| 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, apply_gate_and_accumulate_
|
| from .token_refiner import SingleTokenRefiner
|
| import numpy as np
|
| from mmgp import offload
|
| from shared.attention import pay_attention
|
| from .audio_adapters import AudioProjNet2, PerceiverAttentionCA
|
|
|
| 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
|
|
|
|
|
| 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,
|
| seqlens_q: Optional[torch.Tensor] = None,
|
| seqlens_kv: Optional[torch.Tensor] = None,
|
| freqs_cis: tuple = None,
|
| condition_type: str = None,
|
| token_replace_vec: torch.Tensor = None,
|
| frist_frame_token_num: int = None,
|
| ) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
|
| if condition_type == "token_replace":
|
| img_mod1, token_replace_img_mod1 = self.img_mod(vec, condition_type=condition_type, \
|
| token_replace_vec=token_replace_vec)
|
| (img_mod1_shift,
|
| img_mod1_scale,
|
| img_mod1_gate,
|
| img_mod2_shift,
|
| img_mod2_scale,
|
| img_mod2_gate) = img_mod1.chunk(6, dim=-1)
|
| (tr_img_mod1_shift,
|
| tr_img_mod1_scale,
|
| tr_img_mod1_gate,
|
| tr_img_mod2_shift,
|
| tr_img_mod2_scale,
|
| tr_img_mod2_gate) = token_replace_img_mod1.chunk(6, dim=-1)
|
| else:
|
| (
|
| 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)
|
|
|
|
|
|
|
|
|
|
|
|
|
| img_modulated = self.img_norm1(img)
|
| img_modulated = img_modulated.to(torch.bfloat16)
|
|
|
| if condition_type == "token_replace":
|
| modulate_(img_modulated[:, :frist_frame_token_num], shift=tr_img_mod1_shift, scale=tr_img_mod1_scale)
|
| modulate_(img_modulated[:, frist_frame_token_num:], shift=img_mod1_shift, scale=img_mod1_scale)
|
| else:
|
| 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
|
|
|
|
|
| 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]
|
|
|
| qklist = [img_q, img_k]
|
| del img_q, img_k
|
| img_q, img_k = apply_rotary_emb(qklist, freqs_cis, head_first=False)
|
|
|
| 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
|
|
|
| self.txt_attn_q_norm.apply_(txt_q).to(txt_v)
|
| self.txt_attn_k_norm.apply_(txt_k).to(txt_v)
|
|
|
|
|
| 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
|
|
|
|
|
| qkv_list = [q,k,v]
|
| del q, k, v
|
|
|
| attn = pay_attention(
|
| qkv_list,
|
| attention_mask=attn_mask,
|
| q_lens=seqlens_q,
|
| k_lens=seqlens_kv,
|
| )
|
| b, s, a, d = attn.shape
|
| attn = attn.reshape(b, s, -1)
|
| del qkv_list
|
|
|
|
|
|
|
| img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1] :]
|
| del attn
|
|
|
|
|
| if condition_type == "token_replace":
|
| img_attn = self.img_attn_proj(img_attn)
|
| apply_gate_and_accumulate_(img[:, :frist_frame_token_num], img_attn[:, :frist_frame_token_num], gate=tr_img_mod1_gate)
|
| apply_gate_and_accumulate_(img[:, frist_frame_token_num:], img_attn[:, frist_frame_token_num:], gate=img_mod1_gate)
|
| del img_attn
|
| img_modulated = self.img_norm2(img)
|
| img_modulated = img_modulated.to(torch.bfloat16)
|
| modulate_( img_modulated[:, :frist_frame_token_num], shift=tr_img_mod2_shift, scale=tr_img_mod2_scale)
|
| modulate_( img_modulated[:, frist_frame_token_num:], shift=img_mod2_shift, scale=img_mod2_scale)
|
| self.img_mlp.apply_(img_modulated)
|
| apply_gate_and_accumulate_(img[:, :frist_frame_token_num], img_modulated[:, :frist_frame_token_num], gate=tr_img_mod2_gate)
|
| apply_gate_and_accumulate_(img[:, frist_frame_token_num:], img_modulated[:, frist_frame_token_num:], gate=img_mod2_gate)
|
| del img_modulated
|
| else:
|
| 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
|
|
|
|
|
| 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)
|
| 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
|
|
|
|
|
| self.linear1 = nn.Linear(
|
| hidden_size, hidden_size * 3 + mlp_hidden_dim, **factory_kwargs
|
| )
|
|
|
| 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,
|
|
|
| img: torch.Tensor,
|
| txt: torch.Tensor,
|
| vec: torch.Tensor,
|
| txt_len: int,
|
| attn_mask= None,
|
| seqlens_q: Optional[torch.Tensor] = None,
|
| seqlens_kv: Optional[torch.Tensor] = None,
|
| freqs_cis: Tuple[torch.Tensor, torch.Tensor] = None,
|
| condition_type: str = None,
|
| token_replace_vec: torch.Tensor = None,
|
| frist_frame_token_num: int = None,
|
| ) -> torch.Tensor:
|
|
|
|
|
|
|
|
|
|
|
| if condition_type == "token_replace":
|
| mod, tr_mod = self.modulation(vec,
|
| condition_type=condition_type,
|
| token_replace_vec=token_replace_vec)
|
| (mod_shift,
|
| mod_scale,
|
| mod_gate) = mod.chunk(3, dim=-1)
|
| (tr_mod_shift,
|
| tr_mod_scale,
|
| tr_mod_gate) = tr_mod.chunk(3, dim=-1)
|
| else:
|
| mod_shift, mod_scale, mod_gate = self.modulation(vec).chunk(3, dim=-1)
|
|
|
| img_mod = self.pre_norm(img)
|
| img_mod = img_mod.to(torch.bfloat16)
|
| if condition_type == "token_replace":
|
| modulate_(img_mod[:, :frist_frame_token_num], shift=tr_mod_shift, scale=tr_mod_scale)
|
| modulate_(img_mod[:, frist_frame_token_num:], shift=mod_shift, scale=mod_scale)
|
| else:
|
| modulate_(img_mod, shift=mod_shift, scale=mod_scale)
|
| txt_mod = self.pre_norm(txt)
|
| txt_mod = txt_mod.to(torch.bfloat16)
|
| 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]
|
|
|
|
|
|
|
| self.q_norm.apply_(img_q)
|
| self.k_norm.apply_(img_k)
|
| self.q_norm.apply_(txt_q)
|
| self.k_norm.apply_(txt_k)
|
|
|
| qklist = [img_q, img_k]
|
| del img_q, img_k
|
| img_q, img_k = apply_rotary_emb(qklist, freqs_cis, head_first=False)
|
| img_q_len=img_q.shape[1]
|
| q = torch.cat((img_q, txt_q), dim=1)
|
| del img_q, txt_q
|
| k = torch.cat((img_k, txt_k), dim=1)
|
| img_kv_len=img_k.shape[1]
|
| del img_k, txt_k
|
|
|
| v = torch.cat((img_v, txt_v), dim=1)
|
| del img_v, txt_v
|
|
|
|
|
| qkv_list = [q,k,v]
|
| del q, k, v
|
| attn = pay_attention(
|
| qkv_list,
|
| attention_mask=attn_mask,
|
| q_lens = seqlens_q,
|
| k_lens = seqlens_kv,
|
| )
|
| b, s, a, d = attn.shape
|
| attn = attn.reshape(b, s, -1)
|
| del qkv_list
|
|
|
|
|
| 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[0]/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)
|
|
|
| if condition_type == "token_replace":
|
| apply_gate_and_accumulate_(img[:, :frist_frame_token_num, :], x_mod[:, :frist_frame_token_num, :], gate=tr_mod_gate)
|
| apply_gate_and_accumulate_(img[:, frist_frame_token_num:, :], x_mod[:, frist_frame_token_num:-txt_len, :], gate=mod_gate)
|
| else:
|
| 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):
|
| def preprocess_loras(self, model_type, sd):
|
| if model_type != "hunyuan_i2v" :
|
| return sd
|
| new_sd = {}
|
| for k,v in sd.items():
|
| repl_list = ["double_blocks", "single_blocks", "final_layer", "img_mlp", "img_attn_qkv", "img_attn_proj","img_mod", "txt_mlp", "txt_attn_qkv","txt_attn_proj", "txt_mod", "linear1",
|
| "linear2", "modulation", "mlp_fc1"]
|
| src_list = [k +"_" for k in repl_list] + ["_" + k for k in repl_list]
|
| tgt_list = [k +"." for k in repl_list] + ["." + k for k in repl_list]
|
| if k.startswith("Hunyuan_video_I2V_lora_"):
|
|
|
| k = k.replace("Hunyuan_video_I2V_lora_","diffusion_model.")
|
| k = k.replace("lora_up","lora_B")
|
| k = k.replace("lora_down","lora_A")
|
| if "txt_in_individual" in k:
|
| pass
|
| for s,t in zip(src_list, tgt_list):
|
| k = k.replace(s,t)
|
| if "individual_token_refiner" in k:
|
| k = k.replace("txt_in_individual_token_refiner_blocks_", "txt_in.individual_token_refiner.blocks.")
|
| k = k.replace("_mlp_fc", ".mlp.fc",)
|
| k = k.replace(".mlp_fc", ".mlp.fc",)
|
| new_sd[k] = v
|
| return new_sd
|
| """
|
| 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,
|
| i2v_condition_type,
|
| patch_size: list = [1, 2, 2],
|
| in_channels: int = 4,
|
| 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,
|
| 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",
|
| video_condition: bool = False,
|
| audio_condition: bool = False,
|
| avatar = False,
|
| custom = False,
|
| ):
|
| 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.i2v_condition_type = i2v_condition_type
|
| self.attention_mode = attention_mode
|
| self.video_condition = video_condition
|
| self.audio_condition = audio_condition
|
| self.avatar = avatar
|
| self.custom = custom
|
|
|
|
|
|
|
| self.use_attention_mask = use_attention_mask
|
| self.text_projection = text_projection
|
|
|
| self.text_states_dim = 4096
|
| self.text_states_dim_2 = 768
|
|
|
| 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
|
|
|
|
|
| self.img_in = PatchEmbed(
|
| self.patch_size, self.in_channels, self.hidden_size, **factory_kwargs
|
| )
|
|
|
|
|
| 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}"
|
| )
|
|
|
|
|
| self.time_in = TimestepEmbedder(
|
| self.hidden_size, get_activation_layer("silu"), **factory_kwargs
|
| )
|
|
|
|
|
| self.vector_in = MLPEmbedder(
|
| self.text_states_dim_2, self.hidden_size, **factory_kwargs
|
| )
|
|
|
|
|
| self.guidance_in = (
|
| TimestepEmbedder(
|
| self.hidden_size, get_activation_layer("silu"), **factory_kwargs
|
| )
|
| if guidance_embed
|
| else None
|
| )
|
|
|
|
|
| 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)
|
| ]
|
| )
|
|
|
|
|
| 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,
|
| )
|
|
|
| if self.video_condition:
|
| self.bg_in = PatchEmbed(
|
| self.patch_size, self.in_channels * 2, self.hidden_size, **factory_kwargs
|
| )
|
| self.bg_proj = nn.Linear(self.hidden_size, self.hidden_size)
|
|
|
| if audio_condition:
|
| if avatar:
|
| self.ref_in = PatchEmbed(
|
| self.patch_size, self.in_channels, self.hidden_size, **factory_kwargs
|
| )
|
|
|
|
|
| self.audio_proj = AudioProjNet2(seq_len=10, blocks=5, channels=384, intermediate_dim=1024, output_dim=3072, context_tokens=4)
|
|
|
|
|
| self.motion_exp = TimestepEmbedder(
|
| self.hidden_size // 4,
|
| get_activation_layer("silu"),
|
| **factory_kwargs
|
| )
|
| self.motion_pose = TimestepEmbedder(
|
| self.hidden_size // 4,
|
| get_activation_layer("silu"),
|
| **factory_kwargs
|
| )
|
|
|
| self.fps_proj = TimestepEmbedder(
|
| self.hidden_size,
|
| get_activation_layer("silu"),
|
| **factory_kwargs
|
| )
|
|
|
| self.before_proj = nn.Linear(self.hidden_size, self.hidden_size)
|
|
|
|
|
| self.double_stream_list = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
|
| audio_block_name = "audio_adapter_blocks"
|
| elif custom:
|
| self.audio_proj = AudioProjNet2(seq_len=10, blocks=5, channels=384, intermediate_dim=1024, output_dim=3072, context_tokens=4)
|
| self.double_stream_list = [1, 3, 5, 7, 9, 11]
|
| audio_block_name = "audio_models"
|
|
|
| self.double_stream_map = {str(i): j for j, i in enumerate(self.double_stream_list)}
|
| self.single_stream_list = []
|
| self.single_stream_map = {str(i): j+len(self.double_stream_list) for j, i in enumerate(self.single_stream_list)}
|
| setattr(self, audio_block_name, nn.ModuleList([
|
| PerceiverAttentionCA(dim=3072, dim_head=1024, heads=33) for _ in range(len(self.double_stream_list) + len(self.single_stream_list))
|
| ]))
|
|
|
|
|
|
|
| def lock_layers_dtypes(self, dtype = torch.float32):
|
| layer_list = [self.final_layer, self.final_layer.linear, self.final_layer.adaLN_modulation[1]]
|
| target_dype= dtype
|
|
|
| for current_layer_list, current_dtype in zip([layer_list], [target_dype]):
|
| for layer in current_layer_list:
|
| layer._lock_dtype = dtype
|
|
|
| if hasattr(layer, "weight") and layer.weight.dtype != current_dtype :
|
| layer.weight.data = layer.weight.data.to(current_dtype)
|
| if hasattr(layer, "bias"):
|
| layer.bias.data = layer.bias.data.to(current_dtype)
|
|
|
| self._lock_dtype = dtype
|
|
|
| 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 compute_magcache_threshold(self, start_step, num_inference_steps = 0, speed_factor =0):
|
| skips_step_cache = self.cache
|
|
|
| def nearest_interp(src_array, target_length):
|
| src_length = len(src_array)
|
| if target_length == 1:
|
| return np.array([src_array[-1]])
|
| scale = (src_length - 1) / (target_length - 1)
|
| mapped_indices = np.round(np.arange(target_length) * scale).astype(int)
|
| return src_array[mapped_indices]
|
| def_mag_ratios = np.array([1.0]+ skips_step_cache.def_mag_ratios)
|
| if len(def_mag_ratios) != num_inference_steps:
|
| skips_step_cache.mag_ratios = nearest_interp(def_mag_ratios, num_inference_steps)
|
| else:
|
| skips_step_cache.mag_ratios = def_mag_ratios
|
|
|
| best_deltas = None
|
| best_threshold = 0.01
|
| best_diff = 1000
|
| best_signed_diff = 1000
|
| target_nb_steps= int(num_inference_steps / speed_factor)
|
| threshold = 0.01
|
| while threshold <= 0.6:
|
| nb_steps = 0
|
| diff = 1000
|
| accumulated_err, accumulated_steps, accumulated_ratio = 0, 0, 1.0
|
| for i in range(num_inference_steps):
|
| if i<=start_step:
|
| skip = False
|
| else:
|
| cur_mag_ratio = skips_step_cache.mag_ratios[i]
|
| accumulated_ratio *= cur_mag_ratio
|
| accumulated_steps += 1
|
| cur_skip_err = np.abs(1-accumulated_ratio)
|
| accumulated_err += cur_skip_err
|
| if accumulated_err<threshold and accumulated_steps<=skips_step_cache.magcache_K:
|
| skip = True
|
| else:
|
| skip = False
|
| accumulated_err, accumulated_steps, accumulated_ratio = 0, 0, 1.0
|
| if not skip:
|
| nb_steps += 1
|
| signed_diff = target_nb_steps - nb_steps
|
| diff = abs(signed_diff)
|
| if diff < best_diff:
|
| best_threshold = threshold
|
| best_diff = diff
|
| best_signed_diff = signed_diff
|
| elif diff > best_diff:
|
| break
|
| threshold += 0.01
|
| skips_step_cache.magcache_thresh = best_threshold
|
| print(f"Mag Cache, best threshold found:{best_threshold:0.2f} with gain x{num_inference_steps/(target_nb_steps - best_signed_diff):0.2f} for a target of x{speed_factor}")
|
| return best_threshold
|
|
|
| def forward(
|
| self,
|
| x: torch.Tensor,
|
| t: torch.Tensor,
|
| ref_latents: torch.Tensor=None,
|
| text_states: torch.Tensor = None,
|
| text_mask: torch.Tensor = None,
|
| text_states_2: Optional[torch.Tensor] = None,
|
| freqs_cos: Optional[torch.Tensor] = None,
|
| freqs_sin: Optional[torch.Tensor] = None,
|
| guidance: torch.Tensor = None,
|
| pipeline=None,
|
| x_id = 0,
|
| step_no = 0,
|
| callback = None,
|
| audio_prompts = None,
|
| motion_exp = None,
|
| motion_pose = None,
|
| fps = None,
|
| face_mask = None,
|
| audio_strength = None,
|
| bg_latents = None,
|
| ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
|
|
|
| img = x
|
| bsz, _, 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],
|
| )
|
|
|
|
|
| vec = self.time_in(t)
|
| if motion_exp != None:
|
| vec += self.motion_exp(motion_exp.view(-1)).view(bsz, -1)
|
| if motion_pose != None:
|
| vec += self.motion_pose(motion_pose.view(-1)).view(bsz, -1)
|
| if fps != None:
|
| vec += self.fps_proj(fps)
|
| if audio_prompts != None:
|
| audio_feature_all = self.audio_proj(audio_prompts)
|
| audio_feature_pad = audio_feature_all[:,:1].repeat(1,3,1,1)
|
| audio_feature_all_insert = torch.cat([audio_feature_pad, audio_feature_all], dim=1).view(bsz, ot, 16, 3072)
|
| audio_feature_all = None
|
|
|
| if self.i2v_condition_type == "token_replace":
|
| token_replace_t = torch.zeros_like(t)
|
| token_replace_vec = self.time_in(token_replace_t)
|
| frist_frame_token_num = th * tw
|
| else:
|
| token_replace_vec = None
|
| frist_frame_token_num = None
|
|
|
|
|
|
|
|
|
| vec_2 = self.vector_in(text_states_2)
|
| del text_states_2
|
| vec += vec_2
|
| if self.i2v_condition_type == "token_replace":
|
| token_replace_vec += vec_2
|
| del vec_2
|
|
|
|
|
| if self.guidance_embed:
|
| if guidance is None:
|
| raise ValueError(
|
| "Didn't get guidance strength for guidance distilled model."
|
| )
|
|
|
|
|
| vec += self.guidance_in(guidance)
|
|
|
|
|
| img, shape_mask = self.img_in(img)
|
| if self.avatar:
|
| ref_latents_first = ref_latents[:, :, :1].clone()
|
| ref_latents,_ = self.ref_in(ref_latents)
|
| ref_latents_first,_ = self.img_in(ref_latents_first)
|
| elif self.custom:
|
| if ref_latents != None:
|
| ref_latents, _ = self.img_in(ref_latents)
|
| if bg_latents is not None and self.video_condition:
|
| bg_latents, _ = self.bg_in(bg_latents)
|
| img += self.bg_proj(bg_latents)
|
|
|
| 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}"
|
| )
|
|
|
| if self.avatar:
|
| img += self.before_proj(ref_latents)
|
| ref_length = ref_latents_first.shape[-2]
|
| img = torch.cat([ref_latents_first, img], dim=-2)
|
| img_len = img.shape[1]
|
| mask_len = img_len - ref_length
|
| if face_mask.shape[2] == 1:
|
| face_mask = face_mask.repeat(1,1,ot,1,1)
|
| face_mask = torch.nn.functional.interpolate(face_mask, size=[ot, shape_mask[-2], shape_mask[-1]], mode="nearest")
|
|
|
| face_mask = face_mask.view(-1,mask_len,1).type_as(img)
|
| elif ref_latents == None:
|
| ref_length = None
|
| else:
|
| ref_length = ref_latents.shape[-2]
|
| img = torch.cat([ref_latents, img], dim=-2)
|
| txt_seq_len = txt.shape[1]
|
| img_seq_len = img.shape[1]
|
|
|
| text_len = text_mask.sum(1)
|
| total_len = text_len + img_seq_len
|
| seqlens_q = seqlens_kv = total_len
|
| attn_mask = None
|
|
|
| freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None
|
| should_calc = True
|
| skip_steps_cache = self.cache
|
| if skip_steps_cache is not None:
|
| cache_type = skip_steps_cache.cache_type
|
| if x_id == 0:
|
| skip_steps_cache.should_calc = True
|
| if cache_type == "mag":
|
| if step_no > skip_steps_cache.start_step:
|
| cur_mag_ratio = skip_steps_cache.mag_ratios[step_no]
|
| skip_steps_cache.accumulated_ratio = skip_steps_cache.accumulated_ratio*cur_mag_ratio
|
| cur_skip_err = np.abs(1-skip_steps_cache.accumulated_ratio)
|
| skip_steps_cache.accumulated_err += cur_skip_err
|
| skip_steps_cache.accumulated_steps += 1
|
| if skip_steps_cache.accumulated_err<=skip_steps_cache.magcache_thresh and skip_steps_cache.accumulated_steps<=skip_steps_cache.magcache_K:
|
| skip_steps_cache.should_calc = False
|
| skip_steps_cache.skipped_steps += 1
|
| else:
|
| skip_steps_cache.accumulated_ratio, skip_steps_cache.accumulated_steps, skip_steps_cache.accumulated_err = 1.0, 0, 0
|
| else:
|
| inp = img[0:1]
|
| vec_ = vec[0:1]
|
| ( 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 step_no <= skip_steps_cache.start_step or step_no == skip_steps_cache.num_steps-1:
|
| skip_steps_cache.accumulated_rel_l1_distance = 0
|
| else:
|
| rescale_func = np.poly1d(skip_steps_cache.coefficients)
|
| skip_steps_cache.accumulated_rel_l1_distance += rescale_func(((modulated_inp-skip_steps_cache.previous_modulated_input).abs().mean() / skip_steps_cache.previous_modulated_input.abs().mean()).cpu().item())
|
| if skip_steps_cache.accumulated_rel_l1_distance < skip_steps_cache.rel_l1_thresh:
|
| skip_steps_cache.should_calc = False
|
| skip_steps_cache.skipped_steps += 1
|
| else:
|
| skip_steps_cache.accumulated_rel_l1_distance = 0
|
| skip_steps_cache.previous_modulated_input = modulated_inp
|
| should_calc = skip_steps_cache.should_calc
|
|
|
| if not should_calc:
|
| img += skip_steps_cache.previous_residual[x_id]
|
| else:
|
| if skip_steps_cache is not None:
|
| skip_steps_cache.previous_residual[x_id] = None
|
| ori_img = img[0:1].clone()
|
|
|
| for layer_num, block in enumerate(self.double_blocks):
|
| for i in range(len(img)):
|
| if callback != None:
|
| callback(-1, None, False, True)
|
| if pipeline._interrupt:
|
| return None
|
| double_block_args = [
|
| img[i:i+1],
|
| txt[i:i+1],
|
| vec[i:i+1],
|
| attn_mask,
|
| seqlens_q[i:i+1],
|
| seqlens_kv[i:i+1],
|
| freqs_cis,
|
| self.i2v_condition_type,
|
| token_replace_vec,
|
| frist_frame_token_num,
|
| ]
|
|
|
| img[i], txt[i] = block(*double_block_args)
|
| double_block_args = None
|
|
|
| if audio_prompts != None:
|
| audio_adapter = getattr(self.double_blocks[layer_num], "audio_adapter", None)
|
| if audio_adapter != None:
|
| real_img = img[i:i+1,ref_length:].view(1, ot, -1, 3072)
|
| real_img = audio_adapter(audio_feature_all_insert[i:i+1], real_img).view(1, -1, 3072)
|
| if face_mask != None:
|
| real_img *= face_mask[i:i+1]
|
| if audio_strength != None and audio_strength != 1:
|
| real_img *= audio_strength
|
| img[i:i+1, ref_length:] += real_img
|
| real_img = None
|
|
|
|
|
| for _, block in enumerate(self.single_blocks):
|
| for i in range(len(img)):
|
| if callback != None:
|
| callback(-1, None, False, True)
|
| if pipeline._interrupt:
|
| return None
|
| single_block_args = [
|
|
|
| img[i:i+1],
|
| txt[i:i+1],
|
| vec[i:i+1],
|
| txt_seq_len,
|
| attn_mask,
|
| seqlens_q[i:i+1],
|
| seqlens_kv[i:i+1],
|
| (freqs_cos, freqs_sin),
|
| self.i2v_condition_type,
|
| token_replace_vec,
|
| frist_frame_token_num,
|
| ]
|
|
|
| img[i], txt[i] = block(*single_block_args)
|
| single_block_args = None
|
|
|
|
|
| if skip_steps_cache is not None:
|
| if len(img) > 1:
|
| skip_steps_cache.previous_residual[0] = torch.empty_like(img)
|
| for i, (x, residual) in enumerate(zip(img, skip_steps_cache.previous_residual[0])):
|
| if i < len(img) - 1:
|
| residual[...] = torch.sub(x, ori_img)
|
| else:
|
| residual[...] = ori_img
|
| torch.sub(x, ori_img, out=residual)
|
| x = None
|
| else:
|
| skip_steps_cache.previous_residual[x_id] = ori_img
|
| torch.sub(img, ori_img, out=skip_steps_cache.previous_residual[x_id])
|
|
|
|
|
| if ref_length != None:
|
| img = img[:, ref_length:]
|
|
|
| out_dtype = self.final_layer.linear.weight.dtype
|
| vec = vec.to(out_dtype)
|
| img_list = []
|
| for img_chunk, vec_chunk in zip(img,vec):
|
| img_list.append( self.final_layer(img_chunk.to(out_dtype).unsqueeze(0), vec_chunk.unsqueeze(0)))
|
| img = torch.cat(img_list)
|
| img_list = None
|
|
|
|
|
| img = self.unpatchify(img, tt, th, tw)
|
|
|
| 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
|
|
|
|
|
|
|
|
|
|
|
|
|
| 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,
|
| },
|
| "HYVideo-S/2": {
|
| "mm_double_blocks_depth": 6,
|
| "mm_single_blocks_depth": 12,
|
| "rope_dim_list": [12, 42, 42],
|
| "hidden_size": 480,
|
| "heads_num": 5,
|
| "mlp_width_ratio": 4,
|
| },
|
| 'HYVideo-T/2-custom': {
|
| "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,
|
| 'custom' : True
|
| },
|
| 'HYVideo-T/2-custom-audio': {
|
| "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,
|
| 'custom' : True,
|
| 'audio_condition' : True,
|
| },
|
| 'HYVideo-T/2-custom-edit': {
|
| "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,
|
| 'custom' : True,
|
| 'video_condition' : True,
|
| },
|
| 'HYVideo-T/2-avatar': {
|
| '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,
|
| 'avatar': True,
|
| 'audio_condition' : True,
|
| },
|
|
|
| } |
