import folder_paths from PIL import Image, ImageOps import numpy as np import torch from server import PromptServer, BinaryEventTypes import asyncio from globals import streaming_prompt_metadata, max_output_id_length class ComfyDeployWebscoketImageInput: @classmethod def INPUT_TYPES(s): return { "required": { "input_id": ( "STRING", {"multiline": False, "default": "input_id"}, ), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), }, "optional": { "default_value": ("IMAGE", ), "client_id": ( "STRING", {"multiline": False, "default": ""}, ), } } OUTPUT_NODE = True RETURN_TYPES = ("IMAGE", ) RETURN_NAMES = ("images",) FUNCTION = "run" @classmethod def VALIDATE_INPUTS(s, input_id): try: if len(input_id.encode('ascii')) > max_output_id_length: raise ValueError(f"input_id size is greater than {max_output_id_length} bytes") except UnicodeEncodeError: raise ValueError("input_id is not ASCII encodable") return True def run(self, input_id, seed, default_value=None ,client_id=None): # print(streaming_prompt_metadata[client_id].inputs) if client_id in streaming_prompt_metadata and input_id in streaming_prompt_metadata[client_id].inputs: if isinstance(streaming_prompt_metadata[client_id].inputs[input_id], Image.Image): print("Returning image from websocket input") image = streaming_prompt_metadata[client_id].inputs[input_id] image = ImageOps.exif_transpose(image) image = image.convert("RGB") image = np.array(image).astype(np.float32) / 255.0 image = torch.from_numpy(image)[None,] return [image] print("Returning default value") return [default_value] NODE_CLASS_MAPPINGS = {"ComfyDeployWebscoketImageInput": ComfyDeployWebscoketImageInput} NODE_DISPLAY_NAME_MAPPINGS = {"ComfyDeployWebscoketImageInput": "Image Websocket Input (ComfyDeploy)"}