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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. | |
from dataclasses import dataclass | |
from typing import Dict, Optional, Tuple, Union | |
import torch | |
import torch.nn.functional as F | |
from torch import nn | |
from ...configuration_utils import ConfigMixin, register_to_config | |
from ...schedulers import ConsistencyDecoderScheduler | |
from ...utils import BaseOutput | |
from ...utils.accelerate_utils import apply_forward_hook | |
from ...utils.torch_utils import randn_tensor | |
from ..attention_processor import ( | |
ADDED_KV_ATTENTION_PROCESSORS, | |
CROSS_ATTENTION_PROCESSORS, | |
AttentionProcessor, | |
AttnAddedKVProcessor, | |
AttnProcessor, | |
) | |
from ..modeling_utils import ModelMixin | |
from ..unets.unet_2d import UNet2DModel | |
from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder | |
class ConsistencyDecoderVAEOutput(BaseOutput): | |
""" | |
Output of encoding method. | |
Args: | |
latent_dist (`DiagonalGaussianDistribution`): | |
Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`. | |
`DiagonalGaussianDistribution` allows for sampling latents from the distribution. | |
""" | |
latent_dist: "DiagonalGaussianDistribution" | |
class ConsistencyDecoderVAE(ModelMixin, ConfigMixin): | |
r""" | |
The consistency decoder used with DALL-E 3. | |
Examples: | |
```py | |
>>> import torch | |
>>> from diffusers import StableDiffusionPipeline, ConsistencyDecoderVAE | |
>>> vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder", torch_dtype=torch.float16) | |
>>> pipe = StableDiffusionPipeline.from_pretrained( | |
... "runwayml/stable-diffusion-v1-5", vae=vae, torch_dtype=torch.float16 | |
... ).to("cuda") | |
>>> pipe("horse", generator=torch.manual_seed(0)).images | |
``` | |
""" | |
def __init__( | |
self, | |
scaling_factor: float = 0.18215, | |
latent_channels: int = 4, | |
encoder_act_fn: str = "silu", | |
encoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 512), | |
encoder_double_z: bool = True, | |
encoder_down_block_types: Tuple[str, ...] = ( | |
"DownEncoderBlock2D", | |
"DownEncoderBlock2D", | |
"DownEncoderBlock2D", | |
"DownEncoderBlock2D", | |
), | |
encoder_in_channels: int = 3, | |
encoder_layers_per_block: int = 2, | |
encoder_norm_num_groups: int = 32, | |
encoder_out_channels: int = 4, | |
decoder_add_attention: bool = False, | |
decoder_block_out_channels: Tuple[int, ...] = (320, 640, 1024, 1024), | |
decoder_down_block_types: Tuple[str, ...] = ( | |
"ResnetDownsampleBlock2D", | |
"ResnetDownsampleBlock2D", | |
"ResnetDownsampleBlock2D", | |
"ResnetDownsampleBlock2D", | |
), | |
decoder_downsample_padding: int = 1, | |
decoder_in_channels: int = 7, | |
decoder_layers_per_block: int = 3, | |
decoder_norm_eps: float = 1e-05, | |
decoder_norm_num_groups: int = 32, | |
decoder_num_train_timesteps: int = 1024, | |
decoder_out_channels: int = 6, | |
decoder_resnet_time_scale_shift: str = "scale_shift", | |
decoder_time_embedding_type: str = "learned", | |
decoder_up_block_types: Tuple[str, ...] = ( | |
"ResnetUpsampleBlock2D", | |
"ResnetUpsampleBlock2D", | |
"ResnetUpsampleBlock2D", | |
"ResnetUpsampleBlock2D", | |
), | |
): | |
super().__init__() | |
self.encoder = Encoder( | |
act_fn=encoder_act_fn, | |
block_out_channels=encoder_block_out_channels, | |
double_z=encoder_double_z, | |
down_block_types=encoder_down_block_types, | |
in_channels=encoder_in_channels, | |
layers_per_block=encoder_layers_per_block, | |
norm_num_groups=encoder_norm_num_groups, | |
out_channels=encoder_out_channels, | |
) | |
self.decoder_unet = UNet2DModel( | |
add_attention=decoder_add_attention, | |
block_out_channels=decoder_block_out_channels, | |
down_block_types=decoder_down_block_types, | |
downsample_padding=decoder_downsample_padding, | |
in_channels=decoder_in_channels, | |
layers_per_block=decoder_layers_per_block, | |
norm_eps=decoder_norm_eps, | |
norm_num_groups=decoder_norm_num_groups, | |
num_train_timesteps=decoder_num_train_timesteps, | |
out_channels=decoder_out_channels, | |
resnet_time_scale_shift=decoder_resnet_time_scale_shift, | |
time_embedding_type=decoder_time_embedding_type, | |
up_block_types=decoder_up_block_types, | |
) | |
self.decoder_scheduler = ConsistencyDecoderScheduler() | |
self.register_to_config(block_out_channels=encoder_block_out_channels) | |
self.register_to_config(force_upcast=False) | |
self.register_buffer( | |
"means", | |
torch.tensor([0.38862467, 0.02253063, 0.07381133, -0.0171294])[None, :, None, None], | |
persistent=False, | |
) | |
self.register_buffer( | |
"stds", torch.tensor([0.9654121, 1.0440036, 0.76147926, 0.77022034])[None, :, None, None], persistent=False | |
) | |
self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1) | |
self.use_slicing = False | |
self.use_tiling = False | |
# Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.enable_tiling | |
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 | |
# Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.disable_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) | |
# Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.enable_slicing | |
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 | |
# Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.disable_slicing | |
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 | |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors | |
def attn_processors(self) -> Dict[str, AttentionProcessor]: | |
r""" | |
Returns: | |
`dict` of attention processors: A dictionary containing all attention processors used in the model with | |
indexed by its weight name. | |
""" | |
# set recursively | |
processors = {} | |
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): | |
if hasattr(module, "get_processor"): | |
processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) | |
for sub_name, child in module.named_children(): | |
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) | |
return processors | |
for name, module in self.named_children(): | |
fn_recursive_add_processors(name, module, processors) | |
return processors | |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor | |
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): | |
r""" | |
Sets the attention processor to use to compute attention. | |
Parameters: | |
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): | |
The instantiated processor class or a dictionary of processor classes that will be set as the processor | |
for **all** `Attention` layers. | |
If `processor` is a dict, the key needs to define the path to the corresponding cross attention | |
processor. This is strongly recommended when setting trainable attention processors. | |
""" | |
count = len(self.attn_processors.keys()) | |
if isinstance(processor, dict) and len(processor) != count: | |
raise ValueError( | |
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" | |
f" number of attention layers: {count}. Please make sure to pass {count} processor classes." | |
) | |
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): | |
if hasattr(module, "set_processor"): | |
if not isinstance(processor, dict): | |
module.set_processor(processor) | |
else: | |
module.set_processor(processor.pop(f"{name}.processor")) | |
for sub_name, child in module.named_children(): | |
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) | |
for name, module in self.named_children(): | |
fn_recursive_attn_processor(name, module, processor) | |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor | |
def set_default_attn_processor(self): | |
""" | |
Disables custom attention processors and sets the default attention implementation. | |
""" | |
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): | |
processor = AttnAddedKVProcessor() | |
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): | |
processor = AttnProcessor() | |
else: | |
raise ValueError( | |
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" | |
) | |
self.set_attn_processor(processor) | |
def encode( | |
self, x: torch.FloatTensor, return_dict: bool = True | |
) -> Union[ConsistencyDecoderVAEOutput, Tuple[DiagonalGaussianDistribution]]: | |
""" | |
Encode a batch of images into latents. | |
Args: | |
x (`torch.FloatTensor`): Input batch of images. | |
return_dict (`bool`, *optional*, defaults to `True`): | |
Whether to return a [`~models.consistecy_decoder_vae.ConsistencyDecoderOoutput`] instead of a plain | |
tuple. | |
Returns: | |
The latent representations of the encoded images. If `return_dict` is True, a | |
[`~models.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] is returned, otherwise a plain `tuple` | |
is returned. | |
""" | |
if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size): | |
return self.tiled_encode(x, return_dict=return_dict) | |
if self.use_slicing and x.shape[0] > 1: | |
encoded_slices = [self.encoder(x_slice) for x_slice in x.split(1)] | |
h = torch.cat(encoded_slices) | |
else: | |
h = self.encoder(x) | |
moments = self.quant_conv(h) | |
posterior = DiagonalGaussianDistribution(moments) | |
if not return_dict: | |
return (posterior,) | |
return ConsistencyDecoderVAEOutput(latent_dist=posterior) | |
def decode( | |
self, | |
z: torch.FloatTensor, | |
generator: Optional[torch.Generator] = None, | |
return_dict: bool = True, | |
num_inference_steps: int = 2, | |
) -> Union[DecoderOutput, Tuple[torch.FloatTensor]]: | |
z = (z * self.config.scaling_factor - self.means) / self.stds | |
scale_factor = 2 ** (len(self.config.block_out_channels) - 1) | |
z = F.interpolate(z, mode="nearest", scale_factor=scale_factor) | |
batch_size, _, height, width = z.shape | |
self.decoder_scheduler.set_timesteps(num_inference_steps, device=self.device) | |
x_t = self.decoder_scheduler.init_noise_sigma * randn_tensor( | |
(batch_size, 3, height, width), generator=generator, dtype=z.dtype, device=z.device | |
) | |
for t in self.decoder_scheduler.timesteps: | |
model_input = torch.concat([self.decoder_scheduler.scale_model_input(x_t, t), z], dim=1) | |
model_output = self.decoder_unet(model_input, t).sample[:, :3, :, :] | |
prev_sample = self.decoder_scheduler.step(model_output, t, x_t, generator).prev_sample | |
x_t = prev_sample | |
x_0 = x_t | |
if not return_dict: | |
return (x_0,) | |
return DecoderOutput(sample=x_0) | |
# Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.blend_v | |
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 | |
# Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.blend_h | |
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.FloatTensor, return_dict: bool = True) -> ConsistencyDecoderVAEOutput: | |
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.FloatTensor`): Input batch of images. | |
return_dict (`bool`, *optional*, defaults to `True`): | |
Whether or not to return a [`~models.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] instead of a | |
plain tuple. | |
Returns: | |
[`~models.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] or `tuple`: | |
If return_dict is True, a [`~models.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] is returned, | |
otherwise a plain `tuple` is returned. | |
""" | |
overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)) | |
blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor) | |
row_limit = self.tile_latent_min_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) | |
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 ConsistencyDecoderVAEOutput(latent_dist=posterior) | |
def forward( | |
self, | |
sample: torch.FloatTensor, | |
sample_posterior: bool = False, | |
return_dict: bool = True, | |
generator: Optional[torch.Generator] = None, | |
) -> Union[DecoderOutput, Tuple[torch.FloatTensor]]: | |
r""" | |
Args: | |
sample (`torch.FloatTensor`): 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. | |
generator (`torch.Generator`, *optional*, defaults to `None`): | |
Generator to use for sampling. | |
Returns: | |
[`DecoderOutput`] or `tuple`: | |
If return_dict is True, a [`DecoderOutput`] is returned, otherwise a plain `tuple` is returned. | |
""" | |
x = sample | |
posterior = self.encode(x).latent_dist | |
if sample_posterior: | |
z = posterior.sample(generator=generator) | |
else: | |
z = posterior.mode() | |
dec = self.decode(z, generator=generator).sample | |
if not return_dict: | |
return (dec,) | |
return DecoderOutput(sample=dec) | |