fix(func/projection): Use float32 to avoid inf/-inf

lib/__init__.py

@@ -3,7 +3,7 @@ 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.pos_scale import PosScaleAction
+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 +21,4 @@ register_action(SlerpAction)
 register_action(AverageAction)
 register_action(ScaleDims)
 register_action(SetDims)
-register_action(PosScaleAction)
+register_action(ProjectAction)

lib/action/base.py

@@ -1,6 +1,6 @@
 from abc import ABC, abstractmethod
 from enum import Enum
-from typing import Union, List, TypedDict, Tuple
+from typing import Union, List
 
 from torch import Tensor
 from torch.nn import Embedding
@@ -13,13 +13,6 @@ class ActionArity(Enum):
     SINGLE = 1
     MULTI = 2
 
-class PostModifiers(TypedDict):
-    """
-    A dictionary of post modifiers for an action result.
-    """
-    position_embed_scale: Union[float, None]
-
-
 class Action(ABC):
     @property
     @abstractmethod
@@ -74,7 +67,7 @@ class Action(ABC):
         pass
 
     @abstractmethod
-    def get_result(self, embedding_module: Embedding) -> Union[Tensor, Tuple[Tensor, PostModifiers]]:
+    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.

lib/actions/action_utils.py

@@ -1,3 +1,6 @@
+from typing import List
+
+import torch
 from torch import Tensor
 from torch.nn import Embedding
 
@@ -9,3 +12,9 @@ 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/pos_scale.py

@@ -1,65 +0,0 @@
-from typing import Tuple, List
-
-import torch
-from torch.nn import Embedding
-
-from custom_nodes.KepPromptLang.lib.action.base import (
-    Action,
-    PostModifiers,
-    MultiArgAction,
-)
-from custom_nodes.KepPromptLang.lib.actions.types import SegOrAction
-
-
-class PosScaleAction(MultiArgAction):
-    grammar = 'posScale(" arg+ ")"'
-    name = "posScale"
-    chars = ["[", "]"]
-
-    def __init__(self, args: List[List[SegOrAction]]) -> None:
-        super().__init__(args)
-        if len(args) != 2:
-            raise ValueError("PosScale action should have exactly two arguments")
-
-        self.target_arg = args[0]
-        self._parse_multiplier(args[1])
-
-    def _parse_multiplier(self, arg: List[SegOrAction]) -> None:
-        if len(arg) != 1:
-            raise ValueError(
-                "PosScale actions multiplier should have exactly one segment"
-            )
-
-        multiplier_seg_or_action = arg[0]
-
-        if isinstance(multiplier_seg_or_action, Action):
-            raise ValueError("PosScale actions multiplier must be a number")
-
-        try:
-            self.parsed_multiplier = float(multiplier_seg_or_action.text)
-        except ValueError:
-            raise ValueError(
-                "PosScale action should have an integer/float as the multiplier"
-            )
-
-    def token_length(self) -> int:
-        """
-        PosScale modifies the posional embeddings of the base segment, so the length is the length of the base segment
-        :return:
-        """
-        total_length = 0
-        for seg_or_action in self.target_arg:
-            total_length += seg_or_action.token_length()
-
-        return total_length
-
-    def get_result(self, embedding_module: Embedding) -> Tuple[torch.Tensor, PostModifiers]:
-        all_embeddings = []
-        for seg_or_action in self.target_arg:
-            if isinstance(seg_or_action, Action):
-                all_embeddings.append(seg_or_action.get_result(embedding_module))
-            else:
-                all_embeddings.append(seg_or_action.get_embeddings(embedding_module))
-
-        target_embeddings = torch.cat(all_embeddings, dim=1)
-        return target_embeddings, {"position_embed_scale": self.parsed_multiplier}

lib/actions/project.py

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

lib/clip_config_bigg.json

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

lib/clip_model.py

@@ -7,8 +7,6 @@ from transformers import CLIPTextConfig, modeling_utils
 
 from comfy import model_management
 import comfy.ops
-from comfy.sd1_clip import SD1ClipModel
-from comfy.sdxl_clip import SDXLClipModel
 from custom_nodes.KepPromptLang.lib.action.base import Action
 from custom_nodes.KepPromptLang.lib.actions.types import SegOrAction
 from custom_nodes.KepPromptLang.lib.fun_clip_stuff import PromptLangTextModel
@@ -16,7 +14,7 @@ from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
 
 
 # Methods with no comment can be assumed to be the same as comfy.sd1_clip.SD1ClipModel
-class PromptLangSDClipModel(torch.nn.Module):
+class PromptLangClipModel(torch.nn.Module):
     """Uses the CLIP transformer encoder for text (from huggingface)"""
     LAYERS = [
         "last",
@@ -211,33 +209,3 @@ class PromptLangSDClipModel(torch.nn.Module):
         if (len(output) == 0):
             return z_empty.cpu(), first_pooled.cpu()
         return torch.cat(output, dim=-2).cpu(), first_pooled.cpu()
-
-class PromptLangSD1ClipModel(SD1ClipModel):
-    def __init__(self, device="cpu", dtype=None, clip_name="l", clip_model=PromptLangSDClipModel):
-        super().__init__()
-        self.clip_name = clip_name
-        self.clip = "clip_{}".format(self.clip_name)
-        setattr(self, self.clip, clip_model(device=device, dtype=dtype))
-
-
-class PromptLangSDXLClipModel(SDXLClipModel):
-    def __init__(self, device="cpu", dtype=None) -> None:
-        # Skip SDXLClipModel's init
-        super(SDXLClipModel, self).__init__()
-        self.clip_l = PromptLangSDClipModel(layer="hidden", layer_idx=11, device=device, dtype=dtype)
-        self.clip_l.layer_norm_hidden_state = False
-        self.clip_g = PromptLangSDXLClipG(device, dtype)
-
-class PromptLangSDXLClipG(PromptLangSDClipModel):
-    def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, textmodel_path=None, dtype=None):
-        if layer == "penultimate":
-            layer="hidden"
-            layer_idx=-2
-
-        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json")
-        super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, textmodel_path=textmodel_path, dtype=dtype)
-        self.empty_tokens = [[49406] + [49407] + [0] * 75]
-        self.layer_norm_hidden_state = False
-
-    def load_sd(self, sd):
-        return super().load_sd(sd)

lib/fun_clip_stuff.py

@@ -1,11 +1,10 @@
-from typing import Optional, Tuple, Union, List, TypedDict
-from importlib_metadata import version as import_version
-from packaging import version
+from typing import Optional, Tuple, Union, List
 
 import torch
 from transformers import CLIPTextConfig
 from transformers.modeling_outputs import BaseModelOutputWithPooling
 from transformers.models.clip.modeling_clip import (
+    _expand_mask,
     CLIPTextEmbeddings,
     CLIPTextTransformer,
     CLIPTextModel,
@@ -24,17 +23,6 @@ def slerp(val, low, high):
     res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
     return res
 
-
-class PosModifier(TypedDict):
-    """
-    A dictionary of post modifiers for an action result.
-    """
-
-    position_embed_scale: Union[float]
-    start_idx: Union[int]
-    end_idx: Union[int]
-
-
 class PromptLangCLIPTextEmbeddings(CLIPTextEmbeddings):
     def __init__(self, config: CLIPTextConfig):
         super().__init__(config)
@@ -51,30 +39,14 @@ class PromptLangCLIPTextEmbeddings(CLIPTextEmbeddings):
             raise ValueError("You have to specify input_dicts")
 
         batches = []
-        pos_modifiers: List[List[PosModifier]] = []
         for batch_idx, batch in enumerate(input_dicts):
             results = []
-            batch_pos_modifiers = []
-            token_idx = 0
             for seg_or_action in batch:
                 if isinstance(seg_or_action, Action):
-                    action_result = seg_or_action.get_result(self.token_embedding)
-                    if isinstance(action_result, tuple):
-                        result, post_modifiers = action_result
-                        if post_modifiers["position_embed_scale"] is not None:
-                            post_modifiers["start_idx"] = token_idx
-                            post_modifiers["end_idx"] = (
-                                token_idx + seg_or_action.token_length()
-                            )
-                            batch_pos_modifiers.append(post_modifiers)
-                    else:
-                        result = action_result
+                    results.append(seg_or_action.get_result(self.token_embedding))
                 else:
-                    result = seg_or_action.get_embeddings(self.token_embedding)
-                results.append(result)
-                token_idx += seg_or_action.token_length()
+                    results.append(seg_or_action.get_embeddings(self.token_embedding))
             batches.append(results)
-            pos_modifiers.append(batch_pos_modifiers)
 
         seq_length = batches[0][0].shape[-2]
 
@@ -88,16 +60,8 @@ class PromptLangCLIPTextEmbeddings(CLIPTextEmbeddings):
             else:
                 embeds.append(torch.cat(batch, dim=-2))
 
-        for idx, batch_pos_modifiers in enumerate(pos_modifiers):
-            position_embeddings = self.position_embedding(position_ids)
-            if len(batch_pos_modifiers) > 0:
-                print(f"Found {len(batch_pos_modifiers)} pos modifiers for batch {idx}")
-                for post_modifier in batch_pos_modifiers:
-                    position_embeddings[
-                        0, post_modifier["start_idx"] : post_modifier["end_idx"]
-                    ] *= post_modifier["position_embed_scale"]
-            embeds[idx] = embeds[idx] + position_embeddings
-        embeddings = torch.cat(embeds, dim=0)
+        position_embeddings = self.position_embedding(position_ids)
+        embeddings = torch.cat(embeds, dim=0) + position_embeddings
 
         return embeddings
 
@@ -106,40 +70,6 @@ class PrompLangCLIPTextTransformer(CLIPTextTransformer):
     def __init__(self, config: CLIPTextConfig):
         super().__init__(config)
         self.embeddings = PromptLangCLIPTextEmbeddings(config)
-        self.transformers_version = version.parse(import_version('transformers'))
-
-    def process_attention_mask(self, hidden_states, attention_mask, bsz, seq_len):
-        # Parse the transformer version
-        input_shape = torch.Size([bsz, seq_len])
-
-        v4_30 = version.parse('4.30.0')
-        v4_35 = version.parse('4.35')
-        if self.transformers_version < v4_30:
-            print("Using transformers < 4.30.0")
-            causal_attention_mask = self._build_causal_attention_mask(bsz, seq_len, hidden_states.dtype).to(
-                hidden_states.device)
-        elif v4_30 <= self.transformers_version < v4_35:
-            print("Using transformers >= 4.30.0 and <= 4.34.*")
-            from transformers.models.clip.modeling_clip import _make_causal_mask
-            causal_attention_mask = _make_causal_mask(input_shape, hidden_states.dtype, device=hidden_states.device)
-        else:
-            print("Using transformers >= 4.35")
-            from transformers.modeling_attn_mask_utils import _create_4d_causal_attention_mask
-            causal_attention_mask = _create_4d_causal_attention_mask(
-                input_shape, hidden_states.dtype, device=hidden_states.device
-            )
-
-        # Expand attention_mask if it exists
-        if attention_mask is not None:
-            # Import _expand_mask or _prepare_4d_attention_mask based on version
-            if self.transformers_version < v4_35:
-                from transformers.models.clip.modeling_clip import _expand_mask
-                attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
-            else:
-                from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
-                attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)
-
-        return causal_attention_mask, attention_mask
 
     def forward(
             self,
@@ -171,8 +101,24 @@ class PrompLangCLIPTextTransformer(CLIPTextTransformer):
         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
+        ## VERSION DIFF ##
+        # 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)
 
-        causal_attention_mask, attention_mask = self.process_attention_mask(hidden_states, attention_mask, bsz, seq_len)
+        # expand attention_mask
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
 
         encoder_outputs = self.encoder(
             inputs_embeds=hidden_states,
@@ -197,9 +143,6 @@ class PrompLangCLIPTextTransformer(CLIPTextTransformer):
                 else:
                     if seg_or_action.text == '__PAD__':
                         break
-
-                    # Is a segment, and isn't the pad segment
-                    idx += seg_or_action.token_length()
             eot_idx.append(idx)
         # text_embeds.shape = [batch_size, sequence_length, transformer.width]
         # take features from the eot embedding (eot_token is the highest number in each sequence)

lib/parser/transformer.py

@@ -2,10 +2,15 @@ from typing import List
 
 from lark import Transformer, Token
 
-from comfy.sd1_clip import SDTokenizer
+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
 
 
@@ -13,7 +18,7 @@ class PromptTransformer(Transformer):
     # def WORD(self, items):
     #     return items
 
-    def __init__(self, tokenizer: SDTokenizer):
+    def __init__(self, tokenizer: SD1Tokenizer):
         super().__init__()
         self.tokenizer = tokenizer
 

lib/parser/utils.py

@@ -1,6 +1,6 @@
 from lark import Token
 
-from comfy.sd1_clip import SDTokenizer
+from comfy.sd1_clip import SD1Tokenizer
 from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
 
 
@@ -11,7 +11,7 @@ def flatten_tree(tree):
         return [str(tree.data)] + sum([flatten_tree(child) for child in tree.children], [])
 
 
-def build_prompt_segment(text: str, tokenizer: SDTokenizer) -> PromptSegment:
+def build_prompt_segment(text: str, tokenizer: SD1Tokenizer) -> PromptSegment:
     split_text = text.split(" ")
     tokens = []
     for word in split_text:

lib/tokenizer.py

@@ -1,18 +1,18 @@
-from typing import List, Dict
+from typing import List
 
 from lark import Tree
 
-from comfy.sd1_clip import SD1Tokenizer, SDTokenizer
+from comfy.sd1_clip import SD1Tokenizer
 from custom_nodes.KepPromptLang.lib.actions.types import SegOrAction
 
 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
 
-
-class PromptLangSDTokenizer(SDTokenizer):
-    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l'):
+class PromptLangTokenizer(SD1Tokenizer):
+    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', special_tokens=None):
         super().__init__(tokenizer_path, max_length, pad_with_end, embedding_directory, embedding_size, embedding_key)
+
     """
     Doesn't actually tokenize...
     Returns batches of segments and actions
@@ -43,9 +43,9 @@ class PromptLangSDTokenizer(SDTokenizer):
 
             # If the segment is too large to fit in a single batch, pad the current batch and start a new one
             if num_tokens + batch_size > self.max_length - 1:
-                remaining_length = self.max_length - batch_size
+                remaining_length = self.max_length - batch_size - 1 # -1 for end token
                 # Pad batch
-                batch.append(PromptSegment("__PAD__", [self.end_token] + [pad_token] * (remaining_length - 1))) # -1 for end token
+                batch.append(PromptSegment("__PAD__", [self.end_token] + [pad_token] * remaining_length - 1))
                 batched_segments.append(batch)
 
                 # start new batch
@@ -66,23 +66,3 @@ class PromptLangSDTokenizer(SDTokenizer):
         #     batch_size_info(batch)
 
         return batched_segments
-
-class PromptLangSD1Tokenizer(SD1Tokenizer):
-    def __init__(self, embedding_directory=None, clip_name='l', tokenizer=PromptLangSDTokenizer) -> None:
-        super().__init__(embedding_directory, clip_name, tokenizer)
-
-
-class PromptLangSDXLClipGTokenizer(PromptLangSDTokenizer):
-    def __init__(self, tokenizer_path=None, embedding_directory=None):
-        super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g')
-
-class PromptLangSDXLTokenizer(SD1Tokenizer):
-    def __init__(self, embedding_directory=None) -> None:
-        self.clip_l = PromptLangSDTokenizer(embedding_directory=embedding_directory)
-        self.clip_g = PromptLangSDXLClipGTokenizer(embedding_directory=embedding_directory)
-
-    def tokenize_with_weights(self, text:str, return_word_ids=False) -> Dict[str, List[List[SegOrAction]]]:
-        out = {}
-        out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
-        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
-        return out

nodes.py

@@ -1,3 +1,4 @@
+import random
 import os
 from typing import List, Tuple, Any
 
@@ -7,18 +8,9 @@ from PIL import Image
 import folder_paths
 import comfy.sd
 import comfy.ops
-from comfy.sd2_clip import SD2ClipModel
-from comfy.sdxl_clip import SDXLClipModel
-from comfy.supported_models_base import ClipTarget
-from custom_nodes.KepPromptLang.lib.clip_model import (
-    PromptLangSDXLClipModel,
-    PromptLangSD1ClipModel,
-)
+from custom_nodes.KepPromptLang.lib.clip_model import PromptLangClipModel
 
-from custom_nodes.KepPromptLang.lib.tokenizer import (
-    PromptLangSDXLTokenizer,
-    PromptLangSD1Tokenizer,
-)
+from custom_nodes.KepPromptLang.lib.tokenizer import PromptLangTokenizer
 
 
 class EmptyClass:
@@ -41,22 +33,15 @@ class SpecialClipLoader:
 
     @staticmethod
     def load_clip(source_clip: comfy.sd.CLIP) -> Tuple[comfy.sd.CLIP]:
+        clip_target = EmptyClass()
+        clip_target.params = {}
+        clip_target.clip = PromptLangClipModel
+        clip_target.tokenizer = PromptLangTokenizer
 
-        if isinstance(source_clip.cond_stage_model, SDXLClipModel):
-            clip_target = ClipTarget(PromptLangSDXLTokenizer, PromptLangSDXLClipModel)
-            clip = comfy.sd.CLIP(clip_target, embedding_directory=source_clip.tokenizer.clip_g.embedding_directory)
-            comfy.sd.load_clip_weights(clip.cond_stage_model.clip_g,source_clip.cond_stage_model.clip_g.state_dict())
-            comfy.sd.load_clip_weights(
-                clip.cond_stage_model.clip_l, source_clip.cond_stage_model.clip_l.state_dict()
-            )
-        elif isinstance(source_clip, SD2ClipModel):
-            raise ValueError("SD2 Clip model is not supported.")
-        else:
-            clip_target = ClipTarget(PromptLangSD1Tokenizer, PromptLangSD1ClipModel)
-            clip = comfy.sd.CLIP(clip_target, embedding_directory=source_clip.tokenizer.clip_l.embedding_directory)
-            comfy.sd.load_clip_weights(
-                clip.cond_stage_model, source_clip.cond_stage_model.state_dict()
-            )
+        clip = comfy.sd.CLIP(clip_target, embedding_directory=source_clip.tokenizer.embedding_directory)
+        comfy.sd.load_clip_weights(
+            clip.cond_stage_model, source_clip.cond_stage_model.state_dict()
+        )
         return (clip,)
 
 

requirements.txt

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