feat(func/min): Add function

.github/workflows/action_attempt_two.yml

@@ -0,0 +1,10 @@
+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

@@ -0,0 +1,85 @@
+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 +1,81 @@
 # 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.
+
+## Functions
+
+Here are the available functions and their usage:
+
+1. **Sum Function**:
+    - Syntax: `sum(arg1 | arg2 | ... | argN)`
+    - Adds together multiple embeddings.
+    - Example: `sum(embedding:face1 | dog)`
+
+2. **Negation Function**:
+    - Syntax: `neg(arg)`
+    - Negates the output.
+    - Example: `neg(A embedding:happycats outside)`
+
+3. **Normalization Function**:
+    - Syntax: `norm(arg)`
+    - Normalizes the given vector embedding.
+    - Example: `norm(sum(embedding:face1 | embedding:face2))`
+
+4. **Difference Function**:
+    - Syntax: `diff(arg1 | arg2 | ... | argN)`
+    - Computes the difference between multiple vector embeddings.
+    - Example: `diff(embedding:face1 | embedding:face2)`
+
+### Notes on Arguments:
+- Each function takes one or more arguments.
+- An argument (`arg`) can be an embedding, a word, 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)
+   ```
+   ```

__init__.py

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

lib/__init__.py

@@ -0,0 +1,28 @@
+from custom_nodes.KepPromptLang.lib.actions.abs_max import AbsMaxAction
+from custom_nodes.KepPromptLang.lib.actions.avg import AverageAction
+from custom_nodes.KepPromptLang.lib.actions.diff import DiffAction
+from custom_nodes.KepPromptLang.lib.actions.max import MaxAction
+from custom_nodes.KepPromptLang.lib.actions.min import MinAction
+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.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(MaxAction)
+register_action(AbsMaxAction)
+register_action(MinAction)

lib/action/base.py

@@ -0,0 +1,117 @@
+from abc import ABC, abstractmethod
+from enum import Enum
+from typing import Union, List
+
+from torch import Tensor
+from torch.nn import Embedding
+
+from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
+
+
+class ActionArity(Enum):
+    NONE = 0
+    SINGLE = 1
+    MULTI = 2
+
+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 name(self) -> str:
+        pass
+
+    @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) -> Tensor:
+        """
+        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 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 __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.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 __init__(
+            self,
+            args: List[List[Union[PromptSegment, Action]]],
+    ):
+        self.all_args = args

lib/actions/__init__.py

@@ -1,6 +0,0 @@
-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/abs_max.py

@@ -0,0 +1,104 @@
+from typing import List, Union
+
+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.actions.utils import is_broadcastable
+from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
+
+
+class AbsMaxAction(MultiArgAction):
+    grammar = 'absMax(" arg ("|" arg)+ ")"'
+    name = "absMax"
+    chars = ["+", "+"]
+
+    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:
+        # AbsMax modifies 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 is_broadcastable(result, arg_embedding):
+                positive_mask1 = result > 0
+                positive_mask2 = arg_embedding > 0
+                negative_mask1 = result < 0
+                negative_mask2 = arg_embedding < 0
+                zero_mask1 = result == 0
+                zero_mask2 = arg_embedding == 0
+                
+                # For mixed signs, choose the one with the largest magnitude
+                mixed_mask_pos_neg = positive_mask1 & negative_mask2
+                mixed_mask_neg_pos = negative_mask1 & positive_mask2
+                mixed_mask = mixed_mask_pos_neg | mixed_mask_neg_pos
+                mixed_selection = torch.where(
+                    result.abs() > arg_embedding.abs(), result, arg_embedding
+                )
+                
+                # Apply max for positive dimensions, min for negative dimensions, and handle zeros and mixed signs
+                result = torch.where(
+                    positive_mask1 & positive_mask2,
+                    torch.max(result, arg_embedding),
+                    torch.where(
+                        negative_mask1 & negative_mask2,
+                        torch.min(result, arg_embedding),
+                        torch.where(
+                            positive_mask1 & zero_mask2,
+                            result,
+                            torch.where(
+                                zero_mask1 & positive_mask2,
+                                arg_embedding,
+                                torch.where(mixed_mask, mixed_selection, result),
+                            ),
+                        ),
+                    ),
+                )
+            else:
+                print(
+                    "WARNING: shape mismatch when trying to apply absMax, arg will be averaged"
+                )
+                result = torch.max(result, 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/action_utils.py

@@ -0,0 +1,11 @@
+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)

lib/actions/arith.py

@@ -1,39 +0,0 @@
-from custom_nodes.ClipStuff.lib.actions.base import Action
-
-
-class ArithAction(Action):
-    START_CHAR = "<"
-    END_CHAR = ">"
-
-    def __init__(self, base_segment: str, ops_str: str):
-        self.base_segment = base_segment
-        self.ops = self.process_ops_string(ops_str)
-
-    @classmethod
-    def process_ops_string(cls, ops_string):
-        supported_ops = ["+", "-"]
-        # dict[[Union[Literal['add'], Literal['subtract']]], str]
-        ops_dict = {"+": [], "-": []}
-        buff = ""
-        curr_op_char = ""
-        for char in ops_string:
-            if char in supported_ops:
-                # We have a buffer
-                if buff != "":
-                    # Add op string
-                    ops_dict[curr_op_char] += [buff]
-                    # Reset buffer
-                    buff = ""
-                    # Set new current op char
-                    curr_op_char = char
-                    continue
-                else:
-                    # No buffer, the start of processing
-                    curr_op_char = char
-            else:
-                # Append char to buffer
-                buff += char
-
-        # Add last op to dict
-        ops_dict[curr_op_char] += [buff]
-        return ops_dict

lib/actions/avg.py

@@ -0,0 +1,100 @@
+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 ")"'
+    name = "avg"
+    chars = ["+", "+"]
+
+    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

@@ -1,13 +0,0 @@
-from abc import ABC, abstractmethod
-
-
-class Action(ABC):
-    @property
-    @abstractmethod
-    def START_CHAR(self):
-        pass
-
-    @property
-    @abstractmethod
-    def END_CHAR(self):
-        pass

lib/actions/diff.py

@@ -0,0 +1,77 @@
+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)* ")"'
+    name = "diff"
+    chars = ["-", "-"]
+
+    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

@@ -1,17 +0,0 @@
-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/max.py

@@ -0,0 +1,72 @@
+from typing import List, Union
+
+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.actions.utils import is_broadcastable
+from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
+
+
+class MaxAction(MultiArgAction):
+    grammar = 'max(" arg ("|" arg)+ ")"'
+    name = "max"
+    chars = ["+", "+"]
+
+    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:
+        # Max modifies 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 is_broadcastable(result, arg_embedding):
+                result = torch.max(result, arg_embedding)
+            else:
+                print(
+                    "WARNING: shape mismatch when trying to apply max, arg will be averaged"
+                )
+                result = torch.max(result, 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/min.py

@@ -0,0 +1,72 @@
+from typing import List, Union
+
+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.actions.utils import is_broadcastable
+from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
+
+
+class MinAction(MultiArgAction):
+    grammar = 'min(" arg ("|" arg)+ ")"'
+    name = "min"
+    chars = ["+", "+"]
+
+    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:
+        # Min modifies 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 is_broadcastable(result, arg_embedding):
+                result = torch.min(result, arg_embedding)
+            else:
+                print(
+                    "WARNING: shape mismatch when trying to apply max, arg will be averaged"
+                )
+                result = torch.min(result, 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/mult.py

@@ -0,0 +1,62 @@
+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+ ")"'
+    name = "mult"
+    chars = ["[", "]"]
+
+    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

@@ -0,0 +1,34 @@
+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+ ")"'
+    name = "neg"
+    chars = ["[", "]"]
+
+    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

@@ -0,0 +1,38 @@
+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+ ")"'
+    name = "norm"
+    chars = None
+
+    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

@@ -1,13 +0,0 @@
-from typing import Optional
-
-from custom_nodes.ClipStuff.lib.actions.base import Action
-
-
-class NudgeAction(Action):
-    START_CHAR = "["
-    END_CHAR = "]"
-
-    def __init__(self, base_segment=None, weight: Optional[float] = None, target=None):
-        self.base_segment = base_segment
-        self.weight = weight
-        self.target = target

lib/actions/rand.py

@@ -0,0 +1,87 @@
+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
+
+    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

@@ -0,0 +1,72 @@
+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)* ")"'
+    name = "scaleDims"
+    description = "Scales the specified dimensions of the input embeddings by the specified amount"
+    example = "'The scaleDims(cat|4,1.5|76,1.2) is happy' scales the 4th dimension by 1.5 and the 76th dimension by 1.2 for the word 'cat'"
+    chars = ["-", "-"]
+
+    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

@@ -0,0 +1,72 @@
+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)* ")"'
+    name = "setDims"
+    description = "Sets the specified dimensions of the input embeddings to the specified value"
+    example = "'The scaleDims(cat|4,1.5|76,1.2) is happy' scales the 4th dimension by 1.5 and the 76th dimension by 1.2 for the word 'cat'"
+    chars = ["-", "-"]
+
+    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

@@ -0,0 +1,100 @@
+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 ")"'
+    name = "slerp"
+    chars = ["+", "+"]
+
+    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

@@ -0,0 +1,79 @@
+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)+ ")"'
+    name = "sum"
+    chars = ["+", "+"]
+
+    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

@@ -0,0 +1,6 @@
+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

@@ -0,0 +1,55 @@
+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
+
+def is_broadcastable(tensor1, tensor2) -> bool:
+    """
+    Check if two tensors are broadcastable.
+
+    Parameters:
+    - tensor1 (torch.Tensor): The target tensor against which broadcastability of tensor2 is checked.
+    - tensor2 (torch.Tensor): The tensor whose broadcastability is to be verified against tensor1.
+
+    Returns:
+    - bool: True if tensor2 is broadcastable to tensor1, False otherwise.
+    """
+    try:
+        broadcasted_shape = torch.broadcast_shapes(tensor1.shape, tensor2.shape)
+        return True
+    except RuntimeError:
+        return False

lib/clip_model.py

@@ -1,16 +1,20 @@
 import contextlib
 import os
+from typing import List
 
 import torch
 from transformers import CLIPTextConfig, modeling_utils
 
 from comfy import model_management
 import comfy.ops
-from comfy.sd import CLIP
+from custom_nodes.KepPromptLang.lib.action.base import Action
-from custom_nodes.ClipStuff.lib.fun_clip_stuff import MyCLIPTextModel
+from custom_nodes.KepPromptLang.lib.actions.types import SegOrAction
-from custom_nodes.ClipStuff.lib.tokenizer import TokenDict
+from custom_nodes.KepPromptLang.lib.fun_clip_stuff import PromptLangTextModel
+from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
 
-class SD1FunClipModel(torch.nn.Module):
+
+# Methods with no comment can be assumed to be the same as comfy.sd1_clip.SD1ClipModel
+class PromptLangClipModel(torch.nn.Module):
     """Uses the CLIP transformer encoder for text (from huggingface)"""
     LAYERS = [
         "last",
@@ -20,28 +24,37 @@ class SD1FunClipModel(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):  # clip-vit-base-patch32
+                 textmodel_path=None, dtype=None):  # clip-vit-base-patch32
         super().__init__()
         assert layer in self.LAYERS
         self.num_layers = 12
         if textmodel_path is not None:
-            self.transformer = MyCLIPTextModel.from_pretrained(textmodel_path)
+            # Our transformer
+            self.transformer = PromptLangTextModel.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():
+            with comfy.ops.use_comfy_ops(device, dtype):
                 with modeling_utils.no_init_weights():
-                    self.transformer = MyCLIPTextModel(config)
+                    # Our transformer
+                    self.transformer = PromptLangTextModel(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 = None
+        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.layer_norm_hidden_state = True
         if layer == "hidden":
             assert layer_idx is not None
@@ -66,51 +79,72 @@ class SD1FunClipModel(torch.nn.Module):
         self.layer = self.layer_default[0]
         self.layer_idx = self.layer_default[1]
 
-    def set_up_textual_embeddings(self, tokens: list[list[tuple[TokenDict]]], current_embeds):
+    # Completely changed to support Segments and actions
-        out_tokens = []
+    def set_up_textual_embeddings(self, tokens: List[List[SegOrAction]], current_embeds):
         next_new_token = token_dict_size = current_embeds.weight.shape[0] - 1
         embedding_weights = []
 
+        # For each batch
         for batch in tokens:
-            tokens_temp = []
+            for seg_or_action in batch:
-            for tokenDict in batch:
+                if isinstance(seg_or_action, Action):
-                y = tokenDict[0].token_id
+                    segments = seg_or_action.get_all_segments()
-                if isinstance(y, int):
-                    if y == token_dict_size:  # EOS token
-                        y = -1
-                    tokens_temp += [y]
                 else:
-                    if y.shape[0] == current_embeds.weight.shape[1]:
+                    segments = [seg_or_action]
-                        embedding_weights += [y]
+
-                        tokens_temp += [next_new_token]
+                for segment in segments:
-                        next_new_token += 1
+                    tokens_temp = []
-                    else:
+                    segment_length = segment.token_length()
-                        print("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored",
+                    for tid_or_tensor in segment.tokens:
-                              y.shape[0], current_embeds.weight.shape[1])
+                        if isinstance(tid_or_tensor, int):
-            while len(tokens_temp) < len(batch):
+                            if tid_or_tensor == token_dict_size:  # Is EOS token
-                tokens_temp += [self.empty_tokens[0][-1]]
+                                tid_or_tensor = -1 # Set to -1 so that it can be replaced with the EOS token later
-            out_tokens += [tokens_temp]
+                            tokens_temp += [tid_or_tensor]
+                        else:
+                            if tid_or_tensor.shape[0] == current_embeds.weight.shape[1]:
+                                embedding_weights += [tid_or_tensor]
+                                tokens_temp += [next_new_token]
+                                next_new_token += 1
+                            else:
+                                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:
+                        # 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
         if len(embedding_weights) > 0:
+            # Create new embedding, with size of current embedding + number of new embeddings
             new_embedding = torch.nn.Embedding(next_new_token + 1, current_embeds.weight.shape[1],
                                                device=current_embeds.weight.device, dtype=current_embeds.weight.dtype)
+            # Copy current embedding weights to new embedding
             new_embedding.weight[:token_dict_size] = current_embeds.weight[:-1]
+            # Add new embeddings
             for embed in embedding_weights:
                 new_embedding.weight[n] = embed
                 n += 1
+
+            # Set re-add the EOS token
             new_embedding.weight[n] = current_embeds.weight[-1]  # EOS embedding
             self.transformer.set_input_embeddings(new_embedding)
 
-        for i, out_batch in enumerate(out_tokens):
-            for tokenIdx in range(len(out_batch)):
-                if out_batch[tokenIdx] == -1:
-                    tokens[i][tokenIdx][0].token_id = n # The EOS token should always be the largest one
-                else:
-                    tokens[i][tokenIdx][0].token_id = out_batch[tokenIdx]
 
-        # return processed_tokens
+        for batch in tokens:
-    def forward(self, tokens, **kwargs):
+            for seg_or_action in batch:
+                if isinstance(seg_or_action, Action):
+                    segments = seg_or_action.get_all_segments()
+                else:
+                    segments = [seg_or_action]
+
+                for segment in segments:
+                    for tokenIdx in range(len(segment.tokens)):
+                        if segment.tokens[tokenIdx] == -1:
+                            segment.tokens[tokenIdx] = n
+
+    # Support our set_up_textual_embeddings which modifies the input embeddings
+    def forward(self, tokens):
         backup_embeds = self.transformer.get_input_embeddings()
         device = backup_embeds.weight.device
         self.set_up_textual_embeddings(tokens, backup_embeds)
@@ -121,16 +155,8 @@ class SD1FunClipModel(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":
@@ -144,26 +170,27 @@ class SD1FunClipModel(torch.nn.Module):
 
             pooled_output = outputs.pooler_output
             if self.text_projection is not None:
-                pooled_output = pooled_output.to(self.text_projection.device) @ self.text_projection
+                pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float()
         return z.float(), pooled_output.float()
 
-    def encode(self, tokens, **kwargs):
+    def encode(self, tokens):
-        return self(tokens, **kwargs)
+        return self(tokens)
 
     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)
 
-    def encode_token_weights(self, token_dicts: list[list[tuple[TokenDict]]], **kwargs):
+    # Changed from comfy.sd1_clip.ClipTokenWeightEncoder
-        to_encode = [list(
+    # Changed to use PromptSegments
-            map(
+    def encode_token_weights(self, prompt_segments: List[List[SegOrAction]]):
-                lambda id: (TokenDict(token_id=id, weight=1.0, nudge_id=None),),
+        to_encode = [[PromptSegment(text="_Empty Batch_", tokens=self.empty_tokens[0])]]
-                self.empty_tokens[0]
+        for batch in prompt_segments:
-            )
+            to_encode.append(batch)
-        )]
-        for x in token_dicts:
-            to_encode.append(x)
 
-        out, pooled = self.encode(to_encode, **kwargs)
+        out, pooled = self.encode(to_encode)
         z_empty = out[0:1]
         if pooled.shape[0] > 1:
             first_pooled = pooled[1:2]
@@ -173,10 +200,10 @@ class SD1FunClipModel(torch.nn.Module):
         output = []
         for k in range(1, out.shape[0]):
             z = out[k:k + 1]
-            for i in range(len(z)):
+            # for i in range(len(z)):
-                for j in range(len(z[i])):
+            #     for j in range(len(z[i])):
-                    weight = token_dicts[k - 1][j][0].weight
+            #         weight = token_dicts[k - 1][j][0].weight
-                    z[i][j] = (z[i][j] - z_empty[0][j]) * weight + z_empty[0][j]
+            #         z[i][j] = (z[i][j] - z_empty[0][j]) * weight + z_empty[0][j]
             output.append(z)
 
         if (len(output) == 0):

lib/fun_clip_stuff.py

@@ -1,13 +1,17 @@
-from typing import Optional, Tuple, Union
+from typing import Optional, Tuple, Union, List
 
 import torch
-from torch import device
 from transformers import CLIPTextConfig
 from transformers.modeling_outputs import BaseModelOutputWithPooling
-from transformers.models.clip.modeling_clip import _expand_mask, CLIPTextEmbeddings, CLIPTextTransformer, \
+from transformers.models.clip.modeling_clip import (
-    CLIPTextModel
+    _expand_mask,
+    CLIPTextEmbeddings,
+    CLIPTextTransformer,
+    CLIPTextModel,
+)
 
-from custom_nodes.ClipStuff.lib.tokenizer import TokenDict
+from custom_nodes.KepPromptLang.lib.action.base import Action
+from custom_nodes.KepPromptLang.lib.actions.types import SegOrAction
 
 def slerp(val, low, high):
     low = low.unsqueeze(0)
@@ -19,68 +23,57 @@ 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 MyCLIPTextEmbeddings(CLIPTextEmbeddings):
+class PromptLangCLIPTextEmbeddings(CLIPTextEmbeddings):
     def __init__(self, config: CLIPTextConfig):
         super().__init__(config)
 
     def forward(
             self,
-            input_dicts: Optional[list[list[tuple[TokenDict]]]] = None,
+            input_dicts: Optional[List[List[SegOrAction]]] = None,
             input_ids: Optional[torch.LongTensor] = None,
             position_ids: Optional[torch.LongTensor] = None,
             inputs_embeds: Optional[torch.FloatTensor] = None,
     ) -> torch.Tensor:
-        input_ids = [
-            [
-                tokenDict[0].token_id for tokenDict in batch
-            ] for batch in input_dicts
-        ]
-        tokens = torch.LongTensor(input_ids).to(torch.device('cpu'))
-        input_shape = tokens.size()
-        input_ids = tokens.view(-1, input_shape[-1])
 
-        seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
+        if input_dicts is None:
+            raise ValueError("You have to specify input_dicts")
+
+        batches = []
+        for batch_idx, batch in enumerate(input_dicts):
+            results = []
+            for seg_or_action in batch:
+                if isinstance(seg_or_action, Action):
+                    results.append(seg_or_action.get_result(self.token_embedding))
+                else:
+                    results.append(seg_or_action.get_embeddings(self.token_embedding))
+            batches.append(results)
+
+        seq_length = batches[0][0].shape[-2]
 
         if position_ids is None:
             position_ids = self.position_ids[:, :seq_length]
 
-        if inputs_embeds is None:
+        embeds = []
-            inputs_embeds = self.token_embedding(input_ids)
+        for batch in batches:
-
+            if len(batch) == 1:
-        for batch_idx, batch in enumerate(input_dicts):
+                embeds.append(batch[0])
-            for token_idx, token in enumerate(batch):
+            else:
-                if token[0].nudge_id is not None:
+                embeds.append(torch.cat(batch, dim=-2))
-                    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]
 
         position_embeddings = self.position_embedding(position_ids)
-        embeddings = inputs_embeds + position_embeddings
+        embeddings = torch.cat(embeds, dim=0) + position_embeddings
 
-        return embeddings, input_ids, input_shape
+        return embeddings
 
 
-class MyCLIPTextTransformer(CLIPTextTransformer):
+class PrompLangCLIPTextTransformer(CLIPTextTransformer):
     def __init__(self, config: CLIPTextConfig):
         super().__init__(config)
-        self.embeddings = MyCLIPTextEmbeddings(config)
+        self.embeddings = PromptLangCLIPTextEmbeddings(config)
 
     def forward(
             self,
-            input_ids: Optional[list[list[tuple[TokenDict]]]] = None,
+            input_ids: Optional[List[List[SegOrAction]]] = None,
             attention_mask: Optional[torch.Tensor] = None,
             position_ids: Optional[torch.Tensor] = None,
             output_attentions: Optional[bool] = None,
@@ -103,14 +96,25 @@ class MyCLIPTextTransformer(CLIPTextTransformer):
         # input_shape = input_ids.size()
         # input_ids = input_ids.view(-1, input_shape[-1])
 
-        hidden_states, input_ids, input_shape = self.embeddings(input_dicts=input_ids)
+        hidden_states = self.embeddings(input_dicts=input_ids)
 
-        bsz, seq_len = input_shape
+        bsz = len(input_ids)
+        # TODO: Properly gather this
+        seq_len = 77
+        input_shape = torch.Size([bsz, seq_len])
         # 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(
+        ## VERSION DIFF ##
-            hidden_states.device
+        # transformers < 4.30.0
-        )
+        if hasattr(self, "_build_causal_attention_mask"):
+            print("Using transformers < 4.30.0")
+            causal_attention_mask = self._build_causal_attention_mask(bsz, seq_len, hidden_states.dtype).to(hidden_states.device)
+        else:
+            # transformers >= 4.30.0
+            print("Using transformers >= 4.30.0")
+            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)
+
         # expand attention_mask
         if attention_mask is not None:
             # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
@@ -128,12 +132,25 @@ class MyCLIPTextTransformer(CLIPTextTransformer):
         last_hidden_state = encoder_outputs[0]
         last_hidden_state = self.final_layer_norm(last_hidden_state)
 
+
+        # Hacky way to get idx of first EOT token
+        eot_idx = [1]
+        for batch in input_ids[1:]:
+            idx = 0
+            for seg_or_action in batch:
+                if isinstance(seg_or_action, Action):
+                    idx += seg_or_action.token_length()
+                else:
+                    if seg_or_action.text == '__PAD__':
+                        break
+            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)
         # casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14
+        # TODO: Get the index of the first EOT token
         pooled_output = last_hidden_state[
             torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
-            input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax(dim=-1),
+            eot_idx
         ]
 
         if not return_dict:
@@ -147,37 +164,21 @@ class MyCLIPTextTransformer(CLIPTextTransformer):
         )
 
 
-class MyCLIPTextModel(CLIPTextModel):
+# This is necessary to pass the PromptLangCLIPTextTransformer
+class PromptLangTextModel(CLIPTextModel):
     def __init__(self, config: CLIPTextConfig):
         super().__init__(config)
-        self.text_model = MyCLIPTextTransformer(config)
+        self.text_model = PrompLangCLIPTextTransformer(config)
 
     def forward(
             self,
-            input_ids: Optional[list[list[tuple[TokenDict]]]] = None,
+            input_ids: Optional[List[List[SegOrAction]]] = 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

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

lib/parser/grammar.py

@@ -0,0 +1,20 @@
+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

@@ -0,0 +1,31 @@
+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

@@ -0,0 +1,20 @@
+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.name in action_registry:
+        raise ValueError(f"Action {action.name} already registered")
+    action_registry[str(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

@@ -0,0 +1,63 @@
+from typing import List
+
+from lark import Transformer, Token
+
+from comfy.sd1_clip import SD1Tokenizer
+from custom_nodes.KepPromptLang.lib.action.base import Action, ActionArity
+from custom_nodes.KepPromptLang.lib.actions.diff import DiffAction
+from custom_nodes.KepPromptLang.lib.actions.rand import RandAction
+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.actions.neg import NegAction
+from custom_nodes.KepPromptLang.lib.actions.norm import NormAction
+from custom_nodes.KepPromptLang.lib.actions.sum import SumAction
+from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
+
+
+class PromptTransformer(Transformer):
+    # def WORD(self, items):
+    #     return items
+
+    def __init__(self, tokenizer: SD1Tokenizer):
+        super().__init__()
+        self.tokenizer = tokenizer
+
+    def item(self, items: List[Token]):
+        for item in items:
+            if isinstance(item, Action):
+                return item
+
+            if isinstance(item, 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":
+                return item
+            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

@@ -0,0 +1,38 @@
+from lark import Token
+
+from comfy.sd1_clip import SD1Tokenizer
+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: 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/tokenizer.py

@@ -1,214 +1,68 @@
-from typing import Union
+from typing import List
+
+from lark import Tree
 
 from comfy.sd1_clip import SD1Tokenizer
-from custom_nodes.ClipStuff.lib.actions import (
+from custom_nodes.KepPromptLang.lib.actions.types import SegOrAction
-    NudgeAction,
-    ArithAction,
-    ALL_START_CHARS,
-    ALL_END_CHARS,
-)
-from custom_nodes.ClipStuff.lib.actions.lib import (
-    is_any_action_segment,
-    is_action_segment,
-)
 
+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
 
-def parse_special_tokens(string):
+class PromptLangTokenizer(SD1Tokenizer):
-    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_token_actions(string) -> list[Union[str, NudgeAction, ArithAction]]:
-    out: list[Union[str, NudgeAction, ArithAction]] = []
-    for prompt_segment in parse_special_tokens(string):
-        if prompt_segment == "":
-            continue
-
-        if not is_any_action_segment(prompt_segment):
-            out += [prompt_segment]
-            continue
-
-        is_nudge = is_action_segment(NudgeAction, prompt_segment)
-        is_arith = is_action_segment(ArithAction, prompt_segment)
-
-        prompt_segment = prompt_segment[1:-1]
-        word_sep_idx = prompt_segment.find(":")
-
-        # No word seperator, add whole segment
-        if word_sep_idx < 0:
-            out += [prompt_segment]
-            continue
-
-        base_segment = prompt_segment[:word_sep_idx]
-
-        if is_nudge:
-            trailing_segment = prompt_segment[word_sep_idx + 1 :]
-
-            weight_sep_idx = trailing_segment.find(":")
-            # Has a weight(base_word:nudge_to:1.4)
-            if weight_sep_idx >= 0:
-                [nudge_to, weight] = trailing_segment.split(":")
-                weight = float(weight)
-            else:
-                # No weight(base_word:trailing_segment)
-                nudge_to = trailing_segment
-                weight = None
-
-            out += [NudgeAction(base_segment, weight, nudge_to)]
-        elif is_arith:
-            arith_op_string = prompt_segment[word_sep_idx + 1 :]
-            out += [ArithAction(base_segment, arith_op_string)]
-
-    return out
-
-
-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)
 
     """
-    :return: list of tuples (tokenDict, word_id?)
+    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):
+    def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs) -> List[List[SegOrAction]]:
         if self.pad_with_end:
             pad_token = self.end_token
         else:
             pad_token = 0
 
-        parsed_actions = parse_token_actions(text)
+        parsed_prompt = PromptParser.parse(text)
-
+        parsed_actions = PromptTransformer(self).transform(parsed_prompt)
-        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
-        tokens: list[list[TokenDict]] = []
-
-        for action in parsed_actions:
-            nudge_weight = None
-            nudge_to_id = None
-            arith_ops = None
-            if isinstance(action, str):
-                token_segment = action
-            elif isinstance(action, NudgeAction):
-                token_segment = action.base_segment
-                nudge_to_id = self.tokenizer(action.target)["input_ids"][1:-1][0]
-
-                nudge_weight = action.weight
-                if nudge_weight is None:
-                    nudge_weight = 0.5
-            elif isinstance(action, ArithAction):
-                token_segment = action.base_segment
-                arith_ops = action.ops
-                for op in arith_ops:
-                    arith_ops[op] = [self.tokenizer(word)["input_ids"][1:-1][0] for word in arith_ops[op]]
-            else:
-                raise Exception(f"Unexpected action type: {type(action)}")
-
-            to_tokenize = token_segment.split(' ')
-            to_tokenize = [x for x in to_tokenize if x != ""]
-
-            for word in to_tokenize:
-                # if we find an embedding, deal with the embedding
-                if word.startswith(self.embedding_identifier) and self.embedding_directory is not None:
-                    embedding_name = word[len(self.embedding_identifier):].strip('\n')
-                    embed, leftover = self._try_get_embedding(embedding_name)
-                    if embed is None:
-                        print(f"warning, embedding:{embedding_name} does not exist, ignoring")
-                    else:
-                        if len(embed.shape) == 1:
-                            tokens.append([TokenDict(token_id=embed)])
-                        else:
-                            tokens.append([
-                                TokenDict(token_id=embed[x])
-                                for x in range(embed.shape[0])
-                            ])
-                    # if we accidentally have leftover text, continue parsing using leftover, else move on to next word
-                    if leftover != "":
-                        word = leftover
-                    else:
-                        continue
-                # parse word
-                tokens.append([TokenDict(
-                    token_id=t,
-                    nudge_id=nudge_to_id,
-                    nudge_weight=nudge_weight,
-                    nudge_start=nudge_start,
-                    nudge_end=nudge_end,
-                    arith_ops=arith_ops
-                ) for t in self.tokenizer(word)["input_ids"][1:-1]])
 
         # reshape token array to CLIP input size
-        batched_tokens = []
+        batched_segments = []
-        batch = [(TokenDict(token_id=self.start_token), 0)]
+        batch = [PromptSegment(text="[SOT]", tokens=[self.start_token])]
-        batched_tokens.append(batch)
+        # batched_segments.append(batch)
-        for i, t_group in enumerate(tokens):
+        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:
+            num_tokens = segment.token_length()
             # determine if we're going to try and keep the tokens in a single batch
-            is_large = len(t_group) >= self.max_word_length
+            is_large = num_tokens >= self.max_word_length
 
-            while len(t_group) > 0:
+            # If the segment is too large to fit in a single batch, pad the current batch and start a new one
-                if len(t_group) + len(batch) > self.max_length - 1:
+            if num_tokens + batch_size > self.max_length - 1:
-                    remaining_length = self.max_length - len(batch) - 1
+                remaining_length = self.max_length - batch_size - 1 # -1 for end token
-                    # break word in two and add end token
+                # Pad batch
-                    if is_large:
+                batch.append(PromptSegment("__PAD__", [self.end_token] + [pad_token] * remaining_length - 1))
-                        batch.extend([(tokenDict, i+1) for tokenDict in t_group[:remaining_length]])
+                batched_segments.append(batch)
-                        batch.append((TokenDict(token_id=self.end_token), 0))
-                        t_group = t_group[remaining_length:]
-                    # add end token and pad
-                    else:
-                        batch.append((TokenDict(token_id=self.end_token), 0))
-                        batch.extend([(TokenDict(token_id=pad_token), 0)] * remaining_length)
-                    # start new batch
-                    batch = [(TokenDict(token_id=self.start_token), 1.0, 0)]
-                    batched_tokens.append(batch)
-                else:
-                    batch.extend([(tokenDict, i+1) for tokenDict in t_group])
-                    t_group = []
 
-        # fill last batch
+                # start new batch
-        batch.extend([(TokenDict(token_id=self.end_token), 0)] + [
+                batch = [PromptSegment(text="[SOT]", tokens=[self.start_token]), segment]
-            (TokenDict(token_id=pad_token), 0)] * (self.max_length - len(batch) - 1))
+                batch_size = num_tokens + 1 # +1 for start token
+                continue
 
-        if not return_word_ids:
+            # Since the segment fits in the current batch, add it
-            batched_tokens = [
+            batch.append(segment)
-                [
+            batch_size += num_tokens
-                    (tokenInfo[0],) for tokenInfo in batch
-                ] for batch in batched_tokens
-            ]
 
-        return batched_tokens
+        # Pad the last batch
+        remaining_length = self.max_length - batch_size - 1 # -1 for end token
+        batch.append(PromptSegment("__PAD__", [self.end_token] + [pad_token] * remaining_length))
+        batched_segments.append(batch)
+
+        # for batch in batched_segments:
+        #     batch_size_info(batch)
+
+        return batched_segments

nodes.py

@@ -1,4 +1,6 @@
 import random
+import os
+from typing import List, Tuple, Any
 
 import numpy as np
 from PIL import Image
@@ -6,9 +8,9 @@ from PIL import Image
 import folder_paths
 import comfy.sd
 import comfy.ops
-from custom_nodes.ClipStuff.lib.clip_model import SD1FunClipModel
+from custom_nodes.KepPromptLang.lib.clip_model import PromptLangClipModel
 
-from custom_nodes.ClipStuff.lib.tokenizer import MyTokenizer
+from custom_nodes.KepPromptLang.lib.tokenizer import PromptLangTokenizer
 
 
 class EmptyClass:
@@ -17,7 +19,7 @@ class EmptyClass:
 
 class SpecialClipLoader:
     @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):  # type: ignore
         return {
             "required": {
                 "source_clip": ("CLIP",),
@@ -30,79 +32,36 @@ class SpecialClipLoader:
     CATEGORY = "conditioning"
 
     @staticmethod
-    def load_clip(source_clip):
+    def load_clip(source_clip: comfy.sd.CLIP) -> Tuple[comfy.sd.CLIP]:
         clip_target = EmptyClass()
         clip_target.params = {}
-        clip_target.clip = SD1FunClipModel
+        clip_target.clip = PromptLangClipModel
-        clip_target.tokenizer = MyTokenizer
+        clip_target.tokenizer = PromptLangTokenizer
 
-        # TODO: Extract embedding directory from source_clip
+        clip = comfy.sd.CLIP(clip_target, embedding_directory=source_clip.tokenizer.embedding_directory)
-        clip = comfy.sd.CLIP(clip_target, embedding_directory=None)
         comfy.sd.load_clip_weights(
             clip.cond_stage_model, source_clip.cond_stage_model.state_dict()
         )
         return (clip,)
 
 
-class KepAdvTextEncode:
+def tensor2img(tensor_img) -> Image.Image:
-    @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):
+    def __init__(self) -> None:
+        self.output_dir = folder_paths.get_output_directory()
         pass
 
     @classmethod
-    def INPUT_TYPES(cls):
+    def INPUT_TYPES(cls):  # type: ignore
         return {
             "required": {
                 "images": ("IMAGE",),
                 "split_every": ("INT", {"default": -1}),
+                "frame_duration": ("INT", {"default": 125}),
                 "output_mode": (
                     ["One Per Split", "Big Grid"],
                     {"default": "Big Grid"},
@@ -111,46 +70,53 @@ class BuildGif:
         }
 
     RELOAD_INST = True
-    RETURN_TYPES = ("IMAGE",)
+    RETURN_TYPES = ()
-    RETURN_NAMES = ("Gifs",)
+    # RETURN_NAMES = ("Gifs",)
     INPUT_IS_LIST = True
     FUNCTION = "build_gif"
-    OUTPUT_IS_LIST = (True,)
+    # OUTPUT_IS_LIST = (True,)
-    # OUTPUT_NODE = False
+    OUTPUT_NODE = True
 
     CATEGORY = "List Stuff"
 
-    @staticmethod
+    def build_gif(self, images: List[Any], split_every: List[int], frame_duration: List[int], output_mode: List[str]):
-    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.")
 
-        split_every = split_every[0]
+        if len(output_mode) > 1:
-        batch_size = images[0].size()[0]
+            raise Exception("List input for output_mode is not supported.")
-        if split_every == -1:
+        output_mode = output_mode[0]
-            split_chunks = 1
-            split_every = len(images)
-        else:
-            split_chunks = int(len(images) / split_every)
 
-        out = []
+        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]
+        batch_size = images[0].size()[0]
+        if split_every_val == -1:
+            split_chunks = 1
+            split_every_val = len(images)
+        else:
+            split_chunks = int(len(images) / split_every_val)
 
         num_wide = batch_size
         num_tall = split_chunks
 
         chunked_batches = [
-            images[split_every * chunk_idx : split_every * (chunk_idx + 1)]
+            images[split_every_val * chunk_idx : split_every_val * (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):
+            for idx_in_chunk in range(split_every_val):
                 img_shape = images[0][0].shape
                 img_frame = Image.new(
                     "RGB", size=(num_wide * img_shape[0], num_tall * img_shape[1])
@@ -165,8 +131,9 @@ class BuildGif:
                         )
                 frames.append(img_frame)
 
+            file = f"{filename}_{counter:05}_"
             save_path = (
-                f"{folder_paths.get_output_directory()}/{random.randint(1, 100)}"
+                f"{os.path.join(full_output_folder, file)}"
             )
             frames[0].save(
                 f"{save_path}.webp",
@@ -176,15 +143,24 @@ class BuildGif:
                 save_all=True,
                 append_images=frames[1:],
                 optimize=False,
-                duration=125,
+                duration=frame_duration,
                 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 * split_idx
+                split_start = split_every_val * split_idx
-                split_end = split_every * (split_idx + 1)
+                split_end = split_every_val * (split_idx + 1)
                 for batch_idx in range(batch_size):
-                    save_path = f"{folder_paths.get_output_directory()}/-{batch_idx}-{random.randint(1, 100)}"
+                    file = f"{filename}_{counter:05}_"
+                    save_path = (
+                        f"{os.path.join(full_output_folder, file)}"
+                    )
+                    counter += 1
                     print(save_path)
                     tensor2img(images[split_start][batch_idx]).save(
                         f"{save_path}.webp",
@@ -194,7 +170,12 @@ class BuildGif:
                             for nested_batch in images[split_start + 1 : split_end]
                         ],
                         optimize=False,
-                        duration=125,
+                        duration=frame_duration,
                         loop=0,
                     )
-        return (out,)
+                    results.append({
+                        "filename": f"{file}.webp",
+                        "subfolder": subfolder,
+                        "type": "output"
+                    })
+        return { "ui": { "images": results } }

requirements.txt

@@ -0,0 +1 @@
+lark

test_files/check_and_download_model.py

@@ -0,0 +1,12 @@
+# 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

@@ -0,0 +1,109 @@
+#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

@@ -0,0 +1,84 @@
+{
+  "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"
+  }
+}