# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py from dataclasses import dataclass from typing import List, Optional, Tuple, Union import os import sys sys.path.append(os.path.split(sys.path[0])[0]) import json import math import torch import torch.nn as nn import torch.utils.checkpoint from torch.nn import functional as F import einops from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.models.modeling_utils import ModelMixin from diffusers.models.embeddings import TimestepEmbedding, Timesteps from diffusers.utils import BaseOutput, logging try: from .unet_blocks import ( CrossAttnDownBlock3D, CrossAttnUpBlock3D, DownBlock3D, UNetMidBlock3DCrossAttn, UpBlock3D, get_down_block, get_up_block, ) from .resnet import InflatedConv3d from .temporal_module import TemporalModule3D, EmptyTemporalModule3D except: from unet_blocks import ( CrossAttnDownBlock3D, CrossAttnUpBlock3D, DownBlock3D, UNetMidBlock3DCrossAttn, UpBlock3D, get_down_block, get_up_block, ) from resnet import InflatedConv3d from temporal_module import TemporalModule3D, EmptyTemporalModule3D from rotary_embedding_torch import RotaryEmbedding logger = logging.get_logger(__name__) # pylint: disable=invalid-name def zero_module(module): for p in module.parameters(): nn.init.zeros_(p) return module class RelativePositionBias(nn.Module): def __init__( self, heads=8, num_buckets=32, max_distance=128, ): super().__init__() self.num_buckets = num_buckets self.max_distance = max_distance self.relative_attention_bias = nn.Embedding(num_buckets, heads) @staticmethod def _relative_position_bucket(relative_position, num_buckets=32, max_distance=128): ret = 0 n = -relative_position num_buckets //= 2 ret += (n < 0).long() * num_buckets n = torch.abs(n) max_exact = num_buckets // 2 is_small = n < max_exact val_if_large = max_exact + ( torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) ).long() val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1)) ret += torch.where(is_small, n, val_if_large) return ret def forward(self, n, device): q_pos = torch.arange(n, dtype = torch.long, device = device) k_pos = torch.arange(n, dtype = torch.long, device = device) rel_pos = einops.rearrange(k_pos, 'j -> 1 j') - einops.rearrange(q_pos, 'i -> i 1') rp_bucket = self._relative_position_bucket(rel_pos, num_buckets = self.num_buckets, max_distance = self.max_distance) values = self.relative_attention_bias(rp_bucket) return einops.rearrange(values, 'i j h -> h i j') # num_heads, num_frames, num_frames @dataclass class UNet3DConditionOutput(BaseOutput): sample: torch.FloatTensor class UNet3DVSRModel(ModelMixin, ConfigMixin): _supports_gradient_checkpointing = True @register_to_config def __init__( self, ### Temporal Module Additional Kwargs ### down_temporal_idx = (0,1,2), mid_temporal = False, up_temporal_idx = (0,1,2), video_condition = True, temporal_module_config = None, sample_size: Optional[int] = None, # 80 in_channels: int = 7, out_channels: int = 4, center_input_sample: bool = False, max_noise_level: int = 350, flip_sin_to_cos: bool = True, freq_shift: int = 0, attention_head_dim: Union[int, Tuple[int]] = 8, block_out_channels: Tuple[int] = ( 256, 512, 512, 1024 ), down_block_types: Tuple[str] = ( "DownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D" ), mid_block_type: str = "UNetMidBlock3DCrossAttn", up_block_types: Tuple[str] = ( "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "UpBlock3D" ), only_cross_attention: Union[bool, Tuple[bool]] = ( True, True, True, False ), 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 = 1024, dual_cross_attention: bool = False, use_linear_projection: bool = True, class_embed_type: Optional[str] = None, num_class_embeds: Optional[int] = 1000, upcast_attention: bool = False, resnet_time_scale_shift: str = "default", use_first_frame: bool = False, use_relative_position: bool = False, ): 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) # 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) # VSR for noise level 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([]) self.video_condition = video_condition # Temporal Modules self.down_temporal_blocks = nn.ModuleList([]) self.mid_temporal_block = None self.up_temporal_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) self.temporal_rotary_emb = RotaryEmbedding(32) # 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, 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, use_first_frame=use_first_frame, use_relative_position=use_relative_position, rotary_emb=self.temporal_rotary_emb, ) self.down_blocks.append(down_block) # Down Sample Temporal Modules down_temporal_block = TemporalModule3D( in_channels=output_channel, out_channels=output_channel, temb_channels=time_embed_dim, video_condition=video_condition, **temporal_module_config, ) if i in down_temporal_idx else EmptyTemporalModule3D() self.down_temporal_blocks.append(down_temporal_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, use_first_frame=use_first_frame, use_relative_position=use_relative_position, rotary_emb=self.temporal_rotary_emb, ) else: raise ValueError(f"unknown mid_block_type : {mid_block_type}") self.mid_temporal_block = TemporalModule3D( in_channels=block_out_channels[-1], out_channels=block_out_channels[-1], temb_channels=time_embed_dim, video_condition=video_condition, **temporal_module_config, ) if mid_temporal else EmptyTemporalModule3D() # 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): 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, use_first_frame=use_first_frame, use_relative_position=use_relative_position, rotary_emb=self.temporal_rotary_emb, ) self.up_blocks.append(up_block) prev_output_channel = output_channel up_temporal_block = TemporalModule3D( in_channels=output_channel, out_channels=output_channel, temb_channels=time_embed_dim, video_condition=video_condition, **temporal_module_config, ) if i in up_temporal_idx else EmptyTemporalModule3D() self.up_temporal_blocks.append(up_temporal_block) # out 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) 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"`, maxium 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): 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 {self.config} has {len(sliceable_head_dims)} different" f" attention layers. Make sure to match `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]): 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) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)): module.gradient_checkpointing = value def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], low_res: torch.FloatTensor, # encoder_hidden_states: torch.Tensor, encoder_hidden_states = None, class_labels: Optional[torch.Tensor] = 20, low_res_clean: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, return_dict: bool = True, ): # -> Union[UNet3DConditionOutput, Tuple]: r""" Args: sample (`torch.FloatTensor`): (batch, channel, seq_length, 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 class_labels: noise level return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. Returns: [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: [`~models.unet_2d_condition.UNet2DConditionOutput`] 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. # The overall upsampling factor is equal 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 self.video_condition: low_res_dict = {} low_res_dict[low_res.shape[-1]] = low_res for s in [1/2., 1/4., 1/8.]: low_res_ds = F.interpolate(low_res, scale_factor=(1, s, s), mode='area') low_res_dict[low_res_ds.shape[-1]] = low_res_ds else: low_res_dict = None sample = torch.cat([sample, low_res], dim=1) # concat on C: 4+3=7 #print(f'==============={sample.shape}================') if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): logger.info("Forward upsample size to force interpolation output size.") forward_upsample_size = True # prepare attention_mask if attention_mask is not None: attention_mask = (1 - attention_mask.to(sample.dtype)) * -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 = t_emb.to(dtype=self.dtype) 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") # check noise level if torch.any(class_labels > self.config.max_noise_level): raise ValueError(f"`noise_level` has to be <= {self.config.max_noise_level} but is {class_labels}") 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, down_temporal_block in zip(self.down_blocks, self.down_temporal_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) down_block_res_samples += res_samples # 1. temporal modeling during down sample sample = down_temporal_block( hidden_states=sample, condition_video=low_res_dict, encoder_hidden_states=encoder_hidden_states, timesteps=timesteps, temb=emb, ) # mid sample = self.mid_block( sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask ) # 2. temporal modeling at mid block sample = self.mid_temporal_block( hidden_states=sample, condition_video=low_res_dict, encoder_hidden_states=encoder_hidden_states, timesteps=timesteps, temb=emb, ) # up for i, (upsample_block, up_temporal_block) in enumerate(zip(self.up_blocks, self.up_temporal_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 ) # 3. temporal modeling during up sample sample = up_temporal_block( hidden_states=sample, condition_video=low_res_dict, encoder_hidden_states=encoder_hidden_states, timesteps=timesteps, temb=emb, ) # post-process sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) # print(sample.shape) if not return_dict: return (sample,) return UNet3DConditionOutput(sample=sample) def forward_with_cfg(self, x, t, low_res, encoder_hidden_states = None, class_labels: Optional[torch.Tensor] = 20, cfg_scale=4.0, use_fp16=False): """ Forward, but also batches the unconditional forward pass for classifier-free guidance. """ # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb half = x[: len(x) // 2] combined = torch.cat([half, half], dim=0) if use_fp16: combined = combined.to(dtype=torch.float16) model_out = self.forward(combined, t, low_res, encoder_hidden_states, class_labels).sample # For exact reproducibility reasons, we apply classifier-free guidance on only # three channels by default. The standard approach to cfg applies it to all channels. # This can be done by uncommenting the following line and commenting-out the line following that. eps, rest = model_out[:, :4], model_out[:, 4:] # eps, rest = model_out[:, :3], model_out[:, 3:] # b c f h w cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps) eps = torch.cat([half_eps, half_eps], dim=0) return torch.cat([eps, rest], dim=1) @classmethod def from_pretrained_2d(cls, config_path, pretrained_model_path): if not os.path.isfile(config_path): raise RuntimeError(f"{config_path} does not exist") with open(config_path, "r") as f: config = json.load(f) config["_class_name"] = cls.__name__ freeze_pretrained_2d_upsampler = config["freeze_pretrained_2d_upsampler"] model = cls.from_config(config) model_file = os.path.join(pretrained_model_path) if not os.path.isfile(model_file): raise RuntimeError(f"{model_file} does not exist") state_dict = torch.load(model_file, map_location="cpu") for k, v in model.state_dict().items(): if 'temporal' in k: print(f'New layers: {k}') state_dict.update({k: v}) model.load_state_dict(state_dict, strict=True) if freeze_pretrained_2d_upsampler: print("Freeze pretrained 2d upsampler!") for k, v in model.named_parameters(): if not 'temporal' in k: v.requires_grad = False return model if __name__ == '__main__': import torch device = "cuda" if torch.cuda.is_available() else "cpu" config_path = "./configs/unet_3d_config.json" # pretrained_model_path = "./pretrained_models/unet_diffusion_pytorch_model.bin" # unet = UNet3DVSRModel.from_pretrained_2d(config_path, pretrained_model_path).to(device)