Source code for mmagic.models.editors.animatediff.unet_3d

# Copyright (c) OpenMMLab. All rights reserved.
# Adapted from
# blob/main/src/diffusers/models/

import json
import os
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.utils.checkpoint
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.embeddings import TimestepEmbedding, Timesteps
from diffusers.models.modeling_utils import ModelMixin
from diffusers.utils import BaseOutput
from huggingface_hub import snapshot_download
from mmengine.logging import MMLogger
from mmengine.model import constant_init
from safetensors import safe_open

from mmagic.registry import MODELS
from .resnet_3d import InflatedConv3d, InflatedGroupNorm
from .unet_block import (CrossAttnDownBlock3D, CrossAttnUpBlock3D, DownBlock3D,
                         UNetMidBlock3DCrossAttn, UpBlock3D, get_down_block,

[docs]logger = MMLogger.get_current_instance()
[docs]class UNet3DConditionOutput(BaseOutput): """Output of UNet3DCondtion."""
[docs] sample: torch.FloatTensor
[docs]class UNet3DConditionMotionModel(ModelMixin, ConfigMixin):
[docs] _supports_gradient_checkpointing = True
""" Implementation of UNet3DConditionMotionModel""" @register_to_config def __init__( self, sample_size: Optional[int] = None, in_channels: int = 4, out_channels: int = 4, center_input_sample: bool = False, flip_sin_to_cos: bool = True, freq_shift: int = 0, down_block_types: Tuple[str] = ( 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D', ), mid_block_type: str = 'UNetMidBlock3DCrossAttn', up_block_types: Tuple[str] = ('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D'), only_cross_attention: Union[bool, Tuple[bool]] = False, block_out_channels: Tuple[int] = (320, 640, 1280, 1280), layers_per_block: int = 2, downsample_padding: int = 1, mid_block_scale_factor: float = 1, act_fn: str = 'silu', norm_num_groups: int = 32, norm_eps: float = 1e-5, cross_attention_dim: int = 768, attention_head_dim: Union[int, Tuple[int]] = 8, dual_cross_attention: bool = False, use_linear_projection: bool = False, class_embed_type: Optional[str] = None, num_class_embeds: Optional[int] = None, upcast_attention: bool = False, resnet_time_scale_shift: str = 'default', # Additional use_inflated_groupnorm=False, use_motion_module=False, motion_module_resolutions=(1, 2, 4, 8), motion_module_mid_block=False, motion_module_decoder_only=False, motion_module_type=None, motion_module_kwargs={}, unet_use_cross_frame_attention=None, unet_use_temporal_attention=None, subfolder=None, from_pretrained=None, unet_addtion_kwargs=None, ): super().__init__() self.sample_size = sample_size time_embed_dim = block_out_channels[0] * 4 # input self.conv_in = InflatedConv3d( in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)) # time self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) timestep_input_dim = block_out_channels[0] self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) # class embedding if class_embed_type is None and num_class_embeds is not None: self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) elif class_embed_type == 'timestep': self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) elif class_embed_type == 'identity': self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) else: self.class_embedding = None self.down_blocks = nn.ModuleList([]) self.mid_block = None self.up_blocks = nn.ModuleList([]) if isinstance(only_cross_attention, bool): only_cross_attention = [only_cross_attention ] * len(down_block_types) if isinstance(attention_head_dim, int): attention_head_dim = (attention_head_dim, ) * len(down_block_types) # down output_channel = block_out_channels[0] for i, down_block_type in enumerate(down_block_types): res = 2**i input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 down_block = get_down_block( down_block_type, num_layers=layers_per_block, in_channels=input_channel, out_channels=output_channel, temb_channels=time_embed_dim, add_downsample=not is_final_block, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attention_head_dim[i], downsample_padding=downsample_padding, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention[i], upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, unet_use_cross_frame_attention=unet_use_cross_frame_attention, unet_use_temporal_attention=unet_use_temporal_attention, use_inflated_groupnorm=use_inflated_groupnorm, use_motion_module=use_motion_module and (res in motion_module_resolutions) and (not motion_module_decoder_only), motion_module_type=motion_module_type, motion_module_kwargs=motion_module_kwargs, ) self.down_blocks.append(down_block) # mid if mid_block_type == 'UNetMidBlock3DCrossAttn': self.mid_block = UNetMidBlock3DCrossAttn( in_channels=block_out_channels[-1], temb_channels=time_embed_dim, resnet_eps=norm_eps, resnet_act_fn=act_fn, output_scale_factor=mid_block_scale_factor, resnet_time_scale_shift=resnet_time_scale_shift, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attention_head_dim[-1], resnet_groups=norm_num_groups, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, unet_use_cross_frame_attention=unet_use_cross_frame_attention, unet_use_temporal_attention=unet_use_temporal_attention, use_inflated_groupnorm=use_inflated_groupnorm, use_motion_module=use_motion_module and motion_module_mid_block, motion_module_type=motion_module_type, motion_module_kwargs=motion_module_kwargs, ) else: raise ValueError(f'unknown mid_block_type : {mid_block_type}') # count how many layers upsample the videos self.num_upsamplers = 0 # up reversed_block_out_channels = list(reversed(block_out_channels)) reversed_attention_head_dim = list(reversed(attention_head_dim)) only_cross_attention = list(reversed(only_cross_attention)) output_channel = reversed_block_out_channels[0] for i, up_block_type in enumerate(up_block_types): res = 2**(3 - i) is_final_block = i == len(block_out_channels) - 1 prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] input_channel = reversed_block_out_channels[min( i + 1, len(block_out_channels) - 1)] # add upsample block for all BUT final layer if not is_final_block: add_upsample = True self.num_upsamplers += 1 else: add_upsample = False up_block = get_up_block( up_block_type, num_layers=layers_per_block + 1, in_channels=input_channel, out_channels=output_channel, prev_output_channel=prev_output_channel, temb_channels=time_embed_dim, add_upsample=add_upsample, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, cross_attention_dim=cross_attention_dim, attn_num_head_channels=reversed_attention_head_dim[i], dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention[i], upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, unet_use_cross_frame_attention=unet_use_cross_frame_attention, unet_use_temporal_attention=unet_use_temporal_attention, use_inflated_groupnorm=use_inflated_groupnorm, use_motion_module=use_motion_module and (res in motion_module_resolutions), motion_module_type=motion_module_type, motion_module_kwargs=motion_module_kwargs, ) self.up_blocks.append(up_block) prev_output_channel = output_channel # out if use_inflated_groupnorm: self.conv_norm_out = InflatedGroupNorm( num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps) else: self.conv_norm_out = nn.GroupNorm( num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps) self.conv_norm_out = nn.GroupNorm( num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps) self.conv_act = nn.SiLU() self.conv_out = InflatedConv3d( block_out_channels[0], out_channels, kernel_size=3, padding=1) self.init_weights(subfolder, from_pretrained)
[docs] def init_weights(self, subfolder=None, from_pretrained=None): """Init weights for models. We just use the initialization method proposed in the original paper. Args: pretrained (str, optional): Path for pretrained weights. If given None, pretrained weights will not be loaded. Defaults to None. """ if isinstance(from_pretrained, str): from diffusers.utils import WEIGHTS_NAME model_file = os.path.join(from_pretrained, subfolder, WEIGHTS_NAME) if not os.path.isfile(model_file): cache_file = snapshot_download( 'runwayml/stable-diffusion-v1-5', allow_patterns=['*.json', '*unet*safetensors'], ignore_patterns=[ '*.fp16.safetensors', '*v1-5*', '*ema.safetensors' ]) from diffusers.utils import SAFETENSORS_WEIGHTS_NAME model_file = os.path.join(cache_file, subfolder, SAFETENSORS_WEIGHTS_NAME) state_dict = {} with safe_open(model_file, framework='pt', device='cpu') as f: for key in f.keys(): state_dict[key] = f.get_tensor(key) else: state_dict = torch.load(model_file, map_location='cpu') m, u = self.load_state_dict(state_dict, strict=False) f'### missing keys: {len(m)}; \n### unexpected keys: {len(u)};' ) params = [ p.numel() if 'temporal' in n else 0 for n, p in self.named_parameters() ] f'### Temporal Module Parameters: {sum(params) / 1e6} M') elif from_pretrained is None: # As Improved-DDPM, we apply zero-initialization to # second conv block in ResBlock (keywords: conv_2) # the output layer of the Unet (keywords: 'out' but # not 'out_blocks') # projection layer in Attention layer (keywords: proj) for n, m in self.named_modules(): if isinstance(m, nn.Conv2d) and ('conv2' in n or ('out' in n and 'out_blocks' not in n)): constant_init(m, 0) if isinstance(m, nn.Conv1d) and 'proj' in n: constant_init(m, 0) else: raise TypeError('from_pretrained must be a str or None but'
f' got {type(from_pretrained)} instead.')
[docs] def set_attention_slice(self, slice_size): r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease. Args: slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim' must be a multiple of `slice_size`. """ sliceable_head_dims = [] def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module): """set attention slice recursively.""" if hasattr(module, 'set_attention_slice'): sliceable_head_dims.append(module.sliceable_head_dim) for child in module.children(): fn_recursive_retrieve_slicable_dims(child) # retrieve number of attention layers for module in self.children(): fn_recursive_retrieve_slicable_dims(module) num_slicable_layers = len(sliceable_head_dims) if slice_size == 'auto': # half the attention head size is usually a good trade-off between # speed and memory slice_size = [dim // 2 for dim in sliceable_head_dims] elif slice_size == 'max': # make smallest slice possible slice_size = num_slicable_layers * [1] slice_size = num_slicable_layers * [slice_size] if not isinstance( slice_size, list) else slice_size if len(slice_size) != len(sliceable_head_dims): raise ValueError( f'You have provided {len(slice_size)}, but ' f'{self.config} has {len(sliceable_head_dims)} different' f' attention layers. Make sure to match ' f'`len(slice_size)` to be {len(sliceable_head_dims)}.') for i in range(len(slice_size)): size = slice_size[i] dim = sliceable_head_dims[i] if size is not None and size > dim: raise ValueError( f'size {size} has to be smaller or equal to {dim}.') # Recursively walk through all the children. # Any children which exposes the set_attention_slice method # gets the message def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): """set attention slice recursively.""" if hasattr(module, 'set_attention_slice'): module.set_attention_slice(slice_size.pop()) for child in module.children(): fn_recursive_set_attention_slice(child, slice_size) reversed_slice_size = list(reversed(slice_size)) for module in self.children(): fn_recursive_set_attention_slice(module, reversed_slice_size)
[docs] def _set_gradient_checkpointing(self, module, value=False): """set gradient checkpoint.""" if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)): module.gradient_checkpointing = value
[docs] def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], encoder_hidden_states: torch.Tensor, class_labels: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, return_dict: bool = True, ) -> Union[UNet3DConditionOutput, Tuple]: r""" Args: sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`UNet3DConditionOutput`] instead of a plain tuple. Returns: [`UNet3DConditionOutput`] or `tuple`: [`UNet3DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ # By default samples have to be AT least a multiple of the # overall upsampling factor. he overall upsampling factor is equal # T to 2 ** (# num of upsampling layears). # However, the upsampling interpolation output size # can be forced to fit any upsampling size # on the fly if necessary. default_overall_up_factor = 2**self.num_upsamplers # upsample size should be forwarded when sample is # not a multiple of `default_overall_up_factor` forward_upsample_size = False upsample_size = None if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): 'Forward upsample size to force interpolation output size.') forward_upsample_size = True # prepare attention_mask if attention_mask is not None: attention_mask = (1 - * -10000.0 attention_mask = attention_mask.unsqueeze(1) # center input if necessary if self.config.center_input_sample: sample = 2 * sample - 1.0 # time timesteps = timestep if not torch.is_tensor(timesteps): # This would be a good case for the `match` # statement (Python 3.10+) is_mps = sample.device.type == 'mps' if isinstance(timestep, float): dtype = torch.float32 if is_mps else torch.float64 else: dtype = torch.int32 if is_mps else torch.int64 timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) elif len(timesteps.shape) == 0: timesteps = timesteps[None].to(sample.device) # broadcast to batch dimension in a way # that's compatible with ONNX/Core ML timesteps = timesteps.expand(sample.shape[0]) t_emb = self.time_proj(timesteps) # timesteps does not contain any weights and will # always return f32 tensors # but time_embedding might actually be running in fp16. # so we need to cast here. # there might be better ways to encapsulate this. t_emb = emb = self.time_embedding(t_emb) if self.class_embedding is not None: if class_labels is None: raise ValueError( 'class_labels should be provided when num_class_embeds > 0' ) if self.config.class_embed_type == 'timestep': class_labels = self.time_proj(class_labels) class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) emb = emb + class_emb # pre-process sample = self.conv_in(sample) # down down_block_res_samples = (sample, ) for downsample_block in self.down_blocks: if hasattr(downsample_block, 'has_cross_attention' ) and downsample_block.has_cross_attention: sample, res_samples = downsample_block( hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, ) else: sample, res_samples = downsample_block( hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states) down_block_res_samples += res_samples # mid sample = self.mid_block( sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask) # up for i, upsample_block in enumerate(self.up_blocks): is_final_block = i == len(self.up_blocks) - 1 res_samples = down_block_res_samples[-len(upsample_block.resnets):] down_block_res_samples = down_block_res_samples[:-len( upsample_block.resnets)] # if we have not reached the final block and need to forward the # upsample size, we do it here if not is_final_block and forward_upsample_size: upsample_size = down_block_res_samples[-1].shape[2:] if hasattr(upsample_block, 'has_cross_attention' ) and upsample_block.has_cross_attention: sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, encoder_hidden_states=encoder_hidden_states, upsample_size=upsample_size, attention_mask=attention_mask, ) else: sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, encoder_hidden_states=encoder_hidden_states, ) # post-process sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) if not return_dict: return (sample, ) return UNet3DConditionOutput(sample=sample)
[docs] def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, unet_additional_kwargs=None): """a class method for initialization.""" if subfolder is not None: pretrained_model_path = os.path.join(pretrained_model_path, subfolder)"loaded temporal unet's pretrained weights \ from {pretrained_model_path} ...") config_file = os.path.join(pretrained_model_path, 'config.json') if not os.path.isfile(config_file): raise RuntimeError(f'{config_file} does not exist') with open(config_file, 'r') as f: config = json.load(f) config['_class_name'] = cls.__name__ config['down_block_types'] = [ 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D' ] config['up_block_types'] = [ 'UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D' ] from diffusers.utils import WEIGHTS_NAME model = cls.from_config(config, **unet_additional_kwargs) model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME) if not os.path.isfile(model_file): raise RuntimeError(f'{model_file} does not exist') state_dict = torch.load(model_file, map_location='cpu') m, u = model.load_state_dict(state_dict, strict=False) f'### missing keys: {len(m)}; \n### unexpected keys: {len(u)};') # print(f"### missing keys:\n{m}\n### unexpected keys:\n{u}\n") params = [ p.numel() if 'temporal' in n else 0 for n, p in model.named_parameters() ]'### Temporal Module Parameters: {sum(params) / 1e6} M') return model
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