55 lines
2.3 KiB
Python
55 lines
2.3 KiB
Python
from __future__ import annotations
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from typing import Any
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import json
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import requests
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import comfy.utils
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import numpy as np
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import torch
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from PIL import Image
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import fal_client
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from io import BytesIO
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import base64
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def handle_response(api_result, schema=None, loaded_client=None, response_url=None):
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result_image = None
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image_list = []
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final_batch_img = []
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if type(api_result.data).__name__ == "list" and len(api_result.data) > 1:
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batch_images = []
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for single_result in result.data:
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image_base64 = single_result.b64_json
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image_bytes = base64.b64decode(image_base64)
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result_image = Image.open(BytesIO(image_bytes))
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if result_image is not None:
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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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batch_images.append(result_image)
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if type(batch_images).__name__ == "list" and len(batch_images) > 1:
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image_list = batch_images
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single_image_start = batch_images[0]
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batch_count = 0
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s = None
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for single_image in batch_images:
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if (batch_count + 1) < len(batch_images):
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current_single_image = batch_images[batch_count + 1]
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if single_image_start.shape[1:] != current_single_image.shape[1:]:
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current_single_image = comfy.utils.common_upscale(current_single_image.movedim(-1, 1), single_image_start.shape[2], single_image_start.shape[1], "bilinear", "center").movedim(1, -1)
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batch_count = batch_count + 1
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if s is not None:
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single_image = s
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s = torch.cat((current_single_image, single_image), dim=0)
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result_image = s
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else:
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image_base64 = api_result.data[0].b64_json
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image_bytes = base64.b64decode(image_base64)
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result_image = Image.open(BytesIO(image_bytes))
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if result_image is not None:
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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 |