626 lines
27 KiB
Python
626 lines
27 KiB
Python
from typing import Dict, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.models.attention_processor import (
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ADDED_KV_ATTENTION_PROCESSORS,
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CROSS_ATTENTION_PROCESSORS,
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Attention,
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AttentionProcessor,
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AttnAddedKVProcessor,
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AttnProcessor,
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)
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from diffusers.models.modeling_outputs import AutoencoderKLOutput
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from diffusers.models.modeling_utils import ModelMixin
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from timm.models.layers import drop_path, to_2tuple, trunc_normal_
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from .modeling_enc_dec import (
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DecoderOutput, DiagonalGaussianDistribution,
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CausalVaeDecoder, CausalVaeEncoder,
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)
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from .modeling_causal_conv import CausalConv3d
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#from IPython import embed
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from ..utils import (
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is_context_parallel_initialized,
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get_context_parallel_group,
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get_context_parallel_world_size,
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get_context_parallel_rank,
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get_context_parallel_group_rank,
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)
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from .context_parallel_ops import (
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conv_scatter_to_context_parallel_region,
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conv_gather_from_context_parallel_region,
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)
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class CausalVideoVAE(ModelMixin, ConfigMixin):
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r"""
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A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
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This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
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for all models (such as downloading or saving).
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Parameters:
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in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
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out_channels (int, *optional*, defaults to 3): Number of channels in the output.
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down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
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Tuple of downsample block types.
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up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
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Tuple of upsample block types.
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block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
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Tuple of block output channels.
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act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
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latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
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sample_size (`int`, *optional*, defaults to `32`): Sample input size.
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scaling_factor (`float`, *optional*, defaults to 0.18215):
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The component-wise standard deviation of the trained latent space computed using the first batch of the
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training set. This is used to scale the latent space to have unit variance when training the diffusion
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model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
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diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
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/ scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
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Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
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force_upcast (`bool`, *optional*, default to `True`):
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If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
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can be fine-tuned / trained to a lower range without loosing too much precision in which case
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`force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
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"""
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_supports_gradient_checkpointing = True
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@register_to_config
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def __init__(
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self,
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# encoder related parameters
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encoder_in_channels: int = 3,
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encoder_out_channels: int = 4,
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encoder_layers_per_block: Tuple[int, ...] = (2, 2, 2, 2),
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encoder_down_block_types: Tuple[str, ...] = (
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"DownEncoderBlockCausal3D",
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"DownEncoderBlockCausal3D",
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"DownEncoderBlockCausal3D",
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"DownEncoderBlockCausal3D",
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),
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encoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
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encoder_spatial_down_sample: Tuple[bool, ...] = (True, True, True, False),
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encoder_temporal_down_sample: Tuple[bool, ...] = (True, True, True, False),
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encoder_block_dropout: Tuple[int, ...] = (0.0, 0.0, 0.0, 0.0),
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encoder_act_fn: str = "silu",
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encoder_norm_num_groups: int = 32,
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encoder_double_z: bool = True,
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encoder_type: str = 'causal_vae_conv',
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# decoder related
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decoder_in_channels: int = 4,
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decoder_out_channels: int = 3,
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decoder_layers_per_block: Tuple[int, ...] = (3, 3, 3, 3),
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decoder_up_block_types: Tuple[str, ...] = (
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"UpDecoderBlockCausal3D",
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"UpDecoderBlockCausal3D",
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"UpDecoderBlockCausal3D",
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"UpDecoderBlockCausal3D",
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),
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decoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
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decoder_spatial_up_sample: Tuple[bool, ...] = (True, True, True, False),
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decoder_temporal_up_sample: Tuple[bool, ...] = (True, True, True, False),
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decoder_block_dropout: Tuple[int, ...] = (0.0, 0.0, 0.0, 0.0),
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decoder_act_fn: str = "silu",
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decoder_norm_num_groups: int = 32,
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decoder_type: str = 'causal_vae_conv',
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sample_size: int = 256,
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scaling_factor: float = 0.18215,
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add_post_quant_conv: bool = True,
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interpolate: bool = False,
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downsample_scale: int = 8,
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):
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super().__init__()
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print(f"The latent dimmension channes is {encoder_out_channels}")
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# pass init params to Encoder
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self.encoder = CausalVaeEncoder(
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in_channels=encoder_in_channels,
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out_channels=encoder_out_channels,
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down_block_types=encoder_down_block_types,
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spatial_down_sample=encoder_spatial_down_sample,
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temporal_down_sample=encoder_temporal_down_sample,
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block_out_channels=encoder_block_out_channels,
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layers_per_block=encoder_layers_per_block,
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act_fn=encoder_act_fn,
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norm_num_groups=encoder_norm_num_groups,
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double_z=True,
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block_dropout=encoder_block_dropout,
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)
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# pass init params to Decoder
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self.decoder = CausalVaeDecoder(
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in_channels=decoder_in_channels,
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out_channels=decoder_out_channels,
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up_block_types=decoder_up_block_types,
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spatial_up_sample=decoder_spatial_up_sample,
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temporal_up_sample=decoder_temporal_up_sample,
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block_out_channels=decoder_block_out_channels,
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layers_per_block=decoder_layers_per_block,
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norm_num_groups=decoder_norm_num_groups,
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act_fn=decoder_act_fn,
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interpolate=interpolate,
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block_dropout=decoder_block_dropout,
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)
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self.quant_conv = CausalConv3d(2 * encoder_out_channels, 2 * encoder_out_channels, kernel_size=1, stride=1)
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self.post_quant_conv = CausalConv3d(encoder_out_channels, encoder_out_channels, kernel_size=1, stride=1)
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self.use_tiling = False
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# only relevant if vae tiling is enabled
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self.tile_sample_min_size = self.config.sample_size
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sample_size = (
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self.config.sample_size[0]
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if isinstance(self.config.sample_size, (list, tuple))
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else self.config.sample_size
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)
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self.tile_latent_min_size = int(sample_size / downsample_scale)
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self.encode_tile_overlap_factor = 1 / 8
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self.decode_tile_overlap_factor = 1 / 8
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self.downsample_scale = downsample_scale
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, (nn.Linear, nn.Conv2d, nn.Conv3d)):
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trunc_normal_(m.weight, std=.02)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, (nn.LayerNorm, nn.GroupNorm)):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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def _set_gradient_checkpointing(self, module, value=False):
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if isinstance(module, (Encoder, Decoder)):
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module.gradient_checkpointing = value
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def enable_tiling(self, use_tiling: bool = True):
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r"""
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Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
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compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
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processing larger images.
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"""
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self.use_tiling = use_tiling
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def disable_tiling(self):
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r"""
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Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
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decoding in one step.
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"""
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self.enable_tiling(False)
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@property
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# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
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def attn_processors(self) -> Dict[str, AttentionProcessor]:
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r"""
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Returns:
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`dict` of attention processors: A dictionary containing all attention processors used in the model with
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indexed by its weight name.
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"""
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# set recursively
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processors = {}
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def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
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if hasattr(module, "get_processor"):
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processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
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for sub_name, child in module.named_children():
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fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
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return processors
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for name, module in self.named_children():
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fn_recursive_add_processors(name, module, processors)
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return processors
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# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
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def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
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r"""
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Sets the attention processor to use to compute attention.
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Parameters:
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processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
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The instantiated processor class or a dictionary of processor classes that will be set as the processor
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for **all** `Attention` layers.
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If `processor` is a dict, the key needs to define the path to the corresponding cross attention
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processor. This is strongly recommended when setting trainable attention processors.
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"""
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count = len(self.attn_processors.keys())
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if isinstance(processor, dict) and len(processor) != count:
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raise ValueError(
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f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
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f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
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)
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def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
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if hasattr(module, "set_processor"):
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if not isinstance(processor, dict):
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module.set_processor(processor)
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else:
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module.set_processor(processor.pop(f"{name}.processor"))
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for sub_name, child in module.named_children():
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fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
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for name, module in self.named_children():
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fn_recursive_attn_processor(name, module, processor)
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# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
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def set_default_attn_processor(self):
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"""
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Disables custom attention processors and sets the default attention implementation.
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"""
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if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
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processor = AttnAddedKVProcessor()
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elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
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processor = AttnProcessor()
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else:
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raise ValueError(
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f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
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)
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self.set_attn_processor(processor)
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def encode(
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self, x: torch.FloatTensor, return_dict: bool = True,
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is_init_image=True, temporal_chunk=False, window_size=16, tile_sample_min_size=256,
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) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
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"""
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Encode a batch of images into latents.
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Args:
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x (`torch.FloatTensor`): Input batch of images.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
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Returns:
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The latent representations of the encoded images. If `return_dict` is True, a
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[`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
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"""
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self.tile_sample_min_size = tile_sample_min_size
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self.tile_latent_min_size = int(tile_sample_min_size / self.downsample_scale)
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if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
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return self.tiled_encode(x, return_dict=return_dict, is_init_image=is_init_image,
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temporal_chunk=temporal_chunk, window_size=window_size)
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if temporal_chunk:
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moments = self.chunk_encode(x, window_size=window_size)
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else:
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h = self.encoder(x, is_init_image=is_init_image, temporal_chunk=False)
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moments = self.quant_conv(h, is_init_image=is_init_image, temporal_chunk=False)
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posterior = DiagonalGaussianDistribution(moments)
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if not return_dict:
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return (posterior,)
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return AutoencoderKLOutput(latent_dist=posterior)
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@torch.no_grad()
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def chunk_encode(self, x: torch.FloatTensor, window_size=16):
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# Only used during inference
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# Encode a long video clips through sliding window
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num_frames = x.shape[2]
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assert (num_frames - 1) % self.downsample_scale == 0
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init_window_size = window_size + 1
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frame_list = [x[:,:,:init_window_size]]
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# To chunk the long video
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full_chunk_size = (num_frames - init_window_size) // window_size
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fid = init_window_size
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for idx in range(full_chunk_size):
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frame_list.append(x[:, :, fid:fid+window_size])
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fid += window_size
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if fid < num_frames:
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frame_list.append(x[:, :, fid:])
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latent_list = []
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for idx, frames in enumerate(frame_list):
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if idx == 0:
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h = self.encoder(frames, is_init_image=True, temporal_chunk=True)
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moments = self.quant_conv(h, is_init_image=True, temporal_chunk=True)
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else:
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h = self.encoder(frames, is_init_image=False, temporal_chunk=True)
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moments = self.quant_conv(h, is_init_image=False, temporal_chunk=True)
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latent_list.append(moments)
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latent = torch.cat(latent_list, dim=2)
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return latent
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def get_last_layer(self):
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return self.decoder.conv_out.conv.weight
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@torch.no_grad()
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def chunk_decode(self, z: torch.FloatTensor, window_size=2):
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num_frames = z.shape[2]
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init_window_size = window_size + 1
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frame_list = [z[:,:,:init_window_size]]
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# To chunk the long video
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full_chunk_size = (num_frames - init_window_size) // window_size
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fid = init_window_size
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for idx in range(full_chunk_size):
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frame_list.append(z[:, :, fid:fid+window_size])
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fid += window_size
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if fid < num_frames:
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frame_list.append(z[:, :, fid:])
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dec_list = []
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for idx, frames in enumerate(frame_list):
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if idx == 0:
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z_h = self.post_quant_conv(frames, is_init_image=True, temporal_chunk=True)
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dec = self.decoder(z_h, is_init_image=True, temporal_chunk=True)
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else:
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z_h = self.post_quant_conv(frames, is_init_image=False, temporal_chunk=True)
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dec = self.decoder(z_h, is_init_image=False, temporal_chunk=True)
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dec_list.append(dec)
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dec = torch.cat(dec_list, dim=2)
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return dec
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def decode(self, z: torch.FloatTensor, is_init_image=True, temporal_chunk=False,
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return_dict: bool = True, window_size: int = 2, tile_sample_min_size: int = 256,) -> Union[DecoderOutput, torch.FloatTensor]:
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self.tile_sample_min_size = tile_sample_min_size
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self.tile_latent_min_size = int(tile_sample_min_size / self.downsample_scale)
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if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
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return self.tiled_decode(z, is_init_image=is_init_image,
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temporal_chunk=temporal_chunk, window_size=window_size, return_dict=return_dict)
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if temporal_chunk:
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dec = self.chunk_decode(z, window_size=window_size)
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else:
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z = self.post_quant_conv(z, is_init_image=is_init_image, temporal_chunk=False)
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dec = self.decoder(z, is_init_image=is_init_image, temporal_chunk=False)
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if not return_dict:
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return (dec,)
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return DecoderOutput(sample=dec)
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def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
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blend_extent = min(a.shape[3], b.shape[3], blend_extent)
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for y in range(blend_extent):
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b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, :, y, :] * (y / blend_extent)
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return b
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def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
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blend_extent = min(a.shape[4], b.shape[4], blend_extent)
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for x in range(blend_extent):
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b[:, :, :, :, x] = a[:, :, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, :, x] * (x / blend_extent)
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return b
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def tiled_encode(self, x: torch.FloatTensor, return_dict: bool = True,
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is_init_image=True, temporal_chunk=False, window_size=16,) -> AutoencoderKLOutput:
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r"""Encode a batch of images using a tiled encoder.
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When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several
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steps. This is useful to keep memory use constant regardless of image size. The end result of tiled encoding is
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different from non-tiled encoding because each tile uses a different encoder. To avoid tiling artifacts, the
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tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the
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output, but they should be much less noticeable.
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Args:
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x (`torch.FloatTensor`): Input batch of images.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
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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)
|