ("STRING",),
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"api_key": ("STRING", {"default": "BRIA_API_TOKEN"}), # API Key input with a default value
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},
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"optional": {
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"seed": ("INT", {"default": 123456})
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("output_image",)
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CATEGORY = "API Nodes"
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FUNCTION = "execute" # This is the method that will be executed
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def __init__(self):
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self.api_url = "https://engine.prod.bria-api.com/v1/gen_fill" # Eraser API URL
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# Define the execute method as expected by ComfyUI
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def execute(self, image, mask, prompt, api_key, seed):
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if api_key.strip() == "" or api_key.strip() == "BRIA_API_TOKEN":
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raise Exception("Please insert a valid API key.")
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# Check if image and mask are tensors, if so, convert to NumPy arrays
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if isinstance(image, torch.Tensor):
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image = preprocess_image(image)
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if isinstance(mask, torch.Tensor):
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mask = preprocess_mask(mask)
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# Convert the image and mask directly to Base64 strings
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image_base64 = image_to_base64(image)
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mask_base64 = image_to_base64(mask)
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# Prepare the API request payload
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payload = {
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"file": f"{image_base64}",
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"mask_file": f"{mask_base64}",
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"prompt": prompt,
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"negative_prompt": "blurry",
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"sync": True,
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"seed": seed,
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}
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headers = {
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"Content-Type": "application/json",
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"api_token": f"{api_key}"
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}
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try:
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response = requests.post(self.api_url, json=payload, headers=headers)
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# Check for successful response
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if response.status_code == 200:
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print('response is 200')
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# Process the output image from API response
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response_dict = response.json()
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image_response = requests.get(response_dict['urls'][0])
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result_image = Image.open(io.BytesIO(image_response.content))
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result_image = result_image.convert("RGB")
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result_image = np.array(result_image).astype(np.float32) / 255.0
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result_image = torch.from_numpy(result_image)[None,]
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return (result_image,)
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else:
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raise Exception(f"Error: API request failed with status code {response.status_code}")
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except Exception as e:
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raise Exception(f"{e}")
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