StableSigner / One-to-All-Animation /video-generation /opensora /models /diffusion /udit /modules.py
| from einops import rearrange | |
| from torch import nn | |
| import torch | |
| import numpy as np | |
| from einops import rearrange, repeat | |
| from typing import Any, Dict, Optional, Tuple | |
| from diffusers.utils.torch_utils import maybe_allow_in_graph | |
| from typing import Any, Dict, Optional | |
| import torch | |
| import torch.nn.functional as F | |
| from torch import nn | |
| import diffusers | |
| from diffusers.utils import deprecate, logging | |
| from diffusers.utils.torch_utils import maybe_allow_in_graph | |
| from diffusers.models.attention import FeedForward, GatedSelfAttentionDense | |
| from diffusers.models.attention_processor import Attention as Attention_ | |
| from diffusers.models.embeddings import SinusoidalPositionalEmbedding | |
| from diffusers.models.normalization import AdaLayerNorm, AdaLayerNormContinuous, AdaLayerNormZero, RMSNorm | |
| from .rope import PositionGetter3D, RoPE3D | |
| import re | |
| try: | |
| import torch_npu | |
| from opensora.npu_config import npu_config, set_run_dtype | |
| except: | |
| torch_npu = None | |
| npu_config = None | |
| set_run_dtype = None | |
| logger = logging.get_logger(__name__) | |
| def get_3d_sincos_pos_embed( | |
| embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=16, | |
| ): | |
| """ | |
| grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or | |
| [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) | |
| """ | |
| # if isinstance(grid_size, int): | |
| # grid_size = (grid_size, grid_size) | |
| grid_t = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size[0]) / interpolation_scale[0] | |
| grid_h = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size[1]) / interpolation_scale[1] | |
| grid_w = np.arange(grid_size[2], dtype=np.float32) / (grid_size[2] / base_size[2]) / interpolation_scale[2] | |
| grid = np.meshgrid(grid_w, grid_h, grid_t) # here w goes first | |
| grid = np.stack(grid, axis=0) | |
| grid = grid.reshape([3, 1, grid_size[2], grid_size[1], grid_size[0]]) | |
| pos_embed = get_3d_sincos_pos_embed_from_grid(embed_dim, grid) | |
| if cls_token and extra_tokens > 0: | |
| pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) | |
| return pos_embed | |
| def get_3d_sincos_pos_embed_from_grid(embed_dim, grid): | |
| if embed_dim % 3 != 0: | |
| raise ValueError("embed_dim must be divisible by 3") | |
| # use 1/3 of dimensions to encode grid_t/h/w | |
| emb_t = get_1d_sincos_pos_embed_from_grid(embed_dim // 3, grid[0]) # (T*H*W, D/3) | |
| emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 3, grid[1]) # (T*H*W, D/3) | |
| emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 3, grid[2]) # (T*H*W, D/3) | |
| emb = np.concatenate([emb_t, emb_h, emb_w], axis=1) # (T*H*W, D) | |
| return emb | |
| def get_2d_sincos_pos_embed( | |
| embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=16, | |
| ): | |
| """ | |
| grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or | |
| [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) | |
| """ | |
| # if isinstance(grid_size, int): | |
| # grid_size = (grid_size, grid_size) | |
| grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size[0]) / interpolation_scale[0] | |
| grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size[1]) / interpolation_scale[1] | |
| grid = np.meshgrid(grid_w, grid_h) # here w goes first | |
| grid = np.stack(grid, axis=0) | |
| grid = grid.reshape([2, 1, grid_size[1], grid_size[0]]) | |
| pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) | |
| if cls_token and extra_tokens > 0: | |
| pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) | |
| return pos_embed | |
| def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): | |
| if embed_dim % 2 != 0: | |
| raise ValueError("embed_dim must be divisible by 2") | |
| # use 1/3 of dimensions to encode grid_t/h/w | |
| emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) | |
| emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) | |
| emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) | |
| return emb | |
| def get_1d_sincos_pos_embed( | |
| embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=16, | |
| ): | |
| """ | |
| grid_size: int of the grid return: pos_embed: [grid_size, embed_dim] or | |
| [1+grid_size, embed_dim] (w/ or w/o cls_token) | |
| """ | |
| # if isinstance(grid_size, int): | |
| # grid_size = (grid_size, grid_size) | |
| grid = np.arange(grid_size, dtype=np.float32) / (grid_size / base_size) / interpolation_scale | |
| pos_embed = get_1d_sincos_pos_embed_from_grid(embed_dim, grid) # (H*W, D/2) | |
| if cls_token and extra_tokens > 0: | |
| pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) | |
| return pos_embed | |
| def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): | |
| """ | |
| embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D) | |
| """ | |
| if embed_dim % 2 != 0: | |
| raise ValueError("embed_dim must be divisible by 2") | |
| omega = np.arange(embed_dim // 2, dtype=np.float64) | |
| omega /= embed_dim / 2.0 | |
| omega = 1.0 / 10000**omega # (D/2,) | |
| pos = pos.reshape(-1) # (M,) | |
| out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product | |
| emb_sin = np.sin(out) # (M, D/2) | |
| emb_cos = np.cos(out) # (M, D/2) | |
| emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) | |
| return emb | |
| class FP32_Layernorm(nn.LayerNorm): | |
| def forward(self, inputs: torch.Tensor) -> torch.Tensor: | |
| origin_dtype = inputs.dtype | |
| return F.layer_norm(inputs.float(), self.normalized_shape, self.weight.float() if self.weight is not None else None, | |
| self.bias.float() if self.bias is not None else None, self.eps).to(origin_dtype) | |
| class FP32_SiLU(nn.SiLU): | |
| def forward(self, inputs: torch.Tensor) -> torch.Tensor: | |
| return torch.nn.functional.silu(inputs.float(), inplace=self.inplace).to(inputs.dtype) | |
| class FP32_GELU(nn.GELU): | |
| def forward(self, inputs: torch.Tensor) -> torch.Tensor: | |
| return torch.nn.functional.gelu(inputs.float(), approximate=self.approximate).to(inputs.dtype) | |
| class OverlapPatchEmbed3D(nn.Module): | |
| """2D Image to Patch Embedding but with 3D position embedding""" | |
| def __init__( | |
| self, | |
| num_frames=1, | |
| height=224, | |
| width=224, | |
| patch_size_t=1, | |
| patch_size=16, | |
| in_channels=3, | |
| embed_dim=768, | |
| layer_norm=False, | |
| flatten=True, | |
| bias=True, | |
| interpolation_scale=(1, 1), | |
| interpolation_scale_t=1, | |
| use_abs_pos=False, | |
| ): | |
| super().__init__() | |
| # assert patch_size_t == 1 and patch_size == 1 | |
| self.use_abs_pos = use_abs_pos | |
| self.flatten = flatten | |
| self.layer_norm = layer_norm | |
| self.proj = nn.Conv3d( | |
| in_channels, embed_dim, kernel_size=3, padding=1, stride=(patch_size_t, patch_size, patch_size), bias=bias | |
| ) | |
| if layer_norm: | |
| self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6) | |
| else: | |
| self.norm = None | |
| self.patch_size_t = patch_size_t | |
| self.patch_size = patch_size | |
| # See: | |
| # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L161 | |
| self.height, self.width = height // patch_size, width // patch_size | |
| self.base_size = (height // patch_size, width // patch_size) | |
| self.interpolation_scale = (interpolation_scale[0], interpolation_scale[1]) | |
| pos_embed = get_2d_sincos_pos_embed( | |
| embed_dim, (self.height, self.width), base_size=self.base_size, interpolation_scale=self.interpolation_scale | |
| ) | |
| self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=False) | |
| self.num_frames = (num_frames - 1) // patch_size_t + 1 if num_frames % 2 == 1 else num_frames // patch_size_t | |
| self.base_size_t = (num_frames - 1) // patch_size_t + 1 if num_frames % 2 == 1 else num_frames // patch_size_t | |
| self.interpolation_scale_t = interpolation_scale_t | |
| temp_pos_embed = get_1d_sincos_pos_embed(embed_dim, self.num_frames, base_size=self.base_size_t, interpolation_scale=self.interpolation_scale_t) | |
| self.register_buffer("temp_pos_embed", torch.from_numpy(temp_pos_embed).float().unsqueeze(0), persistent=False) | |
| # self.temp_embed_gate = nn.Parameter(torch.tensor([0.0])) | |
| def forward(self, latent, num_frames): | |
| b, _, _, _, _ = latent.shape | |
| video_latent, image_latent = None, None | |
| # b c 1 h w | |
| # assert latent.shape[-3] == 1 and num_frames == 1 | |
| num_frames = latent.shape[-3] // self.patch_size_t | |
| height, width = latent.shape[-2] // self.patch_size, latent.shape[-1] // self.patch_size | |
| # latent = rearrange(latent, 'b c t h w -> (b t) c h w') | |
| latent = self.proj(latent) | |
| if self.flatten: | |
| # latent = latent.flatten(2).transpose(1, 2) # BT C H W -> BT N C | |
| latent = rearrange(latent, 'b c t h w -> (b t) (h w) c ') | |
| if self.layer_norm: | |
| latent = self.norm(latent) | |
| if self.use_abs_pos: | |
| # Interpolate positional embeddings if needed. | |
| # (For PixArt-Alpha: https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L162C151-L162C160) | |
| if self.height != height or self.width != width: | |
| # raise NotImplementedError | |
| pos_embed = get_2d_sincos_pos_embed( | |
| embed_dim=self.pos_embed.shape[-1], | |
| grid_size=(height, width), | |
| base_size=self.base_size, | |
| interpolation_scale=self.interpolation_scale, | |
| ) | |
| pos_embed = torch.from_numpy(pos_embed) | |
| pos_embed = pos_embed.float().unsqueeze(0).to(latent.device) | |
| else: | |
| pos_embed = self.pos_embed | |
| if self.num_frames != num_frames: | |
| # import ipdb;ipdb.set_trace() | |
| # raise NotImplementedError | |
| temp_pos_embed = get_1d_sincos_pos_embed( | |
| embed_dim=self.temp_pos_embed.shape[-1], | |
| grid_size=num_frames, | |
| base_size=self.base_size_t, | |
| interpolation_scale=self.interpolation_scale_t, | |
| ) | |
| temp_pos_embed = torch.from_numpy(temp_pos_embed) | |
| temp_pos_embed = temp_pos_embed.float().unsqueeze(0).to(latent.device) | |
| else: | |
| temp_pos_embed = self.temp_pos_embed | |
| latent = (latent + pos_embed).to(latent.dtype) | |
| latent = rearrange(latent, '(b t) n c -> b t n c', b=b) | |
| video_latent = latent | |
| if self.use_abs_pos: | |
| # temp_pos_embed = temp_pos_embed.unsqueeze(2) * self.temp_embed_gate.tanh() | |
| temp_pos_embed = temp_pos_embed.unsqueeze(2) | |
| video_latent = (video_latent + temp_pos_embed).to(video_latent.dtype) | |
| video_latent = rearrange(video_latent, 'b t n c -> b (t n) c') | |
| return video_latent | |
| class PatchEmbed2D(nn.Module): | |
| """2D Image to Patch Embedding but with 3D position embedding""" | |
| def __init__( | |
| self, | |
| num_frames=1, | |
| height=224, | |
| width=224, | |
| patch_size_t=1, | |
| patch_size=16, | |
| in_channels=3, | |
| embed_dim=768, | |
| layer_norm=False, | |
| flatten=True, | |
| bias=True, | |
| interpolation_scale=(1, 1), | |
| interpolation_scale_t=1, | |
| use_abs_pos=True, | |
| ): | |
| super().__init__() | |
| # assert num_frames == 1 | |
| self.use_abs_pos = use_abs_pos | |
| self.flatten = flatten | |
| self.layer_norm = layer_norm | |
| self.proj = nn.Conv2d( | |
| in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=(patch_size, patch_size), bias=bias | |
| ) | |
| if layer_norm: | |
| self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6) | |
| else: | |
| self.norm = None | |
| self.patch_size_t = patch_size_t | |
| self.patch_size = patch_size | |
| # See: | |
| # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L161 | |
| self.height, self.width = height // patch_size, width // patch_size | |
| self.base_size = (height // patch_size, width // patch_size) | |
| self.interpolation_scale = (interpolation_scale[0], interpolation_scale[1]) | |
| pos_embed = get_2d_sincos_pos_embed( | |
| embed_dim, (self.height, self.width), base_size=self.base_size, interpolation_scale=self.interpolation_scale | |
| ) | |
| self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=False) | |
| self.num_frames = (num_frames - 1) // patch_size_t + 1 if num_frames % 2 == 1 else num_frames // patch_size_t | |
| self.base_size_t = (num_frames - 1) // patch_size_t + 1 if num_frames % 2 == 1 else num_frames // patch_size_t | |
| self.interpolation_scale_t = interpolation_scale_t | |
| temp_pos_embed = get_1d_sincos_pos_embed(embed_dim, self.num_frames, base_size=self.base_size_t, interpolation_scale=self.interpolation_scale_t) | |
| self.register_buffer("temp_pos_embed", torch.from_numpy(temp_pos_embed).float().unsqueeze(0), persistent=False) | |
| # self.temp_embed_gate = nn.Parameter(torch.tensor([0.0])) | |
| def forward(self, latent, num_frames): | |
| b, _, _, _, _ = latent.shape | |
| video_latent, image_latent = None, None | |
| # b c 1 h w | |
| # assert latent.shape[-3] == 1 and num_frames == 1 | |
| height, width = latent.shape[-2] // self.patch_size, latent.shape[-1] // self.patch_size | |
| latent = rearrange(latent, 'b c t h w -> (b t) c h w') | |
| latent = self.proj(latent) | |
| if self.flatten: | |
| latent = latent.flatten(2).transpose(1, 2) # BT C H W -> BT N C | |
| if self.layer_norm: | |
| latent = self.norm(latent) | |
| if self.use_abs_pos: | |
| # Interpolate positional embeddings if needed. | |
| # (For PixArt-Alpha: https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L162C151-L162C160) | |
| if self.height != height or self.width != width: | |
| # raise NotImplementedError | |
| pos_embed = get_2d_sincos_pos_embed( | |
| embed_dim=self.pos_embed.shape[-1], | |
| grid_size=(height, width), | |
| base_size=self.base_size, | |
| interpolation_scale=self.interpolation_scale, | |
| ) | |
| pos_embed = torch.from_numpy(pos_embed) | |
| pos_embed = pos_embed.float().unsqueeze(0).to(latent.device) | |
| else: | |
| pos_embed = self.pos_embed | |
| if self.num_frames != num_frames: | |
| # import ipdb;ipdb.set_trace() | |
| # raise NotImplementedError | |
| if get_sequence_parallel_state(): | |
| if npu_config is not None: | |
| sp_size = hccl_info.world_size | |
| temp_pos_embed = get_1d_sincos_pos_embed( | |
| embed_dim=self.temp_pos_embed.shape[-1], | |
| grid_size=num_frames * sp_size, | |
| base_size=self.base_size_t, | |
| interpolation_scale=self.interpolation_scale_t, | |
| ) | |
| rank = hccl_info.rank % sp_size | |
| st_frame = rank * num_frames | |
| ed_frame = st_frame + num_frames | |
| temp_pos_embed = temp_pos_embed[st_frame: ed_frame] | |
| else: | |
| sp_size = nccl_info.world_size | |
| temp_pos_embed = get_1d_sincos_pos_embed( | |
| embed_dim=self.temp_pos_embed.shape[-1], | |
| grid_size=num_frames * sp_size, | |
| base_size=self.base_size_t, | |
| interpolation_scale=self.interpolation_scale_t, | |
| ) | |
| rank = nccl_info.rank % sp_size | |
| st_frame = rank * num_frames | |
| ed_frame = st_frame + num_frames | |
| temp_pos_embed = temp_pos_embed[st_frame: ed_frame] | |
| else: | |
| temp_pos_embed = get_1d_sincos_pos_embed( | |
| embed_dim=self.temp_pos_embed.shape[-1], | |
| grid_size=num_frames, | |
| base_size=self.base_size_t, | |
| interpolation_scale=self.interpolation_scale_t, | |
| ) | |
| temp_pos_embed = torch.from_numpy(temp_pos_embed) | |
| temp_pos_embed = temp_pos_embed.float().unsqueeze(0).to(latent.device) | |
| else: | |
| temp_pos_embed = self.temp_pos_embed | |
| latent = (latent + pos_embed).to(latent.dtype) | |
| latent = rearrange(latent, '(b t) n c -> b t n c', b=b) | |
| video_latent = latent | |
| if self.use_abs_pos: | |
| # temp_pos_embed = temp_pos_embed.unsqueeze(2) * self.temp_embed_gate.tanh() | |
| temp_pos_embed = temp_pos_embed.unsqueeze(2) | |
| video_latent = (video_latent + temp_pos_embed).to(video_latent.dtype) | |
| video_latent = rearrange(video_latent, 'b t n c -> b (t n) c') | |
| return video_latent | |
| class Attention(Attention_): | |
| def __init__(self, downsampler, attention_mode, use_rope, interpolation_scale_thw, **kwags): | |
| processor = AttnProcessor2_0(attention_mode=attention_mode, use_rope=use_rope, interpolation_scale_thw=interpolation_scale_thw) | |
| super().__init__(processor=processor, **kwags) | |
| self.downsampler = None | |
| # if downsampler: # downsampler k155_s122 | |
| # downsampler_ker_size = list(re.search(r'k(\d{2,3})', downsampler).group(1)) # 122 | |
| # down_factor = list(re.search(r's(\d{2,3})', downsampler).group(1)) | |
| # downsampler_ker_size = [int(i) for i in downsampler_ker_size] | |
| # downsampler_padding = [(i - 1) // 2 for i in downsampler_ker_size] | |
| # down_factor = [int(i) for i in down_factor] | |
| # if len(downsampler_ker_size) == 2: | |
| # self.downsampler = DownSampler2d(kwags['query_dim'], kwags['query_dim'], kernel_size=downsampler_ker_size, stride=1, | |
| # padding=downsampler_padding, groups=kwags['query_dim'], down_factor=down_factor, | |
| # down_shortcut=True) | |
| # elif len(downsampler_ker_size) == 3: | |
| # self.downsampler = DownSampler3d(kwags['query_dim'], kwags['query_dim'], kernel_size=downsampler_ker_size, stride=1, | |
| # padding=downsampler_padding, groups=kwags['query_dim'], down_factor=down_factor, | |
| # down_shortcut=True) | |
| # self.q_norm = nn.LayerNorm(kwags['dim_head'], elementwise_affine=True, eps=1e-6) | |
| # self.k_norm = nn.LayerNorm(kwags['dim_head'], elementwise_affine=True, eps=1e-6) | |
| class DownSampler3d(nn.Module): | |
| def __init__(self, *args, **kwargs): | |
| ''' Required kwargs: down_factor, downsampler''' | |
| super().__init__() | |
| self.down_factor = kwargs.pop('down_factor') | |
| self.down_shortcut = kwargs.pop('down_shortcut') | |
| self.layer = nn.Conv3d(*args, **kwargs) | |
| def forward(self, x, attention_mask, t, h, w): | |
| b = x.shape[0] | |
| x = rearrange(x, 'b (t h w) d -> b d t h w', t=t, h=h, w=w) | |
| if npu_config is None: | |
| x = self.layer(x) + (x if self.down_shortcut else 0) | |
| else: | |
| x_dtype = x.dtype | |
| x = npu_config.run_conv3d(self.layer, x, x_dtype) + (x if self.down_shortcut else 0) | |
| self.t = t//self.down_factor[0] | |
| self.h = h//self.down_factor[1] | |
| self.w = w//self.down_factor[2] | |
| # import ipdb;ipdb.set_trace() | |
| x = rearrange(x, 'b d (t dt) (h dh) (w dw) -> (b dt dh dw) (t h w) d', | |
| t=t//self.down_factor[0], h=h//self.down_factor[1], w=w//self.down_factor[2], | |
| dt=self.down_factor[0], dh=self.down_factor[1], dw=self.down_factor[2]) | |
| attention_mask = rearrange(attention_mask, 'b 1 (t h w) -> b 1 t h w', t=t, h=h, w=w) | |
| attention_mask = rearrange(attention_mask, 'b 1 (t dt) (h dh) (w dw) -> (b dt dh dw) 1 (t h w)', | |
| t=t//self.down_factor[0], h=h//self.down_factor[1], w=w//self.down_factor[2], | |
| dt=self.down_factor[0], dh=self.down_factor[1], dw=self.down_factor[2]) | |
| return x, attention_mask | |
| def reverse(self, x, t, h, w): | |
| # import ipdb;ipdb.set_trace() | |
| x = rearrange(x, '(b dt dh dw) (t h w) d -> b (t dt h dh w dw) d', | |
| t=t, h=h, w=w, | |
| dt=self.down_factor[0], dh=self.down_factor[1], dw=self.down_factor[2]) | |
| return x | |
| class DownSampler2d(nn.Module): | |
| def __init__(self, *args, **kwargs): | |
| ''' Required kwargs: down_factor, downsampler''' | |
| super().__init__() | |
| self.down_factor = kwargs.pop('down_factor') | |
| self.down_shortcut = kwargs.pop('down_shortcut') | |
| self.layer = nn.Conv2d(*args, **kwargs) | |
| def forward(self, x, attention_mask, t, h, w): | |
| # import ipdb;ipdb.set_trace() | |
| b = x.shape[0] | |
| x = rearrange(x, 'b (t h w) d -> (b t) d h w', t=t, h=h, w=w) | |
| x = self.layer(x) + (x if self.down_shortcut else 0) | |
| self.t = 1 | |
| self.h = h//self.down_factor[0] | |
| self.w = w//self.down_factor[1] | |
| x = rearrange(x, 'b d (h dh) (w dw) -> (b dh dw) (h w) d', | |
| h=h//self.down_factor[0], w=w//self.down_factor[1], | |
| dh=self.down_factor[0], dw=self.down_factor[1]) | |
| attention_mask = rearrange(attention_mask, 'b 1 (t h w) -> (b t) 1 h w', h=h, w=w) | |
| attention_mask = rearrange(attention_mask, 'b 1 (h dh) (w dw) -> (b dh dw) 1 (h w)', | |
| h=h//self.down_factor[0], w=w//self.down_factor[1], | |
| dh=self.down_factor[0], dw=self.down_factor[1]) | |
| return x, attention_mask | |
| def reverse(self, x, t, h, w): | |
| x = rearrange(x, '(b t dh dw) (h w) d -> b (t h dh w dw) d', | |
| t=t, h=h, w=w, | |
| dh=self.down_factor[0], dw=self.down_factor[1]) | |
| return x | |
| class AttnProcessor2_0: | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
| """ | |
| def __init__(self, attention_mode='xformers', use_rope=False, interpolation_scale_thw=(1, 1, 1)): | |
| self.use_rope = use_rope | |
| self.interpolation_scale_thw = interpolation_scale_thw | |
| if self.use_rope: | |
| self._init_rope(interpolation_scale_thw) | |
| self.attention_mode = attention_mode | |
| if not hasattr(F, "scaled_dot_product_attention"): | |
| raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") | |
| def _init_rope(self, interpolation_scale_thw): | |
| self.rope = RoPE3D(interpolation_scale_thw=interpolation_scale_thw) | |
| self.position_getter = PositionGetter3D() | |
| def __call__( | |
| self, | |
| attn: Attention, | |
| hidden_states: torch.FloatTensor, | |
| encoder_hidden_states: Optional[torch.FloatTensor] = None, | |
| attention_mask: Optional[torch.FloatTensor] = None, | |
| temb: Optional[torch.FloatTensor] = None, | |
| frame: int = 8, | |
| height: int = 16, | |
| width: int = 16, | |
| *args, | |
| **kwargs, | |
| ) -> torch.FloatTensor: | |
| if len(args) > 0 or kwargs.get("scale", None) is not None: | |
| deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." | |
| deprecate("scale", "1.0.0", deprecation_message) | |
| if attn.downsampler is not None: | |
| hidden_states, attention_mask = attn.downsampler(hidden_states, attention_mask, t=frame, h=height, w=width) | |
| frame, height, width = attn.downsampler.t, attn.downsampler.h, attn.downsampler.w | |
| residual = hidden_states | |
| if attn.spatial_norm is not None: | |
| hidden_states = attn.spatial_norm(hidden_states, temb) | |
| input_ndim = hidden_states.ndim | |
| # if input_ndim == 4: | |
| # batch_size, channel, height, width = hidden_states.shape | |
| # hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) | |
| batch_size, sequence_length, _ = ( | |
| hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape | |
| ) | |
| if attention_mask is not None: | |
| if npu_config is None: | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| # scaled_dot_product_attention expects attention_mask shape to be | |
| # (batch, heads, source_length, target_length) | |
| attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) | |
| else: | |
| if npu_config.enable_FA: | |
| attention_mask = attention_mask.to(torch.bool) | |
| attention_mask = attention_mask.view(batch_size, 1, -1, attention_mask.shape[-1]) | |
| attention_mask = attention_mask.repeat(1, 1, hidden_states.shape[1], 1) | |
| if attn.group_norm is not None: | |
| hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) | |
| query = attn.to_q(hidden_states) | |
| if encoder_hidden_states is None: | |
| encoder_hidden_states = hidden_states | |
| elif attn.norm_cross: | |
| encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) | |
| key = attn.to_k(encoder_hidden_states) | |
| value = attn.to_v(encoder_hidden_states) | |
| inner_dim = key.shape[-1] | |
| head_dim = inner_dim // attn.heads | |
| if npu_config is not None and npu_config.on_npu: | |
| if npu_config.enable_FA and query.dtype == torch.float32: | |
| dtype = torch.bfloat16 | |
| else: | |
| dtype = None | |
| if self.use_rope: | |
| query = query.view(batch_size, -1, attn.heads, head_dim) | |
| key = key.view(batch_size, -1, attn.heads, head_dim) | |
| # require the shape of (batch_size x nheads x ntokens x dim) | |
| pos_thw = self.position_getter(batch_size, t=frame, h=height, w=width, device=query.device) | |
| query = self.rope(query, pos_thw) | |
| key = self.rope(key, pos_thw) | |
| query = query.view(batch_size, -1, attn.heads * head_dim) | |
| key = key.view(batch_size, -1, attn.heads * head_dim) | |
| with set_run_dtype(query, dtype): | |
| query, key, value = npu_config.set_current_run_dtype([query, key, value]) | |
| hidden_states = npu_config.run_attention(query, key, value, attention_mask, "BSH", | |
| head_dim, attn.heads) | |
| hidden_states = npu_config.restore_dtype(hidden_states) | |
| else: | |
| query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| # qk norm | |
| # query = attn.q_norm(query) | |
| # key = attn.k_norm(key) | |
| if self.use_rope: | |
| # require the shape of (batch_size x nheads x ntokens x dim) | |
| pos_thw = self.position_getter(batch_size, t=frame, h=height, w=width, device=query.device) | |
| query = self.rope(query, pos_thw) | |
| key = self.rope(key, pos_thw) | |
| value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| # 0, -10000 ->(bool) False, True ->(any) True ->(not) False | |
| # 0, 0 ->(bool) False, False ->(any) False ->(not) True | |
| if attention_mask is None or not torch.any(attention_mask.bool()): # 0 mean visible | |
| attention_mask = None | |
| # the output of sdp = (batch, num_heads, seq_len, head_dim) | |
| # TODO: add support for attn.scale when we move to Torch 2.1 | |
| if self.attention_mode == 'flash' and attention_mask is None: | |
| with torch.backends.cuda.sdp_kernel(enable_math=False, enable_flash=True, enable_mem_efficient=False): | |
| hidden_states = F.scaled_dot_product_attention( | |
| query, key, value, dropout_p=0.0, is_causal=False | |
| ) | |
| elif self.attention_mode == 'xformers' or (self.attention_mode == 'flash' and attention_mask is not None): | |
| with torch.backends.cuda.sdp_kernel(enable_math=False, enable_flash=False, enable_mem_efficient=True): | |
| hidden_states = F.scaled_dot_product_attention( | |
| query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
| ) | |
| elif self.attention_mode == 'math': | |
| raise RuntimeError | |
| hidden_states = F.scaled_dot_product_attention( | |
| query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
| ) | |
| else: | |
| raise NotImplementedError(f'Found attention_mode: {self.attention_mode}') | |
| hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
| hidden_states = hidden_states.to(query.dtype) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| if input_ndim == 4: | |
| hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
| if attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| if attn.downsampler is not None: | |
| hidden_states = attn.downsampler.reverse(hidden_states, t=frame, h=height, w=width) | |
| return hidden_states | |
| class PixelUnshuffle(nn.Module): | |
| def __init__(self, ratio, ratio_t=None): | |
| super().__init__() | |
| self.r = ratio | |
| self.r_t = ratio_t if ratio_t else 1 | |
| def forward(self, x): | |
| # if self.r_t is not None and self.r_t != 1: | |
| if x.ndim == 5: | |
| b, c, t, h, w = x.shape | |
| # import ipdb;ipdb.set_trace() | |
| assert t % self.r_t == 0 and h % self.r == 0 and w % self.r == 0 | |
| x = rearrange(x, 'b c (t r1) (h r2) (w r3) -> b (c r1 r2 r3) t h w', r1=self.r_t, r2=self.r, r3=self.r) | |
| else: | |
| b, c, h, w = x.shape | |
| assert h % self.r == 0 and w % self.r == 0 | |
| x = rearrange(x, 'b c (h r2) (w r3) -> b (c r2 r3) h w', r2=self.r, r3=self.r) | |
| return x | |
| class PixelShuffle(nn.Module): | |
| def __init__(self, ratio, ratio_t=None): | |
| super().__init__() | |
| self.r = ratio | |
| self.r_t = ratio_t if ratio_t else 1 | |
| def forward(self, x): | |
| if x.ndim == 5: | |
| b, c, t, h, w = x.shape | |
| # import ipdb;ipdb.set_trace() | |
| assert c % (self.r_t*self.r*self.r) == 0 | |
| x = rearrange(x, 'b (c r1 r2 r3) t h w -> b c (t r1) (h r2) (w r3)', r1=self.r_t, r2=self.r, r3=self.r) | |
| else: | |
| b, c, h, w = x.shape | |
| assert c % (self.r*self.r) == 0 | |
| x = rearrange(x, 'b (c r2 r3) h w -> b c (h r2) (w r3)', r2=self.r, r3=self.r) | |
| return x | |
| class Downsample3d(nn.Module): | |
| def __init__(self, n_feat): | |
| super(Downsample3d, self).__init__() | |
| self.body = nn.Sequential(nn.Conv3d(n_feat, n_feat // 4, kernel_size=3, stride=1, padding=1, bias=False), | |
| PixelUnshuffle(2, 2)) | |
| def forward(self, x, attention_mask, frames, height, width, pad_h=0, pad_w=0): | |
| x = rearrange(x, 'b (t h w) d -> b d t h w', t=frames, h=height, w=width) | |
| # x = F.pad(x, (0, pad_w, 0, pad_h, 0, 0), mode='reflect') | |
| x = F.pad(x, (0, pad_w, 0, pad_h, 0, 0)) | |
| if npu_config is None: | |
| x = self.body(x) | |
| else: | |
| x_dtype = x.dtype | |
| x = npu_config.run_conv3d(self.body, x, x_dtype) | |
| x = rearrange(x, 'b d t h w -> b (t h w) d') | |
| attention_mask = rearrange(attention_mask, 'b 1 (t h w) -> b 1 t h w', t=frames, h=height, w=width) | |
| attention_mask = F.pad(attention_mask, (0, pad_w, 0, pad_h, 0, 0)) | |
| attention_mask = F.max_pool3d(attention_mask, kernel_size=2, stride=2) | |
| attention_mask = rearrange(attention_mask, 'b 1 t h w -> b 1 (t h w)') | |
| attention_bias = (1 - attention_mask.bool().to(x.dtype)) * -10000.0 | |
| return x, attention_bias, attention_mask | |
| class Upsample3d(nn.Module): | |
| def __init__(self, n_feat): | |
| super(Upsample3d, self).__init__() | |
| self.body = nn.Sequential(nn.Conv3d(n_feat, n_feat * 4, kernel_size=3, stride=1, padding=1, bias=False), | |
| PixelShuffle(2, 2)) | |
| def forward(self, x, attention_mask, frames, height, width, pad_h=0, pad_w=0): | |
| x = rearrange(x, 'b (t h w) d -> b d t h w', t=frames, h=height, w=width) | |
| if npu_config is None: | |
| x = self.body(x) | |
| else: | |
| x_dtype = x.dtype | |
| x = npu_config.run_conv3d(self.body, x, x_dtype) | |
| x = x[:, :, :, :height*2-pad_h, :width*2-pad_w] | |
| x = rearrange(x, 'b d t h w -> b (t h w) d') | |
| attention_mask = rearrange(attention_mask, 'b 1 (t h w) -> b 1 t h w', t=frames, h=height, w=width) | |
| attention_mask = attention_mask.repeat_interleave(2, -1).repeat_interleave(2, -2).repeat_interleave(2, -3) | |
| attention_mask = attention_mask[:, :, :, :height*2-pad_h, :width*2-pad_w] | |
| attention_mask = rearrange(attention_mask, 'b 1 t h w -> b 1 (t h w)') | |
| attention_bias = (1 - attention_mask.bool().to(x.dtype)) * -10000.0 | |
| return x, attention_bias, attention_mask | |
| class Downsample2d(nn.Module): | |
| def __init__(self, n_feat, is_video_model=False): | |
| super(Downsample2d, self).__init__() | |
| self.body = nn.Conv2d(n_feat, 2*n_feat, kernel_size=3, stride=2, padding=1, bias=False) | |
| def forward(self, x, attention_mask, frames, height, width, pad_h=0, pad_w=0): | |
| # import ipdb;ipdb.set_trace() | |
| x = rearrange(x, 'b (t h w) d -> (b t) d h w', t=frames, h=height, w=width) | |
| if npu_config is None: | |
| x = F.pad(x, (0, pad_w, 0, pad_h)) | |
| else: | |
| x = npu_config.run_pad_2d(F.pad, x, pad=(0, pad_w, 0, pad_h)) | |
| x = self.body(x) | |
| x = rearrange(x, '(b t) d h w -> b (t h w) d', t=frames) | |
| attention_mask = rearrange(attention_mask, 'b 1 (t h w) -> (b t) 1 h w', t=frames, h=height, w=width) | |
| attention_mask = F.pad(attention_mask, (0, pad_w, 0, pad_h)) | |
| attention_mask = F.max_pool2d(attention_mask.float(), kernel_size=2, stride=2) | |
| attention_mask = rearrange(attention_mask, '(b t) 1 h w -> b 1 (t h w)', t=frames) | |
| attention_bias = (1 - attention_mask.bool().to(x.dtype)) * -10000.0 | |
| return x, attention_bias, attention_mask | |
| class Upsample2d(nn.Module): | |
| def __init__(self, n_feat, is_video_model=False): | |
| super(Upsample2d, self).__init__() | |
| self.body = nn.Conv2d(n_feat, n_feat // 2, kernel_size=3, stride=1, padding=1, bias=False) | |
| def forward(self, x, attention_mask, frames, height, width, pad_h=0, pad_w=0): | |
| x = rearrange(x, 'b (t h w) d -> (b t) d h w', t=frames, h=height, w=width) | |
| x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") | |
| x = self.body(x) | |
| x = x[:, :, :height*2-pad_h, :width*2-pad_w] | |
| x = rearrange(x, '(b t) d h w -> b (t h w) d', t=frames) | |
| attention_mask = rearrange(attention_mask, 'b 1 (t h w) -> (b t) 1 h w', t=frames, h=height, w=width) | |
| attention_mask = attention_mask.repeat_interleave(2, -1).repeat_interleave(2, -2) | |
| attention_mask = attention_mask[:, :, :height*2-pad_h, :width*2-pad_w] | |
| attention_mask = rearrange(attention_mask, '(b t) 1 h w -> b 1 (t h w)', t=frames) | |
| attention_bias = (1 - attention_mask.bool().to(x.dtype)) * -10000.0 | |
| return x, attention_bias, attention_mask | |
| class FeedForward_Conv2d(nn.Module): | |
| def __init__(self, downsampler, dim, hidden_features, bias=True): | |
| super(FeedForward_Conv2d, self).__init__() | |
| self.bias = bias | |
| self.project_in = nn.Linear(dim, hidden_features, bias=bias) | |
| self.act = nn.GELU() | |
| self.dwconv = nn.ModuleList([ | |
| nn.Conv2d(hidden_features, hidden_features, kernel_size=(5, 5), stride=1, padding=(2, 2), dilation=1, | |
| groups=hidden_features, bias=bias), | |
| nn.Conv2d(hidden_features, hidden_features, kernel_size=(3, 3), stride=1, padding=(1, 1), dilation=1, | |
| groups=hidden_features, bias=bias), | |
| nn.Conv2d(hidden_features, hidden_features, kernel_size=(1, 1), stride=1, padding=(0, 0), dilation=1, | |
| groups=hidden_features, bias=bias) | |
| ]) | |
| self.project_out = nn.Linear(hidden_features, dim, bias=bias) | |
| def forward(self, x, t, h, w): | |
| # import ipdb;ipdb.set_trace() | |
| x = self.project_in(x) | |
| x = rearrange(x, 'b (t h w) d -> (b t) d h w', t=t, h=h, w=w) | |
| x = self.act(x) | |
| out = x | |
| for module in self.dwconv: | |
| out = out + module(x) | |
| out = rearrange(out, '(b t) d h w -> b (t h w) d', t=t, h=h, w=w) | |
| x = self.project_out(out) | |
| return x | |
| class BasicTransformerBlock(nn.Module): | |
| r""" | |
| A basic Transformer block. | |
| Parameters: | |
| dim (`int`): The number of channels in the input and output. | |
| num_attention_heads (`int`): The number of heads to use for multi-head attention. | |
| attention_head_dim (`int`): The number of channels in each head. | |
| dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. | |
| cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. | |
| activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. | |
| num_embeds_ada_norm (: | |
| obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. | |
| attention_bias (: | |
| obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. | |
| only_cross_attention (`bool`, *optional*): | |
| Whether to use only cross-attention layers. In this case two cross attention layers are used. | |
| double_self_attention (`bool`, *optional*): | |
| Whether to use two self-attention layers. In this case no cross attention layers are used. | |
| upcast_attention (`bool`, *optional*): | |
| Whether to upcast the attention computation to float32. This is useful for mixed precision training. | |
| norm_elementwise_affine (`bool`, *optional*, defaults to `True`): | |
| Whether to use learnable elementwise affine parameters for normalization. | |
| norm_type (`str`, *optional*, defaults to `"layer_norm"`): | |
| The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`. | |
| final_dropout (`bool` *optional*, defaults to False): | |
| Whether to apply a final dropout after the last feed-forward layer. | |
| attention_type (`str`, *optional*, defaults to `"default"`): | |
| The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`. | |
| positional_embeddings (`str`, *optional*, defaults to `None`): | |
| The type of positional embeddings to apply to. | |
| num_positional_embeddings (`int`, *optional*, defaults to `None`): | |
| The maximum number of positional embeddings to apply. | |
| """ | |
| def __init__( | |
| self, | |
| dim: int, | |
| num_attention_heads: int, | |
| attention_head_dim: int, | |
| dropout=0.0, | |
| cross_attention_dim: Optional[int] = None, | |
| activation_fn: str = "geglu", | |
| num_embeds_ada_norm: Optional[int] = None, | |
| attention_bias: bool = False, | |
| only_cross_attention: bool = False, | |
| double_self_attention: bool = False, | |
| upcast_attention: bool = False, | |
| norm_elementwise_affine: bool = True, | |
| norm_type: str = "layer_norm", # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single', 'ada_norm_continuous', 'layer_norm_i2vgen' | |
| norm_eps: float = 1e-5, | |
| final_dropout: bool = False, | |
| attention_type: str = "default", | |
| positional_embeddings: Optional[str] = None, | |
| num_positional_embeddings: Optional[int] = None, | |
| ada_norm_continous_conditioning_embedding_dim: Optional[int] = None, | |
| ada_norm_bias: Optional[int] = None, | |
| ff_inner_dim: Optional[int] = None, | |
| ff_bias: bool = True, | |
| attention_out_bias: bool = True, | |
| attention_mode: str = "xformers", | |
| downsampler: str = None, | |
| mlp_ratio: int = 4, | |
| use_rope: bool = False, | |
| interpolation_scale_thw: Tuple[int] = (1, 1, 1), | |
| ): | |
| super().__init__() | |
| self.only_cross_attention = only_cross_attention | |
| # We keep these boolean flags for backward-compatibility. | |
| self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero" | |
| self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm" | |
| self.use_ada_layer_norm_single = norm_type == "ada_norm_single" | |
| self.use_layer_norm = norm_type == "layer_norm" | |
| self.use_ada_layer_norm_continuous = norm_type == "ada_norm_continuous" | |
| if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: | |
| raise ValueError( | |
| f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" | |
| f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." | |
| ) | |
| self.norm_type = norm_type | |
| self.num_embeds_ada_norm = num_embeds_ada_norm | |
| if positional_embeddings and (num_positional_embeddings is None): | |
| raise ValueError( | |
| "If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined." | |
| ) | |
| if positional_embeddings == "sinusoidal": | |
| self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings) | |
| else: | |
| self.pos_embed = None | |
| # Define 3 blocks. Each block has its own normalization layer. | |
| # 1. Self-Attn | |
| if norm_type == "ada_norm": | |
| self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) | |
| elif norm_type == "ada_norm_zero": | |
| self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm) | |
| elif norm_type == "ada_norm_continuous": | |
| self.norm1 = AdaLayerNormContinuous( | |
| dim, | |
| ada_norm_continous_conditioning_embedding_dim, | |
| norm_elementwise_affine, | |
| norm_eps, | |
| ada_norm_bias, | |
| "rms_norm", | |
| ) | |
| else: | |
| self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps) | |
| self.attn1 = Attention( | |
| query_dim=dim, | |
| heads=num_attention_heads, | |
| dim_head=attention_head_dim, | |
| dropout=dropout, | |
| bias=attention_bias, | |
| cross_attention_dim=cross_attention_dim if only_cross_attention else None, | |
| upcast_attention=upcast_attention, | |
| out_bias=attention_out_bias, | |
| downsampler=downsampler, | |
| attention_mode=attention_mode, | |
| use_rope=use_rope, | |
| interpolation_scale_thw=interpolation_scale_thw, | |
| ) | |
| # 2. Cross-Attn | |
| if cross_attention_dim is not None or double_self_attention: | |
| # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. | |
| # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during | |
| # the second cross attention block. | |
| if norm_type == "ada_norm": | |
| self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) | |
| elif norm_type == "ada_norm_continuous": | |
| self.norm2 = AdaLayerNormContinuous( | |
| dim, | |
| ada_norm_continous_conditioning_embedding_dim, | |
| norm_elementwise_affine, | |
| norm_eps, | |
| ada_norm_bias, | |
| "rms_norm", | |
| ) | |
| else: | |
| self.norm2 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine) | |
| self.attn2 = Attention( | |
| query_dim=dim, | |
| cross_attention_dim=cross_attention_dim if not double_self_attention else None, | |
| heads=num_attention_heads, | |
| dim_head=attention_head_dim, | |
| dropout=dropout, | |
| bias=attention_bias, | |
| upcast_attention=upcast_attention, | |
| out_bias=attention_out_bias, | |
| downsampler=None, | |
| attention_mode=attention_mode, | |
| use_rope=False, | |
| interpolation_scale_thw=interpolation_scale_thw, | |
| ) # is self-attn if encoder_hidden_states is none | |
| else: | |
| self.norm2 = None | |
| self.attn2 = None | |
| # 3. Feed-forward | |
| if norm_type == "ada_norm_continuous": | |
| self.norm3 = AdaLayerNormContinuous( | |
| dim, | |
| ada_norm_continous_conditioning_embedding_dim, | |
| norm_elementwise_affine, | |
| norm_eps, | |
| ada_norm_bias, | |
| "layer_norm", | |
| ) | |
| elif norm_type in ["ada_norm_zero", "ada_norm", "layer_norm", "ada_norm_continuous"]: | |
| self.norm3 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine) | |
| elif norm_type == "layer_norm_i2vgen": | |
| self.norm3 = None | |
| self.ff = FeedForward( | |
| dim, | |
| dropout=dropout, | |
| activation_fn=activation_fn, | |
| final_dropout=final_dropout, | |
| inner_dim=ff_inner_dim, | |
| bias=ff_bias, | |
| ) | |
| # 4. Fuser | |
| if attention_type == "gated" or attention_type == "gated-text-image": | |
| self.fuser = GatedSelfAttentionDense(dim, cross_attention_dim, num_attention_heads, attention_head_dim) | |
| # 5. Scale-shift for PixArt-Alpha. | |
| if norm_type == "ada_norm_single": | |
| self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5) | |
| # let chunk size default to None | |
| self._chunk_size = None | |
| self._chunk_dim = 0 | |
| def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0): | |
| # Sets chunk feed-forward | |
| self._chunk_size = chunk_size | |
| self._chunk_dim = dim | |
| def forward( | |
| self, | |
| hidden_states: torch.FloatTensor, | |
| attention_mask: Optional[torch.FloatTensor] = None, | |
| encoder_hidden_states: Optional[torch.FloatTensor] = None, | |
| encoder_attention_mask: Optional[torch.FloatTensor] = None, | |
| timestep: Optional[torch.LongTensor] = None, | |
| cross_attention_kwargs: Dict[str, Any] = None, | |
| class_labels: Optional[torch.LongTensor] = None, | |
| frame: int = None, | |
| height: int = None, | |
| width: int = None, | |
| added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, | |
| ) -> torch.FloatTensor: | |
| if cross_attention_kwargs is not None: | |
| if cross_attention_kwargs.get("scale", None) is not None: | |
| logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") | |
| # Notice that normalization is always applied before the real computation in the following blocks. | |
| # 0. Self-Attention | |
| batch_size = hidden_states.shape[0] | |
| if self.norm_type == "ada_norm": | |
| norm_hidden_states = self.norm1(hidden_states, timestep) | |
| elif self.norm_type == "ada_norm_zero": | |
| norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( | |
| hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype | |
| ) | |
| elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]: | |
| norm_hidden_states = self.norm1(hidden_states) | |
| elif self.norm_type == "ada_norm_continuous": | |
| norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"]) | |
| elif self.norm_type == "ada_norm_single": | |
| shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( | |
| self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1) | |
| ).chunk(6, dim=1) | |
| norm_hidden_states = self.norm1(hidden_states) | |
| norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa | |
| norm_hidden_states = norm_hidden_states.squeeze(1) | |
| else: | |
| raise ValueError("Incorrect norm used") | |
| if self.pos_embed is not None: | |
| norm_hidden_states = self.pos_embed(norm_hidden_states) | |
| # 1. Prepare GLIGEN inputs | |
| cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} | |
| gligen_kwargs = cross_attention_kwargs.pop("gligen", None) | |
| # 0, -10000 ->(bool) False, True ->(any) True ->(not) False | |
| # 0, 0 ->(bool) False, False ->(any) False ->(not) True | |
| # assert attention_mask.bool().float().sum() / attention_mask.bool().float().numel() <= 1/16, 'must ~all visible' | |
| attn_output = self.attn1( | |
| norm_hidden_states, | |
| encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, | |
| attention_mask=attention_mask, frame=frame, height=height, width=width, | |
| **cross_attention_kwargs, | |
| ) | |
| if self.norm_type == "ada_norm_zero": | |
| attn_output = gate_msa.unsqueeze(1) * attn_output | |
| elif self.norm_type == "ada_norm_single": | |
| attn_output = gate_msa * attn_output | |
| hidden_states = attn_output + hidden_states | |
| if hidden_states.ndim == 4: | |
| hidden_states = hidden_states.squeeze(1) | |
| # 1.2 GLIGEN Control | |
| if gligen_kwargs is not None: | |
| hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"]) | |
| # 3. Cross-Attention | |
| if self.attn2 is not None: | |
| if self.norm_type == "ada_norm": | |
| norm_hidden_states = self.norm2(hidden_states, timestep) | |
| elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]: | |
| norm_hidden_states = self.norm2(hidden_states) | |
| elif self.norm_type == "ada_norm_single": | |
| # For PixArt norm2 isn't applied here: | |
| # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103 | |
| norm_hidden_states = hidden_states | |
| elif self.norm_type == "ada_norm_continuous": | |
| norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"]) | |
| else: | |
| raise ValueError("Incorrect norm") | |
| if self.pos_embed is not None and self.norm_type != "ada_norm_single": | |
| norm_hidden_states = self.pos_embed(norm_hidden_states) | |
| attn_output = self.attn2( | |
| norm_hidden_states, | |
| encoder_hidden_states=encoder_hidden_states, | |
| attention_mask=encoder_attention_mask, | |
| **cross_attention_kwargs, | |
| ) | |
| hidden_states = attn_output + hidden_states | |
| # 4. Feed-forward | |
| # i2vgen doesn't have this norm 🤷♂️ | |
| if self.norm_type == "ada_norm_continuous": | |
| norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"]) | |
| elif not self.norm_type == "ada_norm_single": | |
| norm_hidden_states = self.norm3(hidden_states) | |
| if self.norm_type == "ada_norm_zero": | |
| norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] | |
| if self.norm_type == "ada_norm_single": | |
| norm_hidden_states = self.norm2(hidden_states) | |
| norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp | |
| # if self.downsampler: | |
| # ff_output = self.ff(norm_hidden_states, t=frame, h=height, w=width) | |
| # else: | |
| ff_output = self.ff(norm_hidden_states) | |
| if self.norm_type == "ada_norm_zero": | |
| ff_output = gate_mlp.unsqueeze(1) * ff_output | |
| elif self.norm_type == "ada_norm_single": | |
| ff_output = gate_mlp * ff_output | |
| hidden_states = ff_output + hidden_states | |
| if hidden_states.ndim == 4: | |
| hidden_states = hidden_states.squeeze(1) | |
| return hidden_states | |