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https://github.com/comfyanonymous/ComfyUI.git
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e1e50e017c
Since model saving is not implemented for the mixed quant system this breaks model saving for every scaled fp8 model which needs to be fixed before this gets merged.
45 lines
2.7 KiB
Python
45 lines
2.7 KiB
Python
from transformers import Qwen2Tokenizer
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from comfy import sd1_clip
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import comfy.text_encoders.llama
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import os
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class Qwen25_3BTokenizer(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='qwen25_3b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data)
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class Omnigen2Tokenizer(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="qwen25_3b", tokenizer=Qwen25_3BTokenizer)
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self.llama_template = '<|im_start|>system\nYou are a helpful assistant that generates high-quality images based on user instructions.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\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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return super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, **kwargs)
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class Qwen25_3BModel(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.Qwen25_3B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
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class Omnigen2Model(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="qwen25_3b", clip_model=Qwen25_3BModel, model_options=model_options)
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def te(dtype_llama=None, llama_quantization_metadata=None):
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class Omnigen2TEModel_(Omnigen2Model):
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def __init__(self, device="cpu", dtype=None, model_options={}):
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if llama_quantization_metadata is not None:
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model_options = model_options.copy()
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model_options["quantization_metadata"] = llama_quantization_metadata
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if dtype_llama is not None:
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dtype = dtype_llama
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super().__init__(device=device, dtype=dtype, model_options=model_options)
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return Omnigen2TEModel_
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