내부적으로 Comfy 서버는 한 노드에서 다음 노드로 흐르는 데이터를 관련 데이터 유형의 파이썬 list로 표현하며, 일반적으로 길이는 1입니다.
일반적인 작동 방식에서는 노드가 출력을 반환할 때 출력 tuple의 각 요소가 개별적으로 리스트(길이 1)로 둘러싸입니다. 이후 다음 노드가 호출되면 데이터가 다시 풀려 메인 함수로 전달됩니다.
일반적으로 이 부분을 신경 쓸 필요가 없습니다. Comfy가 이미 둘러싸고 풀어주는 작업을 해주기 때문입니다.
이는 배치와 관련된 것이 아닙니다. 예를 들어 잠재값이나 이미지의 배치는 리스트의 단일 항목입니다(텐서 데이터 유형 참조)
특정 상황에서는 여러 데이터 인스턴스를 하나의 워크플로우에서 처리하게 되며, 이 경우 내부 데이터는 데이터 인스턴스를 포함한 리스트가 됩니다. 이를테면 VRAM 부족을 피하기 위해 이미지 시리즈를 한 번에 하나씩 처리하거나, 서로 다른 크기의 이미지를 처리하는 경우가 여기에 해당합니다.기본적으로 Comfy는 리스트의 값을 순차적으로 처리합니다:
입력이 서로 다른 길이의 리스트라면, 짧은 리스트는 마지막 값의 반복으로 채워집니다.
메인 메서드는 입력 리스트의 각 값에 대해 한 번씩 호출됩니다.
출력은 리스트이며, 각 리스트의 길이는 가장 긴 입력과 동일합니다.
관련 코드는 execution.py의 map_node_over_list 메서드에서 확인할 수 있습니다.그러나 Comfy가 노드 출력을 길이 1의 리스트로 둘러싸기 때문에, 사용자 정의 노드가 반환하는 tuple에 list가 포함되어 있으면 해당 list도 둘러싸여 하나의 데이터로 취급됩니다. Comfy에게 반환되는 리스트를 둘러싸지 않고 순차적 처리를 위한 데이터 시리즈로 취급하도록 알려주려면, 노드가 클래스 속성 OUTPUT_IS_LIST를 제공해야 합니다. 이 속성은 RETURN_TYPES와 같은 길이의 tuple[bool]이며, 어떤 출력을 그러한 방식으로 처리할지 지정합니다.노드는 기본 입력 동작을 재정의해 전체 리스트를 한 번의 호출로 받을 수도 있습니다. 이를 위해서는 클래스 속성 INPUT_IS_LIST를 True로 설정하면 됩니다.다음은 내장 노드의 예시입니다 - ImageRebatch는 하나 이상의 이미지 배치(리스트로 받으므로 INPUT_IS_LIST - True)를 받아 요청된 크기의 배치로 재배치합니다.
INPUT_IS_LIST는 노드 단위입니다. 모든 입력이 동일한 처리를 받습니다. 따라서 batch_size 위젯의 값은 batch_size[0]로 가져옵니다.
class ImageRebatch: @classmethod def INPUT_TYPES(s): return {"required": { "images": ("IMAGE",), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}) }} RETURN_TYPES = ("IMAGE",) INPUT_IS_LIST = True OUTPUT_IS_LIST = (True, ) FUNCTION = "rebatch" CATEGORY = "image/batch" def rebatch(self, images, batch_size): batch_size = batch_size[0] # 모든 입력이 리스트로 오기 때문에 batch_size는 list[int] output_list = [] all_images = [] for img in images: # 각 img는 이미지 배치입니다 for i in range(img.shape[0]): # 각 i는 단일 이미지입니다 all_images.append(img[i:i+1]) for i in range(0, len(all_images), batch_size): # batch_size만큼의 조각을 떼어 각각 새로운 배치로 만듭니다 output_list.append(torch.cat(all_images[i:i+batch_size], dim=0)) # 이미지 배치의 너비나 높이가 다르면 심각한 오류 발생! return (output_list,)
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