mirror of
https://github.com/invoke-ai/InvokeAI
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Merge branch 'main' into feat_compel_and
This commit is contained in:
Millun Atluri
GitHub
@ -1,56 +1,40 @@
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from typing import Literal, Optional, Union, List, Annotated
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from pydantic import BaseModel, Field
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import re
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from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
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from .model import ClipField
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from ...backend.util.devices import torch_dtype
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from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
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from ...backend.model_management import BaseModelType, ModelType, SubModelType, ModelPatcher
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from dataclasses import dataclass
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from typing import List, Literal, Union
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import torch
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from compel import Compel, ReturnedEmbeddingsType
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from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
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from ...backend.util.devices import torch_dtype
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from ...backend.model_management import ModelType
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from ...backend.model_management.models import ModelNotFoundException
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from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
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from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import (
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BasicConditioningInfo,
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SDXLConditioningInfo,
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)
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from ...backend.model_management.models import ModelType
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from ...backend.model_management.lora import ModelPatcher
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from ...backend.model_management.models import ModelNotFoundException
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from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
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from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
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from ...backend.util.devices import torch_dtype
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from .baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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FieldDescriptions,
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Input,
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InputField,
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InvocationContext,
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OutputField,
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UIComponent,
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tags,
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title,
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)
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from .model import ClipField
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from dataclasses import dataclass
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class ConditioningField(BaseModel):
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conditioning_name: Optional[str] = Field(default=None, description="The name of conditioning data")
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class Config:
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schema_extra = {"required": ["conditioning_name"]}
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@dataclass
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class BasicConditioningInfo:
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# type: Literal["basic_conditioning"] = "basic_conditioning"
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embeds: torch.Tensor
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extra_conditioning: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo]
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# weight: float
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# mode: ConditioningAlgo
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@dataclass
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class SDXLConditioningInfo(BasicConditioningInfo):
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# type: Literal["sdxl_conditioning"] = "sdxl_conditioning"
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pooled_embeds: torch.Tensor
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add_time_ids: torch.Tensor
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ConditioningInfoType = Annotated[Union[BasicConditioningInfo, SDXLConditioningInfo], Field(discriminator="type")]
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@dataclass
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class ConditioningFieldData:
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conditionings: List[Union[BasicConditioningInfo, SDXLConditioningInfo]]
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conditionings: List[BasicConditioningInfo]
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# unconditioned: Optional[torch.Tensor]
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@ -60,32 +44,26 @@ class ConditioningFieldData:
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# PerpNeg = "perp_neg"
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class CompelOutput(BaseInvocationOutput):
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"""Compel parser output"""
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# fmt: off
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type: Literal["compel_output"] = "compel_output"
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conditioning: ConditioningField = Field(default=None, description="Conditioning")
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# fmt: on
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@title("Compel Prompt")
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@tags("prompt", "compel")
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class CompelInvocation(BaseInvocation):
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"""Parse prompt using compel package to conditioning."""
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type: Literal["compel"] = "compel"
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prompt: str = Field(default="", description="Prompt")
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clip: ClipField = Field(None, description="Clip to use")
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# Schema customisation
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class Config(InvocationConfig):
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schema_extra = {
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"ui": {"title": "Prompt (Compel)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
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}
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prompt: str = InputField(
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default="",
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description=FieldDescriptions.compel_prompt,
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ui_component=UIComponent.Textarea,
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)
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clip: ClipField = InputField(
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title="CLIP",
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description=FieldDescriptions.clip,
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input=Input.Connection,
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)
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@torch.no_grad()
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def invoke(self, context: InvocationContext) -> CompelOutput:
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def invoke(self, context: InvocationContext) -> ConditioningOutput:
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tokenizer_info = context.services.model_manager.get_model(
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**self.clip.tokenizer.dict(),
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context=context,
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@ -109,12 +87,15 @@ class CompelInvocation(BaseInvocation):
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name = trigger[1:-1]
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try:
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ti_list.append(
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context.services.model_manager.get_model(
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model_name=name,
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base_model=self.clip.text_encoder.base_model,
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model_type=ModelType.TextualInversion,
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context=context,
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).context.model
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(
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name,
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context.services.model_manager.get_model(
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model_name=name,
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base_model=self.clip.text_encoder.base_model,
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model_type=ModelType.TextualInversion,
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context=context,
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).context.model,
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)
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)
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except ModelNotFoundException:
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# print(e)
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@ -164,7 +145,7 @@ class CompelInvocation(BaseInvocation):
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conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
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context.services.latents.save(conditioning_name, conditioning_data)
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return CompelOutput(
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return ConditioningOutput(
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conditioning=ConditioningField(
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conditioning_name=conditioning_name,
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),
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@ -172,7 +153,15 @@ class CompelInvocation(BaseInvocation):
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class SDXLPromptInvocationBase:
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def run_clip_raw(self, context, clip_field, prompt, get_pooled):
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def run_clip_compel(
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self,
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context: InvocationContext,
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clip_field: ClipField,
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prompt: str,
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get_pooled: bool,
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lora_prefix: str,
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zero_on_empty: bool,
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):
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tokenizer_info = context.services.model_manager.get_model(
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**clip_field.tokenizer.dict(),
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context=context,
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@ -182,79 +171,21 @@ class SDXLPromptInvocationBase:
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context=context,
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)
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def _lora_loader():
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for lora in clip_field.loras:
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lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
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yield (lora_info.context.model, lora.weight)
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del lora_info
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return
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# loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
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ti_list = []
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for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
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name = trigger[1:-1]
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try:
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ti_list.append(
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context.services.model_manager.get_model(
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model_name=name,
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base_model=clip_field.text_encoder.base_model,
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model_type=ModelType.TextualInversion,
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context=context,
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).context.model
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)
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except ModelNotFoundException:
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# print(e)
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# import traceback
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# print(traceback.format_exc())
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print(f'Warn: trigger: "{trigger}" not found')
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with ModelPatcher.apply_lora_text_encoder(
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text_encoder_info.context.model, _lora_loader()
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), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
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tokenizer,
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ti_manager,
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), ModelPatcher.apply_clip_skip(
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text_encoder_info.context.model, clip_field.skipped_layers
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), text_encoder_info as text_encoder:
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text_inputs = tokenizer(
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prompt,
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padding="max_length",
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max_length=tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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prompt_embeds = text_encoder(
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text_input_ids.to(text_encoder.device),
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output_hidden_states=True,
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# return zero on empty
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if prompt == "" and zero_on_empty:
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cpu_text_encoder = text_encoder_info.context.model
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c = torch.zeros(
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(1, cpu_text_encoder.config.max_position_embeddings, cpu_text_encoder.config.hidden_size),
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dtype=text_encoder_info.context.cache.precision,
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)
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if get_pooled:
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c_pooled = prompt_embeds[0]
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c_pooled = torch.zeros(
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(1, cpu_text_encoder.config.hidden_size),
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dtype=c.dtype,
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)
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else:
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c_pooled = None
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c = prompt_embeds.hidden_states[-2]
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del tokenizer
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del text_encoder
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del tokenizer_info
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del text_encoder_info
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c = c.detach().to("cpu")
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if c_pooled is not None:
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c_pooled = c_pooled.detach().to("cpu")
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return c, c_pooled, None
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def run_clip_compel(self, context, clip_field, prompt, get_pooled):
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tokenizer_info = context.services.model_manager.get_model(
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**clip_field.tokenizer.dict(),
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context=context,
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)
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text_encoder_info = context.services.model_manager.get_model(
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**clip_field.text_encoder.dict(),
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context=context,
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)
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return c, c_pooled, None
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def _lora_loader():
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for lora in clip_field.loras:
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@ -270,12 +201,15 @@ class SDXLPromptInvocationBase:
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name = trigger[1:-1]
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try:
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ti_list.append(
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context.services.model_manager.get_model(
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model_name=name,
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base_model=clip_field.text_encoder.base_model,
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model_type=ModelType.TextualInversion,
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context=context,
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).context.model
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(
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name,
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context.services.model_manager.get_model(
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model_name=name,
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base_model=clip_field.text_encoder.base_model,
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model_type=ModelType.TextualInversion,
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context=context,
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).context.model,
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)
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)
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except ModelNotFoundException:
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# print(e)
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@ -283,8 +217,8 @@ class SDXLPromptInvocationBase:
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# print(traceback.format_exc())
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print(f'Warn: trigger: "{trigger}" not found')
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with ModelPatcher.apply_lora_text_encoder(
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text_encoder_info.context.model, _lora_loader()
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with ModelPatcher.apply_lora(
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text_encoder_info.context.model, _lora_loader(), lora_prefix
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), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
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tokenizer,
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ti_manager,
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@ -331,35 +265,37 @@ class SDXLPromptInvocationBase:
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return c, c_pooled, ec
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@title("SDXL Compel Prompt")
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@tags("sdxl", "compel", "prompt")
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class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
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"""Parse prompt using compel package to conditioning."""
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type: Literal["sdxl_compel_prompt"] = "sdxl_compel_prompt"
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prompt: str = Field(default="", description="Prompt")
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style: str = Field(default="", description="Style prompt")
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original_width: int = Field(1024, description="")
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original_height: int = Field(1024, description="")
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crop_top: int = Field(0, description="")
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crop_left: int = Field(0, description="")
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target_width: int = Field(1024, description="")
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target_height: int = Field(1024, description="")
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clip: ClipField = Field(None, description="Clip to use")
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clip2: ClipField = Field(None, description="Clip2 to use")
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# Schema customisation
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class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {"title": "SDXL Prompt (Compel)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
|
||||
}
|
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prompt: str = InputField(default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea)
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style: str = InputField(default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea)
|
||||
original_width: int = InputField(default=1024, description="")
|
||||
original_height: int = InputField(default=1024, description="")
|
||||
crop_top: int = InputField(default=0, description="")
|
||||
crop_left: int = InputField(default=0, description="")
|
||||
target_width: int = InputField(default=1024, description="")
|
||||
target_height: int = InputField(default=1024, description="")
|
||||
clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
|
||||
clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
|
||||
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> CompelOutput:
|
||||
c1, c1_pooled, ec1 = self.run_clip_compel(context, self.clip, self.prompt, False)
|
||||
def invoke(self, context: InvocationContext) -> ConditioningOutput:
|
||||
c1, c1_pooled, ec1 = self.run_clip_compel(
|
||||
context, self.clip, self.prompt, False, "lora_te1_", zero_on_empty=True
|
||||
)
|
||||
if self.style.strip() == "":
|
||||
c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.prompt, True)
|
||||
c2, c2_pooled, ec2 = self.run_clip_compel(
|
||||
context, self.clip2, self.prompt, True, "lora_te2_", zero_on_empty=True
|
||||
)
|
||||
else:
|
||||
c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True)
|
||||
c2, c2_pooled, ec2 = self.run_clip_compel(
|
||||
context, self.clip2, self.style, True, "lora_te2_", zero_on_empty=True
|
||||
)
|
||||
|
||||
original_size = (self.original_height, self.original_width)
|
||||
crop_coords = (self.crop_top, self.crop_left)
|
||||
@ -381,39 +317,34 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
context.services.latents.save(conditioning_name, conditioning_data)
|
||||
|
||||
return CompelOutput(
|
||||
return ConditioningOutput(
|
||||
conditioning=ConditioningField(
|
||||
conditioning_name=conditioning_name,
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@title("SDXL Refiner Compel Prompt")
|
||||
@tags("sdxl", "compel", "prompt")
|
||||
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"},
|
||||
},
|
||||
}
|
||||
style: str = InputField(
|
||||
default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea
|
||||
) # TODO: ?
|
||||
original_width: int = InputField(default=1024, description="")
|
||||
original_height: int = InputField(default=1024, description="")
|
||||
crop_top: int = InputField(default=0, description="")
|
||||
crop_left: int = InputField(default=0, description="")
|
||||
aesthetic_score: float = InputField(default=6.0, description=FieldDescriptions.sdxl_aesthetic)
|
||||
clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
|
||||
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> CompelOutput:
|
||||
c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True)
|
||||
def invoke(self, context: InvocationContext) -> ConditioningOutput:
|
||||
# TODO: if there will appear lora for refiner - write proper prefix
|
||||
c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "<NONE>", zero_on_empty=False)
|
||||
|
||||
original_size = (self.original_height, self.original_width)
|
||||
crop_coords = (self.crop_top, self.crop_left)
|
||||
@ -434,117 +365,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
|
||||
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(
|
||||
return ConditioningOutput(
|
||||
conditioning=ConditioningField(
|
||||
conditioning_name=conditioning_name,
|
||||
),
|
||||
@ -555,21 +376,18 @@ class ClipSkipInvocationOutput(BaseInvocationOutput):
|
||||
"""Clip skip node output"""
|
||||
|
||||
type: Literal["clip_skip_output"] = "clip_skip_output"
|
||||
clip: ClipField = Field(None, description="Clip with skipped layers")
|
||||
clip: ClipField = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
|
||||
|
||||
|
||||
@title("CLIP Skip")
|
||||
@tags("clipskip", "clip", "skip")
|
||||
class ClipSkipInvocation(BaseInvocation):
|
||||
"""Skip layers in clip text_encoder model."""
|
||||
|
||||
type: Literal["clip_skip"] = "clip_skip"
|
||||
|
||||
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"]},
|
||||
}
|
||||
clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP")
|
||||
skipped_layers: int = InputField(default=0, description=FieldDescriptions.skipped_layers)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ClipSkipInvocationOutput:
|
||||
self.clip.skipped_layers += self.skipped_layers
|
||||
|
||||
Reference in New Issue
Block a user