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https://github.com/invoke-ai/InvokeAI
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what have i done
This commit is contained in:
maryhipp
parent
16b3718d6a
commit
ef474a3196
@ -1,40 +1,43 @@
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from dataclasses import dataclass
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from typing import List, Optional, Union
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from typing import Iterator, List, Optional, Tuple, 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 transformers import CLIPTokenizer
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from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
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from invokeai.app.shared.fields import FieldDescriptions
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import invokeai.backend.util.logging as logger
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from invokeai.app.invocations.fields import (
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FieldDescriptions,
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Input,
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InputField,
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OutputField,
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UIComponent,
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)
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from invokeai.app.invocations.primitives import ConditioningOutput
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from invokeai.app.services.model_records import UnknownModelException
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.app.util.ti_utils import extract_ti_triggers_from_prompt
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from invokeai.backend.lora import LoRAModelRaw
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from invokeai.backend.model_manager import ModelType
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from invokeai.backend.model_patcher import ModelPatcher
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from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
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BasicConditioningInfo,
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ConditioningFieldData,
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ExtraConditioningInfo,
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SDXLConditioningInfo,
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)
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from invokeai.backend.textual_inversion import TextualInversionModelRaw
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from invokeai.backend.util.devices import torch_dtype
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from ...backend.model_management.lora import ModelPatcher
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from ...backend.model_management.models import ModelNotFoundException, ModelType
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from ...backend.util.devices import torch_dtype
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from ..util.ti_utils import extract_ti_triggers_from_prompt
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from .baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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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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invocation,
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invocation_output,
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)
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from .model import ClipField
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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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# unconditioned: Optional[torch.Tensor]
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# class ConditioningAlgo(str, Enum):
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@ -48,7 +51,7 @@ class ConditioningFieldData:
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title="Prompt",
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tags=["prompt", "compel"],
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category="conditioning",
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version="1.0.0",
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version="1.0.1",
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)
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class CompelInvocation(BaseInvocation):
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"""Parse prompt using compel package to conditioning."""
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@ -66,49 +69,34 @@ class CompelInvocation(BaseInvocation):
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@torch.no_grad()
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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.model_dump(),
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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.model_dump(),
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context=context,
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)
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tokenizer_info = context.models.load(**self.clip.tokenizer.model_dump())
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text_encoder_info = context.models.load(**self.clip.text_encoder.model_dump())
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def _lora_loader():
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def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
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for lora in self.clip.loras:
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lora_info = context.services.model_manager.get_model(
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**lora.model_dump(exclude={"weight"}), context=context
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)
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yield (lora_info.context.model, lora.weight)
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lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
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assert isinstance(lora_info.model, LoRAModelRaw)
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yield (lora_info.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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# loras = [(context.models.get(**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 extract_ti_triggers_from_prompt(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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loaded_model = context.models.load(key=name).model
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assert isinstance(loaded_model, TextualInversionModelRaw)
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ti_list.append((name, loaded_model))
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except UnknownModelException:
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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 (
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ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
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ModelPatcher.apply_ti(tokenizer_info.model, text_encoder_info.model, ti_list) as (
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tokenizer,
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ti_manager,
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),
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@ -116,7 +104,7 @@ class CompelInvocation(BaseInvocation):
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# Apply the LoRA after text_encoder has been moved to its target device for faster patching.
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ModelPatcher.apply_lora_text_encoder(text_encoder, _lora_loader()),
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# Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
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ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),
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ModelPatcher.apply_clip_skip(text_encoder_info.model, self.clip.skipped_layers),
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):
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compel = Compel(
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tokenizer=tokenizer,
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@ -128,7 +116,7 @@ class CompelInvocation(BaseInvocation):
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conjunction = Compel.parse_prompt_string(self.prompt)
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if context.services.configuration.log_tokenization:
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if context.config.get().log_tokenization:
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log_tokenization_for_conjunction(conjunction, tokenizer)
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c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
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@ -149,17 +137,14 @@ class CompelInvocation(BaseInvocation):
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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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conditioning_name = context.conditioning.save(conditioning_data)
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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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)
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return ConditioningOutput.build(conditioning_name)
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class SDXLPromptInvocationBase:
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"""Prompt processor for SDXL models."""
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def run_clip_compel(
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self,
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context: InvocationContext,
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@ -168,26 +153,21 @@ class SDXLPromptInvocationBase:
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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.model_dump(),
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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.model_dump(),
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context=context,
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)
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[ExtraConditioningInfo]]:
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tokenizer_info = context.models.load(**clip_field.tokenizer.model_dump())
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text_encoder_info = context.models.load(**clip_field.text_encoder.model_dump())
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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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cpu_text_encoder = text_encoder_info.model
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assert isinstance(cpu_text_encoder, torch.nn.Module)
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c = torch.zeros(
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(
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1,
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cpu_text_encoder.config.max_position_embeddings,
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cpu_text_encoder.config.hidden_size,
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),
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dtype=text_encoder_info.context.cache.precision,
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dtype=cpu_text_encoder.dtype,
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)
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if get_pooled:
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c_pooled = torch.zeros(
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@ -198,40 +178,36 @@ class SDXLPromptInvocationBase:
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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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def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
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for lora in clip_field.loras:
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lora_info = context.services.model_manager.get_model(
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**lora.model_dump(exclude={"weight"}), context=context
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)
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yield (lora_info.context.model, lora.weight)
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lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
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lora_model = lora_info.model
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assert isinstance(lora_model, LoRAModelRaw)
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yield (lora_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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# loras = [(context.models.get(**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 extract_ti_triggers_from_prompt(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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ti_model = context.models.load_by_attrs(
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model_name=name, base_model=text_encoder_info.config.base, model_type=ModelType.TextualInversion
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).model
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assert isinstance(ti_model, TextualInversionModelRaw)
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ti_list.append((name, ti_model))
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except UnknownModelException:
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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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logger.warning(f'trigger: "{trigger}" not found')
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except ValueError:
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logger.warning(f'trigger: "{trigger}" more than one similarly-named textual inversion models')
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with (
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ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
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ModelPatcher.apply_ti(tokenizer_info.model, text_encoder_info.model, ti_list) as (
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tokenizer,
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ti_manager,
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),
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@ -239,7 +215,7 @@ class SDXLPromptInvocationBase:
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# Apply the LoRA after text_encoder has been moved to its target device for faster patching.
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ModelPatcher.apply_lora(text_encoder, _lora_loader(), lora_prefix),
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# Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
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ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),
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ModelPatcher.apply_clip_skip(text_encoder_info.model, clip_field.skipped_layers),
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):
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compel = Compel(
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tokenizer=tokenizer,
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@ -253,7 +229,7 @@ class SDXLPromptInvocationBase:
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conjunction = Compel.parse_prompt_string(prompt)
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if context.services.configuration.log_tokenization:
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if context.config.get().log_tokenization:
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# TODO: better logging for and syntax
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log_tokenization_for_conjunction(conjunction, tokenizer)
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@ -286,7 +262,7 @@ class SDXLPromptInvocationBase:
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title="SDXL Prompt",
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tags=["sdxl", "compel", "prompt"],
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category="conditioning",
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version="1.0.0",
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version="1.0.1",
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)
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class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
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"""Parse prompt using compel package to conditioning."""
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@ -357,6 +333,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
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dim=1,
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)
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assert c2_pooled is not None
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conditioning_data = ConditioningFieldData(
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conditionings=[
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SDXLConditioningInfo(
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@ -368,14 +345,9 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
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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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conditioning_name = context.conditioning.save(conditioning_data)
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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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)
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return ConditioningOutput.build(conditioning_name)
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@invocation(
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@ -383,7 +355,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
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title="SDXL Refiner Prompt",
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tags=["sdxl", "compel", "prompt"],
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category="conditioning",
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version="1.0.0",
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version="1.0.1",
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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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@ -410,6 +382,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
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add_time_ids = torch.tensor([original_size + crop_coords + (self.aesthetic_score,)])
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assert c2_pooled is not None
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conditioning_data = ConditioningFieldData(
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conditionings=[
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SDXLConditioningInfo(
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@ -421,14 +394,9 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
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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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conditioning_name = context.conditioning.save(conditioning_data)
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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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)
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return ConditioningOutput.build(conditioning_name)
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@invocation_output("clip_skip_output")
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@ -449,7 +417,7 @@ class ClipSkipInvocation(BaseInvocation):
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"""Skip layers in clip text_encoder model."""
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clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP")
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skipped_layers: int = InputField(default=0, description=FieldDescriptions.skipped_layers)
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skipped_layers: int = InputField(default=0, ge=0, description=FieldDescriptions.skipped_layers)
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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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@ -459,9 +427,9 @@ class ClipSkipInvocation(BaseInvocation):
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def get_max_token_count(
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tokenizer,
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tokenizer: CLIPTokenizer,
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prompt: Union[FlattenedPrompt, Blend, Conjunction],
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truncate_if_too_long=False,
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truncate_if_too_long: bool = 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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@ -473,7 +441,9 @@ def get_max_token_count(
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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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def get_tokens_for_prompt_object(
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tokenizer: CLIPTokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long: bool = True
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) -> 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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@ -486,24 +456,29 @@ def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, trun
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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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tokens: List[str] = 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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def log_tokenization_for_conjunction(
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c: Conjunction, tokenizer: CLIPTokenizer, display_label_prefix: Optional[str] = None
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) -> 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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assert display_label_prefix is not None
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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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def log_tokenization_for_prompt_object(
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p: Union[Blend, FlattenedPrompt], tokenizer: CLIPTokenizer, display_label_prefix: Optional[str] = None
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) -> 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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@ -543,7 +518,12 @@ def log_tokenization_for_prompt_object(p: Union[Blend, FlattenedPrompt], tokeniz
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log_tokenization_for_text(text, tokenizer, display_label=display_label_prefix)
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def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_too_long=False):
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def log_tokenization_for_text(
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text: str,
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tokenizer: CLIPTokenizer,
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display_label: Optional[str] = None,
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truncate_if_too_long: Optional[bool] = False,
|
||||
) -> None:
|
||||
"""shows how the prompt is tokenized
|
||||
# usually tokens have '</w>' to indicate end-of-word,
|
||||
# but for readability it has been replaced with ' '
|
||||
|
||||
Loading…
Reference in New Issue
Block a user