438 lines
18 KiB
Python
438 lines
18 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass
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from typing import Dict, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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from torch import nn
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from ...configuration_utils import ConfigMixin, register_to_config
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from ...schedulers import ConsistencyDecoderScheduler
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from ...utils import BaseOutput
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from ...utils.accelerate_utils import apply_forward_hook
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from ...utils.torch_utils import randn_tensor
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from ..attention_processor import (
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ADDED_KV_ATTENTION_PROCESSORS,
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CROSS_ATTENTION_PROCESSORS,
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AttentionProcessor,
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AttnAddedKVProcessor,
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AttnProcessor,
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)
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from ..modeling_utils import ModelMixin
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from ..unet_2d import UNet2DModel
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from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder
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@dataclass
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class ConsistencyDecoderVAEOutput(BaseOutput):
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"""
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Output of encoding method.
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Args:
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latent_dist (`DiagonalGaussianDistribution`):
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Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
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`DiagonalGaussianDistribution` allows for sampling latents from the distribution.
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"""
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latent_dist: "DiagonalGaussianDistribution"
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class ConsistencyDecoderVAE(ModelMixin, ConfigMixin):
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r"""
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The consistency decoder used with DALL-E 3.
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Examples:
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```py
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>>> import torch
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>>> from diffusers import StableDiffusionPipeline, ConsistencyDecoderVAE
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>>> vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder", torch_dtype=torch.float16)
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>>> pipe = StableDiffusionPipeline.from_pretrained(
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... "runwayml/stable-diffusion-v1-5", vae=vae, torch_dtype=torch.float16
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... ).to("cuda")
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>>> pipe("horse", generator=torch.manual_seed(0)).images
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```
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"""
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@register_to_config
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def __init__(
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self,
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scaling_factor: float = 0.18215,
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latent_channels: int = 4,
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encoder_act_fn: str = "silu",
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encoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
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encoder_double_z: bool = True,
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encoder_down_block_types: Tuple[str, ...] = (
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"DownEncoderBlock2D",
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"DownEncoderBlock2D",
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"DownEncoderBlock2D",
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"DownEncoderBlock2D",
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),
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encoder_in_channels: int = 3,
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encoder_layers_per_block: int = 2,
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encoder_norm_num_groups: int = 32,
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encoder_out_channels: int = 4,
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decoder_add_attention: bool = False,
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decoder_block_out_channels: Tuple[int, ...] = (320, 640, 1024, 1024),
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decoder_down_block_types: Tuple[str, ...] = (
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"ResnetDownsampleBlock2D",
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"ResnetDownsampleBlock2D",
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"ResnetDownsampleBlock2D",
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"ResnetDownsampleBlock2D",
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),
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decoder_downsample_padding: int = 1,
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decoder_in_channels: int = 7,
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decoder_layers_per_block: int = 3,
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decoder_norm_eps: float = 1e-05,
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decoder_norm_num_groups: int = 32,
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decoder_num_train_timesteps: int = 1024,
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decoder_out_channels: int = 6,
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decoder_resnet_time_scale_shift: str = "scale_shift",
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decoder_time_embedding_type: str = "learned",
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decoder_up_block_types: Tuple[str, ...] = (
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"ResnetUpsampleBlock2D",
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"ResnetUpsampleBlock2D",
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"ResnetUpsampleBlock2D",
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"ResnetUpsampleBlock2D",
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),
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):
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super().__init__()
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self.encoder = Encoder(
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act_fn=encoder_act_fn,
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block_out_channels=encoder_block_out_channels,
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double_z=encoder_double_z,
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down_block_types=encoder_down_block_types,
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in_channels=encoder_in_channels,
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layers_per_block=encoder_layers_per_block,
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norm_num_groups=encoder_norm_num_groups,
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out_channels=encoder_out_channels,
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)
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self.decoder_unet = UNet2DModel(
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add_attention=decoder_add_attention,
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block_out_channels=decoder_block_out_channels,
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down_block_types=decoder_down_block_types,
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downsample_padding=decoder_downsample_padding,
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in_channels=decoder_in_channels,
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layers_per_block=decoder_layers_per_block,
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norm_eps=decoder_norm_eps,
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norm_num_groups=decoder_norm_num_groups,
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num_train_timesteps=decoder_num_train_timesteps,
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out_channels=decoder_out_channels,
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resnet_time_scale_shift=decoder_resnet_time_scale_shift,
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time_embedding_type=decoder_time_embedding_type,
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up_block_types=decoder_up_block_types,
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)
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self.decoder_scheduler = ConsistencyDecoderScheduler()
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self.register_to_config(block_out_channels=encoder_block_out_channels)
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self.register_to_config(force_upcast=False)
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self.register_buffer(
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"means",
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torch.tensor([0.38862467, 0.02253063, 0.07381133, -0.0171294])[None, :, None, None],
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persistent=False,
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)
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self.register_buffer(
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"stds", torch.tensor([0.9654121, 1.0440036, 0.76147926, 0.77022034])[None, :, None, None], persistent=False
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)
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self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
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self.use_slicing = False
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self.use_tiling = False
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# Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.enable_tiling
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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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# Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.disable_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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# Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.enable_slicing
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def enable_slicing(self):
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r"""
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Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
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compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
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"""
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self.use_slicing = True
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# Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.disable_slicing
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def disable_slicing(self):
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r"""
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Disable sliced VAE decoding. If `enable_slicing` 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.use_slicing = False
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@property
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# Copied from diffusers.models.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.unet_2d_condition.UNet2DConditionModel.set_attn_processor
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def set_attn_processor(
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self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False
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):
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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, _remove_lora=_remove_lora)
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else:
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module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora)
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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.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, _remove_lora=True)
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@apply_forward_hook
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def encode(
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self, x: torch.FloatTensor, return_dict: bool = True
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) -> Union[ConsistencyDecoderVAEOutput, 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.consistecy_decoder_vae.ConsistencyDecoderOoutput`] instead of a plain
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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.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] is returned, otherwise a plain `tuple`
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is returned.
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"""
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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)
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if self.use_slicing and x.shape[0] > 1:
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encoded_slices = [self.encoder(x_slice) for x_slice in x.split(1)]
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h = torch.cat(encoded_slices)
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else:
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h = self.encoder(x)
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moments = self.quant_conv(h)
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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 ConsistencyDecoderVAEOutput(latent_dist=posterior)
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@apply_forward_hook
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def decode(
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self,
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z: torch.FloatTensor,
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generator: Optional[torch.Generator] = None,
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return_dict: bool = True,
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num_inference_steps: int = 2,
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) -> Union[DecoderOutput, Tuple[torch.FloatTensor]]:
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z = (z * self.config.scaling_factor - self.means) / self.stds
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scale_factor = 2 ** (len(self.config.block_out_channels) - 1)
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z = F.interpolate(z, mode="nearest", scale_factor=scale_factor)
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batch_size, _, height, width = z.shape
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self.decoder_scheduler.set_timesteps(num_inference_steps, device=self.device)
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x_t = self.decoder_scheduler.init_noise_sigma * randn_tensor(
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(batch_size, 3, height, width), generator=generator, dtype=z.dtype, device=z.device
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)
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for t in self.decoder_scheduler.timesteps:
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model_input = torch.concat([self.decoder_scheduler.scale_model_input(x_t, t), z], dim=1)
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model_output = self.decoder_unet(model_input, t).sample[:, :3, :, :]
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prev_sample = self.decoder_scheduler.step(model_output, t, x_t, generator).prev_sample
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x_t = prev_sample
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x_0 = x_t
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if not return_dict:
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return (x_0,)
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return DecoderOutput(sample=x_0)
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# Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.blend_v
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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[2], b.shape[2], 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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# Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.blend_h
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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[3], b.shape[3], 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) -> ConsistencyDecoderVAEOutput:
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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.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] instead of a
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plain tuple.
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Returns:
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[`~models.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] or `tuple`:
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If return_dict is True, a [`~models.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] is returned,
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otherwise a plain `tuple` is returned.
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"""
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overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
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blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor)
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row_limit = self.tile_latent_min_size - blend_extent
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# Split the image into 512x512 tiles and encode them separately.
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rows = []
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for i in range(0, x.shape[2], overlap_size):
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row = []
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for j in range(0, x.shape[3], overlap_size):
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tile = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
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tile = self.encoder(tile)
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tile = self.quant_conv(tile)
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row.append(tile)
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rows.append(row)
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result_rows = []
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for i, row in enumerate(rows):
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result_row = []
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for j, tile in enumerate(row):
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# blend the above tile and the left tile
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# to the current tile and add the current tile to the result row
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if i > 0:
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tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
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if j > 0:
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tile = self.blend_h(row[j - 1], tile, blend_extent)
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result_row.append(tile[:, :, :row_limit, :row_limit])
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result_rows.append(torch.cat(result_row, dim=3))
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moments = torch.cat(result_rows, dim=2)
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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 ConsistencyDecoderVAEOutput(latent_dist=posterior)
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def forward(
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self,
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sample: torch.FloatTensor,
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sample_posterior: bool = False,
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return_dict: bool = True,
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generator: Optional[torch.Generator] = None,
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) -> Union[DecoderOutput, Tuple[torch.FloatTensor]]:
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r"""
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Args:
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sample (`torch.FloatTensor`): Input sample.
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sample_posterior (`bool`, *optional*, defaults to `False`):
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Whether to sample from the posterior.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
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generator (`torch.Generator`, *optional*, defaults to `None`):
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Generator to use for sampling.
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Returns:
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[`DecoderOutput`] or `tuple`:
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If return_dict is True, a [`DecoderOutput`] is returned, otherwise a plain `tuple` is returned.
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"""
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x = sample
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posterior = self.encode(x).latent_dist
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if sample_posterior:
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z = posterior.sample(generator=generator)
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else:
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z = posterior.mode()
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dec = self.decode(z, generator=generator).sample
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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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