66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
# Copyright 2022 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=768):
|
|
super().__init__(config)
|
|
self.proj_size = proj_size
|
|
|
|
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.rand((1, 1, self.proj_size)))
|
|
|
|
def forward(self, pixel_values):
|
|
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)
|
|
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
|