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jblitzar
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github-python

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"""
Embedding layer dropout.
:param embed: embedding layer
:param words: input sequence of words. shape: (batch size, sequence length)
:param dropout: dropout to be applied to the embedding layer
:return:
"""
if dropout:
mask = embed.weight.data.new().resize_((embed.weight.size(0), 1)).bernoulli_(1 - dropout).expand_as(
embed.weight) / (1 - dropout)
masked_embed_weight = mask * embed.weight
else:
masked_embed_weight = embed.weight
padding_idx = embed.padding_idx # be careful here to use the same 'padding_idx' name
if padding_idx is None:
padding_idx = -1
X = torch.nn.functional.embedding(words, masked_embed_weight,
padding_idx, embed.max_norm, embed.norm_type,
embed.scale_grad_by_freq, embed.sparse
)
return X
if __name__ == '__main__':
"""
Main script to check the embedding dropout alone.
"""
V = 50 # vocabulary size
h = 4 # embedding size
bptt = 10 # sequence length
batch_size = 2 # batch size
emb_drop = 0.1 # dropout to be applied to the embedding layer
# dummy input sequence
words = np.random.random_integers(low=0, high=V - 1, size=(batch_size, bptt))
words = torch.LongTensor(words)
# embedding layer
embed = torch.nn.Embedding(V, h)
# without embedding dropout
origX = embed(words)
# with embedding dropout
X = embedded_dropout(embed, words, emb_drop)
# <FILESEP>
import os
import l_network as network
import l_networks as networks
from modules import shared, ui_extra_networks
from modules.ui_extra_networks import quote_js
from l_ui_edit_user_metadata import LoraUserMetadataEditor
class ExtraNetworksPageLyCORIS(ui_extra_networks.ExtraNetworksPage):
def __init__(self):
super().__init__('LyCORIS')
def refresh(self):
networks.list_available_networks()
def create_item(self, name, index=None, enable_filter=True):
lora_on_disk = networks.available_networks.get(name)
if lora_on_disk is None:
return
path, ext = os.path.splitext(lora_on_disk.filename)
alias = lora_on_disk.get_alias()
search_terms = [self.search_terms_from_path(lora_on_disk.filename)]
if lora_on_disk.hash:
search_terms.append(lora_on_disk.hash)
item = {
"name": name,
"filename": lora_on_disk.filename,
"shorthash": lora_on_disk.shorthash,
"preview": self.find_preview(path),
"description": self.find_description(path),
"search_terms": search_terms,
"local_preview": f"{path}.{shared.opts.samples_format}",
"metadata": lora_on_disk.metadata,
"sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)},
"sd_version": lora_on_disk.sd_version.name,
}
self.read_user_metadata(item)
activation_text = item["user_metadata"].get("activation text")
preferred_weight = item["user_metadata"].get("preferred weight", 0.0)
item["prompt"] = quote_js(f"<lyco:{alias}:") + " + " + (str(preferred_weight) if preferred_weight else "opts.extra_networks_default_multiplier") + " + " + quote_js(">")
if activation_text:
item["prompt"] += " + " + quote_js(" " + activation_text)
negative_prompt = item["user_metadata"].get("negative text")