from typing import Any, List, Tuple, Optional, Union, Dict from einops import rearrange import torch import torch.nn as nn import torch.nn.functional as F from diffusers.models import ModelMixin from diffusers.configuration_utils import ConfigMixin, register_to_config from .activation_layers import get_activation_layer from .norm_layers import get_norm_layer from .embed_layers import TimestepEmbedder, PatchEmbed, TextProjection from .attenion import attention, parallel_attention, get_cu_seqlens from .posemb_layers import apply_rotary_emb from .mlp_layers import MLP, MLPEmbedder, FinalLayer from .modulate_layers import ModulateDiT, modulate, modulate_, apply_gate_and_accumulate_ from .token_refiner import SingleTokenRefiner import numpy as np def get_linear_split_map(): hidden_size = 3072 split_linear_modules_map = { "img_attn_qkv" : {"mapped_modules" : ["img_attn_q", "img_attn_k", "img_attn_v"] , "split_sizes": [hidden_size, hidden_size, hidden_size]}, "linear1" : {"mapped_modules" : ["linear1_attn_q", "linear1_attn_k", "linear1_attn_v", "linear1_mlp"] , "split_sizes": [hidden_size, hidden_size, hidden_size, 7*hidden_size- 3*hidden_size]} } return split_linear_modules_map try: from xformers.ops.fmha.attn_bias import BlockDiagonalPaddedKeysMask except ImportError: BlockDiagonalPaddedKeysMask = None class MMDoubleStreamBlock(nn.Module): """ A multimodal dit block with seperate modulation for text and image/video, see more details (SD3): https://arxiv.org/abs/2403.03206 (Flux.1): https://github.com/black-forest-labs/flux """ def __init__( self, hidden_size: int, heads_num: int, mlp_width_ratio: float, mlp_act_type: str = "gelu_tanh", qk_norm: bool = True, qk_norm_type: str = "rms", qkv_bias: bool = False, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None, attention_mode: str = "sdpa", ): factory_kwargs = {"device": device, "dtype": dtype} super().__init__() self.attention_mode = attention_mode self.deterministic = False self.heads_num = heads_num head_dim = hidden_size // heads_num mlp_hidden_dim = int(hidden_size * mlp_width_ratio) self.img_mod = ModulateDiT( hidden_size, factor=6, act_layer=get_activation_layer("silu"), **factory_kwargs, ) self.img_norm1 = nn.LayerNorm( hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs ) self.img_attn_qkv = nn.Linear( hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs ) qk_norm_layer = get_norm_layer(qk_norm_type) self.img_attn_q_norm = ( qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity() ) self.img_attn_k_norm = ( qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity() ) self.img_attn_proj = nn.Linear( hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs ) self.img_norm2 = nn.LayerNorm( hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs ) self.img_mlp = MLP( hidden_size, mlp_hidden_dim, act_layer=get_activation_layer(mlp_act_type), bias=True, **factory_kwargs, ) self.txt_mod = ModulateDiT( hidden_size, factor=6, act_layer=get_activation_layer("silu"), **factory_kwargs, ) self.txt_norm1 = nn.LayerNorm( hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs ) self.txt_attn_qkv = nn.Linear( hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs ) self.txt_attn_q_norm = ( qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity() ) self.txt_attn_k_norm = ( qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity() ) self.txt_attn_proj = nn.Linear( hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs ) self.txt_norm2 = nn.LayerNorm( hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs ) self.txt_mlp = MLP( hidden_size, mlp_hidden_dim, act_layer=get_activation_layer(mlp_act_type), bias=True, **factory_kwargs, ) self.hybrid_seq_parallel_attn = None def enable_deterministic(self): self.deterministic = True def disable_deterministic(self): self.deterministic = False def forward( self, img: torch.Tensor, txt: torch.Tensor, vec: torch.Tensor, attn_mask = None, cu_seqlens_q: Optional[torch.Tensor] = None, cu_seqlens_kv: Optional[torch.Tensor] = None, max_seqlen_q: Optional[int] = None, max_seqlen_kv: Optional[int] = None, freqs_cis: tuple = None, ) -> Tuple[torch.Tensor, torch.Tensor]: ( img_mod1_shift, img_mod1_scale, img_mod1_gate, img_mod2_shift, img_mod2_scale, img_mod2_gate, ) = self.img_mod(vec).chunk(6, dim=-1) ( txt_mod1_shift, txt_mod1_scale, txt_mod1_gate, txt_mod2_shift, txt_mod2_scale, txt_mod2_gate, ) = self.txt_mod(vec).chunk(6, dim=-1) ##### Enjoy this spagheti VRAM optimizations done by DeepBeepMeep ! # I am sure you are a nice person and as you copy this code, you will give me proper credits: # Please link to https://github.com/deepbeepmeep/HunyuanVideoGP and @deepbeepmeep on twitter # Prepare image for attention. img_modulated = self.img_norm1(img) img_modulated = img_modulated.to(torch.bfloat16) modulate_( img_modulated, shift=img_mod1_shift, scale=img_mod1_scale ) shape = (*img_modulated.shape[:2], self.heads_num, int(img_modulated.shape[-1] / self.heads_num) ) img_q = self.img_attn_q(img_modulated).view(*shape) img_k = self.img_attn_k(img_modulated).view(*shape) img_v = self.img_attn_v(img_modulated).view(*shape) del img_modulated # Apply QK-Norm if needed self.img_attn_q_norm.apply_(img_q).to(img_v) img_q_len = img_q.shape[1] self.img_attn_k_norm.apply_(img_k).to(img_v) img_kv_len= img_k.shape[1] batch_size = img_k.shape[0] # Apply RoPE if needed. if freqs_cis is not None: img_qq, img_kk = apply_rotary_emb(img_q, img_k, freqs_cis, head_first=False) assert ( img_qq.shape == img_q.shape and img_kk.shape == img_k.shape ), f"img_kk: {img_qq.shape}, img_q: {img_q.shape}, img_kk: {img_kk.shape}, img_k: {img_k.shape}" img_q, img_k = img_qq, img_kk del img_qq, img_kk # Prepare txt for attention. txt_modulated = self.txt_norm1(txt) modulate_(txt_modulated, shift=txt_mod1_shift, scale=txt_mod1_scale ) txt_qkv = self.txt_attn_qkv(txt_modulated) del txt_modulated txt_q, txt_k, txt_v = rearrange( txt_qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num ) del txt_qkv # Apply QK-Norm if needed. self.txt_attn_q_norm.apply_(txt_q).to(txt_v) self.txt_attn_k_norm.apply_(txt_k).to(txt_v) # Run actual attention. q = torch.cat((img_q, txt_q), dim=1) del img_q, txt_q k = torch.cat((img_k, txt_k), dim=1) del img_k, txt_k v = torch.cat((img_v, txt_v), dim=1) del img_v, txt_v # attention computation start if not self.hybrid_seq_parallel_attn: qkv_list = [q,k,v] del q, k, v attn = attention( qkv_list, mode=self.attention_mode, attn_mask=attn_mask, cu_seqlens_q=cu_seqlens_q, cu_seqlens_kv=cu_seqlens_kv, max_seqlen_q=max_seqlen_q, max_seqlen_kv=max_seqlen_kv, batch_size=batch_size, ) del qkv_list else: attn = parallel_attention( self.hybrid_seq_parallel_attn, q, k, v, img_q_len= img_q_len, img_kv_len= img_kv_len, cu_seqlens_q=cu_seqlens_q, cu_seqlens_kv=cu_seqlens_kv ) del q, k, v # attention computation end img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1] :] del attn # Calculate the img bloks. img_attn = self.img_attn_proj(img_attn) apply_gate_and_accumulate_(img, img_attn, gate=img_mod1_gate) del img_attn img_modulated = self.img_norm2(img) img_modulated = img_modulated.to(torch.bfloat16) modulate_( img_modulated , shift=img_mod2_shift, scale=img_mod2_scale) self.img_mlp.apply_(img_modulated) apply_gate_and_accumulate_(img, img_modulated, gate=img_mod2_gate) del img_modulated # Calculate the txt bloks. txt_attn = self.txt_attn_proj(txt_attn) apply_gate_and_accumulate_(txt, txt_attn, gate=txt_mod1_gate) del txt_attn txt_modulated = self.txt_norm2(txt) txt_modulated = txt_modulated.to(torch.bfloat16) modulate_(txt_modulated, shift=txt_mod2_shift, scale=txt_mod2_scale) txt_mlp = self.txt_mlp(txt_modulated) # should delete txt_modulated halfway in mlp del txt_modulated apply_gate_and_accumulate_(txt, txt_mlp, gate=txt_mod2_gate) return img, txt class MMSingleStreamBlock(nn.Module): """ A DiT block with parallel linear layers as described in https://arxiv.org/abs/2302.05442 and adapted modulation interface. Also refer to (SD3): https://arxiv.org/abs/2403.03206 (Flux.1): https://github.com/black-forest-labs/flux """ def __init__( self, hidden_size: int, heads_num: int, mlp_width_ratio: float = 4.0, mlp_act_type: str = "gelu_tanh", qk_norm: bool = True, qk_norm_type: str = "rms", qk_scale: float = None, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None, attention_mode: str = "sdpa", ): factory_kwargs = {"device": device, "dtype": dtype} super().__init__() self.attention_mode = attention_mode self.deterministic = False self.hidden_size = hidden_size self.heads_num = heads_num head_dim = hidden_size // heads_num mlp_hidden_dim = int(hidden_size * mlp_width_ratio) self.mlp_hidden_dim = mlp_hidden_dim self.scale = qk_scale or head_dim ** -0.5 # qkv and mlp_in self.linear1 = nn.Linear( hidden_size, hidden_size * 3 + mlp_hidden_dim, **factory_kwargs ) # proj and mlp_out self.linear2 = nn.Linear( hidden_size + mlp_hidden_dim, hidden_size, **factory_kwargs ) qk_norm_layer = get_norm_layer(qk_norm_type) self.q_norm = ( qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity() ) self.k_norm = ( qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity() ) self.pre_norm = nn.LayerNorm( hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs ) self.mlp_act = get_activation_layer(mlp_act_type)() self.modulation = ModulateDiT( hidden_size, factor=3, act_layer=get_activation_layer("silu"), **factory_kwargs, ) self.hybrid_seq_parallel_attn = None def enable_deterministic(self): self.deterministic = True def disable_deterministic(self): self.deterministic = False def forward( self, # x: torch.Tensor, img: torch.Tensor, txt: torch.Tensor, vec: torch.Tensor, txt_len: int, attn_mask= None, cu_seqlens_q: Optional[torch.Tensor] = None, cu_seqlens_kv: Optional[torch.Tensor] = None, max_seqlen_q: Optional[int] = None, max_seqlen_kv: Optional[int] = None, freqs_cis: Tuple[torch.Tensor, torch.Tensor] = None, ) -> torch.Tensor: mod_shift, mod_scale, mod_gate = self.modulation(vec).chunk(3, dim=-1) ##### More spagheti VRAM optimizations done by DeepBeepMeep ! # I am sure you are a nice person and as you copy this code, you will give me proper credits: # Please link to https://github.com/deepbeepmeep/HunyuanVideoGP and @deepbeepmeep on twitter img_mod = self.pre_norm(img) img_mod = img_mod.to(torch.bfloat16) txt_mod = self.pre_norm(txt) txt_mod = txt_mod.to(torch.bfloat16) modulate_(img_mod, shift=mod_shift, scale=mod_scale) modulate_(txt_mod, shift=mod_shift, scale=mod_scale) shape = (*img_mod.shape[:2], self.heads_num, int(img_mod.shape[-1] / self.heads_num) ) img_q = self.linear1_attn_q(img_mod).view(*shape) img_k = self.linear1_attn_k(img_mod).view(*shape) img_v = self.linear1_attn_v(img_mod).view(*shape) shape = (*txt_mod.shape[:2], self.heads_num, int(txt_mod.shape[-1] / self.heads_num) ) txt_q = self.linear1_attn_q(txt_mod).view(*shape) txt_k = self.linear1_attn_k(txt_mod).view(*shape) txt_v = self.linear1_attn_v(txt_mod).view(*shape) batch_size = img_mod.shape[0] # Apply QK-Norm if needed. # q = self.q_norm(q).to(v) self.q_norm.apply_(img_q) self.k_norm.apply_(img_k) self.q_norm.apply_(txt_q) self.k_norm.apply_(txt_k) # Apply RoPE if needed. if freqs_cis is not None: img_qq, img_kk = apply_rotary_emb(img_q, img_k, freqs_cis, head_first=False) assert ( img_qq.shape == img_q.shape and img_kk.shape == img_k.shape ), f"img_kk: {img_qq.shape}, img_q: {img_q.shape}, img_kk: {img_kk.shape}, img_k: {img_k.shape}" img_q, img_k = img_qq, img_kk img_q_len=img_q.shape[1] q = torch.cat((img_q, txt_q), dim=1) del img_q, txt_q, img_qq, k = torch.cat((img_k, txt_k), dim=1) img_kv_len=img_k.shape[1] del img_k, txt_k, img_kk v = torch.cat((img_v, txt_v), dim=1) del img_v, txt_v # attention computation start if not self.hybrid_seq_parallel_attn: qkv_list = [q,k,v] del q, k, v attn = attention( qkv_list, mode=self.attention_mode, attn_mask=attn_mask, cu_seqlens_q=cu_seqlens_q, cu_seqlens_kv=cu_seqlens_kv, max_seqlen_q=max_seqlen_q, max_seqlen_kv=max_seqlen_kv, batch_size=batch_size, ) del qkv_list else: attn = parallel_attention( self.hybrid_seq_parallel_attn, q, k, v, img_q_len=img_q_len, img_kv_len=img_kv_len, cu_seqlens_q=cu_seqlens_q, cu_seqlens_kv=cu_seqlens_kv ) del q, k, v # attention computation end x_mod = torch.cat((img_mod, txt_mod), 1) del img_mod, txt_mod x_mod_shape = x_mod.shape x_mod = x_mod.view(-1, x_mod.shape[-1]) chunk_size = int(x_mod_shape[1]/6) x_chunks = torch.split(x_mod, chunk_size) attn = attn.view(-1, attn.shape[-1]) attn_chunks =torch.split(attn, chunk_size) for x_chunk, attn_chunk in zip(x_chunks, attn_chunks): mlp_chunk = self.linear1_mlp(x_chunk) mlp_chunk = self.mlp_act(mlp_chunk) attn_mlp_chunk = torch.cat((attn_chunk, mlp_chunk), -1) del attn_chunk, mlp_chunk x_chunk[...] = self.linear2(attn_mlp_chunk) del attn_mlp_chunk x_mod = x_mod.view(x_mod_shape) apply_gate_and_accumulate_(img, x_mod[:, :-txt_len, :], gate=mod_gate) apply_gate_and_accumulate_(txt, x_mod[:, -txt_len:, :], gate=mod_gate) return img, txt class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin): """ HunyuanVideo Transformer backbone Inherited from ModelMixin and ConfigMixin for compatibility with diffusers' sampler StableDiffusionPipeline. Reference: [1] Flux.1: https://github.com/black-forest-labs/flux [2] MMDiT: http://arxiv.org/abs/2403.03206 Parameters ---------- args: argparse.Namespace The arguments parsed by argparse. patch_size: list The size of the patch. in_channels: int The number of input channels. out_channels: int The number of output channels. hidden_size: int The hidden size of the transformer backbone. heads_num: int The number of attention heads. mlp_width_ratio: float The ratio of the hidden size of the MLP in the transformer block. mlp_act_type: str The activation function of the MLP in the transformer block. depth_double_blocks: int The number of transformer blocks in the double blocks. depth_single_blocks: int The number of transformer blocks in the single blocks. rope_dim_list: list The dimension of the rotary embedding for t, h, w. qkv_bias: bool Whether to use bias in the qkv linear layer. qk_norm: bool Whether to use qk norm. qk_norm_type: str The type of qk norm. guidance_embed: bool Whether to use guidance embedding for distillation. text_projection: str The type of the text projection, default is single_refiner. use_attention_mask: bool Whether to use attention mask for text encoder. dtype: torch.dtype The dtype of the model. device: torch.device The device of the model. """ @register_to_config def __init__( self, args: Any, patch_size: list = [1, 2, 2], in_channels: int = 4, # Should be VAE.config.latent_channels. out_channels: int = None, hidden_size: int = 3072, heads_num: int = 24, mlp_width_ratio: float = 4.0, mlp_act_type: str = "gelu_tanh", mm_double_blocks_depth: int = 20, mm_single_blocks_depth: int = 40, rope_dim_list: List[int] = [16, 56, 56], qkv_bias: bool = True, qk_norm: bool = True, qk_norm_type: str = "rms", guidance_embed: bool = False, # For modulation. text_projection: str = "single_refiner", use_attention_mask: bool = True, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None, attention_mode: Optional[str] = "sdpa" ): factory_kwargs = {"device": device, "dtype": dtype} super().__init__() self.patch_size = patch_size self.in_channels = in_channels self.out_channels = in_channels if out_channels is None else out_channels self.unpatchify_channels = self.out_channels self.guidance_embed = guidance_embed self.rope_dim_list = rope_dim_list self.attention_mode = attention_mode # Text projection. Default to linear projection. # Alternative: TokenRefiner. See more details (LI-DiT): http://arxiv.org/abs/2406.11831 self.use_attention_mask = use_attention_mask self.text_projection = text_projection self.text_states_dim = args.text_states_dim self.text_states_dim_2 = args.text_states_dim_2 if hidden_size % heads_num != 0: raise ValueError( f"Hidden size {hidden_size} must be divisible by heads_num {heads_num}" ) pe_dim = hidden_size // heads_num if sum(rope_dim_list) != pe_dim: raise ValueError( f"Got {rope_dim_list} but expected positional dim {pe_dim}" ) self.hidden_size = hidden_size self.heads_num = heads_num # image projection self.img_in = PatchEmbed( self.patch_size, self.in_channels, self.hidden_size, **factory_kwargs ) # text projection if self.text_projection == "linear": self.txt_in = TextProjection( self.text_states_dim, self.hidden_size, get_activation_layer("silu"), **factory_kwargs, ) elif self.text_projection == "single_refiner": self.txt_in = SingleTokenRefiner( self.text_states_dim, hidden_size, heads_num, depth=2, **factory_kwargs ) else: raise NotImplementedError( f"Unsupported text_projection: {self.text_projection}" ) # time modulation self.time_in = TimestepEmbedder( self.hidden_size, get_activation_layer("silu"), **factory_kwargs ) # text modulation self.vector_in = MLPEmbedder( self.text_states_dim_2, self.hidden_size, **factory_kwargs ) # guidance modulation self.guidance_in = ( TimestepEmbedder( self.hidden_size, get_activation_layer("silu"), **factory_kwargs ) if guidance_embed else None ) # double blocks self.double_blocks = nn.ModuleList( [ MMDoubleStreamBlock( self.hidden_size, self.heads_num, mlp_width_ratio=mlp_width_ratio, mlp_act_type=mlp_act_type, qk_norm=qk_norm, qk_norm_type=qk_norm_type, qkv_bias=qkv_bias, attention_mode = attention_mode, **factory_kwargs, ) for _ in range(mm_double_blocks_depth) ] ) # single blocks self.single_blocks = nn.ModuleList( [ MMSingleStreamBlock( self.hidden_size, self.heads_num, mlp_width_ratio=mlp_width_ratio, mlp_act_type=mlp_act_type, qk_norm=qk_norm, qk_norm_type=qk_norm_type, attention_mode = attention_mode, **factory_kwargs, ) for _ in range(mm_single_blocks_depth) ] ) self.final_layer = FinalLayer( self.hidden_size, self.patch_size, self.out_channels, get_activation_layer("silu"), **factory_kwargs, ) def enable_deterministic(self): for block in self.double_blocks: block.enable_deterministic() for block in self.single_blocks: block.enable_deterministic() def disable_deterministic(self): for block in self.double_blocks: block.disable_deterministic() for block in self.single_blocks: block.disable_deterministic() def forward( self, x: torch.Tensor, t: torch.Tensor, # Should be in range(0, 1000). text_states: torch.Tensor = None, text_mask: torch.Tensor = None, # Now we don't use it. text_states_2: Optional[torch.Tensor] = None, # Text embedding for modulation. freqs_cos: Optional[torch.Tensor] = None, freqs_sin: Optional[torch.Tensor] = None, guidance: torch.Tensor = None, # Guidance for modulation, should be cfg_scale x 1000. pipeline=None, return_dict: bool = True, ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]: out = {} img = x batch_no, _, ot, oh, ow = x.shape del x txt = text_states tt, th, tw = ( ot // self.patch_size[0], oh // self.patch_size[1], ow // self.patch_size[2], ) # Prepare modulation vectors. vec = self.time_in(t) # text modulation vec = vec + self.vector_in(text_states_2) del text_states_2 # guidance modulation if self.guidance_embed: if guidance is None: raise ValueError( "Didn't get guidance strength for guidance distilled model." ) # our timestep_embedding is merged into guidance_in(TimestepEmbedder) vec = vec + self.guidance_in(guidance) # Embed image and text. img = self.img_in(img) if self.text_projection == "linear": txt = self.txt_in(txt) elif self.text_projection == "single_refiner": txt = self.txt_in(txt, t, text_mask if self.use_attention_mask else None) else: raise NotImplementedError( f"Unsupported text_projection: {self.text_projection}" ) txt_seq_len = txt.shape[1] img_seq_len = img.shape[1] # Compute cu_squlens and max_seqlen for flash attention # cu_seqlens_q = get_cu_seqlens(text_mask, img_seq_len) # cu_seqlens_kv = cu_seqlens_q max_seqlen_q = img_seq_len + txt_seq_len max_seqlen_kv = max_seqlen_q if self.attention_mode == "sdpa" or self.attention_mode == "sage2": if batch_no == 1: # newly improved masking code that doesn't require a cumbersome mask.... text_len = text_mask[0].sum().item() total_len = text_len + img_seq_len cu_seqlens_q = cu_seqlens_kv = total_len attn_mask = None else: cu_seqlens_q, cu_seqlens_kv = None, None # thanks to kijai (https://github.com/kijai/ComfyUI-HunyuanVideoWrapper/), for the original code to support sdpa # Create a square boolean mask filled with False attn_mask = torch.zeros((1, max_seqlen_q, max_seqlen_q), dtype=torch.bool, device=text_mask.device) # Calculate the valid attention regions text_len = text_mask[0].sum().item() total_len = text_len + img_seq_len # Allow attention to 6all tokens up to total_len attn_mask[0, :total_len, :total_len] = True elif self.attention_mode == "xformers": text_len = text_mask[0].sum().item() total_len = text_len + img_seq_len cu_seqlens_q, cu_seqlens_kv = None, None attn_mask = BlockDiagonalPaddedKeysMask.from_seqlens([total_len, max_seqlen_q- total_len ],max_seqlen_kv, [total_len, 0] ) else: attn_mask = None # Compute cu_squlens for flash and sage attention cu_seqlens_q = get_cu_seqlens(text_mask, img_seq_len) cu_seqlens_kv = cu_seqlens_q freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None if self.enable_teacache: inp = img vec_ = vec ( img_mod1_shift, img_mod1_scale, _ , _ , _ , _ , ) = self.double_blocks[0].img_mod(vec_).chunk(6, dim=-1) normed_inp = self.double_blocks[0].img_norm1(inp) normed_inp = normed_inp.to(torch.bfloat16) modulated_inp = modulate( normed_inp, shift=img_mod1_shift, scale=img_mod1_scale ) del normed_inp, img_mod1_shift, img_mod1_scale if self.cnt == 0 or self.cnt == self.num_steps-1: should_calc = True self.accumulated_rel_l1_distance = 0 else: coefficients = [7.33226126e+02, -4.01131952e+02, 6.75869174e+01, -3.14987800e+00, 9.61237896e-02] rescale_func = np.poly1d(coefficients) self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()) if self.accumulated_rel_l1_distance < self.rel_l1_thresh: should_calc = False else: should_calc = True self.accumulated_rel_l1_distance = 0 self.previous_modulated_input = modulated_inp self.cnt += 1 if self.cnt == self.num_steps: self.cnt = 0 if self.enable_teacache: if not should_calc: img += self.previous_residual else: ori_img = img.clone() # --------------------- Pass through DiT blocks ------------------------ for _, block in enumerate(self.double_blocks): if pipeline._interrupt: return None double_block_args = [ img, txt, vec, attn_mask, cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv, freqs_cis, ] img, txt = block(*double_block_args) double_block_args = None # Merge txt and img to pass through single stream blocks. # x = torch.cat((img, txt), 1) # del img, txt if len(self.single_blocks) > 0: for _, block in enumerate(self.single_blocks): if pipeline._interrupt: return None single_block_args = [ # x, img, txt, vec, txt_seq_len, attn_mask, cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv, (freqs_cos, freqs_sin), ] img, txt = block(*single_block_args) single_block_args = None # img = x[:, :img_seq_len, ...] self.previous_residual = img - ori_img else: # --------------------- Pass through DiT blocks ------------------------ for _, block in enumerate(self.double_blocks): if pipeline._interrupt: return None double_block_args = [ img, txt, vec, attn_mask, cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv, freqs_cis, ] img, txt = block(*double_block_args) double_block_args = None # Merge txt and img to pass through single stream blocks. # x = torch.cat((img, txt), 1) # del img, txt if len(self.single_blocks) > 0: for _, block in enumerate(self.single_blocks): if pipeline._interrupt: return None single_block_args = [ # x, img, txt, vec, txt_seq_len, attn_mask, cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv, (freqs_cos, freqs_sin), ] img, txt = block(*single_block_args) single_block_args = None # img = x[:, :img_seq_len, ...] del txt # ---------------------------- Final layer ------------------------------ img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) img = self.unpatchify(img, tt, th, tw) if return_dict: out["x"] = img return out return img def unpatchify(self, x, t, h, w): """ x: (N, T, patch_size**2 * C) imgs: (N, H, W, C) """ c = self.unpatchify_channels pt, ph, pw = self.patch_size assert t * h * w == x.shape[1] x = x.reshape(shape=(x.shape[0], t, h, w, c, pt, ph, pw)) x = torch.einsum("nthwcopq->nctohpwq", x) imgs = x.reshape(shape=(x.shape[0], c, t * pt, h * ph, w * pw)) return imgs def params_count(self): counts = { "double": sum( [ sum(p.numel() for p in block.img_attn_qkv.parameters()) + sum(p.numel() for p in block.img_attn_proj.parameters()) + sum(p.numel() for p in block.img_mlp.parameters()) + sum(p.numel() for p in block.txt_attn_qkv.parameters()) + sum(p.numel() for p in block.txt_attn_proj.parameters()) + sum(p.numel() for p in block.txt_mlp.parameters()) for block in self.double_blocks ] ), "single": sum( [ sum(p.numel() for p in block.linear1.parameters()) + sum(p.numel() for p in block.linear2.parameters()) for block in self.single_blocks ] ), "total": sum(p.numel() for p in self.parameters()), } counts["attn+mlp"] = counts["double"] + counts["single"] return counts ################################################################################# # HunyuanVideo Configs # ################################################################################# HUNYUAN_VIDEO_CONFIG = { "HYVideo-T/2": { "mm_double_blocks_depth": 20, "mm_single_blocks_depth": 40, "rope_dim_list": [16, 56, 56], "hidden_size": 3072, "heads_num": 24, "mlp_width_ratio": 4, }, "HYVideo-T/2-cfgdistill": { "mm_double_blocks_depth": 20, "mm_single_blocks_depth": 40, "rope_dim_list": [16, 56, 56], "hidden_size": 3072, "heads_num": 24, "mlp_width_ratio": 4, "guidance_embed": True, }, }