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import torch |
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from transformers import T5EncoderModel, T5Config |
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from .sd_text_encoder import SDTextEncoder |
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class FluxTextEncoder2(T5EncoderModel): |
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def __init__(self, config): |
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super().__init__(config) |
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self.eval() |
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def forward(self, input_ids): |
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outputs = super().forward(input_ids=input_ids) |
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prompt_emb = outputs.last_hidden_state |
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return prompt_emb |
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@staticmethod |
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def state_dict_converter(): |
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return FluxTextEncoder2StateDictConverter() |
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class FluxTextEncoder2StateDictConverter(): |
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def __init__(self): |
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pass |
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def from_diffusers(self, state_dict): |
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state_dict_ = state_dict |
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return state_dict_ |
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def from_civitai(self, state_dict): |
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return self.from_diffusers(state_dict) |
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