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| # Copyright 2023 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 torch | |
| from torch import nn | |
| from transformers import CLIPPreTrainedModel, CLIPVisionModel | |
| from ...models.attention import BasicTransformerBlock | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
| class PaintByExampleImageEncoder(CLIPPreTrainedModel): | |
| def __init__(self, config, proj_size=None): | |
| super().__init__(config) | |
| self.proj_size = proj_size or getattr(config, "projection_dim", 768) | |
| self.model = CLIPVisionModel(config) | |
| self.mapper = PaintByExampleMapper(config) | |
| self.final_layer_norm = nn.LayerNorm(config.hidden_size) | |
| self.proj_out = nn.Linear(config.hidden_size, self.proj_size) | |
| # uncondition for scaling | |
| self.uncond_vector = nn.Parameter(torch.randn((1, 1, self.proj_size))) | |
| def forward(self, pixel_values, return_uncond_vector=False): | |
| clip_output = self.model(pixel_values=pixel_values) | |
| latent_states = clip_output.pooler_output | |
| latent_states = self.mapper(latent_states[:, None]) | |
| latent_states = self.final_layer_norm(latent_states) | |
| latent_states = self.proj_out(latent_states) | |
| if return_uncond_vector: | |
| return latent_states, self.uncond_vector | |
| return latent_states | |
| class PaintByExampleMapper(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| num_layers = (config.num_hidden_layers + 1) // 5 | |
| hid_size = config.hidden_size | |
| num_heads = 1 | |
| self.blocks = nn.ModuleList( | |
| [ | |
| BasicTransformerBlock(hid_size, num_heads, hid_size, activation_fn="gelu", attention_bias=True) | |
| for _ in range(num_layers) | |
| ] | |
| ) | |
| def forward(self, hidden_states): | |
| for block in self.blocks: | |
| hidden_states = block(hidden_states) | |
| return hidden_states | |