Spaces:
Running
on
L40S
Running
on
L40S
File size: 12,175 Bytes
4450790 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 |
#---------------------------------------------------------------------------------------------------------------------#
# 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,
}
'''
|