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mirror of https://github.com/vladmandic/automatic synced 2026-09-19 01:04:32 +02:00
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modernize typing

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This commit is contained in:
Vladimir Mandic
2026-02-19 09:15:37 +01:00
parent 7aded79e8a
commit bfe014f5da
222 changed files with 1538 additions and 1444 deletions
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modules/prompt_parser_diffusers.py
+11 -12
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@@ -1,7 +1,6 @@
import os
import math
import time
import typing
from collections import OrderedDict
import torch
from compel.embeddings_provider import BaseTextualInversionManager, EmbeddingsProvider
@@ -85,7 +84,7 @@ class PromptEmbedder:
return
seen_prompts = {}
# per prompt in batch
for batchidx, (prompt, negative_prompt) in enumerate(zip(self.prompts, self.negative_prompts)):
for batchidx, (prompt, negative_prompt) in enumerate(zip(self.prompts, self.negative_prompts, strict=False)):
self.prepare_schedule(prompt, negative_prompt)
schedule_key = (
tuple(self.positive_schedule) if self.positive_schedule is not None else None,
@@ -300,7 +299,7 @@ class PromptEmbedder:
return None
def compel_hijack(self, token_ids: torch.Tensor, attention_mask: typing.Optional[torch.Tensor] = None) -> torch.Tensor:
def compel_hijack(self, token_ids: torch.Tensor, attention_mask: torch.Tensor | None = None) -> torch.Tensor:
needs_hidden_states = self.returned_embeddings_type != 1
text_encoder_output = self.text_encoder(token_ids, attention_mask, output_hidden_states=needs_hidden_states, return_dict=True)
@@ -323,7 +322,7 @@ def compel_hijack(self, token_ids: torch.Tensor, attention_mask: typing.Optional
return hidden_state
def sd3_compel_hijack(self, token_ids: torch.Tensor, attention_mask: typing.Optional[torch.Tensor] = None) -> torch.Tensor:
def sd3_compel_hijack(self, token_ids: torch.Tensor, attention_mask: torch.Tensor | None = None) -> torch.Tensor:
needs_hidden_states = True
text_encoder_output = self.text_encoder(token_ids, attention_mask, output_hidden_states=needs_hidden_states, return_dict=True)
clip_skip = int(self.returned_embeddings_type)
@@ -353,10 +352,10 @@ class DiffusersTextualInversionManager(BaseTextualInversionManager):
# code from
# https://github.com/huggingface/diffusers/blob/705c592ea98ba4e288d837b9cba2767623c78603/src/diffusers/loaders.py
def maybe_convert_prompt(self, prompt: typing.Union[str, typing.List[str]], tokenizer: PreTrainedTokenizer):
prompts = [prompt] if not isinstance(prompt, typing.List) else prompt
def maybe_convert_prompt(self, prompt: str | list[str], tokenizer: PreTrainedTokenizer):
prompts = [prompt] if not isinstance(prompt, list) else prompt
prompts = [self._maybe_convert_prompt(p, tokenizer) for p in prompts]
if not isinstance(prompt, typing.List):
if not isinstance(prompt, list):
return prompts[0]
return prompts
@@ -378,7 +377,7 @@ class DiffusersTextualInversionManager(BaseTextualInversionManager):
debug(f'Prompt: convert="{prompt}"')
return prompt
def expand_textual_inversion_token_ids_if_necessary(self, token_ids: typing.List[int]) -> typing.List[int]:
def expand_textual_inversion_token_ids_if_necessary(self, token_ids: list[int]) -> list[int]:
if len(token_ids) == 0:
return token_ids
prompt = self.pipe.tokenizer.decode(token_ids)
@@ -470,7 +469,7 @@ def get_prompts_with_weights(pipe, prompt: str):
texts_and_weights = prompt_parser.parse_prompt_attention(prompt)
if shared.opts.prompt_mean_norm:
texts_and_weights = normalize_prompt(texts_and_weights)
texts, text_weights = zip(*texts_and_weights)
texts, text_weights = zip(*texts_and_weights, strict=False)
avg_weight = 0
min_weight = 1
max_weight = 0
@@ -478,7 +477,7 @@ def get_prompts_with_weights(pipe, prompt: str):
try:
all_tokens = 0
for text, weight in zip(texts, text_weights):
for text, weight in zip(texts, text_weights, strict=False):
tokens = get_tokens(pipe, 'section', text)
all_tokens += tokens
avg_weight += tokens*weight
@@ -627,8 +626,8 @@ def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", c
ps = 2 * [get_prompts_with_weights(pipe, prompt)]
ns = 2 * [get_prompts_with_weights(pipe, neg_prompt)]
positives, positive_weights = zip(*ps)
negatives, negative_weights = zip(*ns)
positives, positive_weights = zip(*ps, strict=False)
negatives, negative_weights = zip(*ns, strict=False)
if hasattr(pipe, "tokenizer_2") and not hasattr(pipe, "tokenizer"):
positives.pop(0)
positive_weights.pop(0)