| import torch |
| from .sd_text_encoder import CLIPEncoderLayer |
|
|
|
|
| class CLIPVisionEmbeddings(torch.nn.Module): |
| def __init__(self, embed_dim=1280, image_size=224, patch_size=14, num_channels=3): |
| super().__init__() |
|
|
| |
| self.class_embedding = torch.nn.Parameter(torch.randn(1, 1, embed_dim)) |
|
|
| |
| self.patch_embedding = torch.nn.Conv2d(in_channels=num_channels, out_channels=embed_dim, kernel_size=patch_size, stride=patch_size, bias=False) |
|
|
| |
| self.position_embeds = torch.nn.Parameter(torch.zeros(1, (image_size // patch_size) ** 2 + 1, embed_dim)) |
|
|
| def forward(self, pixel_values): |
| batch_size = pixel_values.shape[0] |
| patch_embeds = self.patch_embedding(pixel_values) |
| patch_embeds = patch_embeds.flatten(2).transpose(1, 2) |
| class_embeds = self.class_embedding.repeat(batch_size, 1, 1) |
| embeddings = torch.cat([class_embeds, patch_embeds], dim=1) + self.position_embeds |
| return embeddings |
|
|
|
|
| class SVDImageEncoder(torch.nn.Module): |
| def __init__(self, embed_dim=1280, layer_norm_eps=1e-5, num_encoder_layers=32, encoder_intermediate_size=5120, projection_dim=1024): |
| super().__init__() |
| self.embeddings = CLIPVisionEmbeddings(embed_dim=embed_dim) |
| self.pre_layernorm = torch.nn.LayerNorm(embed_dim, eps=layer_norm_eps) |
| self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=16, head_dim=80, use_quick_gelu=False) for _ in range(num_encoder_layers)]) |
| self.post_layernorm = torch.nn.LayerNorm(embed_dim, eps=layer_norm_eps) |
| self.visual_projection = torch.nn.Linear(embed_dim, projection_dim, bias=False) |
|
|
| def forward(self, pixel_values): |
| embeds = self.embeddings(pixel_values) |
| embeds = self.pre_layernorm(embeds) |
| for encoder_id, encoder in enumerate(self.encoders): |
| embeds = encoder(embeds) |
| embeds = self.post_layernorm(embeds[:, 0, :]) |
| embeds = self.visual_projection(embeds) |
| return embeds |
|
|
| def state_dict_converter(self): |
| return SVDImageEncoderStateDictConverter() |
|
|
|
|
| class SVDImageEncoderStateDictConverter: |
| def __init__(self): |
| pass |
|
|
| def from_diffusers(self, state_dict): |
| rename_dict = { |
| "vision_model.embeddings.patch_embedding.weight": "embeddings.patch_embedding.weight", |
| "vision_model.embeddings.class_embedding": "embeddings.class_embedding", |
| "vision_model.embeddings.position_embedding.weight": "embeddings.position_embeds", |
| "vision_model.pre_layrnorm.weight": "pre_layernorm.weight", |
| "vision_model.pre_layrnorm.bias": "pre_layernorm.bias", |
| "vision_model.post_layernorm.weight": "post_layernorm.weight", |
| "vision_model.post_layernorm.bias": "post_layernorm.bias", |
| "visual_projection.weight": "visual_projection.weight" |
| } |
| 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 == "vision_model.embeddings.class_embedding": |
| param = state_dict[name].view(1, 1, -1) |
| elif name == "vision_model.embeddings.position_embedding.weight": |
| param = state_dict[name].view(1, 257, 1280) |
| state_dict_[rename_dict[name]] = param |
| elif name.startswith("vision_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_ |
|
|
