InvokeAI/invokeai/app/invocations/compel.py

from typing import Literal, Optional, Union
from pydantic import BaseModel, Field
from contextlib import ExitStack
import re

from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
from .model import ClipField

from ...backend.util.devices import torch_dtype
from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
from ...backend.model_management import BaseModelType, ModelType, SubModelType
from ...backend.model_management.lora import ModelPatcher

from compel import Compel
from compel.prompt_parser import (
    Blend,
    CrossAttentionControlSubstitute,
    FlattenedPrompt,
    Fragment, Conjunction,
)


class ConditioningField(BaseModel):
    conditioning_name: Optional[str] = Field(default=None, description="The name of conditioning data")
    class Config:
        schema_extra = {"required": ["conditioning_name"]}


class CompelOutput(BaseInvocationOutput):
    """Compel parser output"""

    #fmt: off
    type: Literal["compel_output"] = "compel_output"

    conditioning: ConditioningField = Field(default=None, description="Conditioning")
    #fmt: on


class CompelInvocation(BaseInvocation):
    """Parse prompt using compel package to conditioning."""

    type: Literal["compel"] = "compel"

    prompt: str = Field(default="", description="Prompt")
    clip: ClipField = Field(None, description="Clip to use")

    # Schema customisation
    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Prompt (Compel)",
                "tags": ["prompt", "compel"],
                "type_hints": {
                  "model": "model"
                }
            },
        }

    def invoke(self, context: InvocationContext) -> CompelOutput:

        tokenizer_info = context.services.model_manager.get_model(
            **self.clip.tokenizer.dict(),
        )
        text_encoder_info = context.services.model_manager.get_model(
            **self.clip.text_encoder.dict(),
        )
        with tokenizer_info as orig_tokenizer,\
             text_encoder_info as text_encoder:

            loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]

            ti_list = []
            for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
                name = trigger[1:-1]
                try:
                    ti_list.append(
                        context.services.model_manager.get_model(
                            model_name=name,
                            base_model=self.clip.text_encoder.base_model,
                            model_type=ModelType.TextualInversion,
                        ).context.model
                    )
                except Exception:
                    #print(e)
                    #import traceback
                    #print(traceback.format_exc())
                    print(f"Warn: trigger: \"{trigger}\" not found")

            with ModelPatcher.apply_lora_text_encoder(text_encoder, loras),\
                 ModelPatcher.apply_ti(orig_tokenizer, text_encoder, ti_list) as (tokenizer, ti_manager):

                compel = Compel(
                    tokenizer=tokenizer,
                    text_encoder=text_encoder,
                    textual_inversion_manager=ti_manager,
                    dtype_for_device_getter=torch_dtype,
                    truncate_long_prompts=True, # TODO:
                )
                
                conjunction = Compel.parse_prompt_string(self.prompt)
                prompt: Union[FlattenedPrompt, Blend] = conjunction.prompts[0]

                if context.services.configuration.log_tokenization:
                    log_tokenization_for_prompt_object(prompt, tokenizer)

                c, options = compel.build_conditioning_tensor_for_prompt_object(prompt)
                
                # TODO: long prompt support
                #if not self.truncate_long_prompts:
                #    [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
                ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
                    tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
                    cross_attention_control_args=options.get("cross_attention_control", None),
                )
                
            conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"

            # TODO: hacky but works ;D maybe rename latents somehow?
            context.services.latents.save(conditioning_name, (c, ec))

            return CompelOutput(
                conditioning=ConditioningField(
                    conditioning_name=conditioning_name,
                ),
            )


def get_max_token_count(
    tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction], truncate_if_too_long=False
) -> int:
    if type(prompt) is Blend:
        blend: Blend = prompt
        return max(
            [
                get_max_token_count(tokenizer, p, truncate_if_too_long)
                for p in blend.prompts
            ]
        )
    elif type(prompt) is Conjunction:
        conjunction: Conjunction = prompt
        return sum(
            [
                get_max_token_count(tokenizer, p, truncate_if_too_long)
                for p in conjunction.prompts
            ]
        )
    else:
        return len(
            get_tokens_for_prompt_object(tokenizer, prompt, truncate_if_too_long)
        )


def get_tokens_for_prompt_object(
    tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True
) -> [str]:
    if type(parsed_prompt) is Blend:
        raise ValueError(
            "Blend is not supported here - you need to get tokens for each of its .children"
        )

    text_fragments = [
        x.text
        if type(x) is Fragment
        else (
            " ".join([f.text for f in x.original])
            if type(x) is CrossAttentionControlSubstitute
            else str(x)
        )
        for x in parsed_prompt.children
    ]
    text = " ".join(text_fragments)
    tokens = tokenizer.tokenize(text)
    if truncate_if_too_long:
        max_tokens_length = tokenizer.model_max_length - 2  # typically 75
        tokens = tokens[0:max_tokens_length]
    return tokens


def log_tokenization_for_conjunction(
    c: Conjunction, tokenizer, display_label_prefix=None
):
    display_label_prefix = display_label_prefix or ""
    for i, p in enumerate(c.prompts):
        if len(c.prompts)>1:
            this_display_label_prefix = f"{display_label_prefix}(conjunction part {i + 1}, weight={c.weights[i]})"
        else:
            this_display_label_prefix = display_label_prefix
        log_tokenization_for_prompt_object(
            p,
            tokenizer,
            display_label_prefix=this_display_label_prefix
        )


def log_tokenization_for_prompt_object(
    p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None
):
    display_label_prefix = display_label_prefix or ""
    if type(p) is Blend:
        blend: Blend = p
        for i, c in enumerate(blend.prompts):
            log_tokenization_for_prompt_object(
                c,
                tokenizer,
                display_label_prefix=f"{display_label_prefix}(blend part {i + 1}, weight={blend.weights[i]})",
            )
    elif type(p) is FlattenedPrompt:
        flattened_prompt: FlattenedPrompt = p
        if flattened_prompt.wants_cross_attention_control:
            original_fragments = []
            edited_fragments = []
            for f in flattened_prompt.children:
                if type(f) is CrossAttentionControlSubstitute:
                    original_fragments += f.original
                    edited_fragments += f.edited
                else:
                    original_fragments.append(f)
                    edited_fragments.append(f)

            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")