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# Apache License
#
# Copyright 2025 fal - features and labels, inc.
#
# 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
import torch.nn as nn
from diffusers.models import AutoencoderTiny
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.autoencoders.vae import EncoderOutput, DecoderOutput
from diffusers.configuration_utils import ConfigMixin, register_to_config
class Flux2TinyAutoEncoder(ModelMixin, ConfigMixin):
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
latent_channels: int = 128,
encoder_block_out_channels: list[int] = [64, 64, 64, 64],
decoder_block_out_channels: list[int] = [64, 64, 64, 64],
act_fn: str = "silu",
upsampling_scaling_factor: int = 2,
num_encoder_blocks: list[int] = [1, 3, 3, 3],
num_decoder_blocks: list[int] = [3, 3, 3, 1],
latent_magnitude: float = 3.0,
latent_shift: float = 0.5,
force_upcast: bool = False,
scaling_factor: float = 0.13025,
) -> None:
super().__init__()
self.tiny_vae = AutoencoderTiny(
in_channels=in_channels,
out_channels=out_channels,
encoder_block_out_channels=encoder_block_out_channels,
decoder_block_out_channels=decoder_block_out_channels,
act_fn=act_fn,
latent_channels=latent_channels // 4,
upsampling_scaling_factor=upsampling_scaling_factor,
num_encoder_blocks=num_encoder_blocks,
num_decoder_blocks=num_decoder_blocks,
latent_magnitude=latent_magnitude,
latent_shift=latent_shift,
force_upcast=force_upcast,
scaling_factor=scaling_factor,
)
self.extra_encoder = nn.Conv2d(
latent_channels // 4, latent_channels,
kernel_size=4, stride=2, padding=1
)
self.extra_decoder = nn.ConvTranspose2d(
latent_channels, latent_channels // 4,
kernel_size=4, stride=2, padding=1
)
self.residual_encoder = nn.Sequential(
nn.Conv2d(latent_channels, latent_channels, kernel_size=3, padding=1),
nn.GroupNorm(8, latent_channels),
nn.SiLU(),
nn.Conv2d(latent_channels, latent_channels, kernel_size=3, padding=1),
)
self.residual_decoder = nn.Sequential(
nn.Conv2d(latent_channels // 4, latent_channels // 4, kernel_size=3, padding=1),
nn.GroupNorm(8, latent_channels // 4),
nn.SiLU(),
nn.Conv2d(latent_channels // 4, latent_channels // 4, kernel_size=3, padding=1),
)
def encode(self, x: torch.Tensor, return_dict: bool = True) -> EncoderOutput:
encoded = self.tiny_vae.encode(x, return_dict=False)[0]
compressed = self.extra_encoder(encoded)
enhanced = self.residual_encoder(compressed) + compressed
if return_dict:
return EncoderOutput(latent=enhanced)
return enhanced
def decode(self, z: torch.Tensor, return_dict: bool = True) -> DecoderOutput:
decompressed = self.extra_decoder(z)
enhanced = self.residual_decoder(decompressed) + decompressed
decoded = self.tiny_vae.decode(enhanced, return_dict=False)[0]
if return_dict:
return DecoderOutput(sample=decoded)
return decoded
def forward(self, sample: torch.Tensor, return_dict: bool = True) -> DecoderOutput:
encoded = self.encode(sample, return_dict=False)[0]
decoded = self.decode(encoded, return_dict=False)[0]
if return_dict:
return DecoderOutput(sample=decoded)
return decoded
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