import torch import os import json from dataclasses import dataclass from einops import rearrange, repeat from typing import Any, Dict, Optional, Tuple from diffusers.models import Transformer2DModel from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate from diffusers.models.embeddings import get_1d_sincos_pos_embed_from_grid, ImagePositionalEmbeddings, CaptionProjection # from diffusers.models.embeddings import PatchEmbed, CombinedTimestepSizeEmbeddings from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.models.modeling_utils import ModelMixin from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear import torch import torch.nn.functional as F from torch import nn from opensora.models.diffusion.utils.pos_embed import get_1d_sincos_pos_embed from .modules import PatchEmbed, BasicTransformerBlock, BasicTransformerBlock_, AdaLayerNormSingle, Transformer3DModelOutput class Latte(ModelMixin, ConfigMixin): _supports_gradient_checkpointing = True """ A 2D Transformer model for image-like data. Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. in_channels (`int`, *optional*): The number of channels in the input and output (specify if the input is **continuous**). num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**). This is fixed during training since it is used to learn a number of position embeddings. num_vector_embeds (`int`, *optional*): The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**). Includes the class for the masked latent pixel. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. num_embeds_ada_norm ( `int`, *optional*): The number of diffusion steps used during training. Pass if at least one of the norm_layers is `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`. attention_bias (`bool`, *optional*): Configure if the `TransformerBlocks` attention should contain a bias parameter. """ @register_to_config def __init__( self, num_attention_heads: int = 16, patch_size_t: int = 1, attention_head_dim: int = 88, in_channels: Optional[int] = None, out_channels: Optional[int] = None, num_layers: int = 1, dropout: float = 0.0, norm_num_groups: int = 32, cross_attention_dim: Optional[int] = None, attention_bias: bool = False, sample_size: Optional[int] = None, num_vector_embeds: Optional[int] = None, patch_size: Optional[int] = None, activation_fn: str = "geglu", num_embeds_ada_norm: Optional[int] = None, use_linear_projection: bool = False, only_cross_attention: bool = False, double_self_attention: bool = False, upcast_attention: bool = False, norm_type: str = "layer_norm", norm_elementwise_affine: bool = True, norm_eps: float = 1e-5, attention_type: str = "default", caption_channels: int = None, video_length: int = 16, attention_mode: str = 'flash' ): super().__init__() self.use_linear_projection = use_linear_projection self.num_attention_heads = num_attention_heads self.attention_head_dim = attention_head_dim inner_dim = num_attention_heads * attention_head_dim self.video_length = video_length conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)` # Define whether input is continuous or discrete depending on configuration self.is_input_continuous = (in_channels is not None) and (patch_size is None) self.is_input_vectorized = num_vector_embeds is not None self.is_input_patches = in_channels is not None and patch_size is not None if norm_type == "layer_norm" and num_embeds_ada_norm is not None: deprecation_message = ( f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or" " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config." " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect" " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it" " would be very nice if you could open a Pull request for the `transformer/config.json` file" ) deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False) norm_type = "ada_norm" if self.is_input_continuous and self.is_input_vectorized: raise ValueError( f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make" " sure that either `in_channels` or `num_vector_embeds` is None." ) elif self.is_input_vectorized and self.is_input_patches: raise ValueError( f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make" " sure that either `num_vector_embeds` or `num_patches` is None." ) elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches: raise ValueError( f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:" f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None." ) # 2. Define input layers if self.is_input_continuous: self.in_channels = in_channels self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) if use_linear_projection: self.proj_in = linear_cls(in_channels, inner_dim) else: self.proj_in = conv_cls(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) elif self.is_input_vectorized: assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size" assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed" self.height = sample_size[0] self.width = sample_size[1] self.num_vector_embeds = num_vector_embeds self.num_latent_pixels = self.height * self.width self.latent_image_embedding = ImagePositionalEmbeddings( num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width ) elif self.is_input_patches: assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size" self.height = sample_size[0] self.width = sample_size[1] self.patch_size = patch_size interpolation_scale = self.config.sample_size[0] // 64 # => 64 (= 512 pixart) has interpolation scale 1 interpolation_scale = max(interpolation_scale, 1) self.pos_embed = PatchEmbed( height=sample_size[0], width=sample_size[1], patch_size=patch_size, in_channels=in_channels, embed_dim=inner_dim, interpolation_scale=interpolation_scale, ) # 3. Define transformers blocks self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock_( inner_dim, num_attention_heads, attention_head_dim, dropout=dropout, cross_attention_dim=None, ############## unconditon do not need cross attn activation_fn=activation_fn, num_embeds_ada_norm=num_embeds_ada_norm, attention_bias=attention_bias, only_cross_attention=only_cross_attention, double_self_attention=False, upcast_attention=upcast_attention, norm_type=norm_type, norm_elementwise_affine=norm_elementwise_affine, norm_eps=norm_eps, attention_type=attention_type, attention_mode=attention_mode, ) for d in range(num_layers) ] ) # Define temporal transformers blocks self.temporal_transformer_blocks = nn.ModuleList( [ BasicTransformerBlock_( # one attention inner_dim, num_attention_heads, # num_attention_heads attention_head_dim, # attention_head_dim 72 dropout=dropout, cross_attention_dim=None, activation_fn=activation_fn, num_embeds_ada_norm=num_embeds_ada_norm, attention_bias=attention_bias, only_cross_attention=only_cross_attention, double_self_attention=False, upcast_attention=upcast_attention, norm_type=norm_type, norm_elementwise_affine=norm_elementwise_affine, norm_eps=norm_eps, attention_type=attention_type, attention_mode=attention_mode, ) for d in range(num_layers) ] ) # 4. Define output layers self.out_channels = in_channels if out_channels is None else out_channels if self.is_input_continuous: # TODO: should use out_channels for continuous projections if use_linear_projection: self.proj_out = linear_cls(inner_dim, in_channels) else: self.proj_out = conv_cls(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) elif self.is_input_vectorized: self.norm_out = nn.LayerNorm(inner_dim) self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1) elif self.is_input_patches and norm_type != "ada_norm_single": self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6) self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim) self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels) elif self.is_input_patches and norm_type == "ada_norm_single": self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6) self.scale_shift_table = nn.Parameter(torch.randn(2, inner_dim) / inner_dim ** 0.5) self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels) # 5. PixArt-Alpha blocks. self.adaln_single = None self.use_additional_conditions = False if norm_type == "ada_norm_single": # self.use_additional_conditions = self.config.sample_size[0] == 128 # False, 128 -> 1024 # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use # additional conditions until we find better name self.adaln_single = AdaLayerNormSingle(inner_dim, use_additional_conditions=self.use_additional_conditions) self.caption_projection = None if caption_channels is not None: self.caption_projection = CaptionProjection(in_features=caption_channels, hidden_size=inner_dim) self.gradient_checkpointing = False # define temporal positional embedding # temp_pos_embed = self.get_1d_sincos_temp_embed(inner_dim, video_length) # 1152 hidden size # self.register_buffer("temp_pos_embed", torch.from_numpy(temp_pos_embed).float().unsqueeze(0), persistent=False) interpolation_scale = self.config.video_length // 5 # => 5 (= 5 our causalvideovae) has interpolation scale 1 interpolation_scale = max(interpolation_scale, 1) temp_pos_embed = get_1d_sincos_pos_embed(inner_dim, video_length, interpolation_scale=interpolation_scale) # 1152 hidden size self.register_buffer("temp_pos_embed", torch.from_numpy(temp_pos_embed).float().unsqueeze(0), persistent=False) def _set_gradient_checkpointing(self, module, value=False): self.gradient_checkpointing = value def forward( self, hidden_states: torch.Tensor, timestep: Optional[torch.LongTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, added_cond_kwargs: Dict[str, torch.Tensor] = None, class_labels: Optional[torch.LongTensor] = None, cross_attention_kwargs: Dict[str, Any] = None, attention_mask: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, use_image_num: int = 0, enable_temporal_attentions: bool = True, return_dict: bool = True, ): """ The [`Transformer2DModel`] forward method. Args: hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, frame, channel, height, width)` if continuous): Input `hidden_states`. encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): Conditional embeddings for cross attention layer. If not given, cross-attention defaults to self-attention. timestep ( `torch.LongTensor`, *optional*): Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in `AdaLayerZeroNorm`. cross_attention_kwargs ( `Dict[str, Any]`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). attention_mask ( `torch.Tensor`, *optional*): An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large negative values to the attention scores corresponding to "discard" tokens. encoder_attention_mask ( `torch.Tensor`, *optional*): Cross-attention mask applied to `encoder_hidden_states`. Two formats supported: * Mask `(batch, sequence_length)` True = keep, False = discard. * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard. If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format above. This bias will be added to the cross-attention scores. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. Returns: If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a `tuple` where the first element is the sample tensor. """ input_batch_size, c, frame, h, w = hidden_states.shape frame = frame - use_image_num hidden_states = rearrange(hidden_states, 'b c f h w -> (b f) c h w').contiguous() # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. # expects mask of shape: # [batch, key_tokens] # adds singleton query_tokens dimension: # [batch, 1, key_tokens] # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) if attention_mask is not None and attention_mask.ndim == 2: # assume that mask is expressed as: # (1 = keep, 0 = discard) # convert mask into a bias that can be added to attention scores: # (keep = +0, discard = -10000.0) attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 attention_mask = attention_mask.unsqueeze(1) # convert encoder_attention_mask to a bias the same way we do for attention_mask # if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: # ndim == 2 means no image joint # encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0 # encoder_attention_mask = encoder_attention_mask.unsqueeze(1) # encoder_attention_mask = repeat(encoder_attention_mask, 'b 1 l -> (b f) 1 l', f=frame).contiguous() # elif encoder_attention_mask is not None and encoder_attention_mask.ndim == 3: # ndim == 3 means image joint # encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0 # encoder_attention_mask_video = encoder_attention_mask[:, :1, ...] # encoder_attention_mask_video = repeat(encoder_attention_mask_video, 'b 1 l -> b (1 f) l', # f=frame).contiguous() # encoder_attention_mask_image = encoder_attention_mask[:, 1:, ...] # encoder_attention_mask = torch.cat([encoder_attention_mask_video, encoder_attention_mask_image], dim=1) # encoder_attention_mask = rearrange(encoder_attention_mask, 'b n l -> (b n) l').contiguous().unsqueeze(1) # Retrieve lora scale. lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 # 1. Input if self.is_input_patches: # here height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size num_patches = height * width hidden_states = self.pos_embed(hidden_states.to(self.dtype)) # alrady add positional embeddings if self.adaln_single is not None: if self.use_additional_conditions and added_cond_kwargs is None: raise ValueError( "`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`." ) # batch_size = hidden_states.shape[0] batch_size = input_batch_size timestep, embedded_timestep = self.adaln_single( timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype ) # 2. Blocks # if self.caption_projection is not None: # batch_size = hidden_states.shape[0] # encoder_hidden_states = self.caption_projection(encoder_hidden_states) # 3 120 1152 # # if use_image_num != 0 and self.training: # encoder_hidden_states_video = encoder_hidden_states[:, :1, ...] # encoder_hidden_states_video = repeat(encoder_hidden_states_video, 'b 1 t d -> b (1 f) t d', # f=frame).contiguous() # encoder_hidden_states_image = encoder_hidden_states[:, 1:, ...] # encoder_hidden_states = torch.cat([encoder_hidden_states_video, encoder_hidden_states_image], dim=1) # encoder_hidden_states_spatial = rearrange(encoder_hidden_states, 'b f t d -> (b f) t d').contiguous() # else: # encoder_hidden_states_spatial = repeat(encoder_hidden_states, 'b t d -> (b f) t d', # f=frame).contiguous() # prepare timesteps for spatial and temporal block timestep_spatial = repeat(timestep, 'b d -> (b f) d', f=frame + use_image_num).contiguous() timestep_temp = repeat(timestep, 'b d -> (b p) d', p=num_patches).contiguous() for i, (spatial_block, temp_block) in enumerate(zip(self.transformer_blocks, self.temporal_transformer_blocks)): if self.training and self.gradient_checkpointing: hidden_states = torch.utils.checkpoint.checkpoint( spatial_block, hidden_states, attention_mask, None, # encoder_hidden_states_spatial None, # encoder_attention_mask timestep_spatial, cross_attention_kwargs, class_labels, use_reentrant=False, ) if enable_temporal_attentions: hidden_states = rearrange(hidden_states, '(b f) t d -> (b t) f d', b=input_batch_size).contiguous() if use_image_num != 0: # image-video joitn training hidden_states_video = hidden_states[:, :frame, ...] hidden_states_image = hidden_states[:, frame:, ...] if i == 0: hidden_states_video = hidden_states_video + self.temp_pos_embed hidden_states_video = torch.utils.checkpoint.checkpoint( temp_block, hidden_states_video, None, # attention_mask None, # encoder_hidden_states None, # encoder_attention_mask timestep_temp, cross_attention_kwargs, class_labels, use_reentrant=False, ) hidden_states = torch.cat([hidden_states_video, hidden_states_image], dim=1) hidden_states = rearrange(hidden_states, '(b t) f d -> (b f) t d', b=input_batch_size).contiguous() else: if i == 0: hidden_states = hidden_states + self.temp_pos_embed hidden_states = torch.utils.checkpoint.checkpoint( temp_block, hidden_states, None, # attention_mask None, # encoder_hidden_states None, # encoder_attention_mask timestep_temp, cross_attention_kwargs, class_labels, use_reentrant=False, ) hidden_states = rearrange(hidden_states, '(b t) f d -> (b f) t d', b=input_batch_size).contiguous() else: hidden_states = spatial_block( hidden_states, attention_mask, None, # encoder_hidden_states_spatial None, # encoder_attention_mask timestep_spatial, cross_attention_kwargs, class_labels, ) if enable_temporal_attentions: hidden_states = rearrange(hidden_states, '(b f) t d -> (b t) f d', b=input_batch_size).contiguous() if use_image_num != 0 and self.training: hidden_states_video = hidden_states[:, :frame, ...] hidden_states_image = hidden_states[:, frame:, ...] hidden_states_video = temp_block( hidden_states_video, None, # attention_mask None, # encoder_hidden_states None, # encoder_attention_mask timestep_temp, cross_attention_kwargs, class_labels, ) hidden_states = torch.cat([hidden_states_video, hidden_states_image], dim=1) hidden_states = rearrange(hidden_states, '(b t) f d -> (b f) t d', b=input_batch_size).contiguous() else: if i == 0: hidden_states = hidden_states + self.temp_pos_embed hidden_states = temp_block( hidden_states, None, # attention_mask None, # encoder_hidden_states None, # encoder_attention_mask timestep_temp, cross_attention_kwargs, class_labels, ) hidden_states = rearrange(hidden_states, '(b t) f d -> (b f) t d', b=input_batch_size).contiguous() if self.is_input_patches: if self.config.norm_type != "ada_norm_single": conditioning = self.transformer_blocks[0].norm1.emb( timestep, class_labels, hidden_dtype=hidden_states.dtype ) shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1) hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None] hidden_states = self.proj_out_2(hidden_states) elif self.config.norm_type == "ada_norm_single": embedded_timestep = repeat(embedded_timestep, 'b d -> (b f) d', f=frame + use_image_num).contiguous() shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1) hidden_states = self.norm_out(hidden_states) # Modulation hidden_states = hidden_states * (1 + scale) + shift hidden_states = self.proj_out(hidden_states) # unpatchify if self.adaln_single is None: height = width = int(hidden_states.shape[1] ** 0.5) hidden_states = hidden_states.reshape( shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) ) hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) output = hidden_states.reshape( shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) ) output = rearrange(output, '(b f) c h w -> b c f h w', b=input_batch_size).contiguous() if not return_dict: return (output,) return Transformer3DModelOutput(sample=output) # def get_1d_sincos_temp_embed(self, embed_dim, length): # pos = torch.arange(0, length).unsqueeze(1) # return get_1d_sincos_pos_embed_from_grid(embed_dim, pos) @classmethod def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, **kwargs): if subfolder is not None: pretrained_model_path = os.path.join(pretrained_model_path, subfolder) 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) model = cls.from_config(config, **kwargs) # model_files = [ # os.path.join(pretrained_model_path, 'diffusion_pytorch_model.bin'), # os.path.join(pretrained_model_path, 'diffusion_pytorch_model.safetensors') # ] # model_file = None # for fp in model_files: # if os.path.exists(fp): # model_file = fp # if not model_file: # raise RuntimeError(f"{model_file} does not exist") # if model_file.split(".")[-1] == "safetensors": # from safetensors import safe_open # 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") # for k, v in model.state_dict().items(): # if 'temporal_transformer_blocks' in k: # state_dict.update({k: v}) # model.load_state_dict(state_dict) return model def forward_with_cfg(self, x, timestep, class_labels=None, cfg_scale=7.0, attention_mask=None): """ Forward pass of Latte, 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) model_out = self.forward(combined, timestep, class_labels=class_labels, attention_mask=attention_mask) # 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[:, :, :self.in_channels], model_out[:, :, self.in_channels:] 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=2) class LatteT2V(ModelMixin, ConfigMixin): _supports_gradient_checkpointing = True """ A 2D Transformer model for image-like data. Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. in_channels (`int`, *optional*): The number of channels in the input and output (specify if the input is **continuous**). num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**). This is fixed during training since it is used to learn a number of position embeddings. num_vector_embeds (`int`, *optional*): The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**). Includes the class for the masked latent pixel. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. num_embeds_ada_norm ( `int`, *optional*): The number of diffusion steps used during training. Pass if at least one of the norm_layers is `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`. attention_bias (`bool`, *optional*): Configure if the `TransformerBlocks` attention should contain a bias parameter. """ @register_to_config def __init__( self, num_attention_heads: int = 16, patch_size_t: int = 1, attention_head_dim: int = 88, in_channels: Optional[int] = None, out_channels: Optional[int] = None, num_layers: int = 1, dropout: float = 0.0, norm_num_groups: int = 32, cross_attention_dim: Optional[int] = None, attention_bias: bool = False, sample_size: Optional[int] = None, num_vector_embeds: Optional[int] = None, patch_size: Optional[int] = None, activation_fn: str = "geglu", num_embeds_ada_norm: Optional[int] = None, use_linear_projection: bool = False, only_cross_attention: bool = False, double_self_attention: bool = False, upcast_attention: bool = False, norm_type: str = "layer_norm", norm_elementwise_affine: bool = True, norm_eps: float = 1e-5, attention_type: str = "default", caption_channels: int = None, video_length: int = 16, attention_mode: str = 'flash' ): super().__init__() self.use_linear_projection = use_linear_projection self.num_attention_heads = num_attention_heads self.attention_head_dim = attention_head_dim inner_dim = num_attention_heads * attention_head_dim self.video_length = video_length conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)` # Define whether input is continuous or discrete depending on configuration self.is_input_continuous = (in_channels is not None) and (patch_size is None) self.is_input_vectorized = num_vector_embeds is not None self.is_input_patches = in_channels is not None and patch_size is not None if norm_type == "layer_norm" and num_embeds_ada_norm is not None: deprecation_message = ( f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or" " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config." " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect" " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it" " would be very nice if you could open a Pull request for the `transformer/config.json` file" ) deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False) norm_type = "ada_norm" if self.is_input_continuous and self.is_input_vectorized: raise ValueError( f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make" " sure that either `in_channels` or `num_vector_embeds` is None." ) elif self.is_input_vectorized and self.is_input_patches: raise ValueError( f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make" " sure that either `num_vector_embeds` or `num_patches` is None." ) elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches: raise ValueError( f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:" f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None." ) # 2. Define input layers if self.is_input_continuous: self.in_channels = in_channels self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) if use_linear_projection: self.proj_in = linear_cls(in_channels, inner_dim) else: self.proj_in = conv_cls(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) elif self.is_input_vectorized: assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size" assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed" self.height = sample_size[0] self.width = sample_size[1] self.num_vector_embeds = num_vector_embeds self.num_latent_pixels = self.height * self.width self.latent_image_embedding = ImagePositionalEmbeddings( num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width ) elif self.is_input_patches: assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size" self.height = sample_size[0] self.width = sample_size[1] self.patch_size = patch_size interpolation_scale = self.config.sample_size[0] // 64 # => 64 (= 512 pixart) has interpolation scale 1 interpolation_scale = max(interpolation_scale, 1) self.pos_embed = PatchEmbed( height=sample_size[0], width=sample_size[1], patch_size=patch_size, in_channels=in_channels, embed_dim=inner_dim, interpolation_scale=interpolation_scale, ) # 3. Define transformers blocks, spatial attention self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock( inner_dim, num_attention_heads, attention_head_dim, dropout=dropout, cross_attention_dim=cross_attention_dim, activation_fn=activation_fn, num_embeds_ada_norm=num_embeds_ada_norm, attention_bias=attention_bias, only_cross_attention=only_cross_attention, double_self_attention=double_self_attention, upcast_attention=upcast_attention, norm_type=norm_type, norm_elementwise_affine=norm_elementwise_affine, norm_eps=norm_eps, attention_type=attention_type, attention_mode=attention_mode ) for d in range(num_layers) ] ) # Define temporal transformers blocks self.temporal_transformer_blocks = nn.ModuleList( [ BasicTransformerBlock_( # one attention inner_dim, num_attention_heads, # num_attention_heads attention_head_dim, # attention_head_dim 72 dropout=dropout, cross_attention_dim=None, activation_fn=activation_fn, num_embeds_ada_norm=num_embeds_ada_norm, attention_bias=attention_bias, only_cross_attention=only_cross_attention, double_self_attention=False, upcast_attention=upcast_attention, norm_type=norm_type, norm_elementwise_affine=norm_elementwise_affine, norm_eps=norm_eps, attention_type=attention_type, attention_mode=attention_mode ) for d in range(num_layers) ] ) # 4. Define output layers self.out_channels = in_channels if out_channels is None else out_channels if self.is_input_continuous: # TODO: should use out_channels for continuous projections if use_linear_projection: self.proj_out = linear_cls(inner_dim, in_channels) else: self.proj_out = conv_cls(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) elif self.is_input_vectorized: self.norm_out = nn.LayerNorm(inner_dim) self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1) elif self.is_input_patches and norm_type != "ada_norm_single": self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6) self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim) self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels) elif self.is_input_patches and norm_type == "ada_norm_single": self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6) self.scale_shift_table = nn.Parameter(torch.randn(2, inner_dim) / inner_dim ** 0.5) self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels) # 5. PixArt-Alpha blocks. self.adaln_single = None self.use_additional_conditions = False if norm_type == "ada_norm_single": # self.use_additional_conditions = self.config.sample_size[0] == 128 # False, 128 -> 1024 # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use # additional conditions until we find better name self.adaln_single = AdaLayerNormSingle(inner_dim, use_additional_conditions=self.use_additional_conditions) self.caption_projection = None if caption_channels is not None: self.caption_projection = CaptionProjection(in_features=caption_channels, hidden_size=inner_dim) self.gradient_checkpointing = False # define temporal positional embedding # temp_pos_embed = self.get_1d_sincos_temp_embed(inner_dim, video_length) # 1152 hidden size interpolation_scale = self.config.video_length // 5 # => 5 (= 5 our causalvideovae) has interpolation scale 1 interpolation_scale = max(interpolation_scale, 1) temp_pos_embed = get_1d_sincos_pos_embed(inner_dim, video_length, interpolation_scale=interpolation_scale) # 1152 hidden size self.register_buffer("temp_pos_embed", torch.from_numpy(temp_pos_embed).float().unsqueeze(0), persistent=False) def _set_gradient_checkpointing(self, module, value=False): self.gradient_checkpointing = value def forward( self, hidden_states: torch.Tensor, timestep: Optional[torch.LongTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, added_cond_kwargs: Dict[str, torch.Tensor] = None, class_labels: Optional[torch.LongTensor] = None, cross_attention_kwargs: Dict[str, Any] = None, attention_mask: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, use_image_num: int = 0, enable_temporal_attentions: bool = True, return_dict: bool = True, ): """ The [`Transformer2DModel`] forward method. Args: hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, frame, channel, height, width)` if continuous): Input `hidden_states`. encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): Conditional embeddings for cross attention layer. If not given, cross-attention defaults to self-attention. timestep ( `torch.LongTensor`, *optional*): Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in `AdaLayerZeroNorm`. cross_attention_kwargs ( `Dict[str, Any]`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). attention_mask ( `torch.Tensor`, *optional*): An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large negative values to the attention scores corresponding to "discard" tokens. encoder_attention_mask ( `torch.Tensor`, *optional*): Cross-attention mask applied to `encoder_hidden_states`. Two formats supported: * Mask `(batch, sequence_length)` True = keep, False = discard. * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard. If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format above. This bias will be added to the cross-attention scores. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. Returns: If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a `tuple` where the first element is the sample tensor. """ input_batch_size, c, frame, h, w = hidden_states.shape # print(hidden_states.shape, input_batch_size, c, frame, h, w, use_image_num) # print(timestep) # print(encoder_hidden_states.shape) # print(encoder_attention_mask.shape) frame = frame - use_image_num # 20-4=16 hidden_states = rearrange(hidden_states, 'b c f h w -> (b f) c h w').contiguous() # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. # expects mask of shape: # [batch, key_tokens] # adds singleton query_tokens dimension: # [batch, 1, key_tokens] # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) if attention_mask is not None and attention_mask.ndim == 2: # assume that mask is expressed as: # (1 = keep, 0 = discard) # convert mask into a bias that can be added to attention scores: # (keep = +0, discard = -10000.0) attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 attention_mask = attention_mask.unsqueeze(1) attention_mask = attention_mask.to(self.dtype) # 1 + 4, 1 -> video condition, 4 -> image condition # convert encoder_attention_mask to a bias the same way we do for attention_mask if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: # ndim == 2 means no image joint encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0 encoder_attention_mask = encoder_attention_mask.unsqueeze(1) encoder_attention_mask = repeat(encoder_attention_mask, 'b 1 l -> (b f) 1 l', f=frame).contiguous() encoder_attention_mask = encoder_attention_mask.to(self.dtype) elif encoder_attention_mask is not None and encoder_attention_mask.ndim == 3: # ndim == 3 means image joint encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0 encoder_attention_mask_video = encoder_attention_mask[:, :1, ...] encoder_attention_mask_video = repeat(encoder_attention_mask_video, 'b 1 l -> b (1 f) l', f=frame).contiguous() encoder_attention_mask_image = encoder_attention_mask[:, 1:, ...] encoder_attention_mask = torch.cat([encoder_attention_mask_video, encoder_attention_mask_image], dim=1) encoder_attention_mask = rearrange(encoder_attention_mask, 'b n l -> (b n) l').contiguous().unsqueeze(1) encoder_attention_mask = encoder_attention_mask.to(self.dtype) # Retrieve lora scale. lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 # 1. Input if self.is_input_patches: # here height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size num_patches = height * width hidden_states = self.pos_embed(hidden_states.to(self.dtype)) # alrady add positional embeddings if self.adaln_single is not None: if self.use_additional_conditions and added_cond_kwargs is None: raise ValueError( "`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`." ) # batch_size = hidden_states.shape[0] batch_size = input_batch_size timestep, embedded_timestep = self.adaln_single( timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype ) # 2. Blocks if self.caption_projection is not None: batch_size = hidden_states.shape[0] encoder_hidden_states = self.caption_projection(encoder_hidden_states.to(self.dtype)) # 3 120 1152 if use_image_num != 0 and self.training: encoder_hidden_states_video = encoder_hidden_states[:, :1, ...] encoder_hidden_states_video = repeat(encoder_hidden_states_video, 'b 1 t d -> b (1 f) t d', f=frame).contiguous() encoder_hidden_states_image = encoder_hidden_states[:, 1:, ...] encoder_hidden_states = torch.cat([encoder_hidden_states_video, encoder_hidden_states_image], dim=1) encoder_hidden_states_spatial = rearrange(encoder_hidden_states, 'b f t d -> (b f) t d').contiguous() else: encoder_hidden_states_spatial = repeat(encoder_hidden_states, 'b t d -> (b f) t d', f=frame).contiguous() # prepare timesteps for spatial and temporal block timestep_spatial = repeat(timestep, 'b d -> (b f) d', f=frame + use_image_num).contiguous() timestep_temp = repeat(timestep, 'b d -> (b p) d', p=num_patches).contiguous() for i, (spatial_block, temp_block) in enumerate(zip(self.transformer_blocks, self.temporal_transformer_blocks)): if self.training and self.gradient_checkpointing: hidden_states = torch.utils.checkpoint.checkpoint( spatial_block, hidden_states, attention_mask, encoder_hidden_states_spatial, encoder_attention_mask, timestep_spatial, cross_attention_kwargs, class_labels, use_reentrant=False, ) if enable_temporal_attentions: hidden_states = rearrange(hidden_states, '(b f) t d -> (b t) f d', b=input_batch_size).contiguous() if use_image_num != 0: # image-video joitn training hidden_states_video = hidden_states[:, :frame, ...] hidden_states_image = hidden_states[:, frame:, ...] if i == 0: hidden_states_video = hidden_states_video + self.temp_pos_embed hidden_states_video = torch.utils.checkpoint.checkpoint( temp_block, hidden_states_video, None, # attention_mask None, # encoder_hidden_states None, # encoder_attention_mask timestep_temp, cross_attention_kwargs, class_labels, use_reentrant=False, ) hidden_states = torch.cat([hidden_states_video, hidden_states_image], dim=1) hidden_states = rearrange(hidden_states, '(b t) f d -> (b f) t d', b=input_batch_size).contiguous() else: if i == 0: hidden_states = hidden_states + self.temp_pos_embed hidden_states = torch.utils.checkpoint.checkpoint( temp_block, hidden_states, None, # attention_mask None, # encoder_hidden_states None, # encoder_attention_mask timestep_temp, cross_attention_kwargs, class_labels, use_reentrant=False, ) hidden_states = rearrange(hidden_states, '(b t) f d -> (b f) t d', b=input_batch_size).contiguous() else: hidden_states = spatial_block( hidden_states, attention_mask, encoder_hidden_states_spatial, encoder_attention_mask, timestep_spatial, cross_attention_kwargs, class_labels, ) if enable_temporal_attentions: # b c f h w, f = 16 + 4 hidden_states = rearrange(hidden_states, '(b f) t d -> (b t) f d', b=input_batch_size).contiguous() if use_image_num != 0 and self.training: hidden_states_video = hidden_states[:, :frame, ...] hidden_states_image = hidden_states[:, frame:, ...] hidden_states_video = temp_block( hidden_states_video, None, # attention_mask None, # encoder_hidden_states None, # encoder_attention_mask timestep_temp, cross_attention_kwargs, class_labels, ) hidden_states = torch.cat([hidden_states_video, hidden_states_image], dim=1) hidden_states = rearrange(hidden_states, '(b t) f d -> (b f) t d', b=input_batch_size).contiguous() else: if i == 0: hidden_states = hidden_states + self.temp_pos_embed hidden_states = temp_block( hidden_states, None, # attention_mask None, # encoder_hidden_states None, # encoder_attention_mask timestep_temp, cross_attention_kwargs, class_labels, ) hidden_states = rearrange(hidden_states, '(b t) f d -> (b f) t d', b=input_batch_size).contiguous() if self.is_input_patches: if self.config.norm_type != "ada_norm_single": conditioning = self.transformer_blocks[0].norm1.emb( timestep, class_labels, hidden_dtype=hidden_states.dtype ) shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1) hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None] hidden_states = self.proj_out_2(hidden_states) elif self.config.norm_type == "ada_norm_single": embedded_timestep = repeat(embedded_timestep, 'b d -> (b f) d', f=frame + use_image_num).contiguous() shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1) hidden_states = self.norm_out(hidden_states) # Modulation hidden_states = hidden_states * (1 + scale) + shift hidden_states = self.proj_out(hidden_states) # unpatchify if self.adaln_single is None: height = width = int(hidden_states.shape[1] ** 0.5) hidden_states = hidden_states.reshape( shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) ) hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) output = hidden_states.reshape( shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) ) output = rearrange(output, '(b f) c h w -> b c f h w', b=input_batch_size).contiguous() if not return_dict: return (output,) return Transformer3DModelOutput(sample=output) # def get_1d_sincos_temp_embed(self, embed_dim, length): # pos = torch.arange(0, length).unsqueeze(1) # return get_1d_sincos_pos_embed_from_grid(embed_dim, pos) @classmethod def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, **kwargs): if subfolder is not None: pretrained_model_path = os.path.join(pretrained_model_path, subfolder) 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) model = cls.from_config(config, **kwargs) # model_files = [ # os.path.join(pretrained_model_path, 'diffusion_pytorch_model.bin'), # os.path.join(pretrained_model_path, 'diffusion_pytorch_model.safetensors') # ] # model_file = None # for fp in model_files: # if os.path.exists(fp): # model_file = fp # if not model_file: # raise RuntimeError(f"{model_file} does not exist") # if model_file.split(".")[-1] == "safetensors": # from safetensors import safe_open # 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") # for k, v in model.state_dict().items(): # if 'temporal_transformer_blocks' in k: # state_dict.update({k: v}) # model.load_state_dict(state_dict) return model # depth = num_layers * 2 def Latte_XL_122(**kwargs): return Latte(num_layers=28, attention_head_dim=72, num_attention_heads=16, patch_size_t=1, patch_size=2, norm_type="ada_norm_single", **kwargs) def LatteClass_XL_122(**kwargs): return Latte(num_layers=28, attention_head_dim=72, num_attention_heads=16, patch_size_t=1, patch_size=2, norm_type="ada_norm_zero", **kwargs) def LatteT2V_XL_122(**kwargs): return LatteT2V(num_layers=28, attention_head_dim=72, num_attention_heads=16, patch_size_t=1, patch_size=2, norm_type="ada_norm_single", caption_channels=4096, cross_attention_dim=1152, **kwargs) Latte_models = { "Latte-XL/122": Latte_XL_122, "LatteClass-XL/122": LatteClass_XL_122, "LatteT2V-XL/122": LatteT2V_XL_122, } if __name__ == '__main__': a = json.load(open('./config.json', 'r')) model = LatteT2V.from_config(a) ckpt = torch.load(r"E:\下载\t2v.pt", map_location='cpu')['model'] model.load_state_dict(ckpt) print(model)