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fix diffusers base/hires/refiner workflows

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This commit is contained in:
Vladimir Mandic
2023-10-22 11:11:28 -04:00
parent be75ed7e9f
commit fab013d31c
4 changed files with 43 additions and 24 deletions
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modules/prompt_parser_diffusers.py
+6 -7
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@@ -6,8 +6,7 @@ from compel.embeddings_provider import BaseTextualInversionManager, EmbeddingsPr
from modules import shared, prompt_parser
debug_output = os.environ.get('SD_PROMPT_DEBUG', None)
debug = shared.log.info if debug_output is not None else lambda *args, **kwargs: None
debug = shared.log.info if os.environ.get('SD_PROMPT_DEBUG', None) is not None else lambda *args, **kwargs: None
CLIP_SKIP_MAPPING = {
@@ -106,11 +105,11 @@ def prepare_embedding_providers(pipe, clip_skip):
shared.log.warning(f"Prompt parser unsupported: clip_skip={clip_skip}")
clip_skip = 2
embedding_type = CLIP_SKIP_MAPPING[clip_skip]
if hasattr(pipe, "tokenizer") and hasattr(pipe, "text_encoder"):
embedding = EmbeddingsProvider(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder, truncate=False, returned_embeddings_type=embedding_type)
if getattr(pipe, "tokenizer", None) is not None and getattr(pipe, "text_encoder", None) is not None:
embedding = EmbeddingsProvider(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder, truncate=False, returned_embeddings_type=embedding_type, device=pipe.device)
embeddings_providers.append(embedding)
if hasattr(pipe, "tokenizer_2") and hasattr(pipe, "text_encoder_2"):
embedding = EmbeddingsProvider(tokenizer=pipe.tokenizer_2, text_encoder=pipe.text_encoder_2, truncate=False, returned_embeddings_type=embedding_type)
if getattr(pipe, "tokenizer_2", None) is not None and getattr(pipe, "text_encoder_2", None) is not None:
embedding = EmbeddingsProvider(tokenizer=pipe.tokenizer_2, text_encoder=pipe.text_encoder_2, truncate=False, returned_embeddings_type=embedding_type, device=pipe.device)
embeddings_providers.append(embedding)
return embeddings_providers
@@ -140,7 +139,7 @@ def get_weighted_text_embeddings_sdxl(pipe, prompt: str = "", neg_prompt: str =
ns = [get_prompts_with_weights(p) for p in [neg_prompt, neg_prompt_2]]
negatives = [t for t, w in ns]
negative_weights = [w for t, w in ns]
if hasattr(pipe, "tokenizer_2") and not hasattr(pipe, "tokenizer"):
if getattr(pipe, "tokenizer_2", None) is not None and getattr(pipe, "tokenizer", None) is None:
positives.pop(0)
positive_weights.pop(0)
negatives.pop(0)