from transformers import Qwen2Tokenizer from comfy import sd1_clip import os from typing_extensions import override from comfy_api.latest import ComfyExtension, io class Qwen3Tokenizer512Limit(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2560, embedding_key='qwen3_4b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=True, max_length=512, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data) class ZImageTokenizer512Limit(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen3_4b", tokenizer=Qwen3Tokenizer512Limit) self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs): if llama_template is None: llama_text = self.llama_template.format(text) else: llama_text = llama_template.format(text) tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs) return tokens class CapZImageTokenizer512(io.ComfyNode): @classmethod def define_schema(cls) -> io.Schema: return io.Schema( node_id="CapZImageTokenizer512", display_name="Cap ZImage Tokenizer to 512 tokens ", category="conditioning", inputs=[ io.Clip.Input() ], outputs=[ io.Clip.Output() ], ) @classmethod def execute(cls, base) -> io.NodeOutput: base.tokenizer = ZImageTokenizer512Limit() return io.NodeOutput(base) class CapZImageTokenizer512Ext(ComfyExtension): @override async def get_node_list(self) -> list[type[io.ComfyNode]]: return [ CapZImageTokenizer512, ] async def comfy_entrypoint() -> CapZImageTokenizer512Ext: return CapZImageTokenizer512Ext()