Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-12 16:08:43 +02:00
Code Issues Packages Projects Releases Wiki Activity

one more sdxl embeddings fix

Browse Source
This commit is contained in:
Vladimir Mandic
2023-09-28 16:03:18 -04:00
parent a785f3da2e
commit e830b41dfa
3 changed files with 27 additions and 20 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/prompt_parser_diffusers.py
+7 -8
View File
@@ -3,8 +3,7 @@ import typing
import torch
from compel import Compel, ReturnedEmbeddingsType
from compel.embeddings_provider import BaseTextualInversionManager
import modules.shared as shared
import modules.prompt_parser as prompt_parser
from modules import shared, devices, prompt_parser
debug_output = os.environ.get('SD_PROMPT_DEBUG', None)
@@ -142,20 +141,20 @@ def compel_encode_prompt(
prompt_2 = convert_to_compel(prompt_2)
negative_prompt_2 = convert_to_compel(negative_prompt_2)
textual_inversion_manager = DiffusersTextualInversionManager(pipeline)
textual_inversion_manager_te1 = DiffusersTextualInversionManager(pipeline)
compel_te1 = Compel(
tokenizer=pipeline.tokenizer,
text_encoder=pipeline.text_encoder,
returned_embeddings_type=embedding_type,
requires_pooled=False,
# truncate_long_prompts=False,
device=shared.device,
textual_inversion_manager=textual_inversion_manager
device=devices.device,
textual_inversion_manager=textual_inversion_manager_te1
)
if 'XL' in pipeline.__class__.__name__ and not is_refiner:
compel_te2 = Compel(tokenizer=pipeline.tokenizer_2, text_encoder=pipeline.text_encoder_2, returned_embeddings_type=embedding_type, requires_pooled=True, device=shared.device, textual_inversion_manager=textual_inversion_manager)
# TODO textual_inversion_manager=textual_inversion_manager_te2 - DiffusersTextualInversionManager needs to use tokenizer_2
compel_te2 = Compel(tokenizer=pipeline.tokenizer_2, text_encoder=pipeline.text_encoder_2, returned_embeddings_type=embedding_type, requires_pooled=True, device=devices.device)
positive_te1 = compel_te1(prompt)
positive_te2, positive_pooled = compel_te2(prompt_2)
positive = torch.cat((positive_te1, positive_te2), dim=-1)
@@ -169,7 +168,7 @@ def compel_encode_prompt(
return prompt_embed, positive_pooled, negative_embed, negative_pooled
elif 'XL' in pipeline.__class__.__name__ and is_refiner:
compel_te2 = Compel(tokenizer=pipeline.tokenizer_2, text_encoder=pipeline.text_encoder_2, returned_embeddings_type=embedding_type, requires_pooled=True, device=shared.device)
compel_te2 = Compel(tokenizer=pipeline.tokenizer_2, text_encoder=pipeline.text_encoder_2, returned_embeddings_type=embedding_type, requires_pooled=True, device=devices.device)
positive, positive_pooled = compel_te2(prompt)
negative, negative_pooled = compel_te2(negative_prompt)