import os import random import sys from typing import Sequence, Mapping, Any, Union import torch import sys import cv2 import glob from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient args = sys.argv[1:] connection_string = "DefaultEndpointsProtocol=https;AccountName=transcribedblobstorage;AccountKey=1Z7yKPP5DLbxnoHdh7NmHgwg3dFLaDiYHUELdid7dzfzR6/DvkZnnzpJ30lrXIMhtD5GYKo+71jP+AStC1TEvA==;EndpointSuffix=core.windows.net" container_name="saasdev" blob_service_client = BlobServiceClient.from_connection_string(connection_string) for arg in args: def download_blob(blob_name, download_file_path): container_client = blob_service_client.get_container_client(container_name) # Create a blob client blob_client = blob_service_client.get_blob_client(container=container_name, blob=blob_name) # Ensure the directory exists os.makedirs(os.path.dirname(download_file_path), exist_ok=True) # Download the blob to a local file with open(download_file_path, "wb") as download_file: download_stream = blob_client.download_blob() download_file.write(download_stream.readall()) print(f"Blob '{blob_name}' downloaded to '{download_file_path}'.") download_blob(arg, download_file_path="/app/1.jpg") def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any: """Returns the value at the given index of a sequence or mapping. If the object is a sequence (like list or string), returns the value at the given index. If the object is a mapping (like a dictionary), returns the value at the index-th key. Some return a dictionary, in these cases, we look for the "results" key Args: obj (Union[Sequence, Mapping]): The object to retrieve the value from. index (int): The index of the value to retrieve. Returns: Any: The value at the given index. Raises: IndexError: If the index is out of bounds for the object and the object is not a mapping. """ try: return obj[index] except KeyError: return obj["result"][index] def find_path(name: str, path: str = None) -> str: """ Recursively looks at parent folders starting from the given path until it finds the given name. Returns the path as a Path object if found, or None otherwise. """ # If no path is given, use the current working directory if path is None: path = os.getcwd() # Check if the current directory contains the name if name in os.listdir(path): path_name = os.path.join(path, name) print(f"{name} found: {path_name}") return path_name # Get the parent directory parent_directory = os.path.dirname(path) # If the parent directory is the same as the current directory, we've reached the root and stop the search if parent_directory == path: return None # Recursively call the function with the parent directory return find_path(name, parent_directory) def add_comfyui_directory_to_sys_path() -> None: """ Add 'ComfyUI' to the sys.path """ comfyui_path = find_path("ComfyUI") if comfyui_path is not None and os.path.isdir(comfyui_path): sys.path.append(comfyui_path) print(f"'{comfyui_path}' added to sys.path") def add_extra_model_paths() -> None: """ Parse the optional extra_model_paths.yaml file and add the parsed paths to the sys.path. """ from main import load_extra_path_config extra_model_paths = find_path("extra_model_paths.yaml") if extra_model_paths is not None: load_extra_path_config(extra_model_paths) else: print("Could not find the extra_model_paths config file.") add_comfyui_directory_to_sys_path() add_extra_model_paths() def import_custom_nodes() -> None: """Find all custom nodes in the custom_nodes folder and add those node objects to NODE_CLASS_MAPPINGS This function sets up a new asyncio event loop, initializes the PromptServer, creates a PromptQueue, and initializes the custom nodes. """ import asyncio import execution from nodes import init_custom_nodes import server # Creating a new event loop and setting it as the default loop loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) # Creating an instance of PromptServer with the loop server_instance = server.PromptServer(loop) execution.PromptQueue(server_instance) # Initializing custom nodes init_custom_nodes() from nodes import SaveImage, NODE_CLASS_MAPPINGS, LoadImage def img_return() : img_dir = "/output" filename_prefix="SUPIR_00001_.png" data_path = os. path. join(img_dir, filename_prefix) files = glob.glob(data_path) data = [] for f1 in files: img = cv2.imread(f1) data.append(img) return data def main(): import_custom_nodes() with torch.inference_mode(): loadimage = LoadImage() loadimage_13 = loadimage.load_image(image="/app/1.jpg") supir_model_loader = NODE_CLASS_MAPPINGS["SUPIR_model_loader"]() supir_model_loader_58 = supir_model_loader.process( supir_model="supir-voq.ckpt", sdxl_model="juggernautxl.safetensors", fp8_unet=False, diffusion_dtype="auto", ) imageresize = NODE_CLASS_MAPPINGS["ImageResize+"]() imageresize_18 = imageresize.execute( width=1280, height=1280, interpolation="lanczos", keep_proportion=False, condition="always", multiple_of=0, image=get_value_at_index(loadimage_13, 0), ) supir_first_stage = NODE_CLASS_MAPPINGS["SUPIR_first_stage"]() supir_first_stage_7 = supir_first_stage.process( use_tiled_vae=True, encoder_tile_size=512, decoder_tile_size=512, encoder_dtype="auto", SUPIR_VAE=get_value_at_index(supir_model_loader_58, 1), image=get_value_at_index(imageresize_18, 0), ) supir_encode = NODE_CLASS_MAPPINGS["SUPIR_encode"]() supir_encode_21 = supir_encode.encode( use_tiled_vae=True, encoder_tile_size=512, encoder_dtype="auto", SUPIR_VAE=get_value_at_index(supir_first_stage_7, 0), image=get_value_at_index(supir_first_stage_7, 1), ) supir_conditioner = NODE_CLASS_MAPPINGS["SUPIR_conditioner"]() supir_sample = NODE_CLASS_MAPPINGS["SUPIR_sample"]() supir_decode = NODE_CLASS_MAPPINGS["SUPIR_decode"]() image_comparer_rgthree = NODE_CLASS_MAPPINGS["Image Comparer (rgthree)"]() playsoundpysssss = NODE_CLASS_MAPPINGS["PlaySound|pysssss"]() colormatch = NODE_CLASS_MAPPINGS["ColorMatch"]() saveimage = SaveImage() for q in range(10): supir_conditioner_12 = supir_conditioner.condition( positive_prompt="a red car, high quality, detailed", negative_prompt="bad quality, blurry, messy", SUPIR_model=get_value_at_index(supir_model_loader_58, 0), latents=get_value_at_index(supir_first_stage_7, 2), ) supir_sample_8 = supir_sample.sample( seed=random.randint(1, 2**64), steps=10, cfg_scale_start=5, cfg_scale_end=5, EDM_s_churn=5, s_noise=1.003, DPMPP_eta=1, control_scale_start=0.9, control_scale_end=0.9500000000000001, restore_cfg=10, keep_model_loaded=False, sampler="RestoreDPMPP2MSampler", sampler_tile_size=1024, sampler_tile_stride=512, SUPIR_model=get_value_at_index(supir_model_loader_58, 0), latents=get_value_at_index(supir_encode_21, 0), positive=get_value_at_index(supir_conditioner_12, 0), negative=get_value_at_index(supir_conditioner_12, 1), ) supir_decode_9 = supir_decode.decode( use_tiled_vae=True, decoder_tile_size=512, SUPIR_VAE=get_value_at_index(supir_model_loader_58, 1), latents=get_value_at_index(supir_sample_8, 0), ) image_comparer_rgthree_15 = image_comparer_rgthree.compare_images( image_a=get_value_at_index(loadimage_13, 0), image_b=get_value_at_index(supir_first_stage_7, 1), ) playsoundpysssss_38 = playsoundpysssss.nop( mode="always", volume=0.5, file="notify.mp3", any=get_value_at_index(supir_decode_9, 0), ) colormatch_57 = colormatch.colormatch( method="mkl", image_ref=get_value_at_index(loadimage_13, 0), image_target=get_value_at_index(supir_decode_9, 0), ) saveimage_39 = saveimage.save_images( filename_prefix="SUPIR_", images=get_value_at_index(colormatch_57, 0) ) image_comparer_rgthree_59 = image_comparer_rgthree.compare_images( image_a=get_value_at_index(colormatch_57, 0), image_b=get_value_at_index(loadimage_13, 0), ) img_return() if __name__ == "__main__": main()