Working monaco

__init__.py

@@ -1,9 +1,13 @@
 from .nodes import (
     BuildGif,
     SpecialClipLoader,
+    MonacoPrompt,
 )
 
 NODE_CLASS_MAPPINGS = {
     "Build Gif": BuildGif,
     "Special CLIP Loader": SpecialClipLoader,
+    "Monaco Prompt": MonacoPrompt,
 }
+
+WEB_DIRECTORY = ("./web/dist", ["app.bundle.js"])

lib/__init__.py

@@ -3,7 +3,6 @@ from custom_nodes.KepPromptLang.lib.actions.diff import DiffAction
 from custom_nodes.KepPromptLang.lib.actions.mult import MultiplyAction
 from custom_nodes.KepPromptLang.lib.actions.neg import NegAction
 from custom_nodes.KepPromptLang.lib.actions.norm import NormAction
-from custom_nodes.KepPromptLang.lib.actions.project import ProjectAction
 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
@@ -21,4 +20,3 @@ register_action(SlerpAction)
 register_action(AverageAction)
 register_action(ScaleDims)
 register_action(SetDims)
-register_action(ProjectAction)

lib/actions/action_utils.py

@@ -1,6 +1,3 @@
-from typing import List
-
-import torch
 from torch import Tensor
 from torch.nn import Embedding
 
@@ -12,9 +9,3 @@ def get_embedding(seg_or_action: SegOrAction, embedding_module: Embedding) -> Te
     if isinstance(seg_or_action, Action):
         return seg_or_action.get_result(embedding_module)
     return seg_or_action.get_embeddings(embedding_module)
-
-
-def get_embedding_for_segments(
-    segments: List[SegOrAction], embedding_module: Embedding
-) -> Tensor:
-    return torch.cat([get_embedding(segment, embedding_module) for segment in segments], dim=1)

lib/actions/project.py

@@ -1,109 +0,0 @@
-from typing import List, Union
-
-import torch
-from torch.nn import Embedding
-
-from custom_nodes.KepPromptLang.lib.action.base import MultiArgAction, Action
-from custom_nodes.KepPromptLang.lib.actions.action_utils import (
-    get_embedding,
-    get_embedding_for_segments,
-)
-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 ProjectAction(MultiArgAction):
-    grammar = 'project(" arg "|" arg ("|" arg ")?)"'
-    name = "project"
-    chars = ["+", "+"]
-
-    weight = 1.0
-
-    def __init__(self, args: List[List[Union[PromptSegment, Action]]]) -> None:
-        super().__init__(args)
-
-        num_args = len(args)
-        if num_args != 3 and num_args != 2:
-            raise ValueError(
-                "Project action should have exactly three arguments(2 vectors and a weight)"
-            )
-
-        self.source_argument = args[0]
-        self.source_argument_token_length = sum(
-            seg_or_action.token_length() for seg_or_action in self.source_argument
-        )
-        self.onto_argument = args[1]
-        self.onto_argument_token_length = sum(
-            seg_or_action.token_length() for seg_or_action in self.onto_argument
-        )
-
-        if num_args == 3:
-            self._parse_weight(args[2])
-
-        self._validate_args()
-
-    def _parse_weight(self, arg: List[SegOrAction]) -> None:
-        if len(arg) != 1:
-            raise ValueError("Project weight should have exactly one segment")
-
-        weight_seg_or_action = arg[0]
-
-        if isinstance(weight_seg_or_action, Action):
-            raise ValueError("Project weight should not have an action as an argument")
-
-        try:
-            self.weight = float(weight_seg_or_action.text)
-        except ValueError:
-            raise ValueError("Project should have an integer/float as the weight")
-
-    def _validate_args(self) -> None:
-        if (
-            self.source_argument_token_length != self.onto_argument_token_length
-            and self.onto_argument_token_length != 1
-        ):
-            raise ValueError(
-                f"Project source and target arguments should have the same token lengths, or target should be one token. Got {self.source_argument_token_length} source tokens and {self.onto_argument_token_length} target tokens"
-            )
-
-    def token_length(self) -> int:
-        # Project projects the source onto the target, so the length of the result is the length of source
-        return self.source_argument_token_length
-
-    def get_result(self, embedding_module: Embedding) -> torch.Tensor:
-        # Calculate the embeddings for the start segment
-        source_embedding = get_embedding_for_segments(
-            self.source_argument, embedding_module
-        ).to(dtype=torch.float32)
-        onto_embedding = get_embedding_for_segments(
-            self.onto_argument, embedding_module
-        ).to(dtype=torch.float32)
-
-        # Perform the projection
-        return torch.mul(
-            torch.mul(source_embedding, onto_embedding)
-            / torch.mul(onto_embedding, onto_embedding),
-            onto_embedding,
-        ).to(dtype=torch.float16)
-
-    # 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

nodes.py

@@ -17,6 +17,25 @@ class EmptyClass:
     pass
 
 
+class MonacoPrompt:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "clip": ("CLIP",),
+                "prompt": ("MONACO",),
+            }
+        }
+
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "do_crap"
+    OUTPUT_IS_LIST = (False,)
+    CATEGORY = "conditioning"
+
+    @staticmethod
+    def do_crap(clip, prompt):
+        return (clip,)
+
 class SpecialClipLoader:
     @classmethod
     def INPUT_TYPES(cls):  # type: ignore