from shared.utils.phase_progress import vae_decoding_progress, set_phase_status
import os
import math
from typing import Dict, Optional, Tuple, Union
from dataclasses import dataclass
from torch import distributed as dist
import loguru
import torch
import torch.nn as nn
import torch.distributed
RECOMMENDED_DTYPE = torch.float16
def mpi_comm():
from mpi4py import MPI
return MPI.COMM_WORLD
from torch import distributed as dist
def mpi_rank():
return dist.get_rank()
def mpi_world_size():
return dist.get_world_size()
class TorchIGather:
def __init__(self):
if not torch.distributed.is_initialized():
rank = mpi_rank()
world_size = mpi_world_size()
os.environ['RANK'] = str(rank)
os.environ['WORLD_SIZE'] = str(world_size)
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = str(29500)
torch.cuda.set_device(rank)
torch.distributed.init_process_group('nccl')
self.handles = []
self.buffers = []
self.world_size = dist.get_world_size()
self.rank = dist.get_rank()
self.groups_ids = []
self.group = {}
for i in range(self.world_size):
self.groups_ids.append(tuple(range(i + 1)))
for group in self.groups_ids:
new_group = dist.new_group(group)
self.group[group[-1]] = new_group
def gather(self, tensor, n_rank=None):
if n_rank is not None:
group = self.group[n_rank - 1]
else:
group = None
rank = self.rank
tensor = tensor.to(RECOMMENDED_DTYPE)
if rank == 0:
buffer = [torch.empty_like(tensor) for i in range(n_rank)]
else:
buffer = None
self.buffers.append(buffer)
handle = torch.distributed.gather(tensor, buffer, async_op=True, group=group)
self.handles.append(handle)
def wait(self):
for handle in self.handles:
handle.wait()
def clear(self):
self.buffers = []
self.handles = []
from diffusers.configuration_utils import ConfigMixin, register_to_config
try:
# This diffusers is modified and packed in the mirror.
from diffusers.loaders import FromOriginalVAEMixin
except ImportError:
# Use this to be compatible with the original diffusers.
from diffusers.loaders.single_file_model import FromOriginalModelMixin as FromOriginalVAEMixin
from diffusers.utils.accelerate_utils import apply_forward_hook
from diffusers.models.attention_processor import (
ADDED_KV_ATTENTION_PROCESSORS,
CROSS_ATTENTION_PROCESSORS,
Attention,
AttentionProcessor,
AttnAddedKVProcessor,
AttnProcessor,
)
from diffusers.models.modeling_outputs import AutoencoderKLOutput
from diffusers.models.modeling_utils import ModelMixin
from .vae import DecoderCausal3D, BaseOutput, DecoderOutput, DiagonalGaussianDistribution, EncoderCausal3D
# """
# use trt need install polygraphy and onnx-graphsurgeon
# python3 -m pip install --upgrade polygraphy>=0.47.0 onnx-graphsurgeon --extra-index-url https://pypi.ngc.nvidia.com
# """
# try:
# from polygraphy.backend.trt import ( TrtRunner, EngineFromBytes)
# from polygraphy.backend.common import BytesFromPath
# except:
# print("TrtRunner or EngineFromBytes is not available, you can not use trt engine")
@dataclass
class DecoderOutput2(BaseOutput):
sample: torch.FloatTensor
posterior: Optional[DiagonalGaussianDistribution] = None
MODEL_OUTPUT_PATH = os.environ.get('MODEL_OUTPUT_PATH')
MODEL_BASE = os.environ.get('MODEL_BASE')
class AutoencoderKLCausal3D(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
r"""
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.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
Tuple of upsample block types.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
Tuple of block output channels.
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.
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.
force_upcast (`bool`, *optional*, default to `True`):
If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
can be fine-tuned / trained to a lower range without loosing too much precision in which case
`force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
"""
def get_VAE_tile_size(self, vae_config, device_mem_capacity, mixed_precision):
if mixed_precision:
device_mem_capacity /= 1.5
if vae_config == 0:
if device_mem_capacity >= 24000:
use_vae_config = 1
elif device_mem_capacity >= 12000:
use_vae_config = 2
else:
use_vae_config = 3
else:
use_vae_config = vae_config
if use_vae_config == 1:
sample_tsize = 32
sample_size = 256
elif use_vae_config == 2:
sample_tsize = 16
sample_size = 256
else:
sample_tsize = 16
sample_size = 192
VAE_tiling = {
"tile_sample_min_tsize" : sample_tsize,
"tile_latent_min_tsize" : sample_tsize // self.time_compression_ratio,
"tile_sample_min_size" : sample_size,
"tile_latent_min_size" : int(sample_size / (2 ** (len(self.config.block_out_channels) - 1))),
"tile_overlap_factor" : 0.25
}
return VAE_tiling
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: Tuple[str] = ("DownEncoderBlockCausal3D",),
up_block_types: Tuple[str] = ("UpDecoderBlockCausal3D",),
block_out_channels: Tuple[int] = (64,),
layers_per_block: int = 1,
act_fn: str = "silu",
latent_channels: int = 4,
norm_num_groups: int = 32,
sample_size: int = 32,
sample_tsize: int = 64,
scaling_factor: float = 0.18215,
force_upcast: float = True,
spatial_compression_ratio: int = 8,
time_compression_ratio: int = 4,
disable_causal_conv: bool = False,
mid_block_add_attention: bool = True,
mid_block_causal_attn: bool = False,
use_trt_engine: bool = False,
nccl_gather: bool = True,
engine_path: str = f"{MODEL_BASE}/HYVAE_decoder+conv_256x256xT_fp16_H20.engine",
):
super().__init__()
self.disable_causal_conv = disable_causal_conv
self.time_compression_ratio = time_compression_ratio
self.encoder = EncoderCausal3D(
in_channels=in_channels,
out_channels=latent_channels,
down_block_types=down_block_types,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
act_fn=act_fn,
norm_num_groups=norm_num_groups,
double_z=True,
time_compression_ratio=time_compression_ratio,
spatial_compression_ratio=spatial_compression_ratio,
disable_causal=disable_causal_conv,
mid_block_add_attention=mid_block_add_attention,
mid_block_causal_attn=mid_block_causal_attn,
)
self.decoder = DecoderCausal3D(
in_channels=latent_channels,
out_channels=out_channels,
up_block_types=up_block_types,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
norm_num_groups=norm_num_groups,
act_fn=act_fn,
time_compression_ratio=time_compression_ratio,
spatial_compression_ratio=spatial_compression_ratio,
disable_causal=disable_causal_conv,
mid_block_add_attention=mid_block_add_attention,
mid_block_causal_attn=mid_block_causal_attn,
)
self.quant_conv = nn.Conv3d(2 * latent_channels, 2 * latent_channels, kernel_size=1)
self.post_quant_conv = nn.Conv3d(latent_channels, latent_channels, kernel_size=1)
self.use_slicing = False
self.use_spatial_tiling = False
self.use_temporal_tiling = False
# only relevant if vae tiling is enabled
self.tile_sample_min_tsize = sample_tsize
self.tile_latent_min_tsize = sample_tsize // time_compression_ratio
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_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))
self.tile_overlap_factor = 0.25
use_trt_engine = False #if CPU_OFFLOAD else True
# ============= parallism related code ===================
self.parallel_decode = use_trt_engine
self.nccl_gather = nccl_gather
# only relevant if parallel_decode is enabled
self.gather_to_rank0 = self.parallel_decode
self.engine_path = engine_path
self.use_trt_decoder = use_trt_engine
@property
def igather(self):
assert self.nccl_gather and self.gather_to_rank0
if hasattr(self, '_igather'):
return self._igather
else:
self._igather = TorchIGather()
return self._igather
@property
def use_padding(self):
return (
self.use_trt_decoder
# dist.gather demands all processes possess to have the same tile shape.
or (self.nccl_gather and self.gather_to_rank0)
)
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (EncoderCausal3D, DecoderCausal3D)):
module.gradient_checkpointing = value
def enable_temporal_tiling(self, use_tiling: bool = True):
self.use_temporal_tiling = use_tiling
def disable_temporal_tiling(self):
self.enable_temporal_tiling(False)
def enable_spatial_tiling(self, use_tiling: bool = True):
self.use_spatial_tiling = use_tiling
def disable_spatial_tiling(self):
self.enable_spatial_tiling(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.enable_spatial_tiling(use_tiling)
self.enable_temporal_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.disable_spatial_tiling()
self.disable_temporal_tiling()
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 load_trt_decoder(self):
self.use_trt_decoder = True
self.engine = EngineFromBytes(BytesFromPath(self.engine_path))
self.trt_decoder_runner = TrtRunner(self.engine)
self.activate_trt_decoder()
def disable_trt_decoder(self):
self.use_trt_decoder = False
del self.engine
def activate_trt_decoder(self):
self.trt_decoder_runner.activate()
def deactivate_trt_decoder(self):
self.trt_decoder_runner.deactivate()
@property
# Copied from diffusers.models.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.unet_2d_condition.UNet2DConditionModel.set_attn_processor
def set_attn_processor(
self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False
):
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, _remove_lora=_remove_lora)
else:
module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora)
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.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, _remove_lora=True)
@apply_forward_hook
def encode(
self, x: torch.FloatTensor, return_dict: bool = True
) -> Union[AutoencoderKLOutput, 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.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.
"""
set_phase_status("VAE Encoding")
assert len(x.shape) == 5, "The input tensor should have 5 dimensions"
if self.use_temporal_tiling and x.shape[2] > self.tile_sample_min_tsize:
return self.temporal_tiled_encode(x, return_dict=return_dict)
if self.use_spatial_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
return self.spatial_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 AutoencoderKLOutput(latent_dist=posterior)
def _decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
assert len(z.shape) == 5, "The input tensor should have 5 dimensions"
if self.use_temporal_tiling and z.shape[2] > self.tile_latent_min_tsize:
return self.temporal_tiled_decode(z, return_dict=return_dict)
if self.use_spatial_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
return self.spatial_tiled_decode(z, return_dict=return_dict)
if self.use_trt_decoder:
# For unknown reason, `copy_outputs_to_host` must be set to True
dec = self.trt_decoder_runner.infer({"input": z.to(RECOMMENDED_DTYPE).contiguous()}, copy_outputs_to_host=True)["output"].to(device=z.device, dtype=z.dtype)
else:
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.FloatTensor`): 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.
"""
tiles = z.shape[0] if self.use_slicing else 1
if self.use_temporal_tiling and z.shape[2] > self.tile_latent_min_tsize:
tiles *= len(range(0, z.shape[2], int(self.tile_latent_min_tsize * (1 - self.tile_overlap_factor))))
if self.use_spatial_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
stride = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
tiles *= len(range(0, z.shape[-2], stride)) * len(range(0, z.shape[-1], stride))
with vae_decoding_progress(tiles, self.decoder):
if self.parallel_decode:
if z.dtype != RECOMMENDED_DTYPE:
loguru.logger.warning(
f'For better performance, using {RECOMMENDED_DTYPE} for both latent features and model parameters is recommended.'
f'Current latent dtype {z.dtype}. '
f'Please note that the input latent will be cast to {RECOMMENDED_DTYPE} internally when decoding.'
)
z = z.to(RECOMMENDED_DTYPE)
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)
if blend_extent == 0:
return b
a_region = a[..., -blend_extent:, :]
b_region = b[..., :blend_extent, :]
weights = torch.arange(blend_extent, device=a.device, dtype=a.dtype) / blend_extent
weights = weights.view(1, 1, 1, blend_extent, 1)
blended = a_region * (1 - weights) + b_region * weights
b[..., :blend_extent, :] = blended
return b
def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
blend_extent = min(a.shape[-1], b.shape[-1], blend_extent)
if blend_extent == 0:
return b
a_region = a[..., -blend_extent:]
b_region = b[..., :blend_extent]
weights = torch.arange(blend_extent, device=a.device, dtype=a.dtype) / blend_extent
weights = weights.view(1, 1, 1, 1, blend_extent)
blended = a_region * (1 - weights) + b_region * weights
b[..., :blend_extent] = blended
return b
def blend_t(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
blend_extent = min(a.shape[-3], b.shape[-3], blend_extent)
if blend_extent == 0:
return b
a_region = a[..., -blend_extent:, :, :]
b_region = b[..., :blend_extent, :, :]
weights = torch.arange(blend_extent, device=a.device, dtype=a.dtype) / blend_extent
weights = weights.view(1, 1, blend_extent, 1, 1)
blended = a_region * (1 - weights) + b_region * weights
b[..., :blend_extent, :, :] = blended
return b
def spatial_tiled_encode(self, x: torch.FloatTensor, return_dict: bool = True, return_moments: bool = False) -> 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.FloatTensor`): 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.
"""
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 video into tiles and encode them separately.
rows = []
for i in range(0, x.shape[-2], overlap_size):
row = []
for j in range(0, x.shape[-1], 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=-1))
moments = torch.cat(result_rows, dim=-2)
if return_moments:
return moments
posterior = DiagonalGaussianDistribution(moments)
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=posterior)
def spatial_tiled_decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
r"""
Decode a batch of images using a tiled decoder.
Args:
z (`torch.FloatTensor`): 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_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 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
if self.parallel_decode:
rank = mpi_rank()
torch.cuda.set_device(rank) # set device for trt_runner
world_size = mpi_world_size()
tiles = []
afters_if_padding = []
for i in range(0, z.shape[-2], overlap_size):
for j in range(0, z.shape[-1], overlap_size):
tile = z[:, :, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
if self.use_padding and (tile.shape[-2] < self.tile_latent_min_size or tile.shape[-1] < self.tile_latent_min_size):
from torch.nn import functional as F
after_h = tile.shape[-2] * 8
after_w = tile.shape[-1] * 8
padding = (0, self.tile_latent_min_size - tile.shape[-1], 0, self.tile_latent_min_size - tile.shape[-2], 0, 0)
tile = F.pad(tile, padding, "replicate").to(device=tile.device, dtype=tile.dtype)
afters_if_padding.append((after_h, after_w))
else:
afters_if_padding.append(None)
tiles.append(tile)
# balance tasks
ratio = math.ceil(len(tiles) / world_size)
tiles_curr_rank = tiles[rank * ratio: None if rank == world_size - 1 else (rank + 1) * ratio]
decoded_results = []
total = len(tiles)
n_task = ([ratio] * (total // ratio) + ([total % ratio] if total % ratio else []))
n_task = n_task + [0] * (8 - len(n_task))
for i, tile in enumerate(tiles_curr_rank):
if self.use_trt_decoder:
# For unknown reason, `copy_outputs_to_host` must be set to True
decoded = self.trt_decoder_runner.infer(
{"input": tile.to(RECOMMENDED_DTYPE).contiguous()},
copy_outputs_to_host=True
)["output"].to(device=z.device, dtype=z.dtype)
decoded_results.append(decoded)
else:
decoded_results.append(self.decoder(self.post_quant_conv(tile)))
def find(n):
return next((i for i, task_n in enumerate(n_task) if task_n < n), len(n_task))
if self.nccl_gather and self.gather_to_rank0:
self.igather.gather(decoded, n_rank=find(i + 1))
if not self.nccl_gather:
if self.gather_to_rank0:
decoded_results = mpi_comm().gather(decoded_results, root=0)
if rank != 0:
return DecoderOutput(sample=None)
else:
decoded_results = mpi_comm().allgather(decoded_results)
decoded_results = sum(decoded_results, [])
else:
# [Kevin]:
# We expect all tiles obtained from the same rank have the same shape.
# Shapes among ranks can differ due to the imbalance of task assignment.
if self.gather_to_rank0:
if rank == 0:
self.igather.wait()
gather_results = self.igather.buffers
self.igather.clear()
else:
raise NotImplementedError('The old `allgather` implementation is deprecated for nccl plan.')
if rank != 0 and self.gather_to_rank0:
return DecoderOutput(sample=None)
decoded_results = [col[i] for i in range(max([len(k) for k in gather_results])) for col in gather_results if i < len(col)]
# Crop the padding region in pixel level
if self.use_padding:
new_decoded_results = []
for after, dec in zip(afters_if_padding, decoded_results):
if after is not None:
after_h, after_w = after
new_decoded_results.append(dec[:, :, :, :after_h, :after_w])
else:
new_decoded_results.append(dec)
decoded_results = new_decoded_results
rows = []
decoded_results_iter = iter(decoded_results)
for i in range(0, z.shape[-2], overlap_size):
row = []
for j in range(0, z.shape[-1], overlap_size):
row.append(next(decoded_results_iter).to(rank))
rows.append(row)
else:
rows = []
for i in range(0, z.shape[-2], overlap_size):
row = []
for j in range(0, z.shape[-1], overlap_size):
tile = z[:, :, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
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=-1))
dec = torch.cat(result_rows, dim=-2)
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
def temporal_tiled_encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput:
assert not self.disable_causal_conv, "Temporal tiling is only compatible with causal convolutions."
B, C, T, H, W = x.shape
overlap_size = int(self.tile_sample_min_tsize * (1 - self.tile_overlap_factor))
blend_extent = int(self.tile_latent_min_tsize * self.tile_overlap_factor)
t_limit = self.tile_latent_min_tsize - blend_extent
# Split the video into tiles and encode them separately.
row = []
for i in range(0, T, overlap_size):
tile = x[:, :, i : i + self.tile_sample_min_tsize + 1, :, :]
if self.use_spatial_tiling and (tile.shape[-1] > self.tile_sample_min_size or tile.shape[-2] > self.tile_sample_min_size):
tile = self.spatial_tiled_encode(tile, return_moments=True)
else:
tile = self.encoder(tile)
tile = self.quant_conv(tile)
if i > 0:
tile = tile[:, :, 1:, :, :]
row.append(tile)
result_row = []
for i, tile in enumerate(row):
if i > 0:
tile = self.blend_t(row[i - 1], tile, blend_extent)
result_row.append(tile[:, :, :t_limit, :, :])
else:
result_row.append(tile[:, :, :t_limit+1, :, :])
moments = torch.cat(result_row, dim=2)
posterior = DiagonalGaussianDistribution(moments)
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=posterior)
def temporal_tiled_decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
# Split z into overlapping tiles and decode them separately.
B, C, T, H, W = z.shape
overlap_size = int(self.tile_latent_min_tsize * (1 - self.tile_overlap_factor))
blend_extent = int(self.tile_sample_min_tsize * self.tile_overlap_factor)
t_limit = self.tile_sample_min_tsize - blend_extent
row = []
for i in range(0, T, overlap_size):
tile = z[:, :, i: i + self.tile_latent_min_tsize + 1, :, :]
if self.use_spatial_tiling and (tile.shape[-1] > self.tile_latent_min_size or tile.shape[-2] > self.tile_latent_min_size):
decoded = self.spatial_tiled_decode(tile, return_dict=True).sample
else:
tile = self.post_quant_conv(tile)
decoded = self.decoder(tile)
if i > 0:
decoded = decoded[:, :, 1:, :, :]
row.append(decoded)
result_row = []
for i, tile in enumerate(row):
if i > 0:
tile = self.blend_t(row[i - 1], tile, blend_extent)
result_row.append(tile[:, :, :t_limit, :, :])
else:
result_row.append(tile[:, :, :t_limit + 1, :, :])
dec = torch.cat(result_row, dim=2)
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
def forward(
self,
sample: torch.FloatTensor,
sample_posterior: bool = False,
return_dict: bool = True,
return_posterior: bool = False,
generator: Optional[torch.Generator] = None,
) -> Union[DecoderOutput2, 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.
"""
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:
if return_posterior:
return (dec, posterior)
else:
return (dec,)
if return_posterior:
return DecoderOutput2(sample=dec, posterior=posterior)
else:
return DecoderOutput2(sample=dec)
# Copied from diffusers.models.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.
This API is 🧪 experimental.
"""
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)
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
def unfuse_qkv_projections(self):
"""Disables the fused QKV projection if enabled.
This API is 🧪 experimental.
"""
if self.original_attn_processors is not None:
self.set_attn_processor(self.original_attn_processors)