video_vae/modeling_causal_vae.py

from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn

from diffusers.configuration_utils import ConfigMixin, register_to_config
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 timm.models.layers import drop_path, to_2tuple, trunc_normal_
from .modeling_enc_dec import (
 DecoderOutput, DiagonalGaussianDistribution, 
 CausalVaeDecoder, CausalVaeEncoder,
)
from .modeling_causal_conv import CausalConv3d
from IPython import embed

from utils import (
 is_context_parallel_initialized,
 get_context_parallel_group,
 get_context_parallel_world_size,
 get_context_parallel_rank,
 get_context_parallel_group_rank,
)

from .context_parallel_ops import (
 conv_scatter_to_context_parallel_region,
 conv_gather_from_context_parallel_region,
)


class CausalVideoVAE(ModelMixin, ConfigMixin):
 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
 """

 _supports_gradient_checkpointing = True

 @register_to_config
 def __init__(
 self,
 # encoder related parameters
 encoder_in_channels: int = 3,
 encoder_out_channels: int = 4,
 encoder_layers_per_block: Tuple[int, ...] = (2, 2, 2, 2),
 encoder_down_block_types: Tuple[str, ...] = (
 "DownEncoderBlockCausal3D",
 "DownEncoderBlockCausal3D",
 "DownEncoderBlockCausal3D",
 "DownEncoderBlockCausal3D",
 ),
 encoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
 encoder_spatial_down_sample: Tuple[bool, ...] = (True, True, True, False),
 encoder_temporal_down_sample: Tuple[bool, ...] = (True, True, True, False),
 encoder_block_dropout: Tuple[int, ...] = (0.0, 0.0, 0.0, 0.0),
 encoder_act_fn: str = "silu",
 encoder_norm_num_groups: int = 32,
 encoder_double_z: bool = True,
 encoder_type: str = 'causal_vae_conv',
 # decoder related
 decoder_in_channels: int = 4,
 decoder_out_channels: int = 3,
 decoder_layers_per_block: Tuple[int, ...] = (3, 3, 3, 3),
 decoder_up_block_types: Tuple[str, ...] = (
 "UpDecoderBlockCausal3D",
 "UpDecoderBlockCausal3D",
 "UpDecoderBlockCausal3D",
 "UpDecoderBlockCausal3D",
 ),
 decoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
 decoder_spatial_up_sample: Tuple[bool, ...] = (True, True, True, False),
 decoder_temporal_up_sample: Tuple[bool, ...] = (True, True, True, False),
 decoder_block_dropout: Tuple[int, ...] = (0.0, 0.0, 0.0, 0.0),
 decoder_act_fn: str = "silu",
 decoder_norm_num_groups: int = 32,
 decoder_type: str = 'causal_vae_conv',
 sample_size: int = 256,
 scaling_factor: float = 0.18215,
 add_post_quant_conv: bool = True,
 interpolate: bool = False,
 downsample_scale: int = 8,
 ):
 super().__init__()

 print(f"The latent dimmension channes is {encoder_out_channels}")
 # pass init params to Encoder

 self.encoder = CausalVaeEncoder(
 in_channels=encoder_in_channels,
 out_channels=encoder_out_channels,
 down_block_types=encoder_down_block_types,
 spatial_down_sample=encoder_spatial_down_sample,
 temporal_down_sample=encoder_temporal_down_sample,
 block_out_channels=encoder_block_out_channels,
 layers_per_block=encoder_layers_per_block,
 act_fn=encoder_act_fn,
 norm_num_groups=encoder_norm_num_groups,
 double_z=True,
 block_dropout=encoder_block_dropout,
 )

 # pass init params to Decoder
 self.decoder = CausalVaeDecoder(
 in_channels=decoder_in_channels,
 out_channels=decoder_out_channels,
 up_block_types=decoder_up_block_types,
 spatial_up_sample=decoder_spatial_up_sample,
 temporal_up_sample=decoder_temporal_up_sample,
 block_out_channels=decoder_block_out_channels,
 layers_per_block=decoder_layers_per_block,
 norm_num_groups=decoder_norm_num_groups,
 act_fn=decoder_act_fn,
 interpolate=interpolate,
 block_dropout=decoder_block_dropout,
 )

 self.quant_conv = CausalConv3d(2 * encoder_out_channels, 2 * encoder_out_channels, kernel_size=1, stride=1)
 self.post_quant_conv = CausalConv3d(encoder_out_channels, encoder_out_channels, kernel_size=1, stride=1)
 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_size = int(sample_size / downsample_scale) 
 self.encode_tile_overlap_factor = 1 / 8
 self.decode_tile_overlap_factor = 1 / 8
 self.downsample_scale = downsample_scale

 self.apply(self._init_weights)

 def _init_weights(self, m):
 if isinstance(m, (nn.Linear, nn.Conv2d, nn.Conv3d)):
 trunc_normal_(m.weight, std=.02)
 if m.bias is not None:
 nn.init.constant_(m.bias, 0)
 elif isinstance(m, (nn.LayerNorm, nn.GroupNorm)):
 nn.init.constant_(m.bias, 0)
 nn.init.constant_(m.weight, 1.0)

 def _set_gradient_checkpointing(self, module, value=False):
 if isinstance(module, (Encoder, Decoder)):
 module.gradient_checkpointing = value

 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)

 @property
 # 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,
 is_init_image=True, temporal_chunk=False, window_size=16, tile_sample_min_size=256,
 ) -> 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.
 """
 self.tile_sample_min_size = tile_sample_min_size
 self.tile_latent_min_size = int(tile_sample_min_size / self.downsample_scale)

 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, is_init_image=is_init_image, 
 temporal_chunk=temporal_chunk, window_size=window_size)

 if temporal_chunk:
 moments = self.chunk_encode(x, window_size=window_size)
 else:
 h = self.encoder(x, is_init_image=is_init_image, temporal_chunk=False)
 moments = self.quant_conv(h, is_init_image=is_init_image, temporal_chunk=False)
 
 posterior = DiagonalGaussianDistribution(moments)

 if not return_dict:
 return (posterior,)

 return AutoencoderKLOutput(latent_dist=posterior)

 @torch.no_grad()
 def chunk_encode(self, x: torch.FloatTensor, window_size=16):
 # Only used during inference
 # Encode a long video clips through sliding window
 num_frames = x.shape[2]
 assert (num_frames - 1) % self.downsample_scale == 0
 init_window_size = window_size + 1
 frame_list = [x[:,:,:init_window_size]]

 # To chunk the long video 
 full_chunk_size = (num_frames - init_window_size) // window_size
 fid = init_window_size
 for idx in range(full_chunk_size):
 frame_list.append(x[:, :, fid:fid+window_size])
 fid += window_size

 if fid < num_frames:
 frame_list.append(x[:, :, fid:])

 latent_list = []
 for idx, frames in enumerate(frame_list):
 if idx == 0:
 h = self.encoder(frames, is_init_image=True, temporal_chunk=True)
 moments = self.quant_conv(h, is_init_image=True, temporal_chunk=True)
 else:
 h = self.encoder(frames, is_init_image=False, temporal_chunk=True)
 moments = self.quant_conv(h, is_init_image=False, temporal_chunk=True)

 latent_list.append(moments)

 latent = torch.cat(latent_list, dim=2)
 return latent

 def get_last_layer(self):
 return self.decoder.conv_out.conv.weight
 
 @torch.no_grad()
 def chunk_decode(self, z: torch.FloatTensor, window_size=2):
 num_frames = z.shape[2]
 init_window_size = window_size + 1
 frame_list = [z[:,:,:init_window_size]]

 # To chunk the long video 
 full_chunk_size = (num_frames - init_window_size) // window_size
 fid = init_window_size
 for idx in range(full_chunk_size):
 frame_list.append(z[:, :, fid:fid+window_size])
 fid += window_size

 if fid < num_frames:
 frame_list.append(z[:, :, fid:])

 dec_list = []
 for idx, frames in enumerate(frame_list):
 if idx == 0:
 z_h = self.post_quant_conv(frames, is_init_image=True, temporal_chunk=True)
 dec = self.decoder(z_h, is_init_image=True, temporal_chunk=True)
 else:
 z_h = self.post_quant_conv(frames, is_init_image=False, temporal_chunk=True)
 dec = self.decoder(z_h, is_init_image=False, temporal_chunk=True)

 dec_list.append(dec)

 dec = torch.cat(dec_list, dim=2)
 return dec

 def decode(self, z: torch.FloatTensor, is_init_image=True, temporal_chunk=False, 
 return_dict: bool = True, window_size: int = 2, tile_sample_min_size: int = 256,) -> Union[DecoderOutput, torch.FloatTensor]:
 
 self.tile_sample_min_size = tile_sample_min_size
 self.tile_latent_min_size = int(tile_sample_min_size / self.downsample_scale)

 if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
 return self.tiled_decode(z, is_init_image=is_init_image, 
 temporal_chunk=temporal_chunk, window_size=window_size, return_dict=return_dict)

 if temporal_chunk:
 dec = self.chunk_decode(z, window_size=window_size)
 else:
 z = self.post_quant_conv(z, is_init_image=is_init_image, temporal_chunk=False)
 dec = self.decoder(z, is_init_image=is_init_image, temporal_chunk=False)

 if not return_dict:
 return (dec,)

 return DecoderOutput(sample=dec)

 def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
 blend_extent = min(a.shape[3], b.shape[3], 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[4], b.shape[4], 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, 
 is_init_image=True, temporal_chunk=False, window_size=16,) -> 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.encode_tile_overlap_factor))
 blend_extent = int(self.tile_latent_min_size * self.encode_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[3], overlap_size):
 row = []
 for j in range(0, x.shape[4], overlap_size):
 tile = x[:, :, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
 if temporal_chunk:
 tile = self.chunk_encode(tile, window_size=window_size)
 else:
 tile = self.encoder(tile, is_init_image=True, temporal_chunk=False)
 tile = self.quant_conv(tile, is_init_image=True, temporal_chunk=False)
 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=4))

 moments = torch.cat(result_rows, dim=3)

 posterior = DiagonalGaussianDistribution(moments)

 if not return_dict:
 return (posterior,)

 return AutoencoderKLOutput(latent_dist=posterior)

 def tiled_decode(self, z: torch.FloatTensor, is_init_image=True, 
 temporal_chunk=False, window_size=2, 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.decode_tile_overlap_factor))
 blend_extent = int(self.tile_sample_min_size * self.decode_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[3], overlap_size):
 row = []
 for j in range(0, z.shape[4], overlap_size):
 tile = z[:, :, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
 if temporal_chunk:
 decoded = self.chunk_decode(tile, window_size=window_size)
 else:
 tile = self.post_quant_conv(tile, is_init_image=True, temporal_chunk=False)
 decoded = self.decoder(tile, is_init_image=True, temporal_chunk=False)
 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=4))

 dec = torch.cat(result_rows, dim=3)
 if not return_dict:
 return (dec,)

 return DecoderOutput(sample=dec)

 def forward(
 self,
 sample: torch.FloatTensor,
 sample_posterior: bool = True,
 generator: Optional[torch.Generator] = None,
 freeze_encoder: bool = False,
 is_init_image=True, 
 temporal_chunk=False,
 ) -> Union[DecoderOutput, 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

 if is_context_parallel_initialized():
 assert self.training, "Only supports during training now"

 if freeze_encoder:
 with torch.no_grad():
 h = self.encoder(x, is_init_image=True, temporal_chunk=False)
 moments = self.quant_conv(h, is_init_image=True, temporal_chunk=False)
 posterior = DiagonalGaussianDistribution(moments)
 global_posterior = posterior
 else:
 h = self.encoder(x, is_init_image=True, temporal_chunk=False)
 moments = self.quant_conv(h, is_init_image=True, temporal_chunk=False)
 posterior = DiagonalGaussianDistribution(moments)
 global_moments = conv_gather_from_context_parallel_region(moments, dim=2, kernel_size=1)
 global_posterior = DiagonalGaussianDistribution(global_moments)
 
 if sample_posterior:
 z = posterior.sample(generator=generator)
 else:
 z = posterior.mode()

 if get_context_parallel_rank() == 0:
 dec = self.decode(z, is_init_image=True).sample
 else:
 # Do not drop the first upsampled frame
 dec = self.decode(z, is_init_image=False).sample

 return global_posterior, dec

 else:
 # The normal training
 if freeze_encoder:
 with torch.no_grad():
 posterior = self.encode(x, is_init_image=is_init_image, 
 temporal_chunk=temporal_chunk).latent_dist
 else:
 posterior = self.encode(x, is_init_image=is_init_image, 
 temporal_chunk=temporal_chunk).latent_dist
 
 if sample_posterior:
 z = posterior.sample(generator=generator)
 else:
 z = posterior.mode()

 dec = self.decode(z, is_init_image=is_init_image, temporal_chunk=temporal_chunk).sample

 return posterior, 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.

 <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)

 # Copied from diffusers.models.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)