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https://github.com/modelscope/DiffSynth-Studio.git
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94 lines
3.2 KiB
Python
94 lines
3.2 KiB
Python
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 FluxTextEncoder1(SDTextEncoder):
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def __init__(self, vocab_size=49408):
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super().__init__(vocab_size=vocab_size)
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def forward(self, input_ids, clip_skip=2):
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embeds = self.token_embedding(input_ids) + self.position_embeds
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attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
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for encoder_id, encoder in enumerate(self.encoders):
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embeds = encoder(embeds, attn_mask=attn_mask)
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if encoder_id + clip_skip == len(self.encoders):
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hidden_states = embeds
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embeds = self.final_layer_norm(embeds)
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pooled_embeds = embeds[torch.arange(embeds.shape[0]), input_ids.to(dtype=torch.int).argmax(dim=-1)]
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return embeds, pooled_embeds
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@staticmethod
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def state_dict_converter():
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return FluxTextEncoder1StateDictConverter()
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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 FluxTextEncoder1StateDictConverter:
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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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rename_dict = {
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"text_model.embeddings.token_embedding.weight": "token_embedding.weight",
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"text_model.embeddings.position_embedding.weight": "position_embeds",
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"text_model.final_layer_norm.weight": "final_layer_norm.weight",
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"text_model.final_layer_norm.bias": "final_layer_norm.bias"
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}
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attn_rename_dict = {
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"self_attn.q_proj": "attn.to_q",
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"self_attn.k_proj": "attn.to_k",
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"self_attn.v_proj": "attn.to_v",
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"self_attn.out_proj": "attn.to_out",
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"layer_norm1": "layer_norm1",
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"layer_norm2": "layer_norm2",
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"mlp.fc1": "fc1",
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"mlp.fc2": "fc2",
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}
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state_dict_ = {}
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for name in state_dict:
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if name in rename_dict:
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param = state_dict[name]
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if name == "text_model.embeddings.position_embedding.weight":
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param = param.reshape((1, param.shape[0], param.shape[1]))
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state_dict_[rename_dict[name]] = param
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elif name.startswith("text_model.encoder.layers."):
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param = state_dict[name]
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names = name.split(".")
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layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1]
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name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail])
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state_dict_[name_] = param
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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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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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