from dataclasses import dataclass from typing import Dict, Optional, Tuple, Union import torch import torch.nn as nn from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.loaders import UNet2DConditionLoadersMixin from diffusers.utils import BaseOutput, logging from diffusers.models.attention_processor import CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnProcessor from diffusers.models.embeddings import TimestepEmbedding, Timesteps from diffusers.models.modeling_utils import ModelMixin from diffusers.models.unet_3d_blocks import UNetMidBlockSpatioTemporal, get_down_block, get_up_block logger = logging.get_logger(__name__) # pylint: disable=invalid-name @dataclass class UNetSpatioTemporalConditionOutput(BaseOutput): """ The output of [`UNetSpatioTemporalConditionModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`): The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. """ sample: torch.FloatTensor = None class UNetSpatioTemporalConditionControlNetModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): r""" A conditional Spatio-Temporal UNet model that takes a noisy video frames, conditional state, and a timestep and returns a sample shaped output. This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented for all models (such as downloading or saving). Parameters: sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): Height and width of input/output sample. in_channels (`int`, *optional*, defaults to 8): Number of channels in the input sample. out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "DownBlockSpatioTemporal")`): The tuple of downsample blocks to use. up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal")`): The tuple of upsample blocks to use. block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): The tuple of output channels for each block. addition_time_embed_dim: (`int`, defaults to 256): Dimension to to encode the additional time ids. projection_class_embeddings_input_dim (`int`, defaults to 768): The dimension of the projection of encoded `added_time_ids`. layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): The dimension of the cross attention features. transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1): The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for [`~models.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal`], [`~models.unet_3d_blocks.CrossAttnUpBlockSpatioTemporal`], [`~models.unet_3d_blocks.UNetMidBlockSpatioTemporal`]. num_attention_heads (`int`, `Tuple[int]`, defaults to `(5, 10, 10, 20)`): The number of attention heads. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. """ _supports_gradient_checkpointing = True @register_to_config def __init__( self, sample_size: Optional[int] = None, in_channels: int = 8, out_channels: int = 4, down_block_types: Tuple[str] = ( "CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "DownBlockSpatioTemporal", ), up_block_types: Tuple[str] = ( "UpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", ), block_out_channels: Tuple[int] = (320, 640, 1280, 1280), addition_time_embed_dim: int = 256, projection_class_embeddings_input_dim: int = 768, layers_per_block: Union[int, Tuple[int]] = 2, cross_attention_dim: Union[int, Tuple[int]] = 1024, transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, num_attention_heads: Union[int, Tuple[int]] = (5, 10, 10, 20), num_frames: int = 25, ): super().__init__() self.sample_size = sample_size # Check inputs if len(down_block_types) != len(up_block_types): raise ValueError( f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." ) if len(block_out_channels) != len(down_block_types): raise ValueError( f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." ) if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): raise ValueError( f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." ) if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): raise ValueError( f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." ) if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): raise ValueError( f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." ) # input self.conv_in = nn.Conv2d( in_channels, block_out_channels[0], kernel_size=3, padding=1, ) # time time_embed_dim = block_out_channels[0] * 4 self.time_proj = Timesteps(block_out_channels[0], True, downscale_freq_shift=0) timestep_input_dim = block_out_channels[0] self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) self.add_time_proj = Timesteps(addition_time_embed_dim, True, downscale_freq_shift=0) self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) self.down_blocks = nn.ModuleList([]) self.up_blocks = nn.ModuleList([]) if isinstance(num_attention_heads, int): num_attention_heads = (num_attention_heads,) * len(down_block_types) if isinstance(cross_attention_dim, int): cross_attention_dim = (cross_attention_dim,) * len(down_block_types) if isinstance(layers_per_block, int): layers_per_block = [layers_per_block] * len(down_block_types) if isinstance(transformer_layers_per_block, int): transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) blocks_time_embed_dim = time_embed_dim # down output_channel = block_out_channels[0] for i, down_block_type in enumerate(down_block_types): 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[i], transformer_layers_per_block=transformer_layers_per_block[i], in_channels=input_channel, out_channels=output_channel, temb_channels=blocks_time_embed_dim, add_downsample=not is_final_block, resnet_eps=1e-5, cross_attention_dim=cross_attention_dim[i], num_attention_heads=num_attention_heads[i], resnet_act_fn="silu", ) self.down_blocks.append(down_block) # mid self.mid_block = UNetMidBlockSpatioTemporal( block_out_channels[-1], temb_channels=blocks_time_embed_dim, transformer_layers_per_block=transformer_layers_per_block[-1], cross_attention_dim=cross_attention_dim[-1], num_attention_heads=num_attention_heads[-1], ) # count how many layers upsample the images self.num_upsamplers = 0 # up reversed_block_out_channels = list(reversed(block_out_channels)) reversed_num_attention_heads = list(reversed(num_attention_heads)) reversed_layers_per_block = list(reversed(layers_per_block)) reversed_cross_attention_dim = list(reversed(cross_attention_dim)) reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block)) output_channel = reversed_block_out_channels[0] for i, up_block_type in enumerate(up_block_types): 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=reversed_layers_per_block[i] + 1, transformer_layers_per_block=reversed_transformer_layers_per_block[i], in_channels=input_channel, out_channels=output_channel, prev_output_channel=prev_output_channel, temb_channels=blocks_time_embed_dim, add_upsample=add_upsample, resnet_eps=1e-5, resolution_idx=i, cross_attention_dim=reversed_cross_attention_dim[i], num_attention_heads=reversed_num_attention_heads[i], resnet_act_fn="silu", ) self.up_blocks.append(up_block) prev_output_channel = output_channel # out self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=32, eps=1e-5) self.conv_act = nn.SiLU() self.conv_out = nn.Conv2d( block_out_channels[0], out_channels, kernel_size=3, padding=1, ) @property def attn_processors(self) -> Dict[str, AttentionProcessor]: r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with indexed by its weight name. """ # set recursively processors = {} def fn_recursive_add_processors( name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor], ): if hasattr(module, "get_processor"): processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) for sub_name, child in module.named_children(): fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) return processors for name, module in self.named_children(): fn_recursive_add_processors(name, module, processors) return processors def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): The instantiated processor class or a dictionary of processor classes that will be set as the processor for **all** `Attention` layers. If `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors. """ count = len(self.attn_processors.keys()) if isinstance(processor, dict) and len(processor) != count: raise ValueError( f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" f" number of attention layers: {count}. Please make sure to pass {count} processor classes." ) def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): if hasattr(module, "set_processor"): if not isinstance(processor, dict): module.set_processor(processor) else: module.set_processor(processor.pop(f"{name}.processor")) for sub_name, child in module.named_children(): fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) for name, module in self.named_children(): fn_recursive_attn_processor(name, module, processor) def set_default_attn_processor(self): """ Disables custom attention processors and sets the default attention implementation. """ if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): processor = AttnProcessor() else: raise ValueError( f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" ) self.set_attn_processor(processor) def _set_gradient_checkpointing(self, module, value=False): if hasattr(module, "gradient_checkpointing"): module.gradient_checkpointing = value # Copied from diffusers.models.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None: """ Sets the attention processor to use [feed forward chunking](https://huggingface.co./blog/reformer#2-chunked-feed-forward-layers). Parameters: chunk_size (`int`, *optional*): The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually over each tensor of dim=`dim`. dim (`int`, *optional*, defaults to `0`): The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch) or dim=1 (sequence length). """ if dim not in [0, 1]: raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}") # By default chunk size is 1 chunk_size = chunk_size or 1 def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int): if hasattr(module, "set_chunk_feed_forward"): module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim) for child in module.children(): fn_recursive_feed_forward(child, chunk_size, dim) for module in self.children(): fn_recursive_feed_forward(module, chunk_size, dim) def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], encoder_hidden_states: torch.Tensor, down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, mid_block_additional_residual: Optional[torch.Tensor] = None, return_dict: bool = True, added_time_ids: torch.Tensor=None, ) -> Union[UNetSpatioTemporalConditionOutput, Tuple]: r""" The [`UNetSpatioTemporalConditionModel`] forward method. Args: sample (`torch.FloatTensor`): The noisy input tensor with the following shape `(batch, num_frames, channel, height, width)`. timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. encoder_hidden_states (`torch.FloatTensor`): The encoder hidden states with shape `(batch, sequence_length, cross_attention_dim)`. added_time_ids: (`torch.FloatTensor`): The additional time ids with shape `(batch, num_additional_ids)`. These are encoded with sinusoidal embeddings and added to the time embeddings. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] instead of a plain tuple. Returns: [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] or `tuple`: If `return_dict` is True, an [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] is returned, otherwise a `tuple` is returned where the first element is the sample tensor. """ # 1. time timesteps = timestep if not torch.is_tensor(timesteps): # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can # 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 batch_size, num_frames = sample.shape[:2] timesteps = timesteps.expand(batch_size) 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 = t_emb.to(dtype=sample.dtype) emb = self.time_embedding(t_emb) time_embeds = self.add_time_proj(added_time_ids.flatten()) time_embeds = time_embeds.reshape((batch_size, -1)) time_embeds = time_embeds.to(emb.dtype) aug_emb = self.add_embedding(time_embeds) emb = emb + aug_emb # Flatten the batch and frames dimensions # sample: [batch, frames, channels, height, width] -> [batch * frames, channels, height, width] sample = sample.flatten(0, 1) # Repeat the embeddings num_video_frames times # emb: [batch, channels] -> [batch * frames, channels] emb = emb.repeat_interleave(num_frames, dim=0) # encoder_hidden_states: [batch, 1, channels] -> [batch * frames, 1, channels] encoder_hidden_states = encoder_hidden_states.repeat_interleave(num_frames, dim=0) # 2. pre-process sample = self.conv_in(sample) image_only_indicator = torch.zeros(batch_size, num_frames, dtype=sample.dtype, device=sample.device) 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, image_only_indicator=image_only_indicator, ) else: sample, res_samples = downsample_block( hidden_states=sample, temb=emb, image_only_indicator=image_only_indicator, ) down_block_res_samples += res_samples new_down_block_res_samples = () for down_block_res_sample, down_block_additional_residual in zip( down_block_res_samples, down_block_additional_residuals ): down_block_res_sample = down_block_res_sample + down_block_additional_residual new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,) down_block_res_samples = new_down_block_res_samples # 4. mid sample = self.mid_block( hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states, image_only_indicator=image_only_indicator, ) sample = sample + mid_block_additional_residual # 5. up for i, upsample_block in enumerate(self.up_blocks): res_samples = down_block_res_samples[-len(upsample_block.resnets) :] down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] 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, image_only_indicator=image_only_indicator, ) else: sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, image_only_indicator=image_only_indicator, ) # 6. post-process sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) # 7. Reshape back to original shape sample = sample.reshape(batch_size, num_frames, *sample.shape[1:]) if not return_dict: return (sample,) return UNetSpatioTemporalConditionOutput(sample=sample)