mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
526 lines
20 KiB
Python
526 lines
20 KiB
Python
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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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 ...backend.model_management.lora import ModelPatcher
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from ...backend.stable_diffusion import InvokeAIDiffuserComponent, BasicConditioningInfo, SDXLConditioningInfo
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from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
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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 ConditioningFieldData:
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conditionings: List[BasicConditioningInfo]
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# unconditioned: Optional[torch.Tensor]
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# class ConditioningAlgo(str, Enum):
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# Compose = "compose"
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# ComposeEx = "compose_ex"
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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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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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@torch.no_grad()
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def invoke(self, context: InvocationContext) -> CompelOutput:
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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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)
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text_encoder_info = context.services.model_manager.get_model(
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**self.clip.text_encoder.dict(),
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context=context,
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)
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def _lora_loader():
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for lora in self.clip.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., _-]+>", self.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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(
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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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# 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, self.clip.skipped_layers
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), text_encoder_info as text_encoder:
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compel = Compel(
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tokenizer=tokenizer,
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text_encoder=text_encoder,
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textual_inversion_manager=ti_manager,
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dtype_for_device_getter=torch_dtype,
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truncate_long_prompts=True,
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)
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conjunction = Compel.parse_prompt_string(self.prompt)
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prompt: Union[FlattenedPrompt, Blend] = conjunction.prompts[0]
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if context.services.configuration.log_tokenization:
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log_tokenization_for_prompt_object(prompt, tokenizer)
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c, options = compel.build_conditioning_tensor_for_prompt_object(prompt)
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ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
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tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
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cross_attention_control_args=options.get("cross_attention_control", None),
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)
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c = c.detach().to("cpu")
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conditioning_data = ConditioningFieldData(
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conditionings=[
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BasicConditioningInfo(
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embeds=c,
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extra_conditioning=ec,
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)
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]
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)
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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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conditioning=ConditioningField(
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conditioning_name=conditioning_name,
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),
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)
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class SDXLPromptInvocationBase:
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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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)
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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 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 = 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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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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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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(
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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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# 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(
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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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), 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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compel = Compel(
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tokenizer=tokenizer,
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text_encoder=text_encoder,
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textual_inversion_manager=ti_manager,
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dtype_for_device_getter=torch_dtype,
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truncate_long_prompts=True, # TODO:
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returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, # TODO: clip skip
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requires_pooled=True,
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)
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conjunction = Compel.parse_prompt_string(prompt)
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if context.services.configuration.log_tokenization:
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# TODO: better logging for and syntax
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for prompt_obj in conjunction.prompts:
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log_tokenization_for_prompt_object(prompt_obj, tokenizer)
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# TODO: ask for optimizations? to not run text_encoder twice
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c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
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if get_pooled:
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c_pooled = compel.conditioning_provider.get_pooled_embeddings([prompt])
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else:
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c_pooled = None
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ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
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tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
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cross_attention_control_args=options.get("cross_attention_control", None),
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)
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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, ec
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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):
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schema_extra = {
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"ui": {"title": "SDXL Prompt (Compel)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
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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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c1, c1_pooled, ec1 = self.run_clip_compel(
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context, self.clip, self.prompt, False, "lora_te1_", zero_on_empty=False
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)
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if self.style.strip() == "":
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c2, c2_pooled, ec2 = self.run_clip_compel(
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context, self.clip2, self.prompt, True, "lora_te2_", zero_on_empty=True
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)
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else:
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c2, c2_pooled, ec2 = self.run_clip_compel(
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context, self.clip2, self.style, True, "lora_te2_", zero_on_empty=True
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)
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original_size = (self.original_height, self.original_width)
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crop_coords = (self.crop_top, self.crop_left)
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target_size = (self.target_height, self.target_width)
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add_time_ids = torch.tensor([original_size + crop_coords + target_size])
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conditioning_data = ConditioningFieldData(
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conditionings=[
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SDXLConditioningInfo(
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embeds=torch.cat([c1, c2], dim=-1),
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pooled_embeds=c2_pooled,
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add_time_ids=add_time_ids,
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extra_conditioning=ec1,
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)
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]
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)
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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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conditioning=ConditioningField(
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conditioning_name=conditioning_name,
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),
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)
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class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
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"""Parse prompt using compel package to conditioning."""
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type: Literal["sdxl_refiner_compel_prompt"] = "sdxl_refiner_compel_prompt"
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style: str = Field(default="", description="Style prompt") # TODO: ?
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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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aesthetic_score: float = Field(6.0, description="")
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clip2: 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": {
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"title": "SDXL Refiner Prompt (Compel)",
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"tags": ["prompt", "compel"],
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"type_hints": {"model": "model"},
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},
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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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# TODO: if there will appear lora for refiner - write proper prefix
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c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "<NONE>", zero_on_empty=False)
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original_size = (self.original_height, self.original_width)
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crop_coords = (self.crop_top, self.crop_left)
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add_time_ids = torch.tensor([original_size + crop_coords + (self.aesthetic_score,)])
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conditioning_data = ConditioningFieldData(
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conditionings=[
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SDXLConditioningInfo(
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embeds=c2,
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pooled_embeds=c2_pooled,
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add_time_ids=add_time_ids,
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extra_conditioning=ec2, # or None
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)
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]
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)
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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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conditioning=ConditioningField(
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conditioning_name=conditioning_name,
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),
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)
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class ClipSkipInvocationOutput(BaseInvocationOutput):
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"""Clip skip node output"""
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type: Literal["clip_skip_output"] = "clip_skip_output"
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clip: ClipField = Field(None, description="Clip with skipped layers")
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class ClipSkipInvocation(BaseInvocation):
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"""Skip layers in clip text_encoder model."""
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type: Literal["clip_skip"] = "clip_skip"
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clip: ClipField = Field(None, description="Clip to use")
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skipped_layers: int = Field(0, description="Number of layers to skip in text_encoder")
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class Config(InvocationConfig):
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schema_extra = {
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"ui": {"title": "CLIP Skip", "tags": ["clip", "skip"]},
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}
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def invoke(self, context: InvocationContext) -> ClipSkipInvocationOutput:
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self.clip.skipped_layers += self.skipped_layers
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return ClipSkipInvocationOutput(
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clip=self.clip,
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)
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def get_max_token_count(
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tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction], truncate_if_too_long=False
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) -> int:
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if type(prompt) is Blend:
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blend: Blend = prompt
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return max([get_max_token_count(tokenizer, p, truncate_if_too_long) for p in blend.prompts])
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elif type(prompt) is Conjunction:
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conjunction: Conjunction = prompt
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return sum([get_max_token_count(tokenizer, p, truncate_if_too_long) for p in conjunction.prompts])
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else:
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return len(get_tokens_for_prompt_object(tokenizer, prompt, truncate_if_too_long))
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def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True) -> List[str]:
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if type(parsed_prompt) is Blend:
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raise ValueError("Blend is not supported here - you need to get tokens for each of its .children")
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text_fragments = [
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x.text
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if type(x) is Fragment
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else (" ".join([f.text for f in x.original]) if type(x) is CrossAttentionControlSubstitute else str(x))
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for x in parsed_prompt.children
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]
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text = " ".join(text_fragments)
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tokens = tokenizer.tokenize(text)
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if truncate_if_too_long:
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max_tokens_length = tokenizer.model_max_length - 2 # typically 75
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tokens = tokens[0:max_tokens_length]
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return tokens
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def log_tokenization_for_conjunction(c: Conjunction, tokenizer, display_label_prefix=None):
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display_label_prefix = display_label_prefix or ""
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for i, p in enumerate(c.prompts):
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if len(c.prompts) > 1:
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this_display_label_prefix = f"{display_label_prefix}(conjunction part {i + 1}, weight={c.weights[i]})"
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else:
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this_display_label_prefix = display_label_prefix
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log_tokenization_for_prompt_object(p, tokenizer, display_label_prefix=this_display_label_prefix)
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def log_tokenization_for_prompt_object(p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None):
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display_label_prefix = display_label_prefix or ""
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if type(p) is Blend:
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blend: Blend = p
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for i, c in enumerate(blend.prompts):
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log_tokenization_for_prompt_object(
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c,
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tokenizer,
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display_label_prefix=f"{display_label_prefix}(blend part {i + 1}, weight={blend.weights[i]})",
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)
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elif type(p) is FlattenedPrompt:
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flattened_prompt: FlattenedPrompt = p
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if flattened_prompt.wants_cross_attention_control:
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original_fragments = []
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edited_fragments = []
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for f in flattened_prompt.children:
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if type(f) is CrossAttentionControlSubstitute:
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original_fragments += f.original
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edited_fragments += f.edited
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else:
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original_fragments.append(f)
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edited_fragments.append(f)
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original_text = " ".join([x.text for x in original_fragments])
|
|
log_tokenization_for_text(
|
|
original_text,
|
|
tokenizer,
|
|
display_label=f"{display_label_prefix}(.swap originals)",
|
|
)
|
|
edited_text = " ".join([x.text for x in edited_fragments])
|
|
log_tokenization_for_text(
|
|
edited_text,
|
|
tokenizer,
|
|
display_label=f"{display_label_prefix}(.swap replacements)",
|
|
)
|
|
else:
|
|
text = " ".join([x.text for x in flattened_prompt.children])
|
|
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):
|
|
"""shows how the prompt is tokenized
|
|
# usually tokens have '</w>' to indicate end-of-word,
|
|
# but for readability it has been replaced with ' '
|
|
"""
|
|
tokens = tokenizer.tokenize(text)
|
|
tokenized = ""
|
|
discarded = ""
|
|
usedTokens = 0
|
|
totalTokens = len(tokens)
|
|
|
|
for i in range(0, totalTokens):
|
|
token = tokens[i].replace("</w>", " ")
|
|
# alternate color
|
|
s = (usedTokens % 6) + 1
|
|
if truncate_if_too_long and i >= tokenizer.model_max_length:
|
|
discarded = discarded + f"\x1b[0;3{s};40m{token}"
|
|
else:
|
|
tokenized = tokenized + f"\x1b[0;3{s};40m{token}"
|
|
usedTokens += 1
|
|
|
|
if usedTokens > 0:
|
|
print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
|
|
print(f"{tokenized}\x1b[0m")
|
|
|
|
if discarded != "":
|
|
print(f"\n>> [TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
|
|
print(f"{discarded}\x1b[0m")
|