#---------------------------------------------------------------------------------------------------------------------# # CR Animation Nodes by RockOfFire and Akatsuzi https://github.com/Suzie1/CR-Animation-Nodes # for ComfyUI https://github.com/comfyanonymous/ComfyUI #---------------------------------------------------------------------------------------------------------------------# from PIL import Image, ImageSequence import comfy.sd import re import torch import numpy as np import os import sys import folder_paths import math import json import csv from typing import List from PIL.PngImagePlugin import PngInfo from nodes import SaveImage import glob from ..categories import icons #MAX_RESOLUTION=8192 ALLOWED_EXT = ('.jpeg', '.jpg', '.png', '.tiff', '.gif', '.bmp', '.webp') def resolve_pattern(pattern): folder_path, file_pattern = os.path.split(pattern) frame_pattern = re.sub(r"#+", "*", file_pattern) matching_files = glob.glob(os.path.join(folder_path, frame_pattern)) #print(f"[Debug] Found {len(matching_files)} matching files for frame pattern {frame_pattern}") return matching_files def get_files(image_path, sort_by="Index", pattern=None): if pattern is not None: matching_files = resolve_pattern(os.path.join(image_path, pattern)) else: matching_files = os.listdir(image_path) if sort_by == "Index": sorted_files = sorted(matching_files, key=lambda s: sum(((s, int(n)) for s, n in re.findall(r'(\D+)(\d+)', 'a%s0' % s)), ())) elif sort_by == "Alphabetic": sorted_files = sorted(matching_files, key=lambda s: (re.split(r'(\d+)', s), s)) else: raise ValueError("Invalid sort_by value. Use 'Index' or 'Alphabetic'.") return sorted_files #---------------------------------------------------------------------------------------------------------------------# # NODES #---------------------------------------------------------------------------------------------------------------------# class CR_LoadAnimationFrames: #input_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))), 'input') input_dir = folder_paths.input_directory #print(f"CR_LoadAnimationFrames: input directory {input_dir}") @classmethod def INPUT_TYPES(s): #if not os.path.exists(s.input_dir): #os.makedirs(s.input_dir) image_folder = [name for name in os.listdir(s.input_dir) if os.path.isdir(os.path.join(s.input_dir,name)) and len(os.listdir(os.path.join(s.input_dir,name))) != 0] return {"required": {"image_sequence_folder": (sorted(image_folder), ), "start_index": ("INT", {"default": 1, "min": 1, "max": 10000}), "max_frames": ("INT", {"default": 1, "min": 1, "max": 10000}) } } RETURN_TYPES = ("IMAGE", "STRING", ) RETURN_NAMES = ("IMAGE", "show_help", ) FUNCTION = "load_image_sequence" CATEGORY = icons.get("Comfyroll/Animation/IO") def load_image_sequence(self, image_sequence_folder, start_index, max_frames): image_path = os.path.join(self.input_dir, image_sequence_folder) file_list = sorted(os.listdir(image_path), key=lambda s: sum(((s, int(n)) for s, n in re.findall(r'(\D+)(\d+)', 'a%s0' % s)), ())) sample_frames = [] sample_frames_mask = [] sample_index = list(range(start_index-1, len(file_list), 1))[:max_frames] for num in sample_index: i = Image.open(os.path.join(image_path, file_list[num])) image = i.convert("RGB") image = np.array(image).astype(np.float32) / 255.0 image = torch.from_numpy(image)[None,] image = image.squeeze() sample_frames.append(image) show_help = "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/wiki/IO-Nodes#cr-load-animation-frames" return (torch.stack(sample_frames), show_help, ) #---------------------------------------------------------------------------------------------------------------------# class CR_LoadFlowFrames: # based on Load Image Sequence in vid2vid and mtb @classmethod def INPUT_TYPES(s): sort_methods = ["Index", "Alphabetic"] #sort_methods = ["Date modified", "Alphabetic", "Index"] input_dir = folder_paths.input_directory input_folders = [name for name in os.listdir(input_dir) if os.path.isdir(os.path.join(input_dir,name)) and len(os.listdir(os.path.join(input_dir,name))) != 0] return {"required": {"input_folder": (sorted(input_folders), ), "sort_by": (sort_methods, ), "current_frame": ("INT", {"default": 0, "min": 0, "max": 10000, "forceInput": True}), "skip_start_frames": ("INT", {"default": 0, "min": 0, "max": 10000}), }, "optional": {"input_path": ("STRING", {"default": '', "multiline": False}), "file_pattern": ("STRING", {"default": '*.png', "multiline": False}), } } CATEGORY = icons.get("Comfyroll/Animation/IO") RETURN_TYPES = ("IMAGE", "IMAGE", "INT", "STRING", ) RETURN_NAMES = ("current_image", "previous_image", "current_frame", "show_help", ) FUNCTION = "load_images" def load_images(self, file_pattern, skip_start_frames, input_folder, sort_by, current_frame, input_path=''): show_help = "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/wiki/IO-Nodes#cr-load-flow-frames" input_dir = folder_paths.input_directory current_frame = current_frame + skip_start_frames print(f"[Info] CR Load Flow Frames: current_frame {current_frame}") if input_path != '': if not os.path.exists(input_path): print(f"[Warning] CR Load Flow Frames: The input_path `{input_path}` does not exist") return ("", ) image_path = os.path.join('', input_path) else: image_path = os.path.join(input_dir, input_folder) print(f"[Info] CR Load Flow Frames: ComfyUI Input directory is `{image_path}`") file_list = get_files(image_path, sort_by, file_pattern) if os.path.exists(image_path + '.DS_Store'): file_list.remove('.DS_Store') # For Mac users if len(file_list) == 0: print(f"[Warning] CR Load Flow Frames: No matching files found for loading") return () remaining_files = len(file_list) - current_frame print(f"[Info] CR Load Flow Frames: {remaining_files} input files remaining for processing") img = Image.open(os.path.join(image_path, file_list[current_frame])) cur_image = img.convert("RGB") cur_image = np.array(cur_image).astype(np.float32) / 255.0 cur_image = torch.from_numpy(cur_image)[None,] print(f"[Debug] CR Load Flow Frames: Current image {file_list[current_frame]}") # Load first frame as previous frame if no frames skipped if current_frame == 0 and skip_start_frames == 0: img = Image.open(os.path.join(image_path, file_list[current_frame])) pre_image = img.convert("RGB") pre_image = np.array(pre_image).astype(np.float32) / 255.0 pre_image = torch.from_numpy(pre_image)[None,] print(f"[Debug] CR Load Flow Frames: Previous image {file_list[current_frame]}") else: img = Image.open(os.path.join(image_path, file_list[current_frame - 1])) pre_image = img.convert("RGB") pre_image = np.array(pre_image).astype(np.float32) / 255.0 pre_image = torch.from_numpy(pre_image)[None,] print(f"[Debug] CR Load Flow Frames: Previous image {file_list[current_frame - 1]}") return (cur_image, pre_image, current_frame, show_help, ) #---------------------------------------------------------------------------------------------------------------------# class CR_OutputFlowFrames: # based on SaveImageSequence by mtb def __init__(self): self.type = "output" @classmethod def INPUT_TYPES(cls): output_dir = folder_paths.output_directory output_folders = [name for name in os.listdir(output_dir) if os.path.isdir(os.path.join(output_dir,name)) and len(os.listdir(os.path.join(output_dir,name))) != 0] return { "required": {"output_folder": (sorted(output_folders), ), "current_image": ("IMAGE", ), "filename_prefix": ("STRING", {"default": "CR"}), "current_frame": ("INT", {"default": 0, "min": 0, "max": 9999999, "forceInput": True}), }, "optional": {"interpolated_img": ("IMAGE", ), "output_path": ("STRING", {"default": '', "multiline": False}), } } RETURN_TYPES = () FUNCTION = "save_images" OUTPUT_NODE = True CATEGORY = icons.get("Comfyroll/Animation/IO") def save_images(self, output_folder, current_image, current_frame, output_path='', filename_prefix="CR", interpolated_img=None): output_dir = folder_paths.get_output_directory() out_folder = os.path.join(output_dir, output_folder) if output_path != '': if not os.path.exists(output_path): print(f"[Warning] CR Output Flow Frames: The input_path `{output_path}` does not exist") return ("",) out_path = output_path # os.path.join("", output_path) else: out_path = os.path.join(output_dir, out_folder) print(f"[Info] CR Output Flow Frames: Output path is `{out_path}`") if interpolated_img is not None: output_image0 = current_image[0].cpu().numpy() output_image1 = interpolated_img[0].cpu().numpy() img0 = Image.fromarray(np.clip(output_image0 * 255.0, 0, 255).astype(np.uint8)) img1 = Image.fromarray(np.clip(output_image1 * 255.0, 0, 255).astype(np.uint8)) output_filename0 = f"{filename_prefix}_{current_frame:05}_0.png" output_filename1 = f"{filename_prefix}_{current_frame:05}_1.png" print(f"[Warning] CR Output Flow Frames: Saved {filename_prefix}_{current_frame:05}_0.png") print(f"[Warning] CR Output Flow Frames: Saved {filename_prefix}_{current_frame:05}_1.png") resolved_image_path0 = out_path + "/" + output_filename0 resolved_image_path1 = out_path + "/" + output_filename1 img0.save(resolved_image_path0, pnginfo="", compress_level=4) img1.save(resolved_image_path1, pnginfo="", compress_level=4) else: output_image0 = current_image[0].cpu().numpy() img0 = Image.fromarray(np.clip(output_image0 * 255.0, 0, 255).astype(np.uint8)) output_filename0 = f"{filename_prefix}_{current_frame:05}.png" resolved_image_path0 = out_path + "/" + output_filename0 img0.save(resolved_image_path0, pnginfo="", compress_level=4) print(f"[Info] CR Output Flow Frames: Saved {filename_prefix}_{current_frame:05}.png") result = {"ui": {"images": [{"filename": output_filename0,"subfolder": out_path,"type": self.type,}]}} return result #---------------------------------------------------------------------------------------------------------------------# # MAPPINGS #---------------------------------------------------------------------------------------------------------------------# # For reference only, actual mappings are in __init__.py # 3 nodes released ''' NODE_CLASS_MAPPINGS = { # IO "CR Load Animation Frames":CR_LoadAnimationFrames, "CR Load Flow Frames":CR_LoadFlowFrames, "CR Output Flow Frames":CR_OutputFlowFrames, } '''