mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Run python black
This commit is contained in:
Brandon Rising
@ -11,64 +11,63 @@ from ...backend.model_management import BaseModelType, ModelType, SubModelType,
|
||||
|
||||
import torch
|
||||
from compel import Compel, ReturnedEmbeddingsType
|
||||
from compel.prompt_parser import (Blend, Conjunction,
|
||||
CrossAttentionControlSubstitute,
|
||||
FlattenedPrompt, Fragment)
|
||||
from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
|
||||
from ...backend.util.devices import torch_dtype
|
||||
from ...backend.model_management import ModelType
|
||||
from ...backend.model_management.models import ModelNotFoundException
|
||||
from ...backend.model_management.lora import ModelPatcher
|
||||
from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
|
||||
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
|
||||
InvocationConfig, InvocationContext)
|
||||
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
|
||||
from .model import ClipField
|
||||
from dataclasses import dataclass
|
||||
|
||||
|
||||
class ConditioningField(BaseModel):
|
||||
conditioning_name: Optional[str] = Field(
|
||||
default=None, description="The name of conditioning data")
|
||||
conditioning_name: Optional[str] = Field(default=None, description="The name of conditioning data")
|
||||
|
||||
class Config:
|
||||
schema_extra = {"required": ["conditioning_name"]}
|
||||
|
||||
|
||||
@dataclass
|
||||
class BasicConditioningInfo:
|
||||
#type: Literal["basic_conditioning"] = "basic_conditioning"
|
||||
# type: Literal["basic_conditioning"] = "basic_conditioning"
|
||||
embeds: torch.Tensor
|
||||
extra_conditioning: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo]
|
||||
# weight: float
|
||||
# mode: ConditioningAlgo
|
||||
|
||||
|
||||
@dataclass
|
||||
class SDXLConditioningInfo(BasicConditioningInfo):
|
||||
#type: Literal["sdxl_conditioning"] = "sdxl_conditioning"
|
||||
# type: Literal["sdxl_conditioning"] = "sdxl_conditioning"
|
||||
pooled_embeds: torch.Tensor
|
||||
add_time_ids: torch.Tensor
|
||||
|
||||
ConditioningInfoType = Annotated[
|
||||
Union[BasicConditioningInfo, SDXLConditioningInfo],
|
||||
Field(discriminator="type")
|
||||
]
|
||||
|
||||
ConditioningInfoType = Annotated[Union[BasicConditioningInfo, SDXLConditioningInfo], Field(discriminator="type")]
|
||||
|
||||
|
||||
@dataclass
|
||||
class ConditioningFieldData:
|
||||
conditionings: List[Union[BasicConditioningInfo, SDXLConditioningInfo]]
|
||||
#unconditioned: Optional[torch.Tensor]
|
||||
# unconditioned: Optional[torch.Tensor]
|
||||
|
||||
#class ConditioningAlgo(str, Enum):
|
||||
|
||||
# class ConditioningAlgo(str, Enum):
|
||||
# Compose = "compose"
|
||||
# ComposeEx = "compose_ex"
|
||||
# PerpNeg = "perp_neg"
|
||||
|
||||
|
||||
class CompelOutput(BaseInvocationOutput):
|
||||
"""Compel parser output"""
|
||||
|
||||
#fmt: off
|
||||
# fmt: off
|
||||
type: Literal["compel_output"] = "compel_output"
|
||||
|
||||
conditioning: ConditioningField = Field(default=None, description="Conditioning")
|
||||
#fmt: on
|
||||
# fmt: on
|
||||
|
||||
|
||||
class CompelInvocation(BaseInvocation):
|
||||
@ -82,33 +81,28 @@ class CompelInvocation(BaseInvocation):
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Prompt (Compel)",
|
||||
"tags": ["prompt", "compel"],
|
||||
"type_hints": {
|
||||
"model": "model"
|
||||
}
|
||||
},
|
||||
"ui": {"title": "Prompt (Compel)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
|
||||
}
|
||||
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> CompelOutput:
|
||||
tokenizer_info = context.services.model_manager.get_model(
|
||||
**self.clip.tokenizer.dict(), context=context,
|
||||
**self.clip.tokenizer.dict(),
|
||||
context=context,
|
||||
)
|
||||
text_encoder_info = context.services.model_manager.get_model(
|
||||
**self.clip.text_encoder.dict(), context=context,
|
||||
**self.clip.text_encoder.dict(),
|
||||
context=context,
|
||||
)
|
||||
|
||||
def _lora_loader():
|
||||
for lora in self.clip.loras:
|
||||
lora_info = context.services.model_manager.get_model(
|
||||
**lora.dict(exclude={"weight"}), context=context)
|
||||
lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
|
||||
yield (lora_info.context.model, lora.weight)
|
||||
del lora_info
|
||||
return
|
||||
|
||||
#loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
|
||||
# loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
|
||||
|
||||
ti_list = []
|
||||
for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
|
||||
@ -124,15 +118,18 @@ class CompelInvocation(BaseInvocation):
|
||||
)
|
||||
except ModelNotFoundException:
|
||||
# print(e)
|
||||
#import traceback
|
||||
#print(traceback.format_exc())
|
||||
print(f"Warn: trigger: \"{trigger}\" not found")
|
||||
|
||||
with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
|
||||
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
|
||||
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),\
|
||||
text_encoder_info as text_encoder:
|
||||
# import traceback
|
||||
# print(traceback.format_exc())
|
||||
print(f'Warn: trigger: "{trigger}" not found')
|
||||
|
||||
with ModelPatcher.apply_lora_text_encoder(
|
||||
text_encoder_info.context.model, _lora_loader()
|
||||
), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
|
||||
tokenizer,
|
||||
ti_manager,
|
||||
), ModelPatcher.apply_clip_skip(
|
||||
text_encoder_info.context.model, self.clip.skipped_layers
|
||||
), text_encoder_info as text_encoder:
|
||||
compel = Compel(
|
||||
tokenizer=tokenizer,
|
||||
text_encoder=text_encoder,
|
||||
@ -147,14 +144,12 @@ class CompelInvocation(BaseInvocation):
|
||||
if context.services.configuration.log_tokenization:
|
||||
log_tokenization_for_prompt_object(prompt, tokenizer)
|
||||
|
||||
c, options = compel.build_conditioning_tensor_for_prompt_object(
|
||||
prompt)
|
||||
c, options = compel.build_conditioning_tensor_for_prompt_object(prompt)
|
||||
|
||||
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
|
||||
tokens_count_including_eos_bos=get_max_token_count(
|
||||
tokenizer, conjunction),
|
||||
cross_attention_control_args=options.get(
|
||||
"cross_attention_control", None),)
|
||||
tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
|
||||
cross_attention_control_args=options.get("cross_attention_control", None),
|
||||
)
|
||||
|
||||
c = c.detach().to("cpu")
|
||||
|
||||
@ -176,24 +171,26 @@ class CompelInvocation(BaseInvocation):
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
class SDXLPromptInvocationBase:
|
||||
def run_clip_raw(self, context, clip_field, prompt, get_pooled):
|
||||
tokenizer_info = context.services.model_manager.get_model(
|
||||
**clip_field.tokenizer.dict(), context=context,
|
||||
**clip_field.tokenizer.dict(),
|
||||
context=context,
|
||||
)
|
||||
text_encoder_info = context.services.model_manager.get_model(
|
||||
**clip_field.text_encoder.dict(), context=context,
|
||||
**clip_field.text_encoder.dict(),
|
||||
context=context,
|
||||
)
|
||||
|
||||
def _lora_loader():
|
||||
for lora in clip_field.loras:
|
||||
lora_info = context.services.model_manager.get_model(
|
||||
**lora.dict(exclude={"weight"}), context=context)
|
||||
lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
|
||||
yield (lora_info.context.model, lora.weight)
|
||||
del lora_info
|
||||
return
|
||||
|
||||
#loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
|
||||
# loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
|
||||
|
||||
ti_list = []
|
||||
for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
|
||||
@ -209,15 +206,18 @@ class SDXLPromptInvocationBase:
|
||||
)
|
||||
except ModelNotFoundException:
|
||||
# print(e)
|
||||
#import traceback
|
||||
#print(traceback.format_exc())
|
||||
print(f"Warn: trigger: \"{trigger}\" not found")
|
||||
|
||||
with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
|
||||
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
|
||||
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),\
|
||||
text_encoder_info as text_encoder:
|
||||
# import traceback
|
||||
# print(traceback.format_exc())
|
||||
print(f'Warn: trigger: "{trigger}" not found')
|
||||
|
||||
with ModelPatcher.apply_lora_text_encoder(
|
||||
text_encoder_info.context.model, _lora_loader()
|
||||
), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
|
||||
tokenizer,
|
||||
ti_manager,
|
||||
), ModelPatcher.apply_clip_skip(
|
||||
text_encoder_info.context.model, clip_field.skipped_layers
|
||||
), text_encoder_info as text_encoder:
|
||||
text_inputs = tokenizer(
|
||||
prompt,
|
||||
padding="max_length",
|
||||
@ -249,21 +249,22 @@ class SDXLPromptInvocationBase:
|
||||
|
||||
def run_clip_compel(self, context, clip_field, prompt, get_pooled):
|
||||
tokenizer_info = context.services.model_manager.get_model(
|
||||
**clip_field.tokenizer.dict(), context=context,
|
||||
**clip_field.tokenizer.dict(),
|
||||
context=context,
|
||||
)
|
||||
text_encoder_info = context.services.model_manager.get_model(
|
||||
**clip_field.text_encoder.dict(), context=context,
|
||||
**clip_field.text_encoder.dict(),
|
||||
context=context,
|
||||
)
|
||||
|
||||
def _lora_loader():
|
||||
for lora in clip_field.loras:
|
||||
lora_info = context.services.model_manager.get_model(
|
||||
**lora.dict(exclude={"weight"}), context=context)
|
||||
lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
|
||||
yield (lora_info.context.model, lora.weight)
|
||||
del lora_info
|
||||
return
|
||||
|
||||
#loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
|
||||
# loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
|
||||
|
||||
ti_list = []
|
||||
for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
|
||||
@ -279,22 +280,25 @@ class SDXLPromptInvocationBase:
|
||||
)
|
||||
except ModelNotFoundException:
|
||||
# print(e)
|
||||
#import traceback
|
||||
#print(traceback.format_exc())
|
||||
print(f"Warn: trigger: \"{trigger}\" not found")
|
||||
|
||||
with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
|
||||
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
|
||||
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),\
|
||||
text_encoder_info as text_encoder:
|
||||
# import traceback
|
||||
# print(traceback.format_exc())
|
||||
print(f'Warn: trigger: "{trigger}" not found')
|
||||
|
||||
with ModelPatcher.apply_lora_text_encoder(
|
||||
text_encoder_info.context.model, _lora_loader()
|
||||
), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
|
||||
tokenizer,
|
||||
ti_manager,
|
||||
), ModelPatcher.apply_clip_skip(
|
||||
text_encoder_info.context.model, clip_field.skipped_layers
|
||||
), text_encoder_info as text_encoder:
|
||||
compel = Compel(
|
||||
tokenizer=tokenizer,
|
||||
text_encoder=text_encoder,
|
||||
textual_inversion_manager=ti_manager,
|
||||
dtype_for_device_getter=torch_dtype,
|
||||
truncate_long_prompts=True, # TODO:
|
||||
returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, # TODO: clip skip
|
||||
returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, # TODO: clip skip
|
||||
requires_pooled=True,
|
||||
)
|
||||
|
||||
@ -328,6 +332,7 @@ class SDXLPromptInvocationBase:
|
||||
|
||||
return c, c_pooled, ec
|
||||
|
||||
|
||||
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
"""Parse prompt using compel package to conditioning."""
|
||||
|
||||
@ -347,13 +352,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "SDXL Prompt (Compel)",
|
||||
"tags": ["prompt", "compel"],
|
||||
"type_hints": {
|
||||
"model": "model"
|
||||
}
|
||||
},
|
||||
"ui": {"title": "SDXL Prompt (Compel)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
|
||||
}
|
||||
|
||||
@torch.no_grad()
|
||||
@ -368,9 +367,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
crop_coords = (self.crop_top, self.crop_left)
|
||||
target_size = (self.target_height, self.target_width)
|
||||
|
||||
add_time_ids = torch.tensor([
|
||||
original_size + crop_coords + target_size
|
||||
])
|
||||
add_time_ids = torch.tensor([original_size + crop_coords + target_size])
|
||||
|
||||
conditioning_data = ConditioningFieldData(
|
||||
conditionings=[
|
||||
@ -392,12 +389,13 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
"""Parse prompt using compel package to conditioning."""
|
||||
|
||||
type: Literal["sdxl_refiner_compel_prompt"] = "sdxl_refiner_compel_prompt"
|
||||
|
||||
style: str = Field(default="", description="Style prompt") # TODO: ?
|
||||
style: str = Field(default="", description="Style prompt") # TODO: ?
|
||||
original_width: int = Field(1024, description="")
|
||||
original_height: int = Field(1024, description="")
|
||||
crop_top: int = Field(0, description="")
|
||||
@ -411,9 +409,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
|
||||
"ui": {
|
||||
"title": "SDXL Refiner Prompt (Compel)",
|
||||
"tags": ["prompt", "compel"],
|
||||
"type_hints": {
|
||||
"model": "model"
|
||||
}
|
||||
"type_hints": {"model": "model"},
|
||||
},
|
||||
}
|
||||
|
||||
@ -424,9 +420,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
|
||||
original_size = (self.original_height, self.original_width)
|
||||
crop_coords = (self.crop_top, self.crop_left)
|
||||
|
||||
add_time_ids = torch.tensor([
|
||||
original_size + crop_coords + (self.aesthetic_score,)
|
||||
])
|
||||
add_time_ids = torch.tensor([original_size + crop_coords + (self.aesthetic_score,)])
|
||||
|
||||
conditioning_data = ConditioningFieldData(
|
||||
conditionings=[
|
||||
@ -434,7 +428,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
|
||||
embeds=c2,
|
||||
pooled_embeds=c2_pooled,
|
||||
add_time_ids=add_time_ids,
|
||||
extra_conditioning=ec2, # or None
|
||||
extra_conditioning=ec2, # or None
|
||||
)
|
||||
]
|
||||
)
|
||||
@ -448,6 +442,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
"""Pass unmodified prompt to conditioning without compel processing."""
|
||||
|
||||
@ -467,13 +462,7 @@ class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "SDXL Prompt (Raw)",
|
||||
"tags": ["prompt", "compel"],
|
||||
"type_hints": {
|
||||
"model": "model"
|
||||
}
|
||||
},
|
||||
"ui": {"title": "SDXL Prompt (Raw)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
|
||||
}
|
||||
|
||||
@torch.no_grad()
|
||||
@ -488,9 +477,7 @@ class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
crop_coords = (self.crop_top, self.crop_left)
|
||||
target_size = (self.target_height, self.target_width)
|
||||
|
||||
add_time_ids = torch.tensor([
|
||||
original_size + crop_coords + target_size
|
||||
])
|
||||
add_time_ids = torch.tensor([original_size + crop_coords + target_size])
|
||||
|
||||
conditioning_data = ConditioningFieldData(
|
||||
conditionings=[
|
||||
@ -512,12 +499,13 @@ class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
"""Parse prompt using compel package to conditioning."""
|
||||
|
||||
type: Literal["sdxl_refiner_raw_prompt"] = "sdxl_refiner_raw_prompt"
|
||||
|
||||
style: str = Field(default="", description="Style prompt") # TODO: ?
|
||||
style: str = Field(default="", description="Style prompt") # TODO: ?
|
||||
original_width: int = Field(1024, description="")
|
||||
original_height: int = Field(1024, description="")
|
||||
crop_top: int = Field(0, description="")
|
||||
@ -531,9 +519,7 @@ class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
"ui": {
|
||||
"title": "SDXL Refiner Prompt (Raw)",
|
||||
"tags": ["prompt", "compel"],
|
||||
"type_hints": {
|
||||
"model": "model"
|
||||
}
|
||||
"type_hints": {"model": "model"},
|
||||
},
|
||||
}
|
||||
|
||||
@ -544,9 +530,7 @@ class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
original_size = (self.original_height, self.original_width)
|
||||
crop_coords = (self.crop_top, self.crop_left)
|
||||
|
||||
add_time_ids = torch.tensor([
|
||||
original_size + crop_coords + (self.aesthetic_score,)
|
||||
])
|
||||
add_time_ids = torch.tensor([original_size + crop_coords + (self.aesthetic_score,)])
|
||||
|
||||
conditioning_data = ConditioningFieldData(
|
||||
conditionings=[
|
||||
@ -554,7 +538,7 @@ class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
embeds=c2,
|
||||
pooled_embeds=c2_pooled,
|
||||
add_time_ids=add_time_ids,
|
||||
extra_conditioning=ec2, # or None
|
||||
extra_conditioning=ec2, # or None
|
||||
)
|
||||
]
|
||||
)
|
||||
@ -571,11 +555,14 @@ class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
|
||||
class ClipSkipInvocationOutput(BaseInvocationOutput):
|
||||
"""Clip skip node output"""
|
||||
|
||||
type: Literal["clip_skip_output"] = "clip_skip_output"
|
||||
clip: ClipField = Field(None, description="Clip with skipped layers")
|
||||
|
||||
|
||||
class ClipSkipInvocation(BaseInvocation):
|
||||
"""Skip layers in clip text_encoder model."""
|
||||
|
||||
type: Literal["clip_skip"] = "clip_skip"
|
||||
|
||||
clip: ClipField = Field(None, description="Clip to use")
|
||||
@ -583,10 +570,7 @@ class ClipSkipInvocation(BaseInvocation):
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "CLIP Skip",
|
||||
"tags": ["clip", "skip"]
|
||||
},
|
||||
"ui": {"title": "CLIP Skip", "tags": ["clip", "skip"]},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ClipSkipInvocationOutput:
|
||||
@ -597,46 +581,26 @@ class ClipSkipInvocation(BaseInvocation):
|
||||
|
||||
|
||||
def get_max_token_count(
|
||||
tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction],
|
||||
truncate_if_too_long=False) -> int:
|
||||
tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction], truncate_if_too_long=False
|
||||
) -> int:
|
||||
if type(prompt) is Blend:
|
||||
blend: Blend = prompt
|
||||
return max(
|
||||
[
|
||||
get_max_token_count(tokenizer, p, truncate_if_too_long)
|
||||
for p in blend.prompts
|
||||
]
|
||||
)
|
||||
return max([get_max_token_count(tokenizer, p, truncate_if_too_long) for p in blend.prompts])
|
||||
elif type(prompt) is Conjunction:
|
||||
conjunction: Conjunction = prompt
|
||||
return sum(
|
||||
[
|
||||
get_max_token_count(tokenizer, p, truncate_if_too_long)
|
||||
for p in conjunction.prompts
|
||||
]
|
||||
)
|
||||
return sum([get_max_token_count(tokenizer, p, truncate_if_too_long) for p in conjunction.prompts])
|
||||
else:
|
||||
return len(
|
||||
get_tokens_for_prompt_object(
|
||||
tokenizer, prompt, truncate_if_too_long))
|
||||
return len(get_tokens_for_prompt_object(tokenizer, prompt, truncate_if_too_long))
|
||||
|
||||
|
||||
def get_tokens_for_prompt_object(
|
||||
tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True
|
||||
) -> List[str]:
|
||||
def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True) -> List[str]:
|
||||
if type(parsed_prompt) is Blend:
|
||||
raise ValueError(
|
||||
"Blend is not supported here - you need to get tokens for each of its .children"
|
||||
)
|
||||
raise ValueError("Blend is not supported here - you need to get tokens for each of its .children")
|
||||
|
||||
text_fragments = [
|
||||
x.text
|
||||
if type(x) is Fragment
|
||||
else (
|
||||
" ".join([f.text for f in x.original])
|
||||
if type(x) is CrossAttentionControlSubstitute
|
||||
else str(x)
|
||||
)
|
||||
else (" ".join([f.text for f in x.original]) if type(x) is CrossAttentionControlSubstitute else str(x))
|
||||
for x in parsed_prompt.children
|
||||
]
|
||||
text = " ".join(text_fragments)
|
||||
@ -647,25 +611,17 @@ def get_tokens_for_prompt_object(
|
||||
return tokens
|
||||
|
||||
|
||||
def log_tokenization_for_conjunction(
|
||||
c: Conjunction, tokenizer, display_label_prefix=None
|
||||
):
|
||||
def log_tokenization_for_conjunction(c: Conjunction, tokenizer, display_label_prefix=None):
|
||||
display_label_prefix = display_label_prefix or ""
|
||||
for i, p in enumerate(c.prompts):
|
||||
if len(c.prompts) > 1:
|
||||
this_display_label_prefix = f"{display_label_prefix}(conjunction part {i + 1}, weight={c.weights[i]})"
|
||||
else:
|
||||
this_display_label_prefix = display_label_prefix
|
||||
log_tokenization_for_prompt_object(
|
||||
p,
|
||||
tokenizer,
|
||||
display_label_prefix=this_display_label_prefix
|
||||
)
|
||||
log_tokenization_for_prompt_object(p, tokenizer, display_label_prefix=this_display_label_prefix)
|
||||
|
||||
|
||||
def log_tokenization_for_prompt_object(
|
||||
p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None
|
||||
):
|
||||
def log_tokenization_for_prompt_object(p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None):
|
||||
display_label_prefix = display_label_prefix or ""
|
||||
if type(p) is Blend:
|
||||
blend: Blend = p
|
||||
@ -702,13 +658,10 @@ def log_tokenization_for_prompt_object(
|
||||
)
|
||||
else:
|
||||
text = " ".join([x.text for x in flattened_prompt.children])
|
||||
log_tokenization_for_text(
|
||||
text, tokenizer, display_label=display_label_prefix
|
||||
)
|
||||
log_tokenization_for_text(text, tokenizer, display_label=display_label_prefix)
|
||||
|
||||
|
||||
def log_tokenization_for_text(
|
||||
text, tokenizer, display_label=None, truncate_if_too_long=False):
|
||||
def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_too_long=False):
|
||||
"""shows how the prompt is tokenized
|
||||
# usually tokens have '</w>' to indicate end-of-word,
|
||||
# but for readability it has been replaced with ' '
|
||||
|
||||
Reference in New Issue
Block a user