xinyun/ComfyUI
1
0
Fork 0
mirror of https://git.datalinker.icu/comfyanonymous/ComfyUI synced 2026-08-26 04:19:08 +08:00
Code Issues Packages Projects Releases Wiki Activity

LoadImage now loads all the frames from animated images as a batch.

Browse Source
This commit is contained in:
comfyanonymous 2023-12-20 16:39:09 -05:00
parent fcd1d723ac
commit 2bd80b45b2
1 changed files with 24 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

35
nodes.py
View File

@ -9,7 +9,7 @@ import math
import time import time
import random import random
from PIL import Image, ImageOps from PIL import Image, ImageOps, ImageSequence
from PIL.PngImagePlugin import PngInfo from PIL.PngImagePlugin import PngInfo
import numpy as np import numpy as np
import safetensors.torch import safetensors.torch
@ -1410,17 +1410,30 @@ class LoadImage:
FUNCTION = "load_image" FUNCTION = "load_image"
def load_image(self, image): def load_image(self, image):
image_path = folder_paths.get_annotated_filepath(image) image_path = folder_paths.get_annotated_filepath(image)
i = Image.open(image_path) img = Image.open(image_path)
i = ImageOps.exif_transpose(i) output_images = []
image = i.convert("RGB") output_masks = []
image = np.array(image).astype(np.float32) / 255.0 for i in ImageSequence.Iterator(img):
image = torch.from_numpy(image)[None,] i = ImageOps.exif_transpose(i)
if 'A' in i.getbands(): image = i.convert("RGB")
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 image = np.array(image).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask) image = torch.from_numpy(image)[None,]
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
else:
mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
output_images.append(image)
output_masks.append(mask.unsqueeze(0))
if len(output_images) > 1:
output_image = torch.cat(output_images, dim=0)
output_mask = torch.cat(output_masks, dim=0)
else: else:
mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") output_image = output_images[0]
return (image, mask.unsqueeze(0)) output_mask = output_masks[0]
return (output_image, output_mask)
@classmethod @classmethod
def IS_CHANGED(s, image): def IS_CHANGED(s, image):