mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'main' into onnx-testing
This commit is contained in:
Brandon Rising
@ -1,4 +1,4 @@
|
||||
from typing import Literal, Optional, Union, List
|
||||
from typing import Literal, Optional, Union, List, Annotated
|
||||
from pydantic import BaseModel, Field
|
||||
import re
|
||||
|
||||
@ -10,7 +10,7 @@ from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
|
||||
from ...backend.model_management import BaseModelType, ModelType, SubModelType, ModelPatcher
|
||||
|
||||
import torch
|
||||
from compel import Compel
|
||||
from compel import Compel, ReturnedEmbeddingsType
|
||||
from compel.prompt_parser import (Blend, Conjunction,
|
||||
CrossAttentionControlSubstitute,
|
||||
FlattenedPrompt, Fragment)
|
||||
@ -22,6 +22,7 @@ from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
|
||||
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
|
||||
InvocationConfig, InvocationContext)
|
||||
from .model import ClipField
|
||||
from dataclasses import dataclass
|
||||
|
||||
|
||||
class ConditioningField(BaseModel):
|
||||
@ -31,6 +32,34 @@ class ConditioningField(BaseModel):
|
||||
class Config:
|
||||
schema_extra = {"required": ["conditioning_name"]}
|
||||
|
||||
@dataclass
|
||||
class BasicConditioningInfo:
|
||||
#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"
|
||||
pooled_embeds: torch.Tensor
|
||||
add_time_ids: torch.Tensor
|
||||
|
||||
ConditioningInfoType = Annotated[
|
||||
Union[BasicConditioningInfo, SDXLConditioningInfo],
|
||||
Field(discriminator="type")
|
||||
]
|
||||
|
||||
@dataclass
|
||||
class ConditioningFieldData:
|
||||
conditionings: List[Union[BasicConditioningInfo, SDXLConditioningInfo]]
|
||||
#unconditioned: Optional[torch.Tensor]
|
||||
|
||||
#class ConditioningAlgo(str, Enum):
|
||||
# Compose = "compose"
|
||||
# ComposeEx = "compose_ex"
|
||||
# PerpNeg = "perp_neg"
|
||||
|
||||
class CompelOutput(BaseInvocationOutput):
|
||||
"""Compel parser output"""
|
||||
@ -65,10 +94,10 @@ class CompelInvocation(BaseInvocation):
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> CompelOutput:
|
||||
tokenizer_info = context.services.model_manager.get_model(
|
||||
**self.clip.tokenizer.dict(),
|
||||
**self.clip.tokenizer.dict(), context=context,
|
||||
)
|
||||
text_encoder_info = context.services.model_manager.get_model(
|
||||
**self.clip.text_encoder.dict(),
|
||||
**self.clip.text_encoder.dict(), context=context,
|
||||
)
|
||||
|
||||
def _lora_loader():
|
||||
@ -90,6 +119,7 @@ class CompelInvocation(BaseInvocation):
|
||||
model_name=name,
|
||||
base_model=self.clip.text_encoder.base_model,
|
||||
model_type=ModelType.TextualInversion,
|
||||
context=context,
|
||||
).context.model
|
||||
)
|
||||
except ModelNotFoundException:
|
||||
@ -108,7 +138,7 @@ class CompelInvocation(BaseInvocation):
|
||||
text_encoder=text_encoder,
|
||||
textual_inversion_manager=ti_manager,
|
||||
dtype_for_device_getter=torch_dtype,
|
||||
truncate_long_prompts=True, # TODO:
|
||||
truncate_long_prompts=True,
|
||||
)
|
||||
|
||||
conjunction = Compel.parse_prompt_string(self.prompt)
|
||||
@ -120,19 +150,25 @@ class CompelInvocation(BaseInvocation):
|
||||
c, options = compel.build_conditioning_tensor_for_prompt_object(
|
||||
prompt)
|
||||
|
||||
# TODO: long prompt support
|
||||
# if not self.truncate_long_prompts:
|
||||
# [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
|
||||
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
|
||||
tokens_count_including_eos_bos=get_max_token_count(
|
||||
tokenizer, conjunction),
|
||||
cross_attention_control_args=options.get(
|
||||
"cross_attention_control", None),)
|
||||
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
c = c.detach().to("cpu")
|
||||
|
||||
# TODO: hacky but works ;D maybe rename latents somehow?
|
||||
context.services.latents.save(conditioning_name, (c, ec))
|
||||
conditioning_data = ConditioningFieldData(
|
||||
conditionings=[
|
||||
BasicConditioningInfo(
|
||||
embeds=c,
|
||||
extra_conditioning=ec,
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
context.services.latents.save(conditioning_name, conditioning_data)
|
||||
|
||||
return CompelOutput(
|
||||
conditioning=ConditioningField(
|
||||
@ -140,6 +176,397 @@ 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(),
|
||||
)
|
||||
text_encoder_info = context.services.model_manager.get_model(
|
||||
**clip_field.text_encoder.dict(),
|
||||
)
|
||||
|
||||
def _lora_loader():
|
||||
for lora in clip_field.loras:
|
||||
lora_info = context.services.model_manager.get_model(
|
||||
**lora.dict(exclude={"weight"}))
|
||||
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]
|
||||
|
||||
ti_list = []
|
||||
for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
|
||||
name = trigger[1:-1]
|
||||
try:
|
||||
ti_list.append(
|
||||
context.services.model_manager.get_model(
|
||||
model_name=name,
|
||||
base_model=clip_field.text_encoder.base_model,
|
||||
model_type=ModelType.TextualInversion,
|
||||
).context.model
|
||||
)
|
||||
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:
|
||||
|
||||
text_inputs = tokenizer(
|
||||
prompt,
|
||||
padding="max_length",
|
||||
max_length=tokenizer.model_max_length,
|
||||
truncation=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
text_input_ids = text_inputs.input_ids
|
||||
prompt_embeds = text_encoder(
|
||||
text_input_ids.to(text_encoder.device),
|
||||
output_hidden_states=True,
|
||||
)
|
||||
if get_pooled:
|
||||
c_pooled = prompt_embeds[0]
|
||||
else:
|
||||
c_pooled = None
|
||||
c = prompt_embeds.hidden_states[-2]
|
||||
|
||||
del tokenizer
|
||||
del text_encoder
|
||||
del tokenizer_info
|
||||
del text_encoder_info
|
||||
|
||||
c = c.detach().to("cpu")
|
||||
if c_pooled is not None:
|
||||
c_pooled = c_pooled.detach().to("cpu")
|
||||
|
||||
return c, c_pooled, None
|
||||
|
||||
def run_clip_compel(self, context, clip_field, prompt, get_pooled):
|
||||
tokenizer_info = context.services.model_manager.get_model(
|
||||
**clip_field.tokenizer.dict(),
|
||||
)
|
||||
text_encoder_info = context.services.model_manager.get_model(
|
||||
**clip_field.text_encoder.dict(),
|
||||
)
|
||||
|
||||
def _lora_loader():
|
||||
for lora in clip_field.loras:
|
||||
lora_info = context.services.model_manager.get_model(
|
||||
**lora.dict(exclude={"weight"}))
|
||||
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]
|
||||
|
||||
ti_list = []
|
||||
for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
|
||||
name = trigger[1:-1]
|
||||
try:
|
||||
ti_list.append(
|
||||
context.services.model_manager.get_model(
|
||||
model_name=name,
|
||||
base_model=clip_field.text_encoder.base_model,
|
||||
model_type=ModelType.TextualInversion,
|
||||
).context.model
|
||||
)
|
||||
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:
|
||||
|
||||
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
|
||||
requires_pooled=True,
|
||||
)
|
||||
|
||||
conjunction = Compel.parse_prompt_string(prompt)
|
||||
|
||||
if context.services.configuration.log_tokenization:
|
||||
# TODO: better logging for and syntax
|
||||
for prompt_obj in conjunction.prompts:
|
||||
log_tokenization_for_prompt_object(prompt_obj, tokenizer)
|
||||
|
||||
# TODO: ask for optimizations? to not run text_encoder twice
|
||||
c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
|
||||
if get_pooled:
|
||||
c_pooled = compel.conditioning_provider.get_pooled_embeddings([prompt])
|
||||
else:
|
||||
c_pooled = None
|
||||
|
||||
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
|
||||
tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
|
||||
cross_attention_control_args=options.get("cross_attention_control", None),
|
||||
)
|
||||
|
||||
del tokenizer
|
||||
del text_encoder
|
||||
del tokenizer_info
|
||||
del text_encoder_info
|
||||
|
||||
c = c.detach().to("cpu")
|
||||
if c_pooled is not None:
|
||||
c_pooled = c_pooled.detach().to("cpu")
|
||||
|
||||
return c, c_pooled, ec
|
||||
|
||||
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
"""Parse prompt using compel package to conditioning."""
|
||||
|
||||
type: Literal["sdxl_compel_prompt"] = "sdxl_compel_prompt"
|
||||
|
||||
prompt: str = Field(default="", description="Prompt")
|
||||
style: str = Field(default="", description="Style prompt")
|
||||
original_width: int = Field(1024, description="")
|
||||
original_height: int = Field(1024, description="")
|
||||
crop_top: int = Field(0, description="")
|
||||
crop_left: int = Field(0, description="")
|
||||
target_width: int = Field(1024, description="")
|
||||
target_height: int = Field(1024, description="")
|
||||
clip: ClipField = Field(None, description="Clip to use")
|
||||
clip2: ClipField = Field(None, description="Clip2 to use")
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "SDXL Prompt (Compel)",
|
||||
"tags": ["prompt", "compel"],
|
||||
"type_hints": {
|
||||
"model": "model"
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> CompelOutput:
|
||||
c1, c1_pooled, ec1 = self.run_clip_compel(context, self.clip, self.prompt, False)
|
||||
if self.style.strip() == "":
|
||||
c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.prompt, True)
|
||||
else:
|
||||
c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True)
|
||||
|
||||
original_size = (self.original_height, self.original_width)
|
||||
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
|
||||
])
|
||||
|
||||
conditioning_data = ConditioningFieldData(
|
||||
conditionings=[
|
||||
SDXLConditioningInfo(
|
||||
embeds=torch.cat([c1, c2], dim=-1),
|
||||
pooled_embeds=c2_pooled,
|
||||
add_time_ids=add_time_ids,
|
||||
extra_conditioning=ec1,
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
context.services.latents.save(conditioning_name, conditioning_data)
|
||||
|
||||
return CompelOutput(
|
||||
conditioning=ConditioningField(
|
||||
conditioning_name=conditioning_name,
|
||||
),
|
||||
)
|
||||
|
||||
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: ?
|
||||
original_width: int = Field(1024, description="")
|
||||
original_height: int = Field(1024, description="")
|
||||
crop_top: int = Field(0, description="")
|
||||
crop_left: int = Field(0, description="")
|
||||
aesthetic_score: float = Field(6.0, description="")
|
||||
clip2: ClipField = Field(None, description="Clip to use")
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "SDXL Refiner Prompt (Compel)",
|
||||
"tags": ["prompt", "compel"],
|
||||
"type_hints": {
|
||||
"model": "model"
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> CompelOutput:
|
||||
c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True)
|
||||
|
||||
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,)
|
||||
])
|
||||
|
||||
conditioning_data = ConditioningFieldData(
|
||||
conditionings=[
|
||||
SDXLConditioningInfo(
|
||||
embeds=c2,
|
||||
pooled_embeds=c2_pooled,
|
||||
add_time_ids=add_time_ids,
|
||||
extra_conditioning=ec2, # or None
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
context.services.latents.save(conditioning_name, conditioning_data)
|
||||
|
||||
return CompelOutput(
|
||||
conditioning=ConditioningField(
|
||||
conditioning_name=conditioning_name,
|
||||
),
|
||||
)
|
||||
|
||||
class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
"""Pass unmodified prompt to conditioning without compel processing."""
|
||||
|
||||
type: Literal["sdxl_raw_prompt"] = "sdxl_raw_prompt"
|
||||
|
||||
prompt: str = Field(default="", description="Prompt")
|
||||
style: str = Field(default="", description="Style prompt")
|
||||
original_width: int = Field(1024, description="")
|
||||
original_height: int = Field(1024, description="")
|
||||
crop_top: int = Field(0, description="")
|
||||
crop_left: int = Field(0, description="")
|
||||
target_width: int = Field(1024, description="")
|
||||
target_height: int = Field(1024, description="")
|
||||
clip: ClipField = Field(None, description="Clip to use")
|
||||
clip2: ClipField = Field(None, description="Clip2 to use")
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "SDXL Prompt (Raw)",
|
||||
"tags": ["prompt", "compel"],
|
||||
"type_hints": {
|
||||
"model": "model"
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> CompelOutput:
|
||||
c1, c1_pooled, ec1 = self.run_clip_raw(context, self.clip, self.prompt, False)
|
||||
if self.style.strip() == "":
|
||||
c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.prompt, True)
|
||||
else:
|
||||
c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True)
|
||||
|
||||
original_size = (self.original_height, self.original_width)
|
||||
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
|
||||
])
|
||||
|
||||
conditioning_data = ConditioningFieldData(
|
||||
conditionings=[
|
||||
SDXLConditioningInfo(
|
||||
embeds=torch.cat([c1, c2], dim=-1),
|
||||
pooled_embeds=c2_pooled,
|
||||
add_time_ids=add_time_ids,
|
||||
extra_conditioning=ec1,
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
context.services.latents.save(conditioning_name, conditioning_data)
|
||||
|
||||
return CompelOutput(
|
||||
conditioning=ConditioningField(
|
||||
conditioning_name=conditioning_name,
|
||||
),
|
||||
)
|
||||
|
||||
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: ?
|
||||
original_width: int = Field(1024, description="")
|
||||
original_height: int = Field(1024, description="")
|
||||
crop_top: int = Field(0, description="")
|
||||
crop_left: int = Field(0, description="")
|
||||
aesthetic_score: float = Field(6.0, description="")
|
||||
clip2: ClipField = Field(None, description="Clip to use")
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "SDXL Refiner Prompt (Raw)",
|
||||
"tags": ["prompt", "compel"],
|
||||
"type_hints": {
|
||||
"model": "model"
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> CompelOutput:
|
||||
c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True)
|
||||
|
||||
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,)
|
||||
])
|
||||
|
||||
conditioning_data = ConditioningFieldData(
|
||||
conditionings=[
|
||||
SDXLConditioningInfo(
|
||||
embeds=c2,
|
||||
pooled_embeds=c2_pooled,
|
||||
add_time_ids=add_time_ids,
|
||||
extra_conditioning=ec2, # or None
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
context.services.latents.save(conditioning_name, conditioning_data)
|
||||
|
||||
return CompelOutput(
|
||||
conditioning=ConditioningField(
|
||||
conditioning_name=conditioning_name,
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
class ClipSkipInvocationOutput(BaseInvocationOutput):
|
||||
"""Clip skip node output"""
|
||||
type: Literal["clip_skip_output"] = "clip_skip_output"
|
||||
@ -152,6 +579,14 @@ class ClipSkipInvocation(BaseInvocation):
|
||||
clip: ClipField = Field(None, description="Clip to use")
|
||||
skipped_layers: int = Field(0, description="Number of layers to skip in text_encoder")
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "CLIP Skip",
|
||||
"tags": ["clip", "skip"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ClipSkipInvocationOutput:
|
||||
self.clip.skipped_layers += self.skipped_layers
|
||||
return ClipSkipInvocationOutput(
|
||||
|
||||
Reference in New Issue
Block a user