Initial working commit

.github/workflows/action_attempt_two.yml

@@ -1,10 +0,0 @@
-name: Test
-on:
-  workflow_dispatch:
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v2
-      - name: Setup upterm session
-        uses: lhotari/action-upterm@v1

.github/workflows/full_pipeline.yml

@@ -1,85 +0,0 @@
-name: Run Test Workflow
-on: [workflow_dispatch]
-
-jobs:
-  Test:
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: [ "3.7", "3.8", "3.9", "3.10", "3.11" ]
-    steps:
-      - name: Clone Upstream
-        uses: actions/checkout@v3
-        with:
-          repository: comfyanonymous/ComfyUI
-          ref: master
-          fetch-depth: 0
-
-      - name: Clone Node
-        uses: actions/checkout@v3
-        with:
-          ref: master
-          fetch-depth: 0
-          path: custom_nodes/KepPromptLang
-
-      - name: Setup Python
-        uses: actions/setup-python@v4
-        with:
-          # Version range or exact version of Python or PyPy to use, using SemVer's version range syntax. Reads from .python-version if unset.
-          python-version: ${{ matrix.python-version }}
-
-      - name: Cache virtualenv
-        uses: actions/cache@v3
-        id: cache-venv
-        with:
-          path: ./.venv/
-          key: ${{ runner.os }}-venv-${{ matrix.python-version }}-${{ hashFiles('**/requirements.txt') }}
-          restore-keys: |
-            ${{ runner.os }}-venv-${{ matrix.python-version }}-
-
-      - name: Install Requirements
-        if: steps.cache-venv.outputs.cache-hit != 'true'
-        run: |
-          python -m venv ./.venv
-          source ./.venv/bin/activate
-          pip install torch --index-url https://download.pytorch.org/whl/cpu
-          pip install -r requirements.txt
-          pip install -r custom_nodes/KepPromptLang/requirements.txt
-          pip install huggingface_hub websocket-client
-
-#      - name: Cache SD Checkpoint
-#        uses: actions/cache@v3
-#        with:
-#          path: |
-#            models/checkpoints
-#          key: ${{ runner.os }}-sd-15-checkpoint
-
-      - name: Check and Download Model
-        run: |
-          source ./.venv/bin/activate
-          python custom_nodes/KepPromptLang/test_files/check_and_download_model.py
-
-      - name: Run in Background
-        env:
-          PYTHONUNBUFFERED: 1
-        run: |
-          source ./.venv/bin/activate
-          python main.py --cpu &> server.log &
-          sleep 10
-
-#      - name: Setup upterm session
-#        uses: lhotari/action-upterm@v1
-
-      - name: Run Workflow
-        run: |
-          source ./.venv/bin/activate
-          python custom_nodes/KepPromptLang/test_files/run_workflow.py
-
-      - name: Upload Comfy Server Log
-        if: always()
-        uses: actions/upload-artifact@v3
-        with:
-            name: comfy-server-log-${{ matrix.python-version }}
-            path: server.log
-

README.md

@@ -1,79 +1 @@
 # ClipStuff
-## Basic Instructions.
-Clone repo into custom_nodes folder.
-Install the requirements.txt file via pip.
-
-Pass CLIP output from Load Checkpoint into SpecialClipLoader node, then use the outputted clip with standard Clip Text Encode.
-
-See example workflow in examples folder.
-
-### Example Photo
-![Example Photo](assets/first_example.png)
-
-## Functions
-
-## Syntax Elements
-
-1. **Embedding**:
-    - Syntax: `embedding:WORD`
-    - Example: `embedding:face_vector`
-    - Represents a named vector embedding(Textual Inversion).
-
-2. **Word**:
-    - Syntax: Any alphanumeric word including characters such as `,`, `_`, and `-`.
-    - Example: `cat, dog_face, id_123`
-    - Represents simple words or identifiers.
-
-3. **Quoted String**:
-    - Syntax: A string enclosed within double or single quotes. You can escape quotes inside the string using a backslash (`\`).
-    - Example: `"Hello World"`, `'It\'s a sunny day'`
-    - Represents string literals.
-
-
-### Notes on Arguments:
-- Each function takes one or more arguments.
-- An argument (`arg`) can be an embedding, multiple words, another function, or a quoted string.
-- For functions that accept multiple arguments, they are separated by the `|` symbol.
-
-## Examples
-
-1. Add two embeddings and normalize the result:
-   ```
-   norm(sum(cat | dog | horse | parrot))
-   ```
-
-2. Negate an embedding:
-   ```
-   neg(embedding:body_vector)
-   ```
-
-3. King - Man + Woman = Queen:
-   ```
-   sum(diff(king|man)|woman)
-   ```
-   or
-   ```
-   sum(king|neg(man)|woman)
-   ```
-
-## Functions
-
-Here are the available functions and their usage:
-
-| Display Name | Action Name | Description | Usage Examples |
-| --- | --- | --- | --- |
-| Multiply | mult | Multiplies the provided segments or actions by the multiplier. | <ul><li>mult(The cat is\|2.5)</li><li>mult(Cat\|-1)</li></ul> |
-| Set Dimensions | setDims | Sets the specified dimensions of the input embeddings to the specified value | <ul><li>The setDims(cat\|4, -0.01253\|76, 1.2) is happy</li></ul> |
-| Negate | neg | Negates the provided segments or actions. | <ul><li>neg(cat)</li><li>sum(king\|neg(man)\|women)</li></ul> |
-| Normalize | norm | Normalizes the provided segments or actions. | <ul><li>norm(cat)</li><li>sum(cat\|norm(sum(tiger\|fish)))</li></ul> |
-| Positional Embedding Scale | posScale | Scales(Multiplies) the positional embeddings of the provided segments or actions by the multiplier. | <ul><li>A posScale(cat\|1.5) on a rainy day</li></ul> |
-| Random Embedding | rand | Returns a random embedding of the specified token length, with the values optionally bounded by the second and third arguments. | <ul><li>A rand(1) cat</li><li>A rand(1\|-1\|1) cat</li></ul> |
-| Difference | diff | Subtracts the segments in the order they are given. The first segment is subtracted from the second, then the third from the result, and so on. | <ul><li>diff(The cat is\|The dog is)</li><li>diff(Cat\|Dog)</li><li>sum(diff(king\|man)\|woman)</li></ul> |
-| Slerp | slerp | Performs a slerp(Interpolation) between two segments or actions, with the given weight. The recommended weight is 0 - 1 | <ul><li>The slerp(cat\|dog\|0.5) is happy</li></ul> |
-| Pooled Average(Experimental) | _exp-pooledAvg | Processes the provided segments or actions fully through CLIP and creates a pooled average of the last hidden state by averaging the last hidden state of each token. | <ul><li>A cat on a _exp-pooledAvg(beautiful sunny day)</li><li>A _exp-pooledAvg(broken glass) bottle</li></ul> |
-| Sum | sum | Adds the embeddings of the provided segments or actions. | <ul><li>A happy sum(cat\|dog\|shark)</li></ul> |
-| Pooler Output(Experimental) | _exp-pooler | Processes the provided segments or actions fully through CLIP and returns the pooler_output from the transformer | <ul><li>A cat on a _exp-pooler(beautiful sunny day)</li><li>A _exp-pooler(broken glass) bottle</li></ul> |
-| Scale Dimensions | scaleDims | Scales the specified dimensions of the input embeddings by the specified amount | <ul><li>The scaleDims(cat\|4,1.5\|76,1.2) is happy</li></ul> |
-| Ignore Positional Embeddings | postPos | Prevents positional embeddings from being applied to the provided segments or actions. | <ul><li>A postPos(cat) on a rainy day</li></ul> |
-| Average | avg | Performs a weighted average between two segments or actions. The recommended weight is 0 - 1. | <ul><li>avg(The cat is\|The dog is\|0.5)</li><li>avg(Cat\|Dog\|0.5)</li></ul> |
-

__init__.py

@@ -1,9 +1,11 @@
 from .nodes import (
+    KepAdvTextEncode,
     BuildGif,
     SpecialClipLoader,
 )
 
 NODE_CLASS_MAPPINGS = {
+    "Kep Adv Text Encode": KepAdvTextEncode,
     "Build Gif": BuildGif,
     "Special CLIP Loader": SpecialClipLoader,
 }

lib/__init__.py

@@ -1,31 +0,0 @@
-from custom_nodes.KepPromptLang.lib.actions.avg import AverageAction
-from custom_nodes.KepPromptLang.lib.actions.diff import DiffAction
-from custom_nodes.KepPromptLang.lib.actions.mult import MultiplyAction
-from custom_nodes.KepPromptLang.lib.actions.neg import NegAction
-from custom_nodes.KepPromptLang.lib.actions.norm import NormAction
-
-from custom_nodes.KepPromptLang.lib.actions.pooled_avg import PooledAvgAction
-from custom_nodes.KepPromptLang.lib.actions.pooler import PoolerAction
-from custom_nodes.KepPromptLang.lib.actions.pos_scale import PosScaleAction
-from custom_nodes.KepPromptLang.lib.actions.post_pos import PostPosAction
-from custom_nodes.KepPromptLang.lib.actions.rand import RandAction
-from custom_nodes.KepPromptLang.lib.actions.scale_dims import ScaleDims
-from custom_nodes.KepPromptLang.lib.actions.set_dims import SetDims
-from custom_nodes.KepPromptLang.lib.actions.slerp import SlerpAction
-from custom_nodes.KepPromptLang.lib.actions.sum import SumAction
-from custom_nodes.KepPromptLang.lib.parser.registration import register_action
-
-register_action(DiffAction)
-register_action(MultiplyAction)
-register_action(NegAction)
-register_action(NormAction)
-register_action(RandAction)
-register_action(SumAction)
-register_action(SlerpAction)
-register_action(AverageAction)
-register_action(ScaleDims)
-register_action(SetDims)
-register_action(PosScaleAction)
-register_action(PoolerAction)
-register_action(PostPosAction)
-register_action(PooledAvgAction)

lib/action/base.py

@@ -1,158 +0,0 @@
-from abc import ABC, abstractmethod
-from enum import Enum
-from typing import Union, List, TypedDict, Tuple
-
-from torch import Tensor
-from torch.nn import Embedding
-from transformers.models.clip.modeling_clip import CLIPTextTransformer
-
-from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
-
-
-class ActionArity(Enum):
-    NONE = 0
-    SINGLE = 1
-    MULTI = 2
-
-class PostModifiers(TypedDict):
-    """
-    A dictionary of post modifiers for an action result.
-    """
-    position_embed_scale: Union[float, None]
-    bypass_pos_embed: Union[bool, None]
-
-
-class Action(ABC):
-    @property
-    @abstractmethod
-    def chars(self) -> Union[List[str], None]:
-        pass
-
-    @property
-    @abstractmethod
-    def arity(self) -> ActionArity:
-        """
-        Determines the arity of the action. This is used to determine how many arguments the action supports.
-        :return:
-        """
-        pass
-
-    @property
-    @abstractmethod
-    def display_name(self) -> str:
-        pass
-
-    @property
-    @abstractmethod
-    def action_name(self) -> str:
-        pass
-
-    @property
-    @abstractmethod
-    def description(self) -> str:
-        pass
-
-    @property
-    def usage_examples(self) -> List[str]:
-        return []
-
-    @property
-    @abstractmethod
-    def grammar(self) -> str:
-        """
-        The grammar for this action. This is used to parse the action from the prompt.
-        :return:
-        """
-        pass
-
-    @abstractmethod
-    def token_length(self) -> int:
-        """
-        The length of the tokens that this action will add to the prompt.
-        :return:
-        """
-        pass
-
-    @abstractmethod
-    def get_all_segments(self) -> List[PromptSegment]:
-        """
-        Get all segments, including nested segments.
-        :return:
-        """
-        pass
-
-    @abstractmethod
-    def __init__(self, *args, **kwargs) -> None:
-        """
-        Initialize the action. This is called when the action is parsed from the prompt.
-        :param args: The arguments for the action.
-        """
-        pass
-
-    @abstractmethod
-    def get_result(self, embedding_module: Embedding) -> Union[Tensor, Tuple[Tensor, PostModifiers]]:
-        """
-        Get the result of this action. This is called when the embeddings are being calculated.
-        :param embedding_module: The embedding module to use to get the base embeddings for tokens.
-        :return:
-        """
-        pass
-
-    def process_with_transformer(self, transformer: CLIPTextTransformer, embedding_module: Embedding) -> None:
-        """
-        For actions that need access to the TextTransformer, this method is called. Results are expected to be returned via get_result still.
-        :param transformer: An instance of CLIPTextTransformer
-        """
-        pass
-
-    def depth_repr(self, depth: int = 1) -> str:
-        raise NotImplementedError()
-
-
-class SingleArgAction(Action, ABC):
-    arity = ActionArity.SINGLE
-    def get_all_segments(self) -> List[PromptSegment]:
-        segments = []
-        for seg_or_action in self.arg:
-            if isinstance(seg_or_action, Action):
-                segments.extend(seg_or_action.get_all_segments())
-            else:
-                segments.append(seg_or_action)
-        return segments
-
-    def process_with_transformer(self, transformer: CLIPTextTransformer, embedding_module: Embedding) -> None:
-        for seg_or_action in self.arg:
-            if isinstance(seg_or_action, Action):
-                seg_or_action.process_with_transformer(transformer, embedding_module)
-
-    def __init__(self, arg: List[Union[PromptSegment, Action]]):
-        # TODO: Target is a list now... what does this mean for us..
-        self.arg = arg
-
-    def __repr__(self) -> str:
-        return f"{self.display_name}({self.arg})"
-
-class MultiArgAction(Action, ABC):
-    arity = ActionArity.MULTI
-    def get_all_segments(self) -> List[PromptSegment]:
-        segments = []
-        for arg in self.all_args:
-            for seg_or_action in arg:
-                if isinstance(seg_or_action, Action):
-                    segments.extend(seg_or_action.get_all_segments())
-                else:
-                    segments.append(seg_or_action)
-
-        return segments
-
-    def process_with_transformer(self, transformer: CLIPTextTransformer, embedding_module: Embedding) -> None:
-        for arg in self.all_args:
-            for seg_or_action in arg:
-                if isinstance(seg_or_action, Action):
-                    seg_or_action.process_with_transformer(transformer, embedding_module)
-
-    def __init__(
-            self,
-            args: List[List[Union[PromptSegment, Action]]],
-    ):
-        self.all_args = args

lib/actions/__init__.py

@@ -0,0 +1,6 @@
+from custom_nodes.ClipStuff.lib.actions.arith import ArithAction
+from custom_nodes.ClipStuff.lib.actions.nudge import NudgeAction
+
+ALL_ACTIONS = [NudgeAction, ArithAction]
+ALL_START_CHARS = [action.START_CHAR for action in ALL_ACTIONS]
+ALL_END_CHARS = [action.END_CHAR for action in ALL_ACTIONS]

lib/actions/action_utils.py

@@ -1,20 +0,0 @@
-from typing import List
-
-from torch import Tensor
-from torch.nn import Embedding
-
-from custom_nodes.KepPromptLang.lib.action.base import Action
-from custom_nodes.KepPromptLang.lib.actions.types import SegOrAction
-
-
-def get_embedding(seg_or_action: SegOrAction, embedding_module: Embedding) -> Tensor:
-    if isinstance(seg_or_action, Action):
-        return seg_or_action.get_result(embedding_module)
-    return seg_or_action.get_embeddings(embedding_module)
-
-def get_total_length(args: List[SegOrAction]) -> int:
-    total_length = 0
-    for seg_or_action in args:
-        total_length += seg_or_action.token_length()
-
-    return total_length

lib/actions/arith.py

@@ -0,0 +1,131 @@
+from typing import Callable, Union
+
+import torch
+from torch.nn import Embedding
+
+from comfy.sd1_clip import SD1Tokenizer
+from custom_nodes.ClipStuff.lib.actions.base import Action, PromptSegment
+
+
+class ArithAction(Action):
+    START_CHAR = "<"
+    END_CHAR = ">"
+
+    def __init__(self, base_segment: PromptSegment | Action, ops: dict[str, list[PromptSegment | Action]]):
+        self.base_segment = base_segment
+        self.ops = ops
+
+    def __repr__(self):
+        return f"ArithAction(\n\tbase_segment={self.base_segment},\n\tops={self.ops}\n)"
+
+    def depth_repr(self, depth=1):
+        out = "ArithAction(\n"
+        if isinstance(self.base_segment, Action):
+            base_segment_repr = self.base_segment.depth_repr(depth + 1)
+            out += "\t" * depth + f"base_segment={base_segment_repr}\n"
+        elif isinstance(self.base_segment, PromptSegment):
+            out += "\t" * depth + f'base_segment={self.base_segment.depth_repr(depth)}'
+        else:
+            out += "\t" * depth + f'base_segment="{self.base_segment}",'
+
+        for op_key, ops in self.ops.items():
+            for op in ops:
+                out += "\n" + "\t" * depth + f'"{op_key}":[\n'
+                if isinstance(op, Action):
+                    op_repr = op.depth_repr(depth + 2)
+                    out += "\t" * (depth + 1) + f"{op_repr}\n"
+                else:
+                    out += "\t" * (depth + 1) + f'{op.depth_repr()},\n'
+                out += "\t" * depth + "],"
+        out += "\n" + "\t" * (depth - 1) + ")"
+        return out
+
+    def token_length(self):
+        # ArithAction modifies the embeddings of the base segment, so the length is the length of the base segment
+        if isinstance(self.base_segment, Action):
+            return self.base_segment.token_length()
+
+        return len(self.base_segment.tokens)
+
+
+    def get_all_segments(self):
+        segments = []
+        if isinstance(self.base_segment, Action):
+            segments += self.base_segment.get_all_segments()
+        else:
+            segments.append(self.base_segment)
+
+        for op_key, ops in self.ops.items():
+            for op in ops:
+                if isinstance(op, Action):
+                    segments += op.get_all_segments()
+                else:
+                    segments.append(op)
+
+        return segments
+
+    def get_result(self, embedding_module: Embedding):
+        if isinstance(self.base_segment, Action):
+            base_segment_result = self.base_segment.get_result(embedding_module)
+        else:
+            base_segment_result = self.base_segment.get_embeddings(embedding_module)
+
+        for op_key, ops in self.ops.items():
+            for op in ops:
+                if isinstance(op, Action):
+                    op_result = op.get_result(embedding_module)
+                else:
+                    op_result = op.get_embeddings(embedding_module)
+
+
+                if op_result.shape[1] > base_segment_result.shape[1]:
+                    print('[WARN] ArithAction: op_result.shape[1] > base_segment_result.shape[1] - averaging op_result')
+                    op_result = torch.mean(op_result, dim=1, keepdim=True)
+
+                if op_key == "+":
+                    base_segment_result.add(op_result)
+                elif op_key == "-":
+                    base_segment_result.subtract(op_result)
+
+        return base_segment_result
+
+    @classmethod
+    def parse_segment(
+        cls,
+        tokens: list[str],
+        start_chars: list[str],
+        end_chars: list[str],
+        parent_parser: Callable[[list[str], SD1Tokenizer], Union[PromptSegment, 'Action']],
+        tokenizer: SD1Tokenizer,
+    ) -> Action:
+        """
+        Parse an arithmetic action from a list of tokens
+        Supported formats:
+        <base_segment:+op1-op2-op3>
+
+        :param tokens: List of tokens, will be modified
+        :param start_chars: List of start chars for all actions
+        :param end_chars: List of end chars for all actions
+        :param parent_parser: Function to parse segments to allow for nested actions
+        :return:
+        """
+        token = tokens.pop(0)
+        assert token == cls.START_CHAR, "ArithAction must start with " + cls.START_CHAR + " but got " + token
+
+        # Parse base segment
+        base_segment = parent_parser(tokens, tokenizer)
+
+        token = tokens.pop(0)
+        assert token == ":", "ArithAction must have a ':' after the base segment" + " but got " + token
+
+        # Parse ops string
+        ops = {'+': [], '-': []}
+        while tokens[0] != cls.END_CHAR:
+            op_char = tokens.pop(0)
+            assert op_char in ["+", "-"], "ArithAction must have a '+' or '-' as an op char but got " + op_char
+            ops[op_char].append(parent_parser(tokens, tokenizer))
+
+        token = tokens.pop(0)
+        assert token == cls.END_CHAR, "ArithAction must end with " + cls.END_CHAR + " but got " + token
+
+        return cls(base_segment, ops)

lib/actions/avg.py

@@ -1,108 +0,0 @@
-from typing import List, Union
-
-import torch
-from torch.nn import Embedding
-
-from custom_nodes.KepPromptLang.lib.action.base import MultiArgAction, Action
-from custom_nodes.KepPromptLang.lib.actions.action_utils import get_embedding
-from custom_nodes.KepPromptLang.lib.actions.types import SegOrAction
-from custom_nodes.KepPromptLang.lib.actions.utils import slerp
-from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
-
-
-class AverageAction(MultiArgAction):
-    grammar = 'avg(" arg "|" arg "|" arg ")"'
-    chars = ["+", "+"]
-
-    display_name = "Average"
-    action_name = "avg"
-    description = "Performs a weighted average between two segments or actions. The recommended weight is 0 - 1."
-    usage_examples = [
-        "avg(The cat is|The dog is|0.5)",
-        "avg(Cat|Dog|0.5)",
-    ]
-
-
-    def __init__(self, args: List[List[Union[PromptSegment, Action]]]) -> None:
-        super().__init__(args)
-
-        if len(args) != 3:
-            raise ValueError("Average action should have exactly three arguments(2 vectors and a weight)")
-
-        self.first_arg = args[0]
-        self.second_arg = args[1]
-        self._parse_weight(args[2])
-
-        self._validate_args()
-
-
-    def _parse_weight(self, arg: List[SegOrAction]) -> None:
-        if len(arg) != 1:
-            raise ValueError("Average weight should have exactly one segment")
-
-        weight_seg_or_action = arg[0]
-
-        if isinstance(weight_seg_or_action, Action):
-            raise ValueError("Average weight should not have an action as an argument")
-
-        try:
-            self.parsed_weight = float(weight_seg_or_action.text)
-        except ValueError:
-            raise ValueError("Average should have an integer/float as the weight")
-
-    def _validate_args(self) -> None:
-        first_arg_token_length = sum(seg_or_action.token_length() for seg_or_action in self.first_arg)
-        second_arg_token_length = sum(seg_or_action.token_length() for seg_or_action in self.second_arg)
-        if first_arg_token_length != second_arg_token_length:
-            raise ValueError(f"Average start and end arguments should have the same length. Got {start_arg_token_length} and {end_arg_token_length}")
-
-        if self.parsed_weight < 0 or self.parsed_weight > 1:
-            print(f"WARNING: Average weight should be between 0 and 1. Got {self.parsed_weight}")
-
-    def token_length(self) -> int:
-        # Average interpolates between the embeddings of the start and end segments, so the length is the length of the start segment
-        return sum(seg_or_action.token_length() for seg_or_action in self.first_arg)
-
-    def get_result(self, embedding_module: Embedding) -> torch.Tensor:
-        # Calculate the embeddings for the start segment
-        all_start_embeddings = [
-            get_embedding(seg_or_action, embedding_module)
-            for seg_or_action in self.first_arg
-        ]
-
-        start_embedding = torch.cat(all_start_embeddings, dim=1)
-
-        # Calculate the embeddings for the end segment
-        all_end_embeddings = [
-            get_embedding(seg_or_action, embedding_module)
-            for seg_or_action in self.second_arg
-        ]
-
-        end_embedding = torch.cat(all_end_embeddings, dim=1)
-
-        # Perform the weighted average
-        result = start_embedding * (1 - self.parsed_weight) + end_embedding * self.parsed_weight
-
-        return result
-
-    # def __repr__(self):
-    #     return f"sum(\n\tbase_segment={self.base_segment},\n\targs={self.args}\n)"
-    def __repr__(self) -> str:
-        return f"sum({', '.join(map(str, self.additional_args))})"
-
-    def depth_repr(self, depth=1):
-        out = "NudgeAction(\n"
-        if isinstance(self.base_arg, Action):
-            base_segment_repr = self.base_arg.depth_repr(depth + 1)
-            out += "\t" * depth + f"base_segment={base_segment_repr}\n"
-        else:
-            out += "\t" * depth + f"base_segment={self.base_arg.depth_repr()},\n"
-
-        if isinstance(self.additional_args, Action):
-            target_repr = self.additional_args.depth_repr(depth + 1)
-            out += "\t" * depth + f"target={target_repr},\n"
-        else:
-            out += "\t" * depth + f"target={self.additional_args.depth_repr()},\n"
-        out += "\t" * depth + f"weight={self.weight},\n"
-        out += "\t" * (depth - 1) + ")"
-        return out

lib/actions/base.py

@@ -0,0 +1,95 @@
+from abc import ABC, abstractmethod
+from typing import Callable, Union
+
+import torch
+from torch import Tensor
+from torch.nn import Embedding
+
+from comfy.sd1_clip import SD1Tokenizer
+
+
+class Action(ABC):
+    @property
+    @abstractmethod
+    def START_CHAR(self):
+        pass
+
+    @property
+    @abstractmethod
+    def END_CHAR(self):
+        pass
+
+    @abstractmethod
+    def token_length(self):
+        pass
+
+    @abstractmethod
+    def get_all_segments(self):
+        pass
+
+    @abstractmethod
+    def get_result(self, embedding_module: Embedding):
+        pass
+
+    @classmethod
+    @abstractmethod
+    def parse_segment(
+        cls,
+        tokens: list[str],
+        start_chars: list[str],
+        end_chars: list[str],
+        parent_parser: Callable[[list[str], SD1Tokenizer], Union[str, 'Action']],
+        tokenizer: SD1Tokenizer,
+    ) -> 'Action':
+        pass
+
+    def depth_repr(self, depth=1):
+        raise NotImplementedError()
+
+class PromptSegment:
+    def __init__(self, text: str, tokens: list[Union[int, Tensor]]):
+        self.text = text
+        self.tokens = tokens
+
+    def token_length(self):
+        return len(self.tokens)
+
+    def get_embeddings(self, embedding_module: Embedding):
+        tensors = torch.LongTensor(self.tokens).to(torch.device('cpu'))
+        unsqueezed_tensors = tensors.unsqueeze(0)
+        return embedding_module(unsqueezed_tensors)
+
+    def depth_repr(self, depth=1):
+        out = f'"{self.text}"('
+
+        cleaned_tokens = list(map(lambda x: str(x) if isinstance(x, int) else "EMBD", self.tokens))
+        out += ", ".join(cleaned_tokens)
+
+        out += ")"
+        return out
+
+def build_prompt_segment(text: str, tokenizer: SD1Tokenizer) -> PromptSegment:
+    split_text = text.split(" ")
+    tokens = []
+    for word in split_text:
+        if word.startswith(tokenizer.embedding_identifier) and tokenizer.embedding_directory is not None:
+            embedding_name = word[len(tokenizer.embedding_identifier):].strip('\n')
+
+            get_embed_ret = tokenizer._try_get_embedding(embedding_name)
+            embedding = get_embed_ret[0]
+            leftover = get_embed_ret[1]
+            if embedding is None:
+                print(f"warning, embedding:{embedding_name} does not exist, ignoring")
+            else:
+                if len(embedding.shape) == 1:
+                    tokens.append(embedding)
+                else:
+                    tokens.extend(embedding)
+
+            if leftover != "":
+                word = leftover
+            else:
+                continue
+        tokens.extend(tokenizer.tokenizer(word)["input_ids"][1:-1])
+
+    return PromptSegment(text, tokens)

lib/actions/diff.py

@@ -1,85 +0,0 @@
-from typing import Union, List
-
-import torch
-from torch.nn import Embedding
-
-from custom_nodes.KepPromptLang.lib.action.base import Action, MultiArgAction
-from custom_nodes.KepPromptLang.lib.actions.action_utils import get_embedding
-from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
-from custom_nodes.KepPromptLang.lib.parser.registration import register_action
-
-
-class DiffAction(MultiArgAction):
-    grammar = 'diff(" arg ("|" arg)* ")"'
-    chars = ["-", "-"]
-
-    display_name = "Difference"
-    action_name = "diff"
-    description = "Subtracts the segments in the order they are given. The first segment is subtracted from the second, then the third from the result, and so on."
-    usage_examples = [
-        "diff(The cat is|The dog is)",
-        "diff(Cat|Dog)",
-        "sum(diff(king|man)|woman)",
-    ]
-
-    def __init__(self, args: List[List[Union[PromptSegment, Action]]]):
-        super().__init__(args)
-        self.base_arg = args[0]
-        self.additional_args = args[1:]
-
-
-    def token_length(self) -> int:
-        # Sum adds to the embeddings of the base segment, so the length is the length of the base segment
-        return sum(seg_or_action.token_length() for seg_or_action in self.base_arg)
-
-    def get_result(self, embedding_module: Embedding) -> torch.Tensor:
-        # Calculate the embeddings for the base segment
-        all_base_embeddings = [
-            get_embedding(seg_or_action, embedding_module)
-            for seg_or_action in self.base_arg
-        ]
-
-        result = torch.cat(all_base_embeddings, dim=1)
-
-        for arg in self.additional_args:
-            all_arg_embeddings = [
-                get_embedding(seg_or_action, embedding_module) for seg_or_action in arg
-            ]
-
-            arg_embedding = torch.cat(all_arg_embeddings, dim=1)
-
-            if (
-                arg_embedding.shape[-2] == 1
-                or result.shape[-2] == arg_embedding.shape[-2]
-            ):
-                result = result.sub(arg_embedding)
-            else:
-                print(
-                    "WARNING: shape mismatch when trying to apply sum, arg will be averaged"
-                )
-                result = result.sub(torch.mean(arg_embedding, dim=1, keepdim=True))
-
-        return result
-
-    # def __repr__(self):
-    #     return f"sum(\n\tbase_segment={self.base_segment},\n\tadditional_args={self.additional_args}\n)"
-    def __repr__(self) -> str:
-        return f"sum({', '.join(map(str, self.additional_args))})"
-
-    def depth_repr(self, depth=1):
-        out = "NudgeAction(\n"
-        if isinstance(self.base_arg, Action):
-            base_segment_repr = self.base_arg.depth_repr(depth + 1)
-            out += "\t" * depth + f"base_segment={base_segment_repr}\n"
-        else:
-            out += "\t" * depth + f"base_segment={self.base_arg.depth_repr()},\n"
-
-        if isinstance(self.additional_args, Action):
-            target_repr = self.additional_args.depth_repr(depth + 1)
-            out += "\t" * depth + f"target={target_repr},\n"
-        else:
-            out += "\t" * depth + f"target={self.additional_args.depth_repr()},\n"
-        out += "\t" * depth + f"weight={self.weight},\n"
-        out += "\t" * (depth - 1) + ")"
-        return out
-

lib/actions/lib.py

@@ -0,0 +1,17 @@
+from custom_nodes.ClipStuff.lib.actions import ALL_START_CHARS, ALL_END_CHARS
+from custom_nodes.ClipStuff.lib.actions.base import Action
+
+
+def is_action_segment(action_class: Action.__class__, segment: str):
+    if not issubclass(action_class, Action):
+        raise Exception(
+            f"action_class must be a subclass of Action, got {action_class}"
+        )
+
+    return (
+        segment[0] == action_class.START_CHAR and segment[-1] == action_class.END_CHAR
+    )
+
+
+def is_any_action_segment(segment: str):
+    return segment[0] in ALL_START_CHARS and segment[-1] in ALL_END_CHARS

lib/actions/mult.py

@@ -1,69 +0,0 @@
-from typing import List
-
-import torch
-from torch.nn import Embedding
-
-from custom_nodes.KepPromptLang.lib.action.base import (
-    Action,
-    MultiArgAction,
-)
-from custom_nodes.KepPromptLang.lib.actions.types import SegOrAction
-from custom_nodes.KepPromptLang.lib.parser.registration import register_action
-
-
-class MultiplyAction(MultiArgAction):
-    grammar = 'mult(" arg+ ")"'
-    chars = ["[", "]"]
-
-    display_name = "Multiply"
-    action_name = "mult"
-    description = "Multiplies the provided segments or actions by the multiplier."
-    usage_examples = [
-        "mult(The cat is|2.5)",
-        "mult(Cat|-1)",
-    ]
-
-    def __init__(self, args: List[List[SegOrAction]]) -> None:
-        super().__init__(args)
-        if len(args) != 2:
-            raise ValueError("Multiply action should have exactly two arguments")
-
-        self.target_arg = args[0]
-        self._parse_multiplier(args[1])
-
-    def _parse_multiplier(self, arg: List[SegOrAction]) -> None:
-        if len(arg) != 1:
-            raise ValueError("Multiply actions multiplier should have exactly one segment")
-
-        multiplier_seg_or_action = arg[0]
-
-        if isinstance(multiplier_seg_or_action, Action):
-            raise ValueError("Multiply actions multiplier must be a number")
-
-        try:
-            self.parsed_multiplier = float(multiplier_seg_or_action.text)
-        except ValueError:
-            raise ValueError("Multiply action should have an integer/float as the multiplier")
-
-    def token_length(self) -> int:
-        """
-        Mult multiplies the embeddings of the base segment, so the length is the length of the base segment
-        :return:
-        """
-        total_length = 0
-        for seg_or_action in self.target_arg:
-            total_length += seg_or_action.token_length()
-
-        return total_length
-
-    def get_result(self, embedding_module: Embedding) -> torch.Tensor:
-        all_embeddings = []
-        for seg_or_action in self.target_arg:
-            if isinstance(seg_or_action, Action):
-                all_embeddings.append(seg_or_action.get_result(embedding_module))
-            else:
-                all_embeddings.append(seg_or_action.get_embeddings(embedding_module))
-
-        target_embeddings = torch.cat(all_embeddings, dim=1)
-        return target_embeddings * self.parsed_multiplier
-

lib/actions/neg.py

@@ -1,41 +0,0 @@
-import torch
-from torch.nn import Embedding
-
-from custom_nodes.KepPromptLang.lib.action.base import Action, SingleArgAction
-from custom_nodes.KepPromptLang.lib.parser.registration import register_action
-
-
-class NegAction(SingleArgAction):
-    grammar = 'neg(" arg+ ")"'
-    chars = ["[", "]"]
-
-    display_name = "Negate"
-    action_name = "neg"
-    description = "Negates the provided segments or actions."
-    usage_examples = [
-        "neg(cat)",
-        "sum(king|neg(man)|women)",
-    ]
-
-    def token_length(self) -> int:
-        """
-        Neg negates the embeddings of the base segment, so the length is the length of the base segment
-        :return:
-        """
-        total_length = 0
-        for seg_or_action in self.arg:
-            total_length += seg_or_action.token_length()
-
-        return total_length
-
-    def get_result(self, embedding_module: Embedding) -> torch.Tensor:
-        all_embeddings = []
-        for seg_or_action in self.arg:
-            if isinstance(seg_or_action, Action):
-                all_embeddings.append(seg_or_action.get_result(embedding_module))
-            else:
-                all_embeddings.append(seg_or_action.get_embeddings(embedding_module))
-
-        target_embeddings = torch.cat(all_embeddings, dim=1)
-        return target_embeddings * -1
-

lib/actions/norm.py

@@ -1,45 +0,0 @@
-import torch
-from torch.nn import Embedding
-
-from custom_nodes.KepPromptLang.lib.action.base import (
-    Action,
-    SingleArgAction,
-)
-from custom_nodes.KepPromptLang.lib.parser.registration import register_action
-
-
-class NormAction(SingleArgAction):
-    grammar = 'norm(" arg+ ")"'
-    chars = None
-
-    display_name = "Normalize"
-    action_name = "norm"
-    description = "Normalizes the provided segments or actions."
-    usage_examples = [
-        "norm(cat)",
-        "sum(cat|norm(sum(tiger|fish)))",
-    ]
-
-    def token_length(self) -> int:
-        """
-        Norm normalizes the embeddings of the base segment, so the length is the length of the base segment
-        :return:
-        """
-        total_length = 0
-        for seg_or_action in self.arg:
-            total_length += seg_or_action.token_length()
-
-        return total_length
-
-    def get_result(self, embedding_module: Embedding) -> torch.Tensor:
-        all_embeddings = []
-        for seg_or_action in self.arg:
-            if isinstance(seg_or_action, Action):
-                target_embeddings = seg_or_action.get_result(embedding_module)
-            else:
-                target_embeddings = seg_or_action.get_embeddings(embedding_module)
-            all_embeddings.append(target_embeddings)
-
-        target_embeddings = torch.cat(all_embeddings, dim=1)
-        return torch.div(target_embeddings, torch.norm(target_embeddings, dim=-1, keepdim=True))
-

lib/actions/nudge.py

@@ -0,0 +1,128 @@
+from typing import Optional, Union, Callable
+
+import torch
+from torch.nn import Embedding
+
+from comfy.sd1_clip import SD1Tokenizer
+from custom_nodes.ClipStuff.lib.actions.base import Action, PromptSegment
+
+
+class NudgeAction(Action):
+    def token_length(self):
+        # Nudge nudges the embeddings of the base segment, so the length is the length of the base segment
+        if isinstance(self.base_segment, Action):
+            return self.base_segment.token_length()
+
+        return len(self.base_segment.tokens)
+
+    def get_all_segments(self):
+        segments = []
+        if isinstance(self.base_segment, Action):
+            segments += self.base_segment.get_all_segments()
+        else:
+            segments.append(self.base_segment)
+
+        if isinstance(self.target, Action):
+            segments += self.target.get_all_segments()
+        else:
+            segments.append(self.target)
+
+        return segments
+
+    def get_result(self, embedding_module: Embedding):
+        if isinstance(self.base_segment, Action):
+            base_segment_result = self.base_segment.get_result(embedding_module)
+        else:
+            base_segment_result = self.base_segment.get_embeddings(embedding_module)
+
+        if isinstance(self.target, Action):
+            target_segment_result = self.target.get_result(embedding_module)
+        else:
+            target_segment_result = self.target.get_embeddings(embedding_module)
+
+        base_mean = torch.mean(base_segment_result, dim=1, keepdim=True)
+        if target_segment_result.shape[1] == 1:
+            translation_vector = target_segment_result - base_mean
+        else:
+            translation_vector = torch.mean(target_segment_result, dim=1, keepdim=True) - base_mean
+
+        return base_segment_result.add(translation_vector, alpha=self.weight)
+
+    START_CHAR = "["
+    END_CHAR = "]"
+
+    def __init__(
+        self,
+        base_segment: PromptSegment | Action,
+        target: Union[PromptSegment, Action],
+        weight: Optional[float] = None,
+    ):
+        self.base_segment = base_segment
+        self.weight = weight
+        self.target = target
+
+    def __repr__(self):
+        return f"NudgeAction(\n\tbase_segment={self.base_segment},\n\ttarget={self.target},\n\tweight={self.weight}\n)"
+
+    def depth_repr(self, depth=1):
+        out = "NudgeAction(\n"
+        if isinstance(self.base_segment, Action):
+            base_segment_repr = self.base_segment.depth_repr(depth + 1)
+            out += "\t" * depth + f"base_segment={base_segment_repr}\n"
+        else:
+            out += "\t" * depth + f'base_segment={self.base_segment.depth_repr()},\n'
+
+        if isinstance(self.target, Action):
+            target_repr = self.target.depth_repr(depth + 1)
+            out += "\t" * depth + f"target={target_repr},\n"
+        else:
+            out += "\t" * depth + f"target={self.target.depth_repr()},\n"
+        out += "\t" * depth + f"weight={self.weight},\n"
+        out += "\t" * (depth - 1) + ")"
+        return out
+
+    @classmethod
+    def parse_segment(
+        cls,
+        tokens: list[str],
+        start_chars: list[str],
+        end_chars: list[str],
+        parent_parser: Callable[[list[str], SD1Tokenizer], PromptSegment | Action],
+        tokenizer: SD1Tokenizer,
+    ) -> Action:
+        """
+        Parse a nudge action from a list of tokens
+        Supported formats:
+        [base_segment:target_segment]
+        [base_segment:target_segment:weight]
+
+        Weight is optional, if not provided it will be None
+        :param tokens: List of tokens, will be modified
+        :param start_chars: List of start chars for all actions
+        :param end_chars: List of end chars for all actions
+        :param parent_parser: Function to parse segments to allow for nested actions
+        :return:
+        """
+        token = tokens.pop(0)
+        assert token == cls.START_CHAR, "NudgeAction must start with " + cls.START_CHAR + " got " + token
+
+        # Parse base segment
+        base_segment = parent_parser(tokens, tokenizer)
+
+        token = tokens.pop(0)
+        assert token == ":", "NudgeAction must have a ':' after the base segment" + " but got " + token
+
+        # Parse target segment
+        target_segment = parent_parser(tokens, tokenizer)
+
+        # Parse weight if it exists
+        weight = None
+        if tokens[0] == ":":
+            # Parse weight
+            tokens.pop(0)
+            weight = float(tokens.pop(0))
+
+        token = tokens.pop(0)
+        assert token == cls.END_CHAR, "NudgeAction must end with " + cls.END_CHAR + " got " + token
+
+        return cls(base_segment, target_segment, weight)

lib/actions/pooled_avg.py

@@ -1,68 +0,0 @@
-import torch
-from torch.nn import Embedding
-from transformers.modeling_outputs import BaseModelOutputWithPooling
-from transformers.models.clip.modeling_clip import CLIPTextTransformer
-
-from custom_nodes.KepPromptLang.lib.action.base import Action, SingleArgAction
-from custom_nodes.KepPromptLang.lib.actions.action_utils import get_total_length
-from custom_nodes.KepPromptLang.lib.fun_clip_stuff import (
-    PromptLangCLIPTextEmbeddings,
-    PrompLangCLIPTextTransformer,
-)
-from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
-
-
-class PooledAvgAction(SingleArgAction):
-    grammar = 'pooledAvg(" arg+ ")"'
-    chars = ["[", "]"]
-
-    display_name = "Pooled Average(Experimental)"
-    action_name = "_exp-pooledAvg"
-    description = "Processes the provided segments or actions fully through CLIP and creates a pooled average of the last hidden state by averaging the last hidden state of each token."
-    usage_examples = [
-        "A cat on a _exp-pooledAvg(beautiful sunny day)",
-        "A _exp-pooledAvg(broken glass) bottle",
-    ]
-
-    def __init__(self, args):
-        super().__init__(args)
-        self.result = None
-
-    def token_length(self) -> int:
-        """
-        PooledAvg returns the average of the last hidden state, so the length is 1
-        """
-        return 1
-
-    def process_with_transformer(
-        self, transformer: CLIPTextTransformer, embedding_module: Embedding
-    ) -> None:
-        """ """
-        # SOT + tokens + EOT
-        eot_token = embedding_module.num_embeddings - 1
-        print("Using EOT token", eot_token)
-        #TODO: Play with impact of padding on pooled output
-        arg_length = get_total_length(self.arg)
-
-        # SOT + arg length + EOT
-        empty_tokens = [[49406] + [eot_token] * (arg_length + 1)]
-
-        transformer_results: BaseModelOutputWithPooling = transformer(
-            [
-                [PromptSegment(text="_Empty Batch_", tokens=empty_tokens[0])],
-                [PromptSegment(text="[SOT]", tokens=[49406])]
-                + self.arg
-                + [PromptSegment(text="[EOT]", tokens=[eot_token])],
-            ]
-        )
-        self.result = (
-            transformer_results.last_hidden_state[1, 1:-1, :].mean(dim=0).unsqueeze(0).unsqueeze(0)
-        )
-
-    def get_result(self, embedding_module: Embedding) -> torch.Tensor:
-        if self.result is not None:
-            return self.result
-
-        raise Exception(
-            "PooledAvg action result is not set. Did you forget to call process_with_transformer?"
-        )

lib/actions/pooler.py

@@ -1,67 +0,0 @@
-import torch
-from torch.nn import Embedding
-from transformers.modeling_outputs import BaseModelOutputWithPooling
-from transformers.models.clip.modeling_clip import CLIPTextTransformer
-
-from custom_nodes.KepPromptLang.lib.action.base import Action, SingleArgAction
-from custom_nodes.KepPromptLang.lib.actions.action_utils import get_total_length
-from custom_nodes.KepPromptLang.lib.fun_clip_stuff import (
-    PromptLangCLIPTextEmbeddings,
-    PrompLangCLIPTextTransformer,
-)
-from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
-
-
-class PoolerAction(SingleArgAction):
-    grammar = 'pooler(" arg+ ")"'
-    chars = ["[", "]"]
-
-    display_name = "Pooler Output(Experimental)"
-    action_name = "_exp-pooler"
-    description = "Processes the provided segments or actions fully through CLIP and returns the pooler_output from the transformer"
-    usage_examples = [
-        "A cat on a _exp-pooler(beautiful sunny day)",
-        "A _exp-pooler(broken glass) bottle",
-    ]
-
-
-    def __init__(self, args):
-        super().__init__(args)
-        self.result = None
-
-    def token_length(self) -> int:
-        """
-        Pooler returns the embedding of the EOT token, so the length is 1
-        """
-        return 1
-
-    def process_with_transformer(
-        self, transformer: CLIPTextTransformer, embedding_module: Embedding
-    ) -> None:
-        """ """
-        # SOT + tokens + EOT
-        eot_token = embedding_module.num_embeddings - 1
-        print("Using EOT token", eot_token)
-
-        arg_length = get_total_length(self.arg)
-
-        # SOT + arg length + EOT
-        empty_tokens = [[49406] + [eot_token] * (arg_length + 1)]
-
-        transformer_results: BaseModelOutputWithPooling = transformer(
-            [
-                [PromptSegment(text="_Empty Batch_", tokens=empty_tokens[0])],
-                [PromptSegment(text="[SOT]", tokens=[49406])]
-                + self.arg
-                + [PromptSegment(text="[EOT]", tokens=[eot_token])],
-            ]
-        )
-        self.result = transformer_results.pooler_output[1].unsqueeze(0).unsqueeze(0)
-
-    def get_result(self, embedding_module: Embedding) -> torch.Tensor:
-        if self.result is not None:
-            return self.result
-
-        raise Exception(
-            "Pooled action result is not set. Did you forget to call process_with_transformer?"
-        )

lib/actions/pos_scale.py

@@ -1,71 +0,0 @@
-from typing import Tuple, List
-
-import torch
-from torch.nn import Embedding
-
-from custom_nodes.KepPromptLang.lib.action.base import (
-    Action,
-    PostModifiers,
-    MultiArgAction,
-)
-from custom_nodes.KepPromptLang.lib.actions.types import SegOrAction
-
-
-class PosScaleAction(MultiArgAction):
-    grammar = 'posScale(" arg+ ")"'
-    chars = ["[", "]"]
-
-    display_name = "Positional Embedding Scale"
-    action_name = "posScale"
-    description = "Scales(Multiplies) the positional embeddings of the provided segments or actions by the multiplier."
-    usage_examples = [
-        "A posScale(cat|1.5) on a rainy day",
-    ]
-
-    def __init__(self, args: List[List[SegOrAction]]) -> None:
-        super().__init__(args)
-        if len(args) != 2:
-            raise ValueError("PosScale action should have exactly two arguments")
-
-        self.target_arg = args[0]
-        self._parse_multiplier(args[1])
-
-    def _parse_multiplier(self, arg: List[SegOrAction]) -> None:
-        if len(arg) != 1:
-            raise ValueError(
-                "PosScale actions multiplier should have exactly one segment"
-            )
-
-        multiplier_seg_or_action = arg[0]
-
-        if isinstance(multiplier_seg_or_action, Action):
-            raise ValueError("PosScale actions multiplier must be a number")
-
-        try:
-            self.parsed_multiplier = float(multiplier_seg_or_action.text)
-        except ValueError:
-            raise ValueError(
-                "PosScale action should have an integer/float as the multiplier"
-            )
-
-    def token_length(self) -> int:
-        """
-        PosScale modifies the posional embeddings of the base segment, so the length is the length of the base segment
-        :return:
-        """
-        total_length = 0
-        for seg_or_action in self.target_arg:
-            total_length += seg_or_action.token_length()
-
-        return total_length
-
-    def get_result(self, embedding_module: Embedding) -> Tuple[torch.Tensor, PostModifiers]:
-        all_embeddings = []
-        for seg_or_action in self.target_arg:
-            if isinstance(seg_or_action, Action):
-                all_embeddings.append(seg_or_action.get_result(embedding_module))
-            else:
-                all_embeddings.append(seg_or_action.get_embeddings(embedding_module))
-
-        target_embeddings = torch.cat(all_embeddings, dim=1)
-        return target_embeddings, {"position_embed_scale": self.parsed_multiplier}

lib/actions/post_pos.py

@@ -1,47 +0,0 @@
-from typing import Tuple
-
-import torch
-from torch import Tensor
-from torch.nn import Embedding
-
-from custom_nodes.KepPromptLang.lib.action.base import (
-    SingleArgAction,
-    Action,
-    PostModifiers,
-)
-
-
-class PostPosAction(SingleArgAction):
-    grammar = 'postPos(" arg+ ")"'
-    chars = ["[", "]"]
-
-    display_name = "Ignore Positional Embeddings"
-    action_name = "postPos"
-    description = "Prevents positional embeddings from being applied to the provided segments or actions."
-    usage_examples = [
-        "A postPos(cat) on a rainy day",
-    ]
-
-    def __init__(self, args):
-        super().__init__(args)
-        self.result = None
-
-    def token_length(self) -> int:
-        """
-        PostPos returns the results of the wrapped action, so the length is the length of the wrapped action
-        """
-        total_length = 0
-        for seg_or_action in self.arg:
-            total_length += seg_or_action.token_length()
-
-        return total_length
-
-    def get_result(self, embedding_module: Embedding) -> Tuple[Tensor, PostModifiers]:
-        all_embeddings = []
-        for seg_or_action in self.arg:
-            if isinstance(seg_or_action, Action):
-                all_embeddings.append(seg_or_action.get_result(embedding_module))
-            else:
-                all_embeddings.append(seg_or_action.get_embeddings(embedding_module))
-
-        return torch.cat(all_embeddings, dim=1), PostModifiers(position_embed_scale=None, bypass_pos_embed=True)

lib/actions/rand.py

@@ -1,95 +0,0 @@
-from typing import List
-
-import torch
-from torch.nn import Embedding
-
-from custom_nodes.KepPromptLang.lib.action.base import (
-    Action,
-    MultiArgAction,
-)
-from custom_nodes.KepPromptLang.lib.actions.types import SegOrAction
-from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
-from custom_nodes.KepPromptLang.lib.parser.registration import register_action
-
-
-class RandAction(MultiArgAction):
-    grammar = 'rand(" arg ")"'
-    name = "rand"
-    chars = None
-
-    display_name = "Random Embedding"
-    action_name = "rand"
-    description = "Returns a random embedding of the specified token length, with the values optionally bounded by the second and third arguments."
-    usage_examples = [
-        "A rand(1) cat",
-        "A rand(1|-1|1) cat",
-    ]
-
-    parsed_token_length = 0
-    range_min = 0
-    range_max = 1
-
-    def __init__(self, args: List[List[SegOrAction]]) -> None:
-        super().__init__(args)
-        if len(args) != 1 and len(args) != 3:
-            raise ValueError("Random action should have exactly one argument or three arguments")
-
-        self._parse_token_length(args[0])
-
-        if len(args) == 3:
-            self._parse_range(args[1], args[2])
-
-    def _parse_token_length(self, arg: List[SegOrAction]) -> None:
-        if len(arg) != 1:
-            raise ValueError("Random action first argument should have exactly one segment")
-
-        token_length_seg_or_action = arg[0]
-
-        if isinstance(token_length_seg_or_action, Action):
-            raise ValueError("Random action should not have an action as an argument")
-
-        try:
-            self.parsed_token_length = int(token_length_seg_or_action.text)
-        except ValueError:
-            raise ValueError("Random action should have an integer as the first argument")
-
-    def _parse_range(self, min_arg: List[SegOrAction], max_arg: List[SegOrAction]) -> None:
-        if len(min_arg) != 1:
-            raise ValueError("Random action second argument should have exactly one segment")
-
-        if len(max_arg) != 1:
-            raise ValueError("Random action third argument should have exactly one segment")
-
-        min_seg_or_action = min_arg[0]
-        max_seg_or_action = max_arg[0]
-
-        if isinstance(min_seg_or_action, Action):
-            raise ValueError("Random action should not have an action as an argument")
-
-        if isinstance(max_seg_or_action, Action):
-            raise ValueError("Random action should not have an action as an argument")
-
-        try:
-            self.range_min = int(min_seg_or_action.text)
-        except ValueError:
-            raise ValueError("Random action should have an integer as the second argument")
-
-        try:
-            self.range_max = int(max_seg_or_action.text)
-        except ValueError:
-            raise ValueError("Random action should have an integer as the third argument")
-
-        if self.range_min > self.range_max:
-            raise ValueError("Random action should have the second argument be less than the third argument")
-    def token_length(self) -> int:
-        """
-        Random returns a random embedding whose length is the number in the argument
-        :return:
-        """
-        return self.parsed_token_length
-
-    def get_result(self, embedding_module: Embedding) -> torch.Tensor:
-        # Create random tensor of size
-        result = torch.empty(1, self.parsed_token_length, embedding_module.embedding_dim).uniform_(self.range_min, self.range_max)
-        return result
-

lib/actions/scale_dims.py

@@ -1,76 +0,0 @@
-from typing import List, Union
-
-import torch
-from torch.nn import Embedding
-
-from custom_nodes.KepPromptLang.lib.action.base import MultiArgAction, Action
-from custom_nodes.KepPromptLang.lib.actions.action_utils import get_embedding
-from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
-
-
-class ScaleDims(MultiArgAction):
-    grammar = 'scaleDims(" arg ("|" arg)* ")"'
-    chars = ["-", "-"]
-
-    display_name = "Scale Dimensions"
-    action_name = "scaleDims"
-    description = "Scales the specified dimensions of the input embeddings by the specified amount"
-    usage_examples = [
-        "The scaleDims(cat|4,1.5|76,1.2) is happy",
-    ]
-
-    def __init__(self, args: List[List[Union[PromptSegment, Action]]]):
-        super().__init__(args)
-
-        self.base_arg = args[0]
-        self._parse_scale_args(args[1:])
-
-
-    def _parse_scale_args(self, args: List[List[Union[PromptSegment, Action]]]) -> None:
-        # scaleDims scale args should have format of "<dim>,<scale>" where dim is the dimension to scale and scale is the amount to scale it by
-        # scaleDims(some words|4,1.5|76,1.2)
-        self.scale_args = []
-        for arg in args:
-            if isinstance(arg, Action):
-                raise ValueError("ScaleDims scale args must be in the format of <dim>,<scale>(e.g. 4,1.5) but got an action")
-
-            if len(arg) != 1:
-                raise ValueError("ScaleDims scale args must be in the format of <dim>,<scale>(e.g. 4,1.5) but got multiple segments")
-            extracted_arg = arg[0]
-            assert isinstance(extracted_arg, PromptSegment)
-
-            if "," not in extracted_arg.text:
-                raise ValueError("ScaleDims scale args must be in the format of <dim>,<scale>(e.g. 4,1.5) but got a segment with no comma: " + extracted_arg.text)
-
-            # Split prompt segment into text and scale args
-            dim, scale = extracted_arg.text.split(",")
-            try:
-                # TODO: Check that dim is within the bounds of the embedding
-                parsed_dim = int(dim)
-            except ValueError:
-                raise ValueError("ScaleDims scale args must be in the format of <dim>,<scale>(e.g. 4,1.5) but got a segment with a non-integer dim: " + str(dim))
-
-            try:
-                parsed_scale = float(scale)
-            except ValueError:
-                raise ValueError("ScaleDims scale args must be in the format of <dim>,<scale>(e.g. 4,1.5) but got a segment with a non-float scale: " + str(scale))
-
-            self.scale_args.append((parsed_dim, parsed_scale))
-
-
-    def token_length(self) -> int:
-        # scaleDims modifies the embeddings of the base segment, so the length is the length of the base segment
-        return sum(seg_or_action.token_length() for seg_or_action in self.base_arg)
-
-    def get_result(self, embedding_module: Embedding) -> torch.Tensor:
-        # Calculate the embeddings for the base segment
-        all_base_embeddings = [
-            get_embedding(seg_or_action, embedding_module)
-            for seg_or_action in self.base_arg
-        ]
-
-        base_embeddings = torch.cat(all_base_embeddings, dim=1)
-        for dim, scale in self.scale_args:
-            base_embeddings[0, :, dim] *= scale
-
-        return base_embeddings

lib/actions/set_dims.py

@@ -1,76 +0,0 @@
-from typing import List, Union
-
-import torch
-from torch.nn import Embedding
-
-from custom_nodes.KepPromptLang.lib.action.base import MultiArgAction, Action
-from custom_nodes.KepPromptLang.lib.actions.action_utils import get_embedding
-from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
-
-
-class SetDims(MultiArgAction):
-    grammar = 'setDims(" arg ("|" arg)* ")"'
-    chars = ["-", "-"]
-
-    display_name = "Set Dimensions"
-    action_name = "setDims"
-    description = "Sets the specified dimensions of the input embeddings to the specified value"
-    usage_examples = [
-        "The setDims(cat|4, -0.01253|76, 1.2) is happy"
-    ]
-
-    def __init__(self, args: List[List[Union[PromptSegment, Action]]]):
-        super().__init__(args)
-
-        self.base_arg = args[0]
-        self._parse_value_args(args[1:])
-
-
-    def _parse_value_args(self, args: List[List[Union[PromptSegment, Action]]]) -> None:
-        # setDims args should have format of "<dim>,<value>" where dim is the dimension to set and value is the value to set it to
-        # setDims(some words|4,-0.01254|76,1.2)
-        self.value_args = []
-        for arg in args:
-            if isinstance(arg, Action):
-                raise ValueError("SetDims value args must be in the format of <dim>,<value>(e.g. 4,1.5) but got an action")
-
-            if len(arg) != 1:
-                raise ValueError("SetDims value args must be in the format of <dim>,<value>(e.g. 4,1.5) but got multiple segments")
-            extracted_arg = arg[0]
-            assert isinstance(extracted_arg, PromptSegment)
-
-            if "," not in extracted_arg.text:
-                raise ValueError("SetDims value args must be in the format of <dim>,<value>(e.g. 4,1.5) but got a segment with no comma: " + extracted_arg.text)
-
-            # Split prompt segment into text and value args
-            dim, value = extracted_arg.text.split(",")
-            try:
-                # TODO: Check that dim is within the bounds of the embedding
-                parsed_dim = int(dim)
-            except ValueError:
-                raise ValueError("SetDims value args must be in the format of <dim>,<value>(e.g. 4,1.5) but got a segment with a non-integer dim: " + str(dim))
-
-            try:
-                parsed_value = float(value)
-            except ValueError:
-                raise ValueError("SetDims value args must be in the format of <dim>,<value>(e.g. 4,1.5) but got a segment with a non-float scale: " + str(value))
-
-            self.value_args.append((parsed_dim, parsed_value))
-
-
-    def token_length(self) -> int:
-        # setDims modifies the embeddings of the base segment, so the length is the length of the base segment
-        return sum(seg_or_action.token_length() for seg_or_action in self.base_arg)
-
-    def get_result(self, embedding_module: Embedding) -> torch.Tensor:
-        # Calculate the embeddings for the base segment
-        all_base_embeddings = [
-            get_embedding(seg_or_action, embedding_module)
-            for seg_or_action in self.base_arg
-        ]
-
-        base_embeddings = torch.cat(all_base_embeddings, dim=1)
-        for dim, value in self.value_args:
-            base_embeddings[0, :, dim] = value
-
-        return base_embeddings

lib/actions/slerp.py

@@ -1,106 +0,0 @@
-from typing import List, Union
-
-import torch
-from torch.nn import Embedding
-
-from custom_nodes.KepPromptLang.lib.action.base import MultiArgAction, Action
-from custom_nodes.KepPromptLang.lib.actions.action_utils import get_embedding
-from custom_nodes.KepPromptLang.lib.actions.types import SegOrAction
-from custom_nodes.KepPromptLang.lib.actions.utils import slerp
-from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
-
-
-class SlerpAction(MultiArgAction):
-    grammar = 'slerp(" arg "|" arg "|" arg ")"'
-    chars = ["+", "+"]
-
-    display_name = "Slerp"
-    action_name = "slerp"
-    description = "Performs a slerp(Interpolation) between two segments or actions, with the given weight. The recommended weight is 0 - 1"
-    usage_examples = [
-        "The slerp(cat|dog|0.5) is happy",
-    ]
-
-    def __init__(self, args: List[List[Union[PromptSegment, Action]]]) -> None:
-        super().__init__(args)
-
-        if len(args) != 3:
-            raise ValueError("Slerp action should have exactly three arguments(2 vectors and a weight)")
-
-        self.start_argument = args[0]
-        self.end_argument = args[1]
-        self._parse_weight(args[2])
-
-        self._validate_args()
-
-
-    def _parse_weight(self, arg: List[SegOrAction]) -> None:
-        if len(arg) != 1:
-            raise ValueError("Slerp weight should have exactly one segment")
-
-        weight_seg_or_action = arg[0]
-
-        if isinstance(weight_seg_or_action, Action):
-            raise ValueError("Slerp weight should not have an action as an argument")
-
-        try:
-            self.parsed_weight = float(weight_seg_or_action.text)
-        except ValueError:
-            raise ValueError("Slerp should have an integer/float as the weight")
-
-    def _validate_args(self) -> None:
-        start_arg_token_length = sum(seg_or_action.token_length() for seg_or_action in self.start_argument)
-        end_arg_token_length = sum(seg_or_action.token_length() for seg_or_action in self.end_argument)
-        if start_arg_token_length != end_arg_token_length:
-            raise ValueError(f"Slerp start and end arguments should have the same length. Got {start_arg_token_length} and {end_arg_token_length}")
-
-        if self.parsed_weight < 0 or self.parsed_weight > 1:
-            print(f"WARNING: Slerp weight should be between 0 and 1. Got {self.parsed_weight}")
-
-    def token_length(self) -> int:
-        # Slerp interpolates between the embeddings of the start and end segments, so the length is the length of the start segment
-        return sum(seg_or_action.token_length() for seg_or_action in self.start_argument)
-
-    def get_result(self, embedding_module: Embedding) -> torch.Tensor:
-        # Calculate the embeddings for the start segment
-        all_start_embeddings = [
-            get_embedding(seg_or_action, embedding_module)
-            for seg_or_action in self.start_argument
-        ]
-
-        start_embedding = torch.cat(all_start_embeddings, dim=1)
-
-        # Calculate the embeddings for the end segment
-        all_end_embeddings = [
-            get_embedding(seg_or_action, embedding_module)
-            for seg_or_action in self.end_argument
-        ]
-
-        end_embedding = torch.cat(all_end_embeddings, dim=1)
-
-        # Perform the slerp
-        result = slerp(self.parsed_weight, start_embedding, end_embedding)
-
-        return result
-
-    # def __repr__(self):
-    #     return f"sum(\n\tbase_segment={self.base_segment},\n\targs={self.args}\n)"
-    def __repr__(self) -> str:
-        return f"sum({', '.join(map(str, self.additional_args))})"
-
-    def depth_repr(self, depth=1):
-        out = "NudgeAction(\n"
-        if isinstance(self.base_arg, Action):
-            base_segment_repr = self.base_arg.depth_repr(depth + 1)
-            out += "\t" * depth + f"base_segment={base_segment_repr}\n"
-        else:
-            out += "\t" * depth + f"base_segment={self.base_arg.depth_repr()},\n"
-
-        if isinstance(self.additional_args, Action):
-            target_repr = self.additional_args.depth_repr(depth + 1)
-            out += "\t" * depth + f"target={target_repr},\n"
-        else:
-            out += "\t" * depth + f"target={self.additional_args.depth_repr()},\n"
-        out += "\t" * depth + f"weight={self.weight},\n"
-        out += "\t" * (depth - 1) + ")"
-        return out

lib/actions/sum.py

@@ -1,85 +0,0 @@
-from typing import Union, List
-
-import torch
-from torch.nn import Embedding
-
-from custom_nodes.KepPromptLang.lib.actions.action_utils import get_embedding
-from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
-from custom_nodes.KepPromptLang.lib.action.base import Action, MultiArgAction
-from custom_nodes.KepPromptLang.lib.parser.registration import register_action
-
-
-class SumAction(MultiArgAction):
-    grammar = 'sum(" arg ("|" arg)+ ")"'
-    chars = ["+", "+"]
-
-    display_name = "Sum"
-    action_name = "sum"
-    description = "Adds the embeddings of the provided segments or actions."
-    usage_examples = [
-        "A happy sum(cat|dog|shark)",
-    ]
-
-    def __init__(self, args: List[List[Union[PromptSegment, Action]]]) -> None:
-        super().__init__(args)
-
-        self.base_arg = args[0]
-        self.additional_args = args[1:]
-
-    def token_length(self) -> int:
-        # Sum adds to the embeddings of the base segment, so the length is the length of the base segment
-        return sum(seg_or_action.token_length() for seg_or_action in self.base_arg)
-
-    def get_result(self, embedding_module: Embedding) -> torch.Tensor:
-        # Calculate the embeddings for the base segment
-        all_base_embeddings = [
-            get_embedding(seg_or_action, embedding_module)
-            for seg_or_action in self.base_arg
-        ]
-
-        result = torch.cat(all_base_embeddings, dim=1)
-
-        for arg in self.additional_args:
-            all_arg_embeddings = [
-                get_embedding(seg_or_action, embedding_module) for seg_or_action in arg
-            ]
-
-            arg_embedding = torch.cat(all_arg_embeddings, dim=1)
-
-            if (
-                arg_embedding.shape[-2] == 1
-                or result.shape[-2] == arg_embedding.shape[-2]
-            ):
-                result = result.add(arg_embedding)
-            else:
-                print(
-                    "WARNING: shape mismatch when trying to apply sum, arg will be averaged"
-                )
-                result = result.add(
-                    torch.mean(arg_embedding, dim=1, keepdim=True)
-                )
-
-        return result
-
-    # def __repr__(self):
-    #     return f"sum(\n\tbase_segment={self.base_segment},\n\targs={self.args}\n)"
-    def __repr__(self) -> str:
-        return f"sum({', '.join(map(str, self.additional_args))})"
-
-    def depth_repr(self, depth=1):
-        out = "NudgeAction(\n"
-        if isinstance(self.base_arg, Action):
-            base_segment_repr = self.base_arg.depth_repr(depth + 1)
-            out += "\t" * depth + f"base_segment={base_segment_repr}\n"
-        else:
-            out += "\t" * depth + f"base_segment={self.base_arg.depth_repr()},\n"
-
-        if isinstance(self.additional_args, Action):
-            target_repr = self.additional_args.depth_repr(depth + 1)
-            out += "\t" * depth + f"target={target_repr},\n"
-        else:
-            out += "\t" * depth + f"target={self.additional_args.depth_repr()},\n"
-        out += "\t" * depth + f"weight={self.weight},\n"
-        out += "\t" * (depth - 1) + ")"
-        return out
-

lib/actions/types.py

@@ -1,6 +0,0 @@
-from typing import Union
-
-from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
-from custom_nodes.KepPromptLang.lib.action.base import Action
-
-SegOrAction = Union[PromptSegment, Action]

lib/actions/utils.py

@@ -1,38 +0,0 @@
-from typing import List
-
-import torch
-
-from custom_nodes.KepPromptLang.lib.actions.types import SegOrAction
-
-def batch_size_info(batch: List[SegOrAction]):
-    for segment in batch:
-        print("Token Len: " + str(segment.token_length()))
-        print(segment.depth_repr())
-
-def slerp(val: float, low: torch.Tensor, high: torch.Tensor, epsilon=1e-5):
-    # Convert val to tensor and clamp between 0 and 1
-    val = torch.tensor(val, dtype=torch.float32).clamp(0, 1)
-
-    # Normalize the vectors
-    low_norm = low / torch.norm(low, dim=-1, keepdim=True)
-    high_norm = high / torch.norm(high, dim=-1, keepdim=True)
-
-    # Calculate the cosine of the angle between the vectors
-    dot = (low_norm * high_norm).sum(-1, keepdim=True)
-
-    # Clamp to prevent numerical errors
-    dot = torch.clamp(dot, -1, 1)
-
-    omega = torch.acos(dot)
-
-    # Slerp formula
-    sin_omega = torch.sin(omega)
-    scale_0 = torch.sin((1.0 - val) * omega) / (sin_omega + epsilon)
-    scale_1 = torch.sin(val * omega) / (sin_omega + epsilon)
-
-    # Handle the case where omega is small (the vectors are close)
-    close_condition = sin_omega < epsilon
-    scale_0 = torch.where(close_condition, 1.0 - val, scale_0)
-    scale_1 = torch.where(close_condition, val, scale_1)
-
-    return scale_0 * low + scale_1 * high

lib/clip_config_bigg.json

@@ -1,23 +0,0 @@
-{
-  "architectures": [
-    "CLIPTextModel"
-  ],
-  "attention_dropout": 0.0,
-  "bos_token_id": 0,
-  "dropout": 0.0,
-  "eos_token_id": 2,
-  "hidden_act": "gelu",
-  "hidden_size": 1280,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 5120,
-  "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 77,
-  "model_type": "clip_text_model",
-  "num_attention_heads": 20,
-  "num_hidden_layers": 32,
-  "pad_token_id": 1,
-  "projection_dim": 1280,
-  "torch_dtype": "float32",
-  "vocab_size": 49408
-}

lib/clip_model.py

@@ -1,22 +1,18 @@
 import contextlib
 import os
-from typing import List
+from typing import Union
 
 import torch
 from transformers import CLIPTextConfig, modeling_utils
 
 from comfy import model_management
 import comfy.ops
-from comfy.sd1_clip import SD1ClipModel
-from comfy.sdxl_clip import SDXLClipModel
-from custom_nodes.KepPromptLang.lib.action.base import Action
-from custom_nodes.KepPromptLang.lib.actions.types import SegOrAction
-from custom_nodes.KepPromptLang.lib.fun_clip_stuff import PromptLangTextModel
-from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
+from comfy.sd import CLIP
+from custom_nodes.ClipStuff.lib.actions.base import PromptSegment, Action
+from custom_nodes.ClipStuff.lib.fun_clip_stuff import MyCLIPTextModel
+from custom_nodes.ClipStuff.lib.tokenizer import TokenDict
 
-
-# Methods with no comment can be assumed to be the same as comfy.sd1_clip.SD1ClipModel
-class PromptLangSDClipModel(torch.nn.Module):
+class SD1FunClipModel(torch.nn.Module):
     """Uses the CLIP transformer encoder for text (from huggingface)"""
     LAYERS = [
         "last",
@@ -26,37 +22,28 @@ class PromptLangSDClipModel(torch.nn.Module):
 
     def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77,
                  freeze=True, layer="last", layer_idx=None, textmodel_json_config=None,
-                 textmodel_path=None, dtype=None):  # clip-vit-base-patch32
+                 textmodel_path=None):  # clip-vit-base-patch32
         super().__init__()
         assert layer in self.LAYERS
         self.num_layers = 12
         if textmodel_path is not None:
-            # Our transformer
-            self.transformer = PromptLangTextModel.from_pretrained(textmodel_path)
+            self.transformer = MyCLIPTextModel.from_pretrained(textmodel_path)
         else:
             if textmodel_json_config is None:
-                # TODO: Maybe re-use clip config?
-                # Config could come from cond_stage_model.transformer.config
-                # Copied clip_config
                 textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config.json")
             config = CLIPTextConfig.from_json_file(textmodel_json_config)
             self.num_layers = config.num_hidden_layers
-            with comfy.ops.use_comfy_ops(device, dtype):
+            with comfy.ops.use_comfy_ops():
                 with modeling_utils.no_init_weights():
-                    # Our transformer
-                    self.transformer = PromptLangTextModel(config)
+                    self.transformer = MyCLIPTextModel(config)
 
-        if dtype is not None:
-            self.transformer.to(dtype)
         self.max_length = max_length
         if freeze:
             self.freeze()
         self.layer = layer
         self.layer_idx = None
         self.empty_tokens = [[49406] + [49407] * 76]
-        self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1]))
-        self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
-
+        self.text_projection = None
         self.layer_norm_hidden_state = True
         if layer == "hidden":
             assert layer_idx is not None
@@ -81,8 +68,7 @@ class PromptLangSDClipModel(torch.nn.Module):
         self.layer = self.layer_default[0]
         self.layer_idx = self.layer_default[1]
 
-    # Completely changed to support Segments and actions
-    def set_up_textual_embeddings(self, tokens: List[List[SegOrAction]], current_embeds):
+    def set_up_textual_embeddings(self, tokens: list[list[PromptSegment | Action]], current_embeds):
         next_new_token = token_dict_size = current_embeds.weight.shape[0] - 1
         embedding_weights = []
 
@@ -108,11 +94,12 @@ class PromptLangSDClipModel(torch.nn.Module):
                                 tokens_temp += [next_new_token]
                                 next_new_token += 1
                             else:
-                                raise Exception("WARNING: shape mismatch when trying to apply embedding. Should have been caught during tokenization.",
+                                print("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored",
                                       tid_or_tensor.shape[0], current_embeds.weight.shape[1])
                     if len(tokens_temp) < segment_length:
-                        # This should never happen...
-                        raise Exception("Segment size mismatch. Please submit an issue on Github.")
+                        # Pretty sure this is only needed if the embedding is not the same size as the CLIP embedding
+                        print("WARNING: segment length mismatch, padding with EOS token")
+                        tokens_temp.extend([self.empty_tokens[0][-1] * (segment_length - len(tokens_temp))])
                     segment.tokens = tokens_temp
 
         n = token_dict_size
@@ -144,8 +131,7 @@ class PromptLangSDClipModel(torch.nn.Module):
                         if segment.tokens[tokenIdx] == -1:
                             segment.tokens[tokenIdx] = n
 
-    # Support our set_up_textual_embeddings which modifies the input embeddings
-    def forward(self, tokens):
+    def forward(self, tokens, **kwargs):
         backup_embeds = self.transformer.get_input_embeddings()
         device = backup_embeds.weight.device
         self.set_up_textual_embeddings(tokens, backup_embeds)
@@ -156,8 +142,16 @@ class PromptLangSDClipModel(torch.nn.Module):
         else:
             precision_scope = contextlib.nullcontext
 
+
+        if (kwargs.get("position_ids", None) is not None):
+            position_ids = torch.LongTensor(kwargs["position_ids"]).to(device)
+        else:
+            position_ids = None
+
+
         with precision_scope(model_management.get_autocast_device(device)):
-            outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer == "hidden")
+            outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer == "hidden",
+                                       position_ids=position_ids)
             self.transformer.set_input_embeddings(backup_embeds)
 
             if self.layer == "last":
@@ -171,27 +165,21 @@ class PromptLangSDClipModel(torch.nn.Module):
 
             pooled_output = outputs.pooler_output
             if self.text_projection is not None:
-                pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float()
+                pooled_output = pooled_output.to(self.text_projection.device) @ self.text_projection
         return z.float(), pooled_output.float()
 
-    def encode(self, tokens):
-        return self(tokens)
+    def encode(self, tokens, **kwargs):
+        return self(tokens, **kwargs)
 
     def load_sd(self, sd):
-        if "text_projection" in sd:
-            self.text_projection[:] = sd.pop("text_projection")
-        if "text_projection.weight" in sd:
-            self.text_projection[:] = sd.pop("text_projection.weight").transpose(0, 1)
         return self.transformer.load_state_dict(sd, strict=False)
 
-    # Changed from comfy.sd1_clip.ClipTokenWeightEncoder
-    # Changed to use PromptSegments
-    def encode_token_weights(self, prompt_segments: List[List[SegOrAction]]):
+    def encode_token_weights(self, prompt_segments: list[list[Union[PromptSegment | Action]]], **kwargs):
         to_encode = [[PromptSegment(text="_Empty Batch_", tokens=self.empty_tokens[0])]]
         for batch in prompt_segments:
             to_encode.append(batch)
 
-        out, pooled = self.encode(to_encode)
+        out, pooled = self.encode(to_encode, **kwargs)
         z_empty = out[0:1]
         if pooled.shape[0] > 1:
             first_pooled = pooled[1:2]
@@ -210,33 +198,3 @@ class PromptLangSDClipModel(torch.nn.Module):
         if (len(output) == 0):
             return z_empty.cpu(), first_pooled.cpu()
         return torch.cat(output, dim=-2).cpu(), first_pooled.cpu()
-
-class PromptLangSD1ClipModel(SD1ClipModel):
-    def __init__(self, device="cpu", dtype=None, clip_name="l", clip_model=PromptLangSDClipModel):
-        super().__init__()
-        self.clip_name = clip_name
-        self.clip = "clip_{}".format(self.clip_name)
-        setattr(self, self.clip, clip_model(device=device, dtype=dtype))
-
-
-class PromptLangSDXLClipModel(SDXLClipModel):
-    def __init__(self, device="cpu", dtype=None) -> None:
-        # Skip SDXLClipModel's init
-        super(SDXLClipModel, self).__init__()
-        self.clip_l = PromptLangSDClipModel(layer="hidden", layer_idx=11, device=device, dtype=dtype)
-        self.clip_l.layer_norm_hidden_state = False
-        self.clip_g = PromptLangSDXLClipG(device, dtype)
-
-class PromptLangSDXLClipG(PromptLangSDClipModel):
-    def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, textmodel_path=None, dtype=None):
-        if layer == "penultimate":
-            layer="hidden"
-            layer_idx=-2
-
-        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json")
-        super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, textmodel_path=textmodel_path, dtype=dtype)
-        self.empty_tokens = [[49406] + [49407] + [0] * 75]
-        self.layer_norm_hidden_state = False
-
-    def load_sd(self, sd):
-        return super().load_sd(sd)

lib/fun_clip_stuff.py

@@ -1,23 +1,14 @@
-from typing import Optional, Tuple, Union, List, TypedDict, TYPE_CHECKING
-from importlib.metadata import version as import_version
-from packaging import version
+from typing import Optional, Tuple, Union
 
 import torch
+from torch import device
 from transformers import CLIPTextConfig
 from transformers.modeling_outputs import BaseModelOutputWithPooling
-from transformers.models.clip.modeling_clip import (
-    CLIPTextEmbeddings,
-    CLIPTextTransformer,
-    CLIPTextModel,
-)
-
-from custom_nodes.KepPromptLang.lib.action.base import Action
-from custom_nodes.KepPromptLang.lib.actions.types import SegOrAction
-
-if TYPE_CHECKING:
-    from torch import Tensor
-    from custom_nodes.KepPromptLang.lib.action.base import PostModifiers
+from transformers.models.clip.modeling_clip import _expand_mask, CLIPTextEmbeddings, CLIPTextTransformer, \
+    CLIPTextModel
 
+from custom_nodes.ClipStuff.lib.actions.base import PromptSegment, Action
+from custom_nodes.ClipStuff.lib.tokenizer import TokenDict
 
 def slerp(val, low, high):
     low = low.unsqueeze(0)
@@ -29,70 +20,57 @@ def slerp(val, low, high):
     res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
     return res
 
-
-class PosModifier(TypedDict):
-    """
-    A dictionary of post modifiers for an action result.
-    """
-
-    position_embed_scale: Union[float]
-    start_idx: Union[int]
-    end_idx: Union[int]
-
-
-class PromptLangCLIPTextEmbeddings(CLIPTextEmbeddings):
+class MyCLIPTextEmbeddings(CLIPTextEmbeddings):
     def __init__(self, config: CLIPTextConfig):
         super().__init__(config)
 
     def forward(
             self,
-            input_dicts: Optional[List[List[SegOrAction]]] = None,
+            input_dicts: Optional[list[list[tuple[TokenDict]]]] = None,
             input_ids: Optional[torch.LongTensor] = None,
             position_ids: Optional[torch.LongTensor] = None,
             inputs_embeds: Optional[torch.FloatTensor] = None,
     ) -> torch.Tensor:
-        if input_dicts is None:
-            raise ValueError("You have to specify input_dicts")
 
-        batches: List[List[Tensor | Tuple[Tensor, PostModifiers] | Action]] = []
-        pos_modifiers: List[List[PosModifier]] = []
+
+        batches = []
         for batch_idx, batch in enumerate(input_dicts):
             results = []
-            batch_pos_modifiers = []
-            token_idx = 0
             for seg_or_action in batch:
                 if isinstance(seg_or_action, Action):
-                    action_result: Union[
-                        Tensor, Tuple[Tensor, PostModifiers]
-                    ] = seg_or_action.get_result(self.token_embedding)
-                    if isinstance(action_result, tuple):
-                        result, post_modifiers = action_result
-                        if post_modifiers.get("position_embed_scale", None) is not None:
-                            post_modifiers["start_idx"] = token_idx
-                            post_modifiers["end_idx"] = (
-                                token_idx + seg_or_action.token_length()
-                            )
-
-                        if post_modifiers.get("bypass_pos_embed", False):
-                            post_modifiers["start_idx"] = token_idx
-                            post_modifiers["end_idx"] = (
-                                token_idx + seg_or_action.token_length()
-                            )
-                        batch_pos_modifiers.append(post_modifiers)
-                    else:
-                        result = action_result
+                    results.append(seg_or_action.get_result(self.token_embedding))
                 else:
-                    result = seg_or_action.get_embeddings(self.token_embedding)
-                results.append(result)
-                token_idx += seg_or_action.token_length()
+                    results.append(seg_or_action.get_embeddings(self.token_embedding))
             batches.append(results)
-            pos_modifiers.append(batch_pos_modifiers)
 
         seq_length = batches[0][0].shape[-2]
 
         if position_ids is None:
             position_ids = self.position_ids[:, :seq_length]
 
+        # if inputs_embeds is None:
+        #     inputs_embeds = self.token_embedding(input_ids)
+
+        # for batch_idx, batch in enumerate(input_dicts):
+        #     for token_idx, token in enumerate(batch):
+        #         if token[0].nudge_id is not None:
+        #             nudged_embed = inputs_embeds[batch_idx, token_idx][:] + self.token_embedding(torch.LongTensor([token[0].nudge_id]).to(torch.device('cpu')))[0]
+        #             if token[0].nudge_index_start is not None and token[0].nudge_index_stop is not None:
+        #                 nudge_start = token[0].nudge_index_start
+        #                 nudge_end = token[0].nudge_index_stop
+        #             else:
+        #                 nudge_start = 0
+        #                 nudge_end = 768
+        #             inputs_embeds[batch_idx, token_idx][nudge_start:nudge_end] = (slerp(token[0].nudge_weight, inputs_embeds[batch_idx, token_idx][:], nudged_embed)[0][nudge_start:nudge_end])
+        #         elif token[0].arith_ops is not None:
+        #             for op, id_list in token[0].arith_ops.items():
+        #                 if op == '+':
+        #                     for this_id in id_list:
+        #                         inputs_embeds[batch_idx, token_idx] += self.token_embedding(torch.LongTensor([this_id]).to(torch.device('cpu')))[0]
+        #                 elif op == '-':
+        #                     for this_id in id_list:
+        #                         inputs_embeds[batch_idx, token_idx] -= self.token_embedding(torch.LongTensor([this_id]).to(torch.device('cpu')))[0]
+
         embeds = []
         for batch in batches:
             if len(batch) == 1:
@@ -100,74 +78,20 @@ class PromptLangCLIPTextEmbeddings(CLIPTextEmbeddings):
             else:
                 embeds.append(torch.cat(batch, dim=-2))
 
-        # Iterate over the batches and apply the pos modifiers to the position embeddings then add them to the embeddings
-        for idx, batch_pos_modifiers in enumerate(pos_modifiers):
-            position_embeddings = self.position_embedding(position_ids)
-            if len(batch_pos_modifiers) > 0:
-                print(f"Found {len(batch_pos_modifiers)} pos modifiers for batch {idx}")
-                # Apply each pos modifier to the position embeddings at the specified indices
-                for post_modifier in batch_pos_modifiers:
-                    if post_modifier.get("bypass_pos_embed", False):
-                        position_embeddings[
-                            0, post_modifier["start_idx"] : post_modifier["end_idx"]
-                        ] = 0
-                    elif post_modifier["position_embed_scale"] is not None:
-                        position_embeddings[
-                            0, post_modifier["start_idx"] : post_modifier["end_idx"]
-                        ] *= post_modifier["position_embed_scale"]
-                    else:
-                        raise ValueError(
-                            "Pos modifier must have a scale or bypass_pos_embed"
-                        )
-            # Add the possibly modified position embeddings to the embeddings
-            embeds[idx] = embeds[idx] + position_embeddings
-        embeddings = torch.cat(embeds, dim=0)
+        position_embeddings = self.position_embedding(position_ids)
+        embeddings = torch.cat(embeds, dim=0) + position_embeddings
 
         return embeddings
 
 
-class PrompLangCLIPTextTransformer(CLIPTextTransformer):
+class MyCLIPTextTransformer(CLIPTextTransformer):
     def __init__(self, config: CLIPTextConfig):
         super().__init__(config)
-        self.embeddings = PromptLangCLIPTextEmbeddings(config)
-        self.transformers_version = version.parse(import_version('transformers'))
-
-    def process_attention_mask(self, hidden_states, attention_mask, bsz, seq_len):
-        # Parse the transformer version
-        input_shape = torch.Size([bsz, seq_len])
-
-        v4_30 = version.parse('4.30.0')
-        v4_35 = version.parse('4.35')
-        if self.transformers_version < v4_30:
-            print("Using transformers < 4.30.0")
-            causal_attention_mask = self._build_causal_attention_mask(bsz, seq_len, hidden_states.dtype).to(
-                hidden_states.device)
-        elif v4_30 <= self.transformers_version < v4_35:
-            print("Using transformers >= 4.30.0 and <= 4.34.*")
-            from transformers.models.clip.modeling_clip import _make_causal_mask
-            causal_attention_mask = _make_causal_mask(input_shape, hidden_states.dtype, device=hidden_states.device)
-        else:
-            print("Using transformers >= 4.35")
-            from transformers.modeling_attn_mask_utils import _create_4d_causal_attention_mask
-            causal_attention_mask = _create_4d_causal_attention_mask(
-                input_shape, hidden_states.dtype, device=hidden_states.device
-            )
-
-        # Expand attention_mask if it exists
-        if attention_mask is not None:
-            # Import _expand_mask or _prepare_4d_attention_mask based on version
-            if self.transformers_version < v4_35:
-                from transformers.models.clip.modeling_clip import _expand_mask
-                attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
-            else:
-                from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
-                attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)
-
-        return causal_attention_mask, attention_mask
+        self.embeddings = MyCLIPTextEmbeddings(config)
 
     def forward(
             self,
-            input_ids: Optional[List[List[SegOrAction]]] = None,
+            input_ids: Optional[list[list[PromptSegment | Action]]] = None,
             attention_mask: Optional[torch.Tensor] = None,
             position_ids: Optional[torch.Tensor] = None,
             output_attentions: Optional[bool] = None,
@@ -190,19 +114,21 @@ class PrompLangCLIPTextTransformer(CLIPTextTransformer):
         # input_shape = input_ids.size()
         # input_ids = input_ids.view(-1, input_shape[-1])
 
-        for batch_idx, batch in enumerate(input_ids):
-            for seg_or_action in batch:
-                if isinstance(seg_or_action, Action):
-                    seg_or_action.process_with_transformer(
-                        self, self.embeddings.token_embedding
-                    )
-
         hidden_states = self.embeddings(input_dicts=input_ids)
 
         bsz = len(input_ids)
-        seq_len = hidden_states.shape[1]
-
-        causal_attention_mask, attention_mask = self.process_attention_mask(hidden_states, attention_mask, bsz, seq_len)
+        # TODO: Properly gather this
+        seq_len = 77
+        # bsz, seq_len = input_shape
+        # CLIP's text model uses causal mask, prepare it here.
+        # https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
+        causal_attention_mask = self._build_causal_attention_mask(bsz, seq_len, hidden_states.dtype).to(
+            hidden_states.device
+        )
+        # expand attention_mask
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
 
         encoder_outputs = self.encoder(
             inputs_embeds=hidden_states,
@@ -225,11 +151,8 @@ class PrompLangCLIPTextTransformer(CLIPTextTransformer):
                 if isinstance(seg_or_action, Action):
                     idx += seg_or_action.token_length()
                 else:
-                    if seg_or_action.text == "__PAD__" or seg_or_action.text == "[EOT]":
+                    if seg_or_action.text == '__PAD__':
                         break
-
-                    # Is a segment, and isn't the pad segment
-                    idx += seg_or_action.token_length()
             eot_idx.append(idx)
         # text_embeds.shape = [batch_size, sequence_length, transformer.width]
         # take features from the eot embedding (eot_token is the highest number in each sequence)
@@ -251,21 +174,37 @@ class PrompLangCLIPTextTransformer(CLIPTextTransformer):
         )
 
 
-# This is necessary to pass the PromptLangCLIPTextTransformer
-class PromptLangTextModel(CLIPTextModel):
+class MyCLIPTextModel(CLIPTextModel):
     def __init__(self, config: CLIPTextConfig):
         super().__init__(config)
-        self.text_model = PrompLangCLIPTextTransformer(config)
+        self.text_model = MyCLIPTextTransformer(config)
 
     def forward(
             self,
-            input_ids: Optional[List[List[SegOrAction]]] = None,
+            input_ids: Optional[list[list[tuple[TokenDict]]]] = None,
             attention_mask: Optional[torch.Tensor] = None,
             position_ids: Optional[torch.Tensor] = None,
             output_attentions: Optional[bool] = None,
             output_hidden_states: Optional[bool] = None,
             return_dict: Optional[bool] = None,
     ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        r"""
+        Returns:
+
+        Examples:
+
+        ```python
+        >>> from transformers import AutoTokenizer, CLIPTextModel
+
+        >>> model = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32")
+        >>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")
+
+        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
+
+        >>> outputs = model(**inputs)
+        >>> last_hidden_state = outputs.last_hidden_state
+        >>> pooled_output = outputs.pooler_output  # pooled (EOS token) states
+        ```"""
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         return self.text_model(

lib/parser/__init__.py

@@ -1,5 +0,0 @@
-from lark import Lark
-
-from .grammar import grammar
-
-PromptParser = Lark(grammar, start="start", parser="earley")

lib/parser/grammar.py

@@ -1,20 +0,0 @@
-grammar = """
-?start: item+
-
-item: embedding
-    | WORD
-    | generic_function
-    | QUOTED_STRING
-
-generic_function: FUNC_NAME "(" arg ("|" arg)* ")"
-
-arg: item+
-
-embedding: "embedding:" WORD
-FUNC_NAME: /[A-Za-z_-]+/
-WORD: /[A-Za-z0-9,_\.-]+/
-QUOTED_STRING: /"([^"\\\]*(\\\.[^"\\\]*)*)"|'([^'\\\]*(\\\.[^'\\\]*)*)'/
-
-%import common.WS
-%ignore WS
-"""

lib/parser/prompt_segment.py

@@ -1,31 +0,0 @@
-from typing import Union, List
-
-import torch
-from torch import Tensor
-from torch.nn import Embedding
-
-
-class PromptSegment:
-    def __init__(self, text: str, tokens: List[Union[int, Tensor]]):
-        self.text = text
-        self.tokens = tokens
-
-    def __repr__(self):
-        return f'"{self.text}"{self.tokens}'
-
-    def token_length(self):
-        return len(self.tokens)
-
-    def get_embeddings(self, embedding_module: Embedding) -> Tensor:
-        tensors = torch.LongTensor(self.tokens).to(embedding_module.weight.device)
-        unsqueezed_tensors = tensors.unsqueeze(0)
-        return embedding_module(unsqueezed_tensors)
-
-    def depth_repr(self, depth=1):
-        out = f'"{self.text}"('
-
-        cleaned_tokens = list(map(lambda x: str(x) if isinstance(x, int) else "EMBD", self.tokens))
-        out += ", ".join(cleaned_tokens)
-
-        out += ")"
-        return out

lib/parser/registration.py

@@ -1,20 +0,0 @@
-from typing import Type, Dict
-
-from custom_nodes.KepPromptLang.lib.action.base import Action
-
-action_registry: Dict[str, Type[Action]] = {}
-
-def register_action(action: Type[Action]) -> None:
-    """
-
-    :rtype: object
-    """
-    if action.action_name in action_registry:
-        raise ValueError(f"Action {action.action_name} already registered")
-    action_registry[str(action.action_name)] = action
-
-def get_action_by_name(name: str) -> Type[Action]:
-    if name not in action_registry:
-        raise ValueError(f"Action {name} not found in registry")
-
-    return action_registry[name]

lib/parser/transformer.py

@@ -1,59 +0,0 @@
-from typing import List
-
-from lark import Transformer, Token
-
-from comfy.sd1_clip import SDTokenizer
-from custom_nodes.KepPromptLang.lib.action.base import Action, ActionArity
-from custom_nodes.KepPromptLang.lib.parser.registration import get_action_by_name
-from custom_nodes.KepPromptLang.lib.parser.utils import build_prompt_segment
-from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
-
-
-class PromptTransformer(Transformer):
-    """
-    Transforms the parsed prompt into a list of segments and actions
-    Types from the grammar are mapped to the methods in this class
-    """
-    # def WORD(self, items):
-    #     return items
-
-    def __init__(self, tokenizer: SDTokenizer):
-        super().__init__()
-        self.tokenizer = tokenizer
-
-    def item(self, items: List[Token]):
-        for item in items:
-            if isinstance(item, (Action, PromptSegment)):
-                return item
-
-            if item.type == "WORD":
-                return build_prompt_segment(str(item), self.tokenizer)
-            elif item.type == "QUOTED_STRING":
-                # Remove the quotes
-                unquoted = item[1:-1]
-                # Replace escaped quotes with quotes
-                unescaped = unquoted.replace("\\\"", "\"").replace("\\\'", "\'")
-                return build_prompt_segment(unescaped, self.tokenizer)
-            elif item.type == "embedding":
-                return build_prompt_segment(item, self.tokenizer)
-            elif item.type == "function":
-                raise Exception("Unexpected type in prompt transformer: function. Please report this issue on GitHub.")
-            else:
-                raise Exception("Unknown item type: " + str(item.type))
-
-    def arg(self, items):
-        return items
-
-    def embedding(self, items):
-        return build_prompt_segment(f'{self.tokenizer.embedding_identifier}{items[0]}', self.tokenizer)
-
-    def generic_function(self, items):
-        action = get_action_by_name(items[0])
-        if action.arity == ActionArity.SINGLE:
-            if len(items) != 2:
-                raise ValueError(f"Action {action.name} should have exactly one argument")
-            return action(items[1])
-        elif action.arity == ActionArity.MULTI:
-            return action(items[1:][:])
-        else:
-            raise ValueError(f"Unknown action arity: {action.arity}")

lib/parser/utils.py

@@ -1,41 +0,0 @@
-from lark import Token
-
-from comfy.sd1_clip import SDTokenizer
-from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
-
-
-def flatten_tree(tree):
-    if isinstance(tree, Token):
-        return [str(tree)]
-    else:
-        return [str(tree.data)] + sum([flatten_tree(child) for child in tree.children], [])
-
-
-def build_prompt_segment(text: str, tokenizer: SDTokenizer) -> PromptSegment:
-    split_text = text.split(" ")
-    tokens = []
-    for word in split_text:
-        if word.startswith(tokenizer.embedding_identifier) and tokenizer.embedding_directory is not None:
-            embedding_name = word[len(tokenizer.embedding_identifier):].strip('\n')
-
-            get_embed_ret = tokenizer._try_get_embedding(embedding_name)
-            embedding = get_embed_ret[0]
-            leftover = get_embed_ret[1]
-            if embedding is None:
-                print(f"warning, embedding:{embedding_name} does not exist, ignoring")
-            else:
-                if embedding.shape[1] != tokenizer.embedding_size:
-                    print(f"warning, embedding:{embedding_name} has size {embedding.shape[1]}, expected {tokenizer.embedding_size}, ignoring")
-                else:
-                    if len(embedding.shape) == 1:
-                        tokens.append(embedding)
-                    else:
-                        tokens.extend(embedding)
-
-            if leftover != "":
-                word = leftover
-            else:
-                continue
-        tokens.extend(tokenizer.tokenizer(word)["input_ids"][1:-1])
-
-    return PromptSegment(text, tokens)

lib/tokenizer.py

@@ -1,51 +1,161 @@
-from typing import List, Dict
+import re
+from typing import Union
 
-from lark import Tree
+from comfy.sd1_clip import SD1Tokenizer
+from custom_nodes.ClipStuff.lib.actions import (
+    NudgeAction,
+    ArithAction,
+    ALL_START_CHARS,
+    ALL_END_CHARS,
+    ALL_ACTIONS,
+)
+from custom_nodes.ClipStuff.lib.actions.base import (
+    Action,
+    PromptSegment,
+    build_prompt_segment,
+)
+from custom_nodes.ClipStuff.lib.actions.lib import (
+    is_any_action_segment,
+    is_action_segment,
+)
+from custom_nodes.ClipStuff.lib.actions.utils import batch_size_info
 
-from comfy.sd1_clip import SD1Tokenizer, SDTokenizer
-from custom_nodes.KepPromptLang.lib.actions.types import SegOrAction
+arith_action = r'(<[a-zA-Z0-9\-_]+:[a-zA-Z0-9\-_]+>)'
 
-from custom_nodes.KepPromptLang.lib.parser import PromptParser
-from custom_nodes.KepPromptLang.lib.parser.transformer import PromptTransformer
-from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
+# TODO: Get embedding identifier from tokenizer
+tokenizer_regex = re.compile(
+    fr"""
+    \d+\.\d+                  # Capture decimals
+    |
+    (?:(?!embedding:)[\w\s]|embedding:[a-zA-Z0-9_]+)+ # Capture sequences of characters, including "embedding:"
+    |
+    \d+                       # Capture whole numbers
+    |
+    [:+-{re.escape("".join(ALL_START_CHARS))}{re.escape("".join(ALL_END_CHARS))}] # Capture special characters including start and end characters
+    """,
+    re.VERBOSE
+)
+def tokenize(text: str) -> list[str]:
+    # Captures:
+    # 1. Words
+    # 2. Numbers(1.0, 1)
+    # 3. Special characters(ALL_START_CHARS, ALL_END_CHARS, :, +, -)
+    tokens = re.findall(tokenizer_regex, text)
+    print(tokens)
+    return [token.strip() for token in tokens]
 
 
-class PromptLangSDTokenizer(SDTokenizer):
-    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l'):
+
+def parse_segment(tokens: list[str], tokenizer: SD1Tokenizer) -> PromptSegment | Action:
+    print("Parse segment: Checking token: " + tokens[0])
+    for action in ALL_ACTIONS:
+        if tokens[0] == action.START_CHAR:
+            return action.parse_segment(tokens, ALL_START_CHARS, ALL_END_CHARS, parse_segment, tokenizer)
+    # If we get here, it's a text segment
+    return build_prompt_segment(tokens.pop(0), tokenizer)
+
+def parse(tokens: list[str], tokenizer: SD1Tokenizer) -> list[PromptSegment | Action]:
+    parsed = []
+    while tokens:
+        if tokens[0] == '':
+            tokens.pop(0)
+            continue
+        print("Parse: Checking token: " + tokens[0])
+        if tokens[0] in ALL_START_CHARS:
+            parsed.append(parse_segment(tokens, tokenizer))
+        else:
+            parsed.append(build_prompt_segment(tokens.pop(0), tokenizer))
+    return parsed
+
+
+def parse_special_tokens(string) -> list[str]:
+    out = []
+    current = ""
+
+    for char in string:
+        if char in ALL_START_CHARS:
+            out += [current]
+            current = char
+        elif char in ALL_END_CHARS:
+            out += [current + char]
+            current = ""
+        else:
+            current += char
+    out += [current]
+    return out
+
+
+def parse_segment_actions(string, tokenizer: SD1Tokenizer) -> list[PromptSegment | NudgeAction | ArithAction]:
+    tokens = tokenize(string)
+    parsed = parse(tokens, tokenizer)
+    return parsed
+
+class TokenDict:
+    def __init__(self,
+                 token_id: int,
+                 weight: float = None,
+                 nudge_id=None, nudge_weight=None, nudge_start: int = None, nudge_end: int = None,
+                 arith_ops: dict[str, list[str]] = None):
+        if weight is None:
+            self.weight = 1.0
+        else:
+            self.weight = weight
+
+        self.token_id = token_id
+        self.nudge_id = nudge_id
+        self.nudge_weight = nudge_weight
+        self.nudge_index_start = nudge_start
+        self.nudge_index_stop = nudge_end
+
+        self.arith_ops = arith_ops
+
+
+class MyTokenizer(SD1Tokenizer):
+    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', special_tokens=None):
         super().__init__(tokenizer_path, max_length, pad_with_end, embedding_directory, embedding_size, embedding_key)
+
     """
     Doesn't actually tokenize...
     Returns batches of segments and actions
     :return: List of list(batches) of segments and actions
     """
-    def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs) -> List[List[SegOrAction]]:
+    def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs) -> list[list[PromptSegment | Action]]:
         if self.pad_with_end:
             pad_token = self.end_token
         else:
             pad_token = 0
 
-        parsed_prompt = PromptParser.parse(text)
-        parsed_actions = PromptTransformer(self).transform(parsed_prompt)
+        parsed_actions = parse_segment_actions(text, self)
+
+        # nudge_start = kwargs.get("nudge_start")
+        # nudge_end = kwargs.get("nudge_end")
+        #
+        # if nudge_start is not None and nudge_end is not None:
+        #     nudge_start = int(nudge_start)
+        #     nudge_end = int(nudge_end)
+        #
+        # # tokenize words
+        for segment in parsed_actions:
+            if isinstance(segment, Action):
+                print(segment.depth_repr())
+            else:
+                print(segment.depth_repr())
 
         # reshape token array to CLIP input size
         batched_segments = []
         batch = [PromptSegment(text="[SOT]", tokens=[self.start_token])]
         # batched_segments.append(batch)
         batch_size = 1
-        if isinstance(parsed_actions, Tree):
-            segments_to_process = parsed_actions.children
-        else:
-            segments_to_process = [parsed_actions]
-        for segment in segments_to_process:
+        for segment in parsed_actions:
             num_tokens = segment.token_length()
             # determine if we're going to try and keep the tokens in a single batch
             is_large = num_tokens >= self.max_word_length
 
             # If the segment is too large to fit in a single batch, pad the current batch and start a new one
             if num_tokens + batch_size > self.max_length - 1:
-                remaining_length = self.max_length - batch_size
+                remaining_length = self.max_length - batch_size - 1 # -1 for end token
                 # Pad batch
-                batch.append(PromptSegment("__PAD__", [self.end_token] + [pad_token] * (remaining_length - 1))) # -1 for end token
+                batch.append(PromptSegment("__PAD__", [self.end_token] + [pad_token] * remaining_length - 1))
                 batched_segments.append(batch)
 
                 # start new batch
@@ -53,7 +163,7 @@ class PromptLangSDTokenizer(SDTokenizer):
                 batch_size = num_tokens + 1 # +1 for start token
                 continue
 
-            # Since the segment fits in the current batch, add it
+            # If the segment is small enough to fit in the current batch, add it
             batch.append(segment)
             batch_size += num_tokens
 
@@ -62,27 +172,7 @@ class PromptLangSDTokenizer(SDTokenizer):
         batch.append(PromptSegment("__PAD__", [self.end_token] + [pad_token] * remaining_length))
         batched_segments.append(batch)
 
-        # for batch in batched_segments:
-        #     batch_size_info(batch)
+        for batch in batched_segments:
+            batch_size_info(batch)
 
         return batched_segments
-
-class PromptLangSD1Tokenizer(SD1Tokenizer):
-    def __init__(self, embedding_directory=None, clip_name='l', tokenizer=PromptLangSDTokenizer) -> None:
-        super().__init__(embedding_directory, clip_name, tokenizer)
-
-
-class PromptLangSDXLClipGTokenizer(PromptLangSDTokenizer):
-    def __init__(self, tokenizer_path=None, embedding_directory=None):
-        super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g')
-
-class PromptLangSDXLTokenizer(SD1Tokenizer):
-    def __init__(self, embedding_directory=None) -> None:
-        self.clip_l = PromptLangSDTokenizer(embedding_directory=embedding_directory)
-        self.clip_g = PromptLangSDXLClipGTokenizer(embedding_directory=embedding_directory)
-
-    def tokenize_with_weights(self, text:str, return_word_ids=False) -> Dict[str, List[List[SegOrAction]]]:
-        out = {}
-        out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
-        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
-        return out

nodes.py

@@ -1,5 +1,4 @@
-import os
-from typing import List, Tuple, Any
+import random
 
 import numpy as np
 from PIL import Image
@@ -7,18 +6,9 @@ from PIL import Image
 import folder_paths
 import comfy.sd
 import comfy.ops
-from comfy.sd2_clip import SD2ClipModel
-from comfy.sdxl_clip import SDXLClipModel
-from comfy.supported_models_base import ClipTarget
-from custom_nodes.KepPromptLang.lib.clip_model import (
-    PromptLangSDXLClipModel,
-    PromptLangSD1ClipModel,
-)
+from custom_nodes.ClipStuff.lib.clip_model import SD1FunClipModel
 
-from custom_nodes.KepPromptLang.lib.tokenizer import (
-    PromptLangSDXLTokenizer,
-    PromptLangSD1Tokenizer,
-)
+from custom_nodes.ClipStuff.lib.tokenizer import MyTokenizer
 
 
 class EmptyClass:
@@ -27,7 +17,7 @@ class EmptyClass:
 
 class SpecialClipLoader:
     @classmethod
-    def INPUT_TYPES(cls):  # type: ignore
+    def INPUT_TYPES(s):
         return {
             "required": {
                 "source_clip": ("CLIP",),
@@ -40,43 +30,79 @@ class SpecialClipLoader:
     CATEGORY = "conditioning"
 
     @staticmethod
-    def load_clip(source_clip: comfy.sd.CLIP) -> Tuple[comfy.sd.CLIP]:
+    def load_clip(source_clip):
+        clip_target = EmptyClass()
+        clip_target.params = {}
+        clip_target.clip = SD1FunClipModel
+        clip_target.tokenizer = MyTokenizer
 
-        if isinstance(source_clip.cond_stage_model, SDXLClipModel):
-            clip_target = ClipTarget(PromptLangSDXLTokenizer, PromptLangSDXLClipModel)
-            clip = comfy.sd.CLIP(clip_target, embedding_directory=source_clip.tokenizer.clip_g.embedding_directory)
-            comfy.sd.load_clip_weights(clip.cond_stage_model.clip_g,source_clip.cond_stage_model.clip_g.state_dict())
-            comfy.sd.load_clip_weights(
-                clip.cond_stage_model.clip_l, source_clip.cond_stage_model.clip_l.state_dict()
-            )
-        elif isinstance(source_clip, SD2ClipModel):
-            raise ValueError("SD2 Clip model is not supported.")
-        else:
-            clip_target = ClipTarget(PromptLangSD1Tokenizer, PromptLangSD1ClipModel)
-            clip = comfy.sd.CLIP(clip_target, embedding_directory=source_clip.tokenizer.clip_l.embedding_directory)
-            comfy.sd.load_clip_weights(
-                clip.cond_stage_model, source_clip.cond_stage_model.state_dict()
-            )
+        # TODO: Extract embedding directory from source_clip
+        clip = comfy.sd.CLIP(clip_target, embedding_directory=source_clip.tokenizer.embedding_directory)
+        comfy.sd.load_clip_weights(
+            clip.cond_stage_model, source_clip.cond_stage_model.state_dict()
+        )
         return (clip,)
 
 
-def tensor2img(tensor_img) -> Image.Image:
+class KepAdvTextEncode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "text": ("STRING", {"multiline": True}),
+                "clip": ("CLIP",),
+                "nudge_start": ("INT", {}),
+                "nudge_end": ("INT", {}),
+                "split_newlines": ("BOOL", {"default": True}),
+            }
+        }
+
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "encode"
+    OUTPUT_IS_LIST = (True,)
+    CATEGORY = "conditioning"
+
+    @staticmethod
+    def encode(clip, text, nudge_start, nudge_end, split_newlines):
+        ret = []
+        if split_newlines:
+            prompts = text.split("\n")
+        else:
+            prompts = [text]
+
+        for prompt in prompts:
+            if prompt.strip() == "":
+                continue
+            tokens = clip.tokenizer.tokenize_with_weights(
+                text,
+                return_word_ids=False,
+                nudge_start=nudge_start,
+                nudge_end=nudge_end,
+            )
+            cond, pooled = clip.encode_from_tokens(
+                tokens, return_pooled=True, position_ids=[0] * 77
+            )
+            cond = [[cond, {"pooled_output": pooled}]]
+            ret.append(cond)
+        return (ret,)
+
+
+def tensor2img(tensor_img):
     i = 255.0 * tensor_img.cpu().numpy()
     i_np_arr = np.clip(i, 0, 255, out=i).astype(np.uint8, copy=False)
     return Image.fromarray(i_np_arr)
 
+
 class BuildGif:
-    def __init__(self) -> None:
-        self.output_dir = folder_paths.get_output_directory()
+    def __init__(self):
         pass
 
     @classmethod
-    def INPUT_TYPES(cls):  # type: ignore
+    def INPUT_TYPES(cls):
         return {
             "required": {
                 "images": ("IMAGE",),
                 "split_every": ("INT", {"default": -1}),
-                "frame_duration": ("INT", {"default": 125}),
                 "output_mode": (
                     ["One Per Split", "Big Grid"],
                     {"default": "Big Grid"},
@@ -85,53 +111,46 @@ class BuildGif:
         }
 
     RELOAD_INST = True
-    RETURN_TYPES = ()
-    # RETURN_NAMES = ("Gifs",)
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("Gifs",)
     INPUT_IS_LIST = True
     FUNCTION = "build_gif"
-    # OUTPUT_IS_LIST = (True,)
-    OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (True,)
+    # OUTPUT_NODE = False
 
     CATEGORY = "List Stuff"
 
-    def build_gif(self, images: List[Any], split_every: List[int], frame_duration: List[int], output_mode: List[str]):
+    @staticmethod
+    def build_gif(images: list, split_every: list[int], output_mode: str):
         print("Build GIF called!")
         print(f"{type(images)}")
 
         if len(split_every) > 1:
             raise Exception("List input for split every is not supported.")
 
-        if len(output_mode) > 1:
-            raise Exception("List input for output_mode is not supported.")
-        output_mode = output_mode[0]
-
-        if len(frame_duration) > 1:
-            raise Exception("List input for frame_duration is not supported.")
-        frame_duration = frame_duration[0]
-
-        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix="Gif", output_dir=self.output_dir, image_width=0, image_height=0)
-        split_every_val = split_every[0]
+        split_every = split_every[0]
         batch_size = images[0].size()[0]
-        if split_every_val == -1:
+        if split_every == -1:
             split_chunks = 1
-            split_every_val = len(images)
+            split_every = len(images)
         else:
-            split_chunks = int(len(images) / split_every_val)
+            split_chunks = int(len(images) / split_every)
+
+        out = []
 
         num_wide = batch_size
         num_tall = split_chunks
 
         chunked_batches = [
-            images[split_every_val * chunk_idx : split_every_val * (chunk_idx + 1)]
+            images[split_every * chunk_idx : split_every * (chunk_idx + 1)]
             for chunk_idx in range(split_chunks)
         ]
 
         frames = []
-        results = list()
 
         if output_mode == "Big Grid":
             # For every image in gif
-            for idx_in_chunk in range(split_every_val):
+            for idx_in_chunk in range(split_every):
                 img_shape = images[0][0].shape
                 img_frame = Image.new(
                     "RGB", size=(num_wide * img_shape[0], num_tall * img_shape[1])
@@ -146,9 +165,8 @@ class BuildGif:
                         )
                 frames.append(img_frame)
 
-            file = f"{filename}_{counter:05}_"
             save_path = (
-                f"{os.path.join(full_output_folder, file)}"
+                f"{folder_paths.get_output_directory()}/{random.randint(1, 100)}"
             )
             frames[0].save(
                 f"{save_path}.webp",
@@ -158,24 +176,15 @@ class BuildGif:
                 save_all=True,
                 append_images=frames[1:],
                 optimize=False,
-                duration=frame_duration,
+                duration=125,
                 loop=0,
             )
-            results.append({
-                "filename": f"{file}.webp",
-                "subfolder": subfolder,
-                "type": "output"
-            })
         elif output_mode == "One Per Split":
             for split_idx in range(int(split_chunks)):
-                split_start = split_every_val * split_idx
-                split_end = split_every_val * (split_idx + 1)
+                split_start = split_every * split_idx
+                split_end = split_every * (split_idx + 1)
                 for batch_idx in range(batch_size):
-                    file = f"{filename}_{counter:05}_"
-                    save_path = (
-                        f"{os.path.join(full_output_folder, file)}"
-                    )
-                    counter += 1
+                    save_path = f"{folder_paths.get_output_directory()}/-{batch_idx}-{random.randint(1, 100)}"
                     print(save_path)
                     tensor2img(images[split_start][batch_idx]).save(
                         f"{save_path}.webp",
@@ -185,12 +194,7 @@ class BuildGif:
                             for nested_batch in images[split_start + 1 : split_end]
                         ],
                         optimize=False,
-                        duration=frame_duration,
+                        duration=125,
                         loop=0,
                     )
-                    results.append({
-                        "filename": f"{file}.webp",
-                        "subfolder": subfolder,
-                        "type": "output"
-                    })
-        return { "ui": { "images": results } }
+        return (out,)

requirements.txt

@@ -1,2 +0,0 @@
-lark
-packaging

test_files/check_and_download_model.py

@@ -1,12 +0,0 @@
-# If the models/checkpoints folder does not have test.txt, then download the model.
-import os
-
-from huggingface_hub import hf_hub_download
-
-FILE = "v1-5-pruned-emaonly.safetensors"
-REPO_ID = "runwayml/stable-diffusion-v1-5"
-if not os.path.exists(f"models/checkpoints/{FILE}"):
-    print("Downloading model...")
-    hf_hub_download(repo_id=REPO_ID, filename=FILE, local_dir="models/checkpoints", local_dir_use_symlinks=False)
-else:
-    print("Model already downloaded.")

test_files/run_workflow.py

@@ -1,109 +0,0 @@
-#This is an example that uses the websockets api to know when a prompt execution is done
-#Once the prompt execution is done it downloads the images using the /history endpoint
-import os
-
-import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
-import uuid
-import json
-import urllib.request
-import urllib.parse
-
-server_address = "127.0.0.1:8188"
-client_id = str(uuid.uuid4())
-
-def queue_prompt(prompt):
-    p = {"prompt": prompt, "client_id": client_id}
-    data = json.dumps(p).encode('utf-8')
-    req =  urllib.request.Request("http://{}/prompt".format(server_address), data=data)
-
-    try:
-        response = urllib.request.urlopen(req)
-        return json.loads(response.read())
-    except urllib.error.HTTPError as e:
-        print(f"HTTP Error {e.code}: {e.reason}")
-        error_body = e.read()
-        # Attempt to read and print the JSON error body
-        try:
-            json_error_body = json.loads(error_body)
-            print(json_error_body)
-            raise e
-        except json.JSONDecodeError:
-            print("Failed to decode error response as JSON.")
-            print(error_body)
-            raise e
-    except Exception as e:
-        print(f"An unexpected error occurred: {e}")
-        raise e
-
-def get_image(filename, subfolder, folder_type):
-    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
-    url_values = urllib.parse.urlencode(data)
-    with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response:
-        return response.read()
-
-def get_history(prompt_id):
-    with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response:
-        return json.loads(response.read())
-
-def get_images(ws, prompt):
-    prompt_id = queue_prompt(prompt)['prompt_id']
-    output_images = {}
-    while True:
-        out = ws.recv()
-        if isinstance(out, str):
-            message = json.loads(out)
-            if message['type'] == 'executing':
-                data = message['data']
-                if data['node'] is None and data['prompt_id'] == prompt_id:
-                    break #Execution is done
-        else:
-            continue #previews are binary data
-
-    history = get_history(prompt_id)[prompt_id]
-    for o in history['outputs']:
-        for node_id in history['outputs']:
-            node_output = history['outputs'][node_id]
-            if 'images' in node_output:
-                images_output = []
-                for image in node_output['images']:
-                    image_data = get_image(image['filename'], image['subfolder'], image['type'])
-                    images_output.append(image_data)
-            output_images[node_id] = images_output
-
-    return output_images
-
-# Load json from file relative to this script
-prompt = json.load(open(os.path.join(os.path.dirname(os.path.realpath(__file__)), "workflow_api.json")))
-
-#set the text prompt for our positive CLIPTextEncode
-# prompt["6"]["inputs"]["text"] = "masterpiece best quality man"
-
-#set the seed for our KSampler node
-print(queue_prompt(prompt))
-ws = websocket.WebSocket()
-ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
-while True:
-    out = ws.recv()
-    if isinstance(out, str):
-        message = json.loads(out)
-        # print(message)
-        if message["type"] == "executing" and message["data"]["node"] is None:
-            print("Execution is done")
-            break
-
-        if message["type"] == "execution_error":
-            print("Execution error")
-            print(json.dumps(message["data"], indent=4))
-            raise Exception("Execution error")
-
-# images = get_images(ws, prompt)
-
-#Commented out code to display the output images:
-
-# for node_id in images:
-#     for image_data in images[node_id]:
-#         from PIL import Image
-#         import io
-#         image = Image.open(io.BytesIO(image_data))
-#         image.show()
-

test_files/workflow_api.json

@@ -1,84 +0,0 @@
-{
-  "1": {
-    "inputs": {
-      "text": "A sum(cat|norm(sum(neg(parrot)|rabbit))) outside",
-      "clip": [
-        "2",
-        0
-      ]
-    },
-    "class_type": "CLIPTextEncode"
-  },
-  "2": {
-    "inputs": {
-      "source_clip": [
-        "4",
-        1
-      ]
-    },
-    "class_type": "Special CLIP Loader"
-  },
-  "3": {
-    "inputs": {
-      "seed": 556492279461741,
-      "steps": 1,
-      "cfg": 8,
-      "sampler_name": "euler",
-      "scheduler": "normal",
-      "denoise": 1,
-      "model": [
-        "4",
-        0
-      ],
-      "positive": [
-        "1",
-        0
-      ],
-      "negative": [
-        "1",
-        0
-      ],
-      "latent_image": [
-        "7",
-        0
-      ]
-    },
-    "class_type": "KSampler"
-  },
-  "4": {
-    "inputs": {
-      "ckpt_name": "v1-5-pruned-emaonly.safetensors"
-    },
-    "class_type": "CheckpointLoaderSimple"
-  },
-  "5": {
-    "inputs": {
-      "samples": [
-        "3",
-        0
-      ],
-      "vae": [
-        "4",
-        2
-      ]
-    },
-    "class_type": "VAEDecode"
-  },
-  "6": {
-    "inputs": {
-      "images": [
-        "5",
-        0
-      ]
-    },
-    "class_type": "PreviewImage"
-  },
-  "7": {
-    "inputs": {
-      "width": 512,
-      "height": 512,
-      "batch_size": 1
-    },
-    "class_type": "EmptyLatentImage"
-  }
-}

tools/build_docs.py

@@ -1,83 +0,0 @@
-import importlib
-import inspect
-import os
-from typing import List, Type
-
-from custom_nodes.KepPromptLang.lib.action.base import Action
-
-
-EXCLUDED_MODULES = ["utils.py", "action_utils.py", "types.py"]
-def import_module_from_path(path: str):
-    module_name = path.replace("/", ".")[:-3]
-    return importlib.import_module(module_name, package="custom_nodes.KepPromptLang.lib.actions")
-
-
-# Function to find and import action classes
-def find_action_classes(directory: str) -> List[Type[Action]]:
-    action_classes = []
-    for filename in os.listdir(directory):
-        if filename.endswith(".py") and not filename.startswith("__") and filename not in EXCLUDED_MODULES:
-            module_path = os.path.join(directory, filename)
-            module = import_module_from_path(module_path)
-            for name, obj in inspect.getmembers(module, inspect.isclass):
-                if issubclass(obj, Action) and obj is not Action and obj.__name__ != "MultiArgAction" and obj.__name__ != "SingleArgAction":
-                    action_classes.append(obj)
-    return action_classes
-
-
-# Function to extract info from an action class
-def extract_class_info(cls: Type[Action]) -> dict:
-    class_info = {
-        'class_name': cls.__name__,
-        'properties': {
-            'display_name': getattr(cls, 'display_name', None),
-            'action_name': getattr(cls, 'action_name', None),
-            'description': getattr(cls, 'description', None),
-            'usage_examples': getattr(cls, 'usage_examples', None)
-        }
-    }
-    return class_info
-
-def escape_pipes(text: str) -> str:
-    return text.replace('|', '\\|')
-
-def generate_markdown_documentation(classes_info: List[dict]) -> str:
-    documentation = "# Actions Documentation\n\n"
-
-    # Define table columns
-    # columns = ["Class", "Display Name", "Action Name", "Description", "Usage Examples"]
-    columns = ["Display Name", "Action Name", "Description", "Usage Examples"]
-    documentation += "| " + " | ".join(columns) + " |\n"
-    documentation += "| --- " * len(columns) + "|\n"
-
-    for cls_info in classes_info:
-        # row = [cls_info['class_name']]
-        row = []
-        # Iterate over properties in a predefined order
-        for prop in ["display_name", "action_name", "description", "usage_examples"]:
-            prop_doc = cls_info['properties'].get(prop, 'N/A')
-
-            # Format and escape usage examples
-            if isinstance(prop_doc, list):
-                escaped_examples = [escape_pipes(example) for example in prop_doc]
-                prop_doc = "<ul>" + "".join([f"<li>{example}</li>" for example in escaped_examples]) + "</ul>"
-            else:
-                prop_doc = escape_pipes(prop_doc)
-
-            row.append(prop_doc)
-
-        documentation += "| " + " | ".join(row) + " |\n"
-
-    return documentation
-
-
-
-# Main execution
-if __name__ == "__main__":
-    actions_directory = (
-        "../lib/actions"  # Update this path as per your project structure
-    )
-    action_classes = find_action_classes(actions_directory)
-    class_infos = [extract_class_info(cls) for cls in action_classes]
-    docs = generate_markdown_documentation(class_infos)
-    print(docs)  # Or write to a file