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# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py
import torch
class LatentRebatch:
@classmethod
def INPUT_TYPES(s):
return {"required": { "latents": ("LATENT",),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
}}
RETURN_TYPES = ("LATENT",)
INPUT_IS_LIST = True
OUTPUT_IS_LIST = (True, )
FUNCTION = "rebatch"
CATEGORY = "latent/batch"
@staticmethod
def get_batch(latents, list_ind, offset):
'''prepare a batch out of the list of latents'''
samples = latents[list_ind]['samples']
shape = samples.shape
mask = latents[list_ind]['noise_mask'] if 'noise_mask' in latents[list_ind] else torch.ones((shape[0], 1, shape[2]*8, shape[3]*8), device='cpu')
if mask.shape[-1] != shape[-1] * 8 or mask.shape[-2] != shape[-2]:
torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[-2]*8, shape[-1]*8), mode="bilinear")
if mask.shape[0] < samples.shape[0]:
mask = mask.repeat((shape[0] - 1) // mask.shape[0] + 1, 1, 1, 1)[:shape[0]]
if 'batch_index' in latents[list_ind]:
batch_inds = latents[list_ind]['batch_index']
else:
batch_inds = [x+offset for x in range(shape[0])]
return samples, mask, batch_inds
@staticmethod
def get_slices(indexable, num, batch_size):
'''divides an indexable object into num slices of length batch_size, and a remainder'''
slices = []
for i in range(num):
slices.append(indexable[i*batch_size:(i+1)*batch_size])
if num * batch_size < len(indexable):
return slices, indexable[num * batch_size:]
else:
return slices, None
@staticmethod
def slice_batch(batch, num, batch_size):
result = [LatentRebatch.get_slices(x, num, batch_size) for x in batch]
return list(zip(*result))
@staticmethod
def cat_batch(batch1, batch2):
if batch1[0] is None:
return batch2
result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)]
return result
def rebatch(self, latents, batch_size):
batch_size = batch_size[0]
output_list = []
current_batch = (None, None, None)
processed = 0
for i in range(len(latents)):
# fetch new entry of list
#samples, masks, indices = self.get_batch(latents, i)
next_batch = self.get_batch(latents, i, processed)
processed += len(next_batch[2])
# set to current if current is None
if current_batch[0] is None:
current_batch = next_batch
# add previous to list if dimensions do not match
elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]:
sliced, _ = self.slice_batch(current_batch, 1, batch_size)
output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]})
current_batch = next_batch
# cat if everything checks out
else:
current_batch = self.cat_batch(current_batch, next_batch)
# add to list if dimensions gone above target batch size
if current_batch[0].shape[0] > batch_size:
num = current_batch[0].shape[0] // batch_size
sliced, remainder = self.slice_batch(current_batch, num, batch_size)
for i in range(num):
output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]})
current_batch = remainder
#add remainder
if current_batch[0] is not None:
sliced, _ = self.slice_batch(current_batch, 1, batch_size)
output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]})
#get rid of empty masks
for s in output_list:
if s['noise_mask'].mean() == 1.0:
del s['noise_mask']
return (output_list,)
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]
output_list = []
all_images = []
for img in images:
for i in range(img.shape[0]):
all_images.append(img[i:i+1])
for i in range(0, len(all_images), batch_size):
output_list.append(torch.cat(all_images[i:i+batch_size], dim=0))
return (output_list,)
NODE_CLASS_MAPPINGS = {
"RebatchLatents": LatentRebatch,
"RebatchImages": ImageRebatch,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"RebatchLatents": "Rebatch Latents",
"RebatchImages": "Rebatch Images",
}
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