import torch from .attention import Attention class CLIPEncoderLayer(torch.nn.Module): def __init__(self, embed_dim, intermediate_size, num_heads=12, head_dim=64, use_quick_gelu=True): super().__init__() self.attn = Attention(q_dim=embed_dim, num_heads=num_heads, head_dim=head_dim, bias_q=True, bias_kv=True, bias_out=True) self.layer_norm1 = torch.nn.LayerNorm(embed_dim) self.layer_norm2 = torch.nn.LayerNorm(embed_dim) self.fc1 = torch.nn.Linear(embed_dim, intermediate_size) self.fc2 = torch.nn.Linear(intermediate_size, embed_dim) self.use_quick_gelu = use_quick_gelu def quickGELU(self, x): return x * torch.sigmoid(1.702 * x) def forward(self, hidden_states, attn_mask=None): residual = hidden_states hidden_states = self.layer_norm1(hidden_states) hidden_states = self.attn(hidden_states, attn_mask=attn_mask) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.layer_norm2(hidden_states) hidden_states = self.fc1(hidden_states) if self.use_quick_gelu: hidden_states = self.quickGELU(hidden_states) else: hidden_states = torch.nn.functional.gelu(hidden_states) hidden_states = self.fc2(hidden_states) hidden_states = residual + hidden_states return hidden_states class SDTextEncoder(torch.nn.Module): def __init__(self, embed_dim=768, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=12, encoder_intermediate_size=3072): super().__init__() # token_embedding self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim) # position_embeds (This is a fixed tensor) self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim)) # encoders self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)]) # attn_mask self.attn_mask = self.attention_mask(max_position_embeddings) # final_layer_norm self.final_layer_norm = torch.nn.LayerNorm(embed_dim) def attention_mask(self, length): mask = torch.empty(length, length) mask.fill_(float("-inf")) mask.triu_(1) return mask def forward(self, input_ids, clip_skip=1): embeds = self.token_embedding(input_ids) + self.position_embeds attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype) for encoder_id, encoder in enumerate(self.encoders): embeds = encoder(embeds, attn_mask=attn_mask) if encoder_id + clip_skip == len(self.encoders): break embeds = self.final_layer_norm(embeds) return embeds def state_dict_converter(self): return SDTextEncoderStateDictConverter() class SDTextEncoderStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): rename_dict = { "text_model.embeddings.token_embedding.weight": "token_embedding.weight", "text_model.embeddings.position_embedding.weight": "position_embeds", "text_model.final_layer_norm.weight": "final_layer_norm.weight", "text_model.final_layer_norm.bias": "final_layer_norm.bias" } attn_rename_dict = { "self_attn.q_proj": "attn.to_q", "self_attn.k_proj": "attn.to_k", "self_attn.v_proj": "attn.to_v", "self_attn.out_proj": "attn.to_out", "layer_norm1": "layer_norm1", "layer_norm2": "layer_norm2", "mlp.fc1": "fc1", "mlp.fc2": "fc2", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if name == "text_model.embeddings.position_embedding.weight": param = param.reshape((1, param.shape[0], param.shape[1])) state_dict_[rename_dict[name]] = param elif name.startswith("text_model.encoder.layers."): param = state_dict[name] names = name.split(".") layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1] name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail]) state_dict_[name_] = param return state_dict_ def from_civitai(self, state_dict): rename_dict = { "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight", "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias", 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"cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.out_proj.bias": "encoders.7.attn.to_out.bias", "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.out_proj.weight": "encoders.7.attn.to_out.weight", "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.q_proj.bias": "encoders.7.attn.to_q.bias", "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.q_proj.weight": "encoders.7.attn.to_q.weight", "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.v_proj.bias": "encoders.7.attn.to_v.bias", "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.v_proj.weight": "encoders.7.attn.to_v.weight", "cond_stage_model.transformer.text_model.encoder.layers.8.layer_norm1.bias": "encoders.8.layer_norm1.bias", "cond_stage_model.transformer.text_model.encoder.layers.8.layer_norm1.weight": "encoders.8.layer_norm1.weight", "cond_stage_model.transformer.text_model.encoder.layers.8.layer_norm2.bias": "encoders.8.layer_norm2.bias", "cond_stage_model.transformer.text_model.encoder.layers.8.layer_norm2.weight": "encoders.8.layer_norm2.weight", "cond_stage_model.transformer.text_model.encoder.layers.8.mlp.fc1.bias": "encoders.8.fc1.bias", "cond_stage_model.transformer.text_model.encoder.layers.8.mlp.fc1.weight": "encoders.8.fc1.weight", "cond_stage_model.transformer.text_model.encoder.layers.8.mlp.fc2.bias": "encoders.8.fc2.bias", "cond_stage_model.transformer.text_model.encoder.layers.8.mlp.fc2.weight": "encoders.8.fc2.weight", "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.k_proj.bias": "encoders.8.attn.to_k.bias", "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.k_proj.weight": "encoders.8.attn.to_k.weight", "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.out_proj.bias": "encoders.8.attn.to_out.bias", "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.out_proj.weight": "encoders.8.attn.to_out.weight", "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.q_proj.bias": "encoders.8.attn.to_q.bias", "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.q_proj.weight": "encoders.8.attn.to_q.weight", "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.v_proj.bias": "encoders.8.attn.to_v.bias", "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.v_proj.weight": "encoders.8.attn.to_v.weight", "cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm1.bias": "encoders.9.layer_norm1.bias", "cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm1.weight": "encoders.9.layer_norm1.weight", "cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm2.bias": "encoders.9.layer_norm2.bias", "cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm2.weight": "encoders.9.layer_norm2.weight", "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc1.bias": "encoders.9.fc1.bias", "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc1.weight": "encoders.9.fc1.weight", "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc2.bias": "encoders.9.fc2.bias", "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc2.weight": "encoders.9.fc2.weight", "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.k_proj.bias": "encoders.9.attn.to_k.bias", "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.k_proj.weight": "encoders.9.attn.to_k.weight", "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.out_proj.bias": "encoders.9.attn.to_out.bias", "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.out_proj.weight": "encoders.9.attn.to_out.weight", "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.q_proj.bias": "encoders.9.attn.to_q.bias", "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight", "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias", "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight", "cond_stage_model.transformer.text_model.final_layer_norm.bias": "final_layer_norm.bias", "cond_stage_model.transformer.text_model.final_layer_norm.weight": "final_layer_norm.weight", "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight": "position_embeds" } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if name == "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight": param = param.reshape((1, param.shape[0], param.shape[1])) state_dict_[rename_dict[name]] = param return state_dict_