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from comfy import sd1_clip
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from transformers import BertTokenizer
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from .spiece_tokenizer import SPieceTokenizer
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from .bert import BertModel
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import comfy.text_encoders.t5
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import os
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import torch
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class HyditBertModel(sd1_clip.SDClipModel):
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def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
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textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip.json")
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super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 101, "end": 102, "pad": 0}, model_class=BertModel, enable_attention_masks=True, return_attention_masks=True)
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class HyditBertTokenizer(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__)), "hydit_clip_tokenizer")
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super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1024, embedding_key='chinese_roberta', tokenizer_class=BertTokenizer, pad_to_max_length=False, max_length=512, min_length=77)
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class MT5XLModel(sd1_clip.SDClipModel):
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def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
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textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_config_xl.json")
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super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, return_attention_masks=True)
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class MT5XLTokenizer(sd1_clip.SDTokenizer):
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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tokenizer = tokenizer_data.get("spiece_model", None)
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super().__init__(tokenizer, pad_with_end=False, embedding_size=2048, embedding_key='mt5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256)
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def state_dict(self):
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return {"spiece_model": self.tokenizer.serialize_model()}
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class HyditTokenizer:
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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mt5_tokenizer_data = tokenizer_data.get("mt5xl.spiece_model", None)
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self.hydit_clip = HyditBertTokenizer(embedding_directory=embedding_directory)
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self.mt5xl = MT5XLTokenizer(tokenizer_data={"spiece_model": mt5_tokenizer_data}, embedding_directory=embedding_directory)
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def tokenize_with_weights(self, text:str, return_word_ids=False):
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out = {}
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out["hydit_clip"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids)
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out["mt5xl"] = self.mt5xl.tokenize_with_weights(text, return_word_ids)
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return out
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def untokenize(self, token_weight_pair):
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return self.hydit_clip.untokenize(token_weight_pair)
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def state_dict(self):
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return {"mt5xl.spiece_model": self.mt5xl.state_dict()["spiece_model"]}
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class HyditModel(torch.nn.Module):
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def __init__(self, device="cpu", dtype=None):
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super().__init__()
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self.hydit_clip = HyditBertModel(dtype=dtype)
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self.mt5xl = MT5XLModel(dtype=dtype)
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self.dtypes = set()
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if dtype is not None:
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self.dtypes.add(dtype)
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def encode_token_weights(self, token_weight_pairs):
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hydit_out = self.hydit_clip.encode_token_weights(token_weight_pairs["hydit_clip"])
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mt5_out = self.mt5xl.encode_token_weights(token_weight_pairs["mt5xl"])
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return hydit_out[0], hydit_out[1], {"attention_mask": hydit_out[2]["attention_mask"], "conditioning_mt5xl": mt5_out[0], "attention_mask_mt5xl": mt5_out[2]["attention_mask"]}
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def load_sd(self, sd):
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if "bert.encoder.layer.0.attention.self.query.weight" in sd:
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return self.hydit_clip.load_sd(sd)
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else:
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return self.mt5xl.load_sd(sd)
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def set_clip_options(self, options):
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self.hydit_clip.set_clip_options(options)
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self.mt5xl.set_clip_options(options)
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def reset_clip_options(self):
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self.hydit_clip.reset_clip_options()
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self.mt5xl.reset_clip_options()
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