lllyasviel/stable-diffusion-webui-forge
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forge 2.0.0

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lllyasviel
2024-08-10 19:24:19 -07:00
committed by GitHub
parent 4014013d05
commit cfa5242a75
28 changed files with 785 additions and 1249 deletions
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51
backend/text_processing/t5_engine.py
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@@ -1,9 +1,7 @@
import math
import torch
from collections import namedtuple
from backend.text_processing import parsing, emphasis
from backend.text_processing.textual_inversion import EmbeddingDatabase
from backend import memory_management
@@ -50,9 +48,6 @@ class T5TextProcessingEngine:
if mult != 1.0:
self.token_mults[ident] = mult
def get_target_prompt_token_count(self, token_count):
return token_count
def tokenize(self, texts):
tokenized = self.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
return tokenized
@@ -112,45 +107,33 @@ class T5TextProcessingEngine:
return chunks, token_count
def process_texts(self, texts):
token_count = 0
def __call__(self, texts):
zs = []
cache = {}
batch_chunks = []
for line in texts:
if line in cache:
chunks = cache[line]
line_z_values = cache[line]
else:
chunks, current_token_count = self.tokenize_line(line)
token_count = max(current_token_count, token_count)
chunks, token_count = self.tokenize_line(line)
line_z_values = []
for chunk in chunks:
tokens = chunk.tokens
multipliers = chunk.multipliers
z = self.process_tokens([tokens], [multipliers])[0]
line_z_values.append(z)
cache[line] = line_z_values
cache[line] = chunks
zs.extend(line_z_values)
batch_chunks.append(chunks)
return torch.stack(zs)
return batch_chunks, token_count
def __call__(self, texts):
batch_chunks, token_count = self.process_texts(texts)
chunk_count = max([len(x) for x in batch_chunks])
zs = []
for i in range(chunk_count):
batch_chunk = [chunks[i] for chunks in batch_chunks]
tokens = [x.tokens for x in batch_chunk]
multipliers = [x.multipliers for x in batch_chunk]
z = self.process_tokens(tokens, multipliers)
zs.append(z)
return torch.hstack(zs)
def process_tokens(self, remade_batch_tokens, batch_multipliers):
tokens = torch.asarray(remade_batch_tokens)
def process_tokens(self, batch_tokens, batch_multipliers):
tokens = torch.asarray(batch_tokens)
z = self.encode_with_transformers(tokens)
self.emphasis.tokens = remade_batch_tokens
self.emphasis.tokens = batch_tokens
self.emphasis.multipliers = torch.asarray(batch_multipliers).to(z)
self.emphasis.z = z
self.emphasis.after_transformers()