139 lines
5.1 KiB
Python
139 lines
5.1 KiB
Python
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",
|
|
}
|