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mirror of https://github.com/vladmandic/automatic synced 2026-09-02 11:00:46 +02:00
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Clean up code.

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2023-08-04 15:12:37 +00:00
parent b166dcbfad
commit fdbafbd713
1 changed files with 14 additions and 7 deletions
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modules/prompt_parser_diffusers.py
+14 -7
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@@ -1,11 +1,16 @@
import torch
import modules.shared as shared
from compel import Compel, ReturnedEmbeddingsType
import diffusers
import typing
def compel_encode_prompt(pipeline, prompt, negative_prompt, prompt_2=None, negative_prompt_2=None, refiner=False):
if "XL" not in pipeline.__class__.__name__:
print(f"Compel parser is not configured for: {pipeline.__class__.__name__}")
return None, None, None, None
def compel_encode_prompt(pipeline: typing.Any, *args, **kwargs):
compel_encode_fn = COMPEL_ENCODE_FN_DICT.get(type(pipeline), None)
if compel_encode_fn is None:
raise TypeError(f"Compel encoding not yet supported for {type(pipeline).__name__}.")
return compel_encode_fn(pipeline, *args, **kwargs)
def compel_encode_prompt_sdxl(pipeline: diffusers.StableDiffusionXLPipeline, prompt: str, negative_prompt: str, prompt_2: typing.Optional[str]=None, negative_prompt_2: typing.Optional[str]=None, refiner=False):
compel_te1 = Compel(
tokenizer=pipeline.tokenizer,
text_encoder=pipeline.text_encoder,
@@ -19,7 +24,7 @@ def compel_encode_prompt(pipeline, prompt, negative_prompt, prompt_2=None, negat
returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
requires_pooled=True,
)
if not refiner:
if refiner is None:
positive_te1 = compel_te1(prompt)
positive_te2, pooled = compel_te2(prompt_2)
positive = torch.cat((positive_te1, positive_te2), dim=-1)
@@ -27,10 +32,12 @@ def compel_encode_prompt(pipeline, prompt, negative_prompt, prompt_2=None, negat
negative_te1 = compel_te1(negative_prompt)
negative_te2, negative_pooled = compel_te2(negative_prompt_2)
negative = torch.cat((negative_te1, negative_te2), dim=-1)
if refiner:
else:
positive, pooled = compel_te2(prompt)
negative, negative_pooled = compel_te2(negative_prompt)
[prompt_embed, negative_embed] = compel_te2.pad_conditioning_tensors_to_same_length([positive, negative])
return prompt_embed, pooled, negative_embed, negative_pooled
return prompt_embed, pooled, negative_embed, negative_pooled
COMPEL_ENCODE_FN_DICT = {diffusers.StableDiffusionXLPipeline: compel_encode_prompt_sdxl}