| import torch |
| from einops import rearrange |
|
|
|
|
| def low_version_attention(query, key, value, attn_bias=None): |
| scale = 1 / query.shape[-1] ** 0.5 |
| query = query * scale |
| attn = torch.matmul(query, key.transpose(-2, -1)) |
| if attn_bias is not None: |
| attn = attn + attn_bias |
| attn = attn.softmax(-1) |
| return attn @ value |
|
|
|
|
| class Attention(torch.nn.Module): |
|
|
| def __init__(self, q_dim, num_heads, head_dim, kv_dim=None, bias_q=False, bias_kv=False, bias_out=False): |
| super().__init__() |
| dim_inner = head_dim * num_heads |
| kv_dim = kv_dim if kv_dim is not None else q_dim |
| self.num_heads = num_heads |
| self.head_dim = head_dim |
|
|
| self.to_q = torch.nn.Linear(q_dim, dim_inner, bias=bias_q) |
| self.to_k = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv) |
| self.to_v = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv) |
| self.to_out = torch.nn.Linear(dim_inner, q_dim, bias=bias_out) |
|
|
| def interact_with_ipadapter(self, hidden_states, q, ip_k, ip_v, scale=1.0): |
| batch_size = q.shape[0] |
| ip_k = ip_k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) |
| ip_v = ip_v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) |
| ip_hidden_states = torch.nn.functional.scaled_dot_product_attention(q, ip_k, ip_v) |
| hidden_states = hidden_states + scale * ip_hidden_states |
| return hidden_states |
|
|
| def torch_forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None, ipadapter_kwargs=None, qkv_preprocessor=None): |
| if encoder_hidden_states is None: |
| encoder_hidden_states = hidden_states |
|
|
| batch_size = encoder_hidden_states.shape[0] |
|
|
| q = self.to_q(hidden_states) |
| k = self.to_k(encoder_hidden_states) |
| v = self.to_v(encoder_hidden_states) |
|
|
| q = q.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) |
| k = k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) |
| v = v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) |
|
|
| if qkv_preprocessor is not None: |
| q, k, v = qkv_preprocessor(q, k, v) |
|
|
| hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) |
| if ipadapter_kwargs is not None: |
| hidden_states = self.interact_with_ipadapter(hidden_states, q, **ipadapter_kwargs) |
| hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) |
| hidden_states = hidden_states.to(q.dtype) |
|
|
| hidden_states = self.to_out(hidden_states) |
|
|
| return hidden_states |
| |
| def xformers_forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None): |
| if encoder_hidden_states is None: |
| encoder_hidden_states = hidden_states |
|
|
| q = self.to_q(hidden_states) |
| k = self.to_k(encoder_hidden_states) |
| v = self.to_v(encoder_hidden_states) |
|
|
| q = rearrange(q, "b f (n d) -> (b n) f d", n=self.num_heads) |
| k = rearrange(k, "b f (n d) -> (b n) f d", n=self.num_heads) |
| v = rearrange(v, "b f (n d) -> (b n) f d", n=self.num_heads) |
|
|
| if attn_mask is not None: |
| hidden_states = low_version_attention(q, k, v, attn_bias=attn_mask) |
| else: |
| import xformers.ops as xops |
| hidden_states = xops.memory_efficient_attention(q, k, v) |
| hidden_states = rearrange(hidden_states, "(b n) f d -> b f (n d)", n=self.num_heads) |
|
|
| hidden_states = hidden_states.to(q.dtype) |
| hidden_states = self.to_out(hidden_states) |
|
|
| return hidden_states |
|
|
| def forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None, ipadapter_kwargs=None, qkv_preprocessor=None): |
| return self.torch_forward(hidden_states, encoder_hidden_states=encoder_hidden_states, attn_mask=attn_mask, ipadapter_kwargs=ipadapter_kwargs, qkv_preprocessor=qkv_preprocessor) |
|
|
|
|
|
|
|
|
|
|
| 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__() |
|
|
| |
| self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim) |
|
|
| |
| self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim)) |
|
|
| |
| self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)]) |
|
|
| |
| self.attn_mask = self.attention_mask(max_position_embeddings) |
|
|
| |
| 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 |
| |
| @staticmethod |
| def state_dict_converter(): |
| 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", |
| "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight": "encoders.0.attn.to_q.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight": "encoders.0.attn.to_v.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm1.weight": "encoders.1.layer_norm1.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm2.weight": "encoders.1.layer_norm2.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc1.bias": "encoders.1.fc1.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc1.weight": "encoders.1.fc1.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc2.bias": "encoders.1.fc2.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc2.weight": "encoders.1.fc2.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.k_proj.bias": "encoders.1.attn.to_k.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.k_proj.weight": "encoders.1.attn.to_k.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.out_proj.bias": "encoders.1.attn.to_out.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.out_proj.weight": "encoders.1.attn.to_out.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.q_proj.bias": "encoders.1.attn.to_q.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.q_proj.weight": "encoders.1.attn.to_q.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.v_proj.bias": "encoders.1.attn.to_v.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.v_proj.weight": "encoders.1.attn.to_v.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.10.layer_norm1.bias": "encoders.10.layer_norm1.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.10.layer_norm1.weight": "encoders.10.layer_norm1.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.10.layer_norm2.bias": "encoders.10.layer_norm2.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.10.layer_norm2.weight": "encoders.10.layer_norm2.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.10.mlp.fc1.bias": "encoders.10.fc1.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.10.mlp.fc1.weight": "encoders.10.fc1.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.10.mlp.fc2.bias": "encoders.10.fc2.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.10.mlp.fc2.weight": "encoders.10.fc2.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.k_proj.bias": "encoders.10.attn.to_k.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.k_proj.weight": "encoders.10.attn.to_k.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.out_proj.bias": "encoders.10.attn.to_out.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.out_proj.weight": "encoders.10.attn.to_out.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.q_proj.bias": "encoders.10.attn.to_q.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.q_proj.weight": "encoders.10.attn.to_q.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.v_proj.bias": "encoders.10.attn.to_v.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.v_proj.weight": "encoders.10.attn.to_v.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm1.bias": "encoders.11.layer_norm1.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm1.weight": "encoders.11.layer_norm1.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm2.bias": "encoders.11.layer_norm2.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm2.weight": "encoders.11.layer_norm2.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc1.bias": "encoders.11.fc1.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc1.weight": "encoders.11.fc1.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc2.bias": "encoders.11.fc2.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc2.weight": "encoders.11.fc2.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.k_proj.bias": "encoders.11.attn.to_k.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.k_proj.weight": "encoders.11.attn.to_k.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.out_proj.bias": "encoders.11.attn.to_out.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.out_proj.weight": "encoders.11.attn.to_out.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.q_proj.bias": "encoders.11.attn.to_q.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.q_proj.weight": "encoders.11.attn.to_q.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.v_proj.bias": "encoders.11.attn.to_v.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.v_proj.weight": "encoders.11.attn.to_v.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.2.layer_norm1.bias": "encoders.2.layer_norm1.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.2.layer_norm1.weight": "encoders.2.layer_norm1.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.2.layer_norm2.bias": "encoders.2.layer_norm2.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.2.layer_norm2.weight": "encoders.2.layer_norm2.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc1.bias": "encoders.2.fc1.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc1.weight": "encoders.2.fc1.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc2.bias": "encoders.2.fc2.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc2.weight": "encoders.2.fc2.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.k_proj.bias": "encoders.2.attn.to_k.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.k_proj.weight": "encoders.2.attn.to_k.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.out_proj.bias": "encoders.2.attn.to_out.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.out_proj.weight": "encoders.2.attn.to_out.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.q_proj.bias": "encoders.2.attn.to_q.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.q_proj.weight": "encoders.2.attn.to_q.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.v_proj.bias": "encoders.2.attn.to_v.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.v_proj.weight": "encoders.2.attn.to_v.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.3.layer_norm1.bias": "encoders.3.layer_norm1.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.3.layer_norm1.weight": "encoders.3.layer_norm1.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.3.layer_norm2.bias": "encoders.3.layer_norm2.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.3.layer_norm2.weight": "encoders.3.layer_norm2.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc1.bias": "encoders.3.fc1.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc1.weight": "encoders.3.fc1.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc2.bias": "encoders.3.fc2.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc2.weight": "encoders.3.fc2.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.k_proj.bias": "encoders.3.attn.to_k.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.k_proj.weight": "encoders.3.attn.to_k.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.out_proj.bias": "encoders.3.attn.to_out.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.out_proj.weight": "encoders.3.attn.to_out.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.q_proj.bias": "encoders.3.attn.to_q.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.q_proj.weight": "encoders.3.attn.to_q.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.v_proj.bias": "encoders.3.attn.to_v.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.v_proj.weight": "encoders.3.attn.to_v.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.4.layer_norm1.bias": "encoders.4.layer_norm1.bias", |
| "cond_stage_model.transformer.text_model.encoder.layers.4.layer_norm1.weight": "encoders.4.layer_norm1.weight", |
| "cond_stage_model.transformer.text_model.encoder.layers.4.layer_norm2.bias": "encoders.4.layer_norm2.bias", |
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| "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", |
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| "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_ |
|
|
|
|
|
|
| class LoRALayerBlock(torch.nn.Module): |
| def __init__(self, L, dim_in, dim_out): |
| super().__init__() |
| self.x = torch.nn.Parameter(torch.randn(1, L, dim_in)) |
| self.layer_norm = torch.nn.LayerNorm(dim_out) |
|
|
| def forward(self, lora_A, lora_B): |
| x = self.x @ lora_A.T @ lora_B.T |
| x = self.layer_norm(x) |
| return x |
| |
|
|
| class LoRAEmbedder(torch.nn.Module): |
| def __init__(self, lora_patterns=None, L=1, out_dim=2048): |
| super().__init__() |
| if lora_patterns is None: |
| lora_patterns = self.default_lora_patterns() |
| |
| model_dict = {} |
| for lora_pattern in lora_patterns: |
| name, dim = lora_pattern["name"], lora_pattern["dim"] |
| model_dict[name.replace(".", "___")] = LoRALayerBlock(L, dim[0], dim[1]) |
| self.model_dict = torch.nn.ModuleDict(model_dict) |
| |
| proj_dict = {} |
| for lora_pattern in lora_patterns: |
| layer_type, dim = lora_pattern["type"], lora_pattern["dim"] |
| if layer_type not in proj_dict: |
| proj_dict[layer_type.replace(".", "___")] = torch.nn.Linear(dim[1], out_dim) |
| self.proj_dict = torch.nn.ModuleDict(proj_dict) |
| |
| self.lora_patterns = lora_patterns |
| |
| |
| def default_lora_patterns(self): |
| lora_patterns = [] |
| lora_dict = { |
| "attn.a_to_qkv": (3072, 9216), "attn.a_to_out": (3072, 3072), "ff_a.0": (3072, 12288), "ff_a.2": (12288, 3072), "norm1_a.linear": (3072, 18432), |
| "attn.b_to_qkv": (3072, 9216), "attn.b_to_out": (3072, 3072), "ff_b.0": (3072, 12288), "ff_b.2": (12288, 3072), "norm1_b.linear": (3072, 18432), |
| } |
| for i in range(19): |
| for suffix in lora_dict: |
| lora_patterns.append({ |
| "name": f"blocks.{i}.{suffix}", |
| "dim": lora_dict[suffix], |
| "type": suffix, |
| }) |
| lora_dict = {"to_qkv_mlp": (3072, 21504), "proj_out": (15360, 3072), "norm.linear": (3072, 9216)} |
| for i in range(38): |
| for suffix in lora_dict: |
| lora_patterns.append({ |
| "name": f"single_blocks.{i}.{suffix}", |
| "dim": lora_dict[suffix], |
| "type": suffix, |
| }) |
| return lora_patterns |
| |
| def forward(self, lora): |
| lora_emb = [] |
| for lora_pattern in self.lora_patterns: |
| name, layer_type = lora_pattern["name"], lora_pattern["type"] |
| lora_A = lora[name + ".lora_A.weight"] |
| lora_B = lora[name + ".lora_B.weight"] |
| lora_out = self.model_dict[name.replace(".", "___")](lora_A, lora_B) |
| lora_out = self.proj_dict[layer_type.replace(".", "___")](lora_out) |
| lora_emb.append(lora_out) |
| lora_emb = torch.concat(lora_emb, dim=1) |
| return lora_emb |
| |
| |
| class FluxLoRAEncoder(torch.nn.Module): |
| def __init__(self, embed_dim=4096, encoder_intermediate_size=8192, num_encoder_layers=1, num_embeds_per_lora=16, num_special_embeds=1): |
| super().__init__() |
| self.num_embeds_per_lora = num_embeds_per_lora |
| |
| self.embedder = LoRAEmbedder(L=num_embeds_per_lora, out_dim=embed_dim) |
| |
| |
| self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=32, head_dim=128) for _ in range(num_encoder_layers)]) |
|
|
| |
| self.special_embeds = torch.nn.Parameter(torch.randn(1, num_special_embeds, embed_dim)) |
| self.num_special_embeds = num_special_embeds |
| |
| |
| self.final_layer_norm = torch.nn.LayerNorm(embed_dim) |
| self.final_linear = torch.nn.Linear(embed_dim, embed_dim) |
|
|
| def forward(self, lora): |
| lora_embeds = self.embedder(lora) |
| special_embeds = self.special_embeds.to(dtype=lora_embeds.dtype, device=lora_embeds.device) |
| embeds = torch.concat([special_embeds, lora_embeds], dim=1) |
| for encoder_id, encoder in enumerate(self.encoders): |
| embeds = encoder(embeds) |
| embeds = embeds[:, :self.num_special_embeds] |
| embeds = self.final_layer_norm(embeds) |
| embeds = self.final_linear(embeds) |
| return embeds |
| |
| @staticmethod |
| def state_dict_converter(): |
| return FluxLoRAEncoderStateDictConverter() |
|
|
|
|
| class FluxLoRAEncoderStateDictConverter: |
| def from_civitai(self, state_dict): |
| return state_dict |
|
|
