mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2025-12-08 21:44:33 +08:00
67 lines
3.5 KiB
Python
67 lines
3.5 KiB
Python
from transformers import Qwen2Tokenizer
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import comfy.text_encoders.llama
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from comfy import sd1_clip
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import os
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import torch
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import numbers
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class Qwen3Tokenizer(sd1_clip.SDTokenizer):
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer")
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super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='qwen3_2b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=284, pad_token=151643, tokenizer_data=tokenizer_data)
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class OvisTokenizer(sd1_clip.SD1Tokenizer):
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen3_2b", tokenizer=Qwen3Tokenizer)
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self.llama_template = "<|im_start|>user\nDescribe the image by detailing the color, quantity, text, shape, size, texture, spatial relationships of the objects and background: {}<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
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def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs):
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if llama_template is None:
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llama_text = self.llama_template.format(text)
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else:
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llama_text = llama_template.format(text)
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tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
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return tokens
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class Ovis25_2BModel(sd1_clip.SDClipModel):
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def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
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super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Ovis25_2B, enable_attention_masks=attention_mask, return_attention_masks=False, zero_out_masked=True, model_options=model_options)
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class OvisTEModel(sd1_clip.SD1ClipModel):
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def __init__(self, device="cpu", dtype=None, model_options={}):
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super().__init__(device=device, dtype=dtype, name="qwen3_2b", clip_model=Ovis25_2BModel, model_options=model_options)
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def encode_token_weights(self, token_weight_pairs, template_end=-1):
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out, pooled = super().encode_token_weights(token_weight_pairs)
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tok_pairs = token_weight_pairs["qwen3_2b"][0]
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count_im_start = 0
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if template_end == -1:
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for i, v in enumerate(tok_pairs):
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elem = v[0]
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if not torch.is_tensor(elem):
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if isinstance(elem, numbers.Integral):
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if elem == 4004 and count_im_start < 1:
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template_end = i
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count_im_start += 1
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if out.shape[1] > (template_end + 1):
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if tok_pairs[template_end + 1][0] == 25:
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template_end += 1
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out = out[:, template_end:]
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return out, pooled, {}
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def te(dtype_llama=None, llama_quantization_metadata=None):
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class OvisTEModel_(OvisTEModel):
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def __init__(self, device="cpu", dtype=None, model_options={}):
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if dtype_llama is not None:
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dtype = dtype_llama
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if llama_quantization_metadata is not None:
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model_options["quantization_metadata"] = llama_quantization_metadata
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super().__init__(device=device, dtype=dtype, model_options=model_options)
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return OvisTEModel_
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