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---
license: cc-by-nc-4.0
library_name: diffusers
---
It's simple upscaler using AsymmetricAutoencoderKL. I was playing around with code used for training in the middle of it a lot so it's nothing scientific. I was just pleased with results from something that easy to train.
For optimizers, training was done with AdEMAMix optimizer, dataset of ~4k images mostly including photos, digital art and small amount of PBR textures. I did some finetuning with same dataset, but Adopt optimizer with OrthoGrad from <a href="https://arxiv.org/abs/2501.04697" target="_blank"><i>Grokking at the Edge of Numerical Stability</i></a> (arXiv: 2501.04697). Model was trained at 96px x 96px resolution (so 192px x 192ox output).
For loss, I was using most of the time simple HSL loss (1 - cosine of difference between target and pred H and L1 loss for S and L channels), LPIPS+ and DISTS.
Model have issues with handling jpeg artifacts because I couldn't train it on random compression levels due to lack of support of ROCm by torchvision.transforms.v2.JPEG. In this case it's better to scale down image a bit before upscaling.
This is some proof of concept model. It can't be used commercially as is, but there is a chance that I'll train new version on some CC0 dataset with license permiting commercial usage and with better jpeg artifacts handling in future.
You can run model using code below
```
import torch
from torchvision import transforms, utils
import diffusers
from diffusers import AsymmetricAutoencoderKL
from diffusers.utils import load_image
def crop_image_to_nearest_divisible_by_8(img):
# Check if the image height and width are divisible by 8
if img.shape[1] % 8 == 0 and img.shape[2] % 8 == 0:
return img
else:
# Calculate the closest lower resolution divisible by 8
new_height = img.shape[1] - (img.shape[1] % 8)
new_width = img.shape[2] - (img.shape[2] % 8)
# Use CenterCrop to crop the image
transform = transforms.CenterCrop((new_height, new_width), interpolation=transforms.InterpolationMode.BILINEAR)
img = transform(img).to(torch.float32).clamp(-1, 1)
return img
to_tensor = transforms.ToTensor()
vae = AsymmetricAutoencoderKL.from_pretrained("Heasterian/AsymmetricAutoencoderKLUpscaler", weight_dtype=torch.float32)
vae.requires_grad_(False)
image = load_image(r"/home/heasterian/test/a/F8VlGmCWEAAUVpc (copy).jpeg")
image = crop_image_to_nearest_divisible_by_8(to_tensor(image)).unsqueeze(0)
upscaled_image = vae(image).sample
# Save the reconstructed image
utils.save_image(upscaled_image, "test.png")
```
In case you want to run it on GPU and VRAM usage is too high, below you can find modified AsymmetricAutoencoderKL class with tiling support (and maybe slicing - it does not reduce VRAM usage for me, but it can be issue with ROCm on my platform). It's copy paste from AutoencoderKL with separated tile size for encode and decode.
```
class AsymmetricAutoencoderKL(ModelMixin, ConfigMixin):
r"""
Designing a Better Asymmetric VQGAN for StableDiffusion https://arxiv.org/abs/2306.04632 . A VAE model with KL loss
for encoding images into latents and decoding latent representations into images.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
out_channels (int, *optional*, defaults to 3): Number of channels in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
Tuple of downsample block types.
down_block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
Tuple of down block output channels.
layers_per_down_block (`int`, *optional*, defaults to `1`):
Number layers for down block.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
Tuple of upsample block types.
up_block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
Tuple of up block output channels.
layers_per_up_block (`int`, *optional*, defaults to `1`):
Number layers for up block.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
sample_size (`int`, *optional*, defaults to `32`): Sample input size.
norm_num_groups (`int`, *optional*, defaults to `32`):
Number of groups to use for the first normalization layer in ResNet blocks.
scaling_factor (`float`, *optional*, defaults to 0.18215):
The component-wise standard deviation of the trained latent space computed using the first batch of the
training set. This is used to scale the latent space to have unit variance when training the diffusion
model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
/ scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
"""
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: Tuple[str, ...] = ("DownEncoderBlock2D",),
down_block_out_channels: Tuple[int, ...] = (64,),
layers_per_down_block: int = 1,
up_block_types: Tuple[str, ...] = ("UpDecoderBlock2D",),
up_block_out_channels: Tuple[int, ...] = (64,),
layers_per_up_block: int = 1,
act_fn: str = "silu",
latent_channels: int = 4,
norm_num_groups: int = 32,
sample_size: int = 32,
scaling_factor: float = 0.18215,
use_quant_conv: bool = True,
use_post_quant_conv: bool = True,
) -> None:
super().__init__()
# pass init params to Encoder
self.encoder = Encoder(
in_channels=in_channels,
out_channels=latent_channels,
down_block_types=down_block_types,
block_out_channels=down_block_out_channels,
layers_per_block=layers_per_down_block,
act_fn=act_fn,
norm_num_groups=norm_num_groups,
double_z=True,
)
# pass init params to Decoder
self.decoder = MaskConditionDecoder(
in_channels=latent_channels,
out_channels=out_channels,
up_block_types=up_block_types,
block_out_channels=up_block_out_channels,
layers_per_block=layers_per_up_block,
act_fn=act_fn,
norm_num_groups=norm_num_groups,
)
self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1) if use_quant_conv else None
self.post_quant_conv = nn.Conv2d(latent_channels, latent_channels, 1) if use_post_quant_conv else None
self.use_slicing = False
self.use_tiling = False
# only relevant if vae tiling is enabled
self.tile_sample_min_size = self.config.sample_size
sample_size = (
self.config.sample_size[0]
if isinstance(self.config.sample_size, (list, tuple))
else self.config.sample_size
)
self.tile_latent_min_up_size = int(sample_size / (2 ** (len(self.config.up_block_out_channels) - 1)))
self.tile_latent_min_down_size = int(sample_size / (2 ** (len(self.config.down_block_out_channels) - 1)))
self.tile_overlap_factor = 0.25
self.register_to_config(block_out_channels=up_block_out_channels)
self.register_to_config(force_upcast=False)
def enable_tiling(self, use_tiling: bool = True):
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.
"""
self.use_tiling = use_tiling
def disable_tiling(self):
r"""
Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
decoding in one step.
"""
self.enable_tiling(False)
def enable_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.use_slicing = True
def disable_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
decoding in one step.
"""
self.use_slicing = False
def _encode(self, x: torch.Tensor) -> torch.Tensor:
batch_size, num_channels, height, width = x.shape
if self.use_tiling and (width > self.tile_sample_min_size or height > self.tile_sample_min_size):
return self._tiled_encode(x)
enc = self.encoder(x)
if self.quant_conv is not None:
enc = self.quant_conv(enc)
return enc
@apply_forward_hook
def encode(
self, x: torch.Tensor, return_dict: bool = True
) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
"""
Encode a batch of images into latents.
Args:
x (`torch.Tensor`): Input batch of images.
return_dict (`bool`, *optional*, defaults to `True`):
Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
Returns:
The latent representations of the encoded images. If `return_dict` is True, a
[`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
"""
if self.use_slicing and x.shape[0] > 1:
encoded_slices = [self._encode(x_slice) for x_slice in x.split(1)]
h = torch.cat(encoded_slices)
else:
h = self._encode(x)
posterior = DiagonalGaussianDistribution(h)
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=posterior)
def _decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
if self.use_tiling and (z.shape[-1] > self.tile_latent_min_up_size or z.shape[-2] > self.tile_latent_min_up_size):
return self.tiled_decode(z, return_dict=return_dict)
if self.post_quant_conv is not None:
z = self.post_quant_conv(z)
dec = self.decoder(z)
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
@apply_forward_hook
def decode(
self, z: torch.FloatTensor, return_dict: bool = True, generator=None
) -> Union[DecoderOutput, torch.FloatTensor]:
"""
Decode a batch of images.
Args:
z (`torch.Tensor`): Input batch of latent vectors.
return_dict (`bool`, *optional*, defaults to `True`):
Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
Returns:
[`~models.vae.DecoderOutput`] or `tuple`:
If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
returned.
"""
if self.use_slicing and z.shape[0] > 1:
decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)]
decoded = torch.cat(decoded_slices)
else:
decoded = self._decode(z).sample
if not return_dict:
return (decoded,)
return DecoderOutput(sample=decoded)
def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
blend_extent = min(a.shape[2], b.shape[2], blend_extent)
for y in range(blend_extent):
b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
return b
def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
blend_extent = min(a.shape[3], b.shape[3], blend_extent)
for x in range(blend_extent):
b[:, :, :, x] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
return b
def _tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
r"""Encode a batch of images using a tiled encoder.
When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several
steps. This is useful to keep memory use constant regardless of image size. The end result of tiled encoding is
different from non-tiled encoding because each tile uses a different encoder. To avoid tiling artifacts, the
tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the
output, but they should be much less noticeable.
Args:
x (`torch.Tensor`): Input batch of images.
Returns:
`torch.Tensor`:
The latent representation of the encoded videos.
"""
overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
blend_extent = int(self.tile_latent_min_down_size * self.tile_overlap_factor)
row_limit = self.tile_latent_min_down_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
rows = []
for i in range(0, x.shape[2], overlap_size):
row = []
for j in range(0, x.shape[3], overlap_size):
tile = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
tile = self.encoder(tile)
if self.config.use_quant_conv:
tile = self.quant_conv(tile)
row.append(tile)
rows.append(row)
result_rows = []
for i, row in enumerate(rows):
result_row = []
for j, tile in enumerate(row):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
if j > 0:
tile = self.blend_h(row[j - 1], tile, blend_extent)
result_row.append(tile[:, :, :row_limit, :row_limit])
result_rows.append(torch.cat(result_row, dim=3))
enc = torch.cat(result_rows, dim=2)
return enc
def tiled_encode(self, x: torch.Tensor, return_dict: bool = True) -> AutoencoderKLOutput:
r"""Encode a batch of images using a tiled encoder.
When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several
steps. This is useful to keep memory use constant regardless of image size. The end result of tiled encoding is
different from non-tiled encoding because each tile uses a different encoder. To avoid tiling artifacts, the
tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the
output, but they should be much less noticeable.
Args:
x (`torch.Tensor`): Input batch of images.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
Returns:
[`~models.autoencoder_kl.AutoencoderKLOutput`] or `tuple`:
If return_dict is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain
`tuple` is returned.
"""
deprecation_message = (
"The tiled_encode implementation supporting the `return_dict` parameter is deprecated. In the future, the "
"implementation of this method will be replaced with that of `_tiled_encode` and you will no longer be able "
"to pass `return_dict`. You will also have to create a `DiagonalGaussianDistribution()` from the returned value."
)
deprecate("tiled_encode", "1.0.0", deprecation_message, standard_warn=False)
overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
blend_extent = int(self.tile_latent_min_up_size * self.tile_overlap_factor)
row_limit = self.tile_latent_min_up_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
rows = []
for i in range(0, x.shape[2], overlap_size):
row = []
for j in range(0, x.shape[3], overlap_size):
tile = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
tile = self.encoder(tile)
if self.config.use_quant_conv:
tile = self.quant_conv(tile)
row.append(tile)
rows.append(row)
result_rows = []
for i, row in enumerate(rows):
result_row = []
for j, tile in enumerate(row):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
if j > 0:
tile = self.blend_h(row[j - 1], tile, blend_extent)
result_row.append(tile[:, :, :row_limit, :row_limit])
result_rows.append(torch.cat(result_row, dim=3))
moments = torch.cat(result_rows, dim=2)
posterior = DiagonalGaussianDistribution(moments)
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=posterior)
def tiled_decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
r"""
Decode a batch of images using a tiled decoder.
Args:
z (`torch.Tensor`): Input batch of latent vectors.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
Returns:
[`~models.vae.DecoderOutput`] or `tuple`:
If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
returned.
"""
overlap_size = int(self.tile_latent_min_up_size * (1 - self.tile_overlap_factor))
blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor)
row_limit = self.tile_sample_min_size - blend_extent
# Split z into overlapping 64x64 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
rows = []
for i in range(0, z.shape[2], overlap_size):
row = []
for j in range(0, z.shape[3], overlap_size):
tile = z[:, :, i : i + self.tile_latent_min_up_size, j : j + self.tile_latent_min_up_size]
if self.config.use_post_quant_conv:
tile = self.post_quant_conv(tile)
decoded = self.decoder(tile)
row.append(decoded)
rows.append(row)
result_rows = []
for i, row in enumerate(rows):
result_row = []
for j, tile in enumerate(row):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
if j > 0:
tile = self.blend_h(row[j - 1], tile, blend_extent)
result_row.append(tile[:, :, :row_limit, :row_limit])
result_rows.append(torch.cat(result_row, dim=3))
dec = torch.cat(result_rows, dim=2)
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
def forward(
self,
sample: torch.Tensor,
sample_posterior: bool = False,
return_dict: bool = True,
generator: Optional[torch.Generator] = None,
) -> Union[DecoderOutput, torch.Tensor]:
r"""
Args:
sample (`torch.Tensor`): Input sample.
sample_posterior (`bool`, *optional*, defaults to `False`):
Whether to sample from the posterior.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
"""
x = sample
posterior = self.encode(x).latent_dist
if sample_posterior:
z = posterior.sample(generator=generator)
else:
z = posterior.mode()
dec = self.decode(z).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
def fuse_qkv_projections(self):
"""
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
are fused. For cross-attention modules, key and value projection matrices are fused.
<Tip warning={true}>
This API is 🧪 experimental.
</Tip>
"""
self.original_attn_processors = None
for _, attn_processor in self.attn_processors.items():
if "Added" in str(attn_processor.__class__.__name__):
raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
self.original_attn_processors = self.attn_processors
for module in self.modules():
if isinstance(module, Attention):
module.fuse_projections(fuse=True)
self.set_attn_processor(FusedAttnProcessor2_0())
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
def unfuse_qkv_projections(self):
"""Disables the fused QKV projection if enabled.
<Tip warning={true}>
This API is 🧪 experimental.
</Tip>
"""
if self.original_attn_processors is not None:
self.set_attn_processor(self.original_attn_processors)
``` |