mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
761 lines
26 KiB
Python
761 lines
26 KiB
Python
from typing import Any, List, Tuple, Optional, Union, Dict
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from einops import rearrange
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from diffusers.models import ModelMixin
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from .activation_layers import get_activation_layer
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from .norm_layers import get_norm_layer
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from .embed_layers import TimestepEmbedder, PatchEmbed, TextProjection
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from .attenion import attention, parallel_attention, get_cu_seqlens
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from .posemb_layers import apply_rotary_emb
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from .mlp_layers import MLP, MLPEmbedder, FinalLayer
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from .modulate_layers import ModulateDiT, modulate, apply_gate
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from .token_refiner import SingleTokenRefiner
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class MMDoubleStreamBlock(nn.Module):
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"""
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A multimodal dit block with seperate modulation for
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text and image/video, see more details (SD3): https://arxiv.org/abs/2403.03206
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(Flux.1): https://github.com/black-forest-labs/flux
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"""
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def __init__(
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self,
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hidden_size: int,
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heads_num: int,
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mlp_width_ratio: float,
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mlp_act_type: str = "gelu_tanh",
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qk_norm: bool = True,
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qk_norm_type: str = "rms",
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qkv_bias: bool = False,
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dtype: Optional[torch.dtype] = None,
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device: Optional[torch.device] = None,
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):
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factory_kwargs = {"device": device, "dtype": dtype}
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super().__init__()
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self.deterministic = False
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self.heads_num = heads_num
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head_dim = hidden_size // heads_num
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mlp_hidden_dim = int(hidden_size * mlp_width_ratio)
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self.img_mod = ModulateDiT(
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hidden_size,
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factor=6,
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act_layer=get_activation_layer("silu"),
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**factory_kwargs,
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)
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self.img_norm1 = nn.LayerNorm(
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hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs
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)
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self.img_attn_qkv = nn.Linear(
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hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs
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)
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qk_norm_layer = get_norm_layer(qk_norm_type)
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self.img_attn_q_norm = (
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qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
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if qk_norm
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else nn.Identity()
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)
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self.img_attn_k_norm = (
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qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
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if qk_norm
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else nn.Identity()
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)
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self.img_attn_proj = nn.Linear(
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hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs
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)
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self.img_norm2 = nn.LayerNorm(
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hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs
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)
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self.img_mlp = MLP(
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hidden_size,
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mlp_hidden_dim,
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act_layer=get_activation_layer(mlp_act_type),
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bias=True,
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**factory_kwargs,
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)
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self.txt_mod = ModulateDiT(
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hidden_size,
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factor=6,
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act_layer=get_activation_layer("silu"),
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**factory_kwargs,
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)
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self.txt_norm1 = nn.LayerNorm(
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hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs
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)
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self.txt_attn_qkv = nn.Linear(
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hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs
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)
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self.txt_attn_q_norm = (
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qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
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if qk_norm
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else nn.Identity()
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)
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self.txt_attn_k_norm = (
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qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
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if qk_norm
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else nn.Identity()
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)
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self.txt_attn_proj = nn.Linear(
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hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs
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)
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self.txt_norm2 = nn.LayerNorm(
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hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs
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)
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self.txt_mlp = MLP(
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hidden_size,
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mlp_hidden_dim,
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act_layer=get_activation_layer(mlp_act_type),
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bias=True,
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**factory_kwargs,
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)
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self.hybrid_seq_parallel_attn = None
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def enable_deterministic(self):
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self.deterministic = True
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def disable_deterministic(self):
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self.deterministic = False
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def forward(
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self,
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img: torch.Tensor,
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txt: torch.Tensor,
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vec: torch.Tensor,
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cu_seqlens_q: Optional[torch.Tensor] = None,
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cu_seqlens_kv: Optional[torch.Tensor] = None,
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max_seqlen_q: Optional[int] = None,
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max_seqlen_kv: Optional[int] = None,
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freqs_cis: tuple = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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(
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img_mod1_shift,
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img_mod1_scale,
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img_mod1_gate,
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img_mod2_shift,
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img_mod2_scale,
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img_mod2_gate,
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) = self.img_mod(vec).chunk(6, dim=-1)
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(
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txt_mod1_shift,
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txt_mod1_scale,
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txt_mod1_gate,
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txt_mod2_shift,
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txt_mod2_scale,
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txt_mod2_gate,
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) = self.txt_mod(vec).chunk(6, dim=-1)
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# Prepare image for attention.
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img_modulated = self.img_norm1(img)
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img_modulated = modulate(
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img_modulated, shift=img_mod1_shift, scale=img_mod1_scale
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)
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img_qkv = self.img_attn_qkv(img_modulated)
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img_q, img_k, img_v = rearrange(
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img_qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num
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)
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# Apply QK-Norm if needed
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img_q = self.img_attn_q_norm(img_q).to(img_v)
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img_k = self.img_attn_k_norm(img_k).to(img_v)
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# Apply RoPE if needed.
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if freqs_cis is not None:
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img_qq, img_kk = apply_rotary_emb(img_q, img_k, freqs_cis, head_first=False)
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assert (
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img_qq.shape == img_q.shape and img_kk.shape == img_k.shape
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), f"img_kk: {img_qq.shape}, img_q: {img_q.shape}, img_kk: {img_kk.shape}, img_k: {img_k.shape}"
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img_q, img_k = img_qq, img_kk
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# Prepare txt for attention.
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txt_modulated = self.txt_norm1(txt)
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txt_modulated = modulate(
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txt_modulated, shift=txt_mod1_shift, scale=txt_mod1_scale
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)
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txt_qkv = self.txt_attn_qkv(txt_modulated)
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txt_q, txt_k, txt_v = rearrange(
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txt_qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num
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)
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# Apply QK-Norm if needed.
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txt_q = self.txt_attn_q_norm(txt_q).to(txt_v)
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txt_k = self.txt_attn_k_norm(txt_k).to(txt_v)
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# Run actual attention.
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q = torch.cat((img_q, txt_q), dim=1)
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k = torch.cat((img_k, txt_k), dim=1)
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v = torch.cat((img_v, txt_v), dim=1)
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assert (
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cu_seqlens_q.shape[0] == 2 * img.shape[0] + 1
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), f"cu_seqlens_q.shape:{cu_seqlens_q.shape}, img.shape[0]:{img.shape[0]}"
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# attention computation start
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if not self.hybrid_seq_parallel_attn:
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attn = attention(
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q,
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k,
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v,
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cu_seqlens_q=cu_seqlens_q,
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cu_seqlens_kv=cu_seqlens_kv,
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max_seqlen_q=max_seqlen_q,
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max_seqlen_kv=max_seqlen_kv,
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batch_size=img_k.shape[0],
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)
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else:
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attn = parallel_attention(
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self.hybrid_seq_parallel_attn,
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q,
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k,
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v,
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img_q_len=img_q.shape[1],
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img_kv_len=img_k.shape[1],
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cu_seqlens_q=cu_seqlens_q,
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cu_seqlens_kv=cu_seqlens_kv
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)
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# attention computation end
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img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1] :]
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# Calculate the img bloks.
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img = img + apply_gate(self.img_attn_proj(img_attn), gate=img_mod1_gate)
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img = img + apply_gate(
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self.img_mlp(
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modulate(
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self.img_norm2(img), shift=img_mod2_shift, scale=img_mod2_scale
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)
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),
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gate=img_mod2_gate,
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)
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# Calculate the txt bloks.
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txt = txt + apply_gate(self.txt_attn_proj(txt_attn), gate=txt_mod1_gate)
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txt = txt + apply_gate(
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self.txt_mlp(
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modulate(
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self.txt_norm2(txt), shift=txt_mod2_shift, scale=txt_mod2_scale
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)
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),
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gate=txt_mod2_gate,
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)
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return img, txt
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class MMSingleStreamBlock(nn.Module):
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"""
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A DiT block with parallel linear layers as described in
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https://arxiv.org/abs/2302.05442 and adapted modulation interface.
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Also refer to (SD3): https://arxiv.org/abs/2403.03206
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(Flux.1): https://github.com/black-forest-labs/flux
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"""
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def __init__(
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self,
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hidden_size: int,
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heads_num: int,
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mlp_width_ratio: float = 4.0,
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mlp_act_type: str = "gelu_tanh",
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qk_norm: bool = True,
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qk_norm_type: str = "rms",
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qk_scale: float = None,
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dtype: Optional[torch.dtype] = None,
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device: Optional[torch.device] = None,
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):
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factory_kwargs = {"device": device, "dtype": dtype}
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super().__init__()
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self.deterministic = False
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self.hidden_size = hidden_size
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self.heads_num = heads_num
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head_dim = hidden_size // heads_num
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mlp_hidden_dim = int(hidden_size * mlp_width_ratio)
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self.mlp_hidden_dim = mlp_hidden_dim
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self.scale = qk_scale or head_dim ** -0.5
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# qkv and mlp_in
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self.linear1 = nn.Linear(
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hidden_size, hidden_size * 3 + mlp_hidden_dim, **factory_kwargs
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)
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# proj and mlp_out
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self.linear2 = nn.Linear(
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hidden_size + mlp_hidden_dim, hidden_size, **factory_kwargs
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)
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qk_norm_layer = get_norm_layer(qk_norm_type)
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self.q_norm = (
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qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
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if qk_norm
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else nn.Identity()
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)
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self.k_norm = (
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qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
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if qk_norm
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else nn.Identity()
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)
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self.pre_norm = nn.LayerNorm(
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hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs
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)
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self.mlp_act = get_activation_layer(mlp_act_type)()
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self.modulation = ModulateDiT(
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hidden_size,
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factor=3,
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act_layer=get_activation_layer("silu"),
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**factory_kwargs,
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)
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self.hybrid_seq_parallel_attn = None
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def enable_deterministic(self):
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self.deterministic = True
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def disable_deterministic(self):
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self.deterministic = False
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def forward(
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self,
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x: torch.Tensor,
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vec: torch.Tensor,
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txt_len: int,
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cu_seqlens_q: Optional[torch.Tensor] = None,
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cu_seqlens_kv: Optional[torch.Tensor] = None,
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max_seqlen_q: Optional[int] = None,
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max_seqlen_kv: Optional[int] = None,
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freqs_cis: Tuple[torch.Tensor, torch.Tensor] = None,
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) -> torch.Tensor:
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mod_shift, mod_scale, mod_gate = self.modulation(vec).chunk(3, dim=-1)
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x_mod = modulate(self.pre_norm(x), shift=mod_shift, scale=mod_scale)
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qkv, mlp = torch.split(
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self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1
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)
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q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num)
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# Apply QK-Norm if needed.
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q = self.q_norm(q).to(v)
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k = self.k_norm(k).to(v)
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# Apply RoPE if needed.
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if freqs_cis is not None:
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img_q, txt_q = q[:, :-txt_len, :, :], q[:, -txt_len:, :, :]
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img_k, txt_k = k[:, :-txt_len, :, :], k[:, -txt_len:, :, :]
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img_qq, img_kk = apply_rotary_emb(img_q, img_k, freqs_cis, head_first=False)
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assert (
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img_qq.shape == img_q.shape and img_kk.shape == img_k.shape
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), f"img_kk: {img_qq.shape}, img_q: {img_q.shape}, img_kk: {img_kk.shape}, img_k: {img_k.shape}"
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img_q, img_k = img_qq, img_kk
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q = torch.cat((img_q, txt_q), dim=1)
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k = torch.cat((img_k, txt_k), dim=1)
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# Compute attention.
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assert (
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cu_seqlens_q.shape[0] == 2 * x.shape[0] + 1
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), f"cu_seqlens_q.shape:{cu_seqlens_q.shape}, x.shape[0]:{x.shape[0]}"
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# attention computation start
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if not self.hybrid_seq_parallel_attn:
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attn = attention(
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q,
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k,
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v,
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cu_seqlens_q=cu_seqlens_q,
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cu_seqlens_kv=cu_seqlens_kv,
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max_seqlen_q=max_seqlen_q,
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max_seqlen_kv=max_seqlen_kv,
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batch_size=x.shape[0],
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)
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else:
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attn = parallel_attention(
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self.hybrid_seq_parallel_attn,
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q,
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k,
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v,
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img_q_len=img_q.shape[1],
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img_kv_len=img_k.shape[1],
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cu_seqlens_q=cu_seqlens_q,
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cu_seqlens_kv=cu_seqlens_kv
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)
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# attention computation end
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# Compute activation in mlp stream, cat again and run second linear layer.
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output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
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return x + apply_gate(output, gate=mod_gate)
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class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
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"""
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HunyuanVideo Transformer backbone
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Inherited from ModelMixin and ConfigMixin for compatibility with diffusers' sampler StableDiffusionPipeline.
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Reference:
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[1] Flux.1: https://github.com/black-forest-labs/flux
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[2] MMDiT: http://arxiv.org/abs/2403.03206
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Parameters
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----------
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args: argparse.Namespace
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The arguments parsed by argparse.
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patch_size: list
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The size of the patch.
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in_channels: int
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The number of input channels.
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out_channels: int
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The number of output channels.
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hidden_size: int
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The hidden size of the transformer backbone.
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heads_num: int
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The number of attention heads.
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mlp_width_ratio: float
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The ratio of the hidden size of the MLP in the transformer block.
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mlp_act_type: str
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The activation function of the MLP in the transformer block.
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depth_double_blocks: int
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The number of transformer blocks in the double blocks.
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depth_single_blocks: int
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The number of transformer blocks in the single blocks.
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rope_dim_list: list
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The dimension of the rotary embedding for t, h, w.
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qkv_bias: bool
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Whether to use bias in the qkv linear layer.
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qk_norm: bool
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Whether to use qk norm.
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qk_norm_type: str
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The type of qk norm.
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guidance_embed: bool
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Whether to use guidance embedding for distillation.
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text_projection: str
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The type of the text projection, default is single_refiner.
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use_attention_mask: bool
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Whether to use attention mask for text encoder.
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dtype: torch.dtype
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The dtype of the model.
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device: torch.device
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The device of the model.
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"""
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@register_to_config
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def __init__(
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self,
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args: Any,
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patch_size: list = [1, 2, 2],
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in_channels: int = 4, # Should be VAE.config.latent_channels.
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out_channels: int = None,
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hidden_size: int = 3072,
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heads_num: int = 24,
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mlp_width_ratio: float = 4.0,
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mlp_act_type: str = "gelu_tanh",
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mm_double_blocks_depth: int = 20,
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mm_single_blocks_depth: int = 40,
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rope_dim_list: List[int] = [16, 56, 56],
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qkv_bias: bool = True,
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qk_norm: bool = True,
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qk_norm_type: str = "rms",
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guidance_embed: bool = False, # For modulation.
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text_projection: str = "single_refiner",
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use_attention_mask: bool = True,
|
|
dtype: Optional[torch.dtype] = None,
|
|
device: Optional[torch.device] = None,
|
|
):
|
|
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
|
|
|
|
# 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,
|
|
**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,
|
|
**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.
|
|
return_dict: bool = True,
|
|
) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
|
|
out = {}
|
|
img = x
|
|
txt = text_states
|
|
_, _, ot, oh, ow = x.shape
|
|
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)
|
|
|
|
# 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
|
|
|
|
freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None
|
|
# --------------------- Pass through DiT blocks ------------------------
|
|
for _, block in enumerate(self.double_blocks):
|
|
double_block_args = [
|
|
img,
|
|
txt,
|
|
vec,
|
|
cu_seqlens_q,
|
|
cu_seqlens_kv,
|
|
max_seqlen_q,
|
|
max_seqlen_kv,
|
|
freqs_cis,
|
|
]
|
|
|
|
img, txt = block(*double_block_args)
|
|
|
|
# Merge txt and img to pass through single stream blocks.
|
|
x = torch.cat((img, txt), 1)
|
|
if len(self.single_blocks) > 0:
|
|
for _, block in enumerate(self.single_blocks):
|
|
single_block_args = [
|
|
x,
|
|
vec,
|
|
txt_seq_len,
|
|
cu_seqlens_q,
|
|
cu_seqlens_kv,
|
|
max_seqlen_q,
|
|
max_seqlen_kv,
|
|
(freqs_cos, freqs_sin),
|
|
]
|
|
|
|
x = block(*single_block_args)
|
|
|
|
img = x[:, :img_seq_len, ...]
|
|
|
|
# ---------------------------- 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,
|
|
},
|
|
}
|