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