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| # Copyright 2022 Kakao Brain and The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import paddle | |
| from paddle import nn | |
| from ...configuration_utils import ConfigMixin, register_to_config | |
| from ...modeling_utils import ModelMixin | |
| class UnCLIPTextProjModel(ModelMixin, ConfigMixin): | |
| """ | |
| Utility class for CLIP embeddings. Used to combine the image and text embeddings into a format usable by the | |
| decoder. | |
| For more details, see the original paper: https://arxiv.org/abs/2204.06125 section 2.1 | |
| """ | |
| def __init__( | |
| self, | |
| *, | |
| clip_extra_context_tokens: int = 4, | |
| clip_embeddings_dim: int = 768, | |
| time_embed_dim: int, | |
| cross_attention_dim, | |
| ): | |
| super().__init__() | |
| self.learned_classifier_free_guidance_embeddings = self.create_parameter( | |
| (clip_embeddings_dim,), dtype=paddle.get_default_dtype(), default_initializer=nn.initializer.Constant(0.0) | |
| ) | |
| # parameters for additional clip time embeddings | |
| self.embedding_proj = nn.Linear(clip_embeddings_dim, time_embed_dim) | |
| self.clip_image_embeddings_project_to_time_embeddings = nn.Linear(clip_embeddings_dim, time_embed_dim) | |
| # parameters for encoder hidden states | |
| self.clip_extra_context_tokens = clip_extra_context_tokens | |
| self.clip_extra_context_tokens_proj = nn.Linear( | |
| clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim | |
| ) | |
| self.encoder_hidden_states_proj = nn.Linear(clip_embeddings_dim, cross_attention_dim) | |
| self.text_encoder_hidden_states_norm = nn.LayerNorm(cross_attention_dim) | |
| def forward(self, *, image_embeddings, text_embeddings, text_encoder_hidden_states, do_classifier_free_guidance): | |
| if do_classifier_free_guidance: | |
| # Add the classifier free guidance embeddings to the image embeddings | |
| image_embeddings_batch_size = image_embeddings.shape[0] | |
| classifier_free_guidance_embeddings = self.learned_classifier_free_guidance_embeddings.unsqueeze(0) | |
| classifier_free_guidance_embeddings = classifier_free_guidance_embeddings.expand( | |
| [image_embeddings_batch_size, -1] | |
| ) | |
| image_embeddings = paddle.concat([classifier_free_guidance_embeddings, image_embeddings], axis=0) | |
| # The image embeddings batch size and the text embeddings batch size are equal | |
| assert image_embeddings.shape[0] == text_embeddings.shape[0] | |
| batch_size = text_embeddings.shape[0] | |
| # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and | |
| # adding CLIP embeddings to the existing timestep embedding, ... | |
| time_projected_text_embeddings = self.embedding_proj(text_embeddings) | |
| time_projected_image_embeddings = self.clip_image_embeddings_project_to_time_embeddings(image_embeddings) | |
| additive_clip_time_embeddings = time_projected_image_embeddings + time_projected_text_embeddings | |
| # ... and by projecting CLIP embeddings into four | |
| # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder" | |
| clip_extra_context_tokens = self.clip_extra_context_tokens_proj(image_embeddings) | |
| clip_extra_context_tokens = clip_extra_context_tokens.reshape([batch_size, -1, self.clip_extra_context_tokens]) | |
| text_encoder_hidden_states = self.encoder_hidden_states_proj(text_encoder_hidden_states) | |
| text_encoder_hidden_states = self.text_encoder_hidden_states_norm(text_encoder_hidden_states) | |
| text_encoder_hidden_states = text_encoder_hidden_states.transpose([0, 2, 1]) | |
| text_encoder_hidden_states = paddle.concat([clip_extra_context_tokens, text_encoder_hidden_states], axis=2) | |
| return text_encoder_hidden_states, additive_clip_time_embeddings | |