411 lines
16 KiB
Python
411 lines
16 KiB
Python
from typing import Any
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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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import comfy.ops
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from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed, RMSNorm
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from comfy.ldm.modules.diffusionmodules.util import timestep_embedding
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from torch.utils import checkpoint
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from .attn_layers import Attention, CrossAttention
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from .poolers import AttentionPool
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from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop
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def calc_rope(x, patch_size, head_size):
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th = (x.shape[2] + (patch_size // 2)) // patch_size
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tw = (x.shape[3] + (patch_size // 2)) // patch_size
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base_size = 512 // 8 // patch_size
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start, stop = get_fill_resize_and_crop((th, tw), base_size)
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sub_args = [start, stop, (th, tw)]
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# head_size = HUNYUAN_DIT_CONFIG['DiT-g/2']['hidden_size'] // HUNYUAN_DIT_CONFIG['DiT-g/2']['num_heads']
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rope = get_2d_rotary_pos_embed(head_size, *sub_args)
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rope = (rope[0].to(x), rope[1].to(x))
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return rope
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def modulate(x, shift, scale):
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return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
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class HunYuanDiTBlock(nn.Module):
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"""
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A HunYuanDiT block with `add` conditioning.
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"""
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def __init__(self,
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hidden_size,
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c_emb_size,
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num_heads,
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mlp_ratio=4.0,
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text_states_dim=1024,
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qk_norm=False,
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norm_type="layer",
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skip=False,
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attn_precision=None,
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dtype=None,
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device=None,
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operations=None,
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):
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super().__init__()
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use_ele_affine = True
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if norm_type == "layer":
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norm_layer = operations.LayerNorm
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elif norm_type == "rms":
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norm_layer = RMSNorm
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else:
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raise ValueError(f"Unknown norm_type: {norm_type}")
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# ========================= Self-Attention =========================
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self.norm1 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device)
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self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)
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# ========================= FFN =========================
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self.norm2 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device)
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mlp_hidden_dim = int(hidden_size * mlp_ratio)
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approx_gelu = lambda: nn.GELU(approximate="tanh")
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self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0, dtype=dtype, device=device, operations=operations)
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# ========================= Add =========================
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# Simply use add like SDXL.
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self.default_modulation = nn.Sequential(
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nn.SiLU(),
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operations.Linear(c_emb_size, hidden_size, bias=True, dtype=dtype, device=device)
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)
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# ========================= Cross-Attention =========================
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self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=True,
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qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)
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self.norm3 = norm_layer(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)
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# ========================= Skip Connection =========================
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if skip:
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self.skip_norm = norm_layer(2 * hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)
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self.skip_linear = operations.Linear(2 * hidden_size, hidden_size, dtype=dtype, device=device)
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else:
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self.skip_linear = None
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self.gradient_checkpointing = False
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def _forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None):
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# Long Skip Connection
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if self.skip_linear is not None:
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cat = torch.cat([x, skip], dim=-1)
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if cat.dtype != x.dtype:
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cat = cat.to(x.dtype)
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cat = self.skip_norm(cat)
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x = self.skip_linear(cat)
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# Self-Attention
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shift_msa = self.default_modulation(c).unsqueeze(dim=1)
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attn_inputs = (
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self.norm1(x) + shift_msa, freq_cis_img,
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)
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x = x + self.attn1(*attn_inputs)[0]
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# Cross-Attention
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cross_inputs = (
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self.norm3(x), text_states, freq_cis_img
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)
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x = x + self.attn2(*cross_inputs)[0]
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# FFN Layer
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mlp_inputs = self.norm2(x)
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x = x + self.mlp(mlp_inputs)
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return x
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def forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None):
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if self.gradient_checkpointing and self.training:
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return checkpoint.checkpoint(self._forward, x, c, text_states, freq_cis_img, skip)
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return self._forward(x, c, text_states, freq_cis_img, skip)
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class FinalLayer(nn.Module):
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"""
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The final layer of HunYuanDiT.
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"""
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def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None):
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super().__init__()
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self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
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self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device)
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)
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def forward(self, x, c):
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shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
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x = modulate(self.norm_final(x), shift, scale)
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x = self.linear(x)
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return x
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class HunYuanDiT(nn.Module):
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"""
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HunYuanDiT: Diffusion model with a Transformer backbone.
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Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.
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Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline.
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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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input_size: tuple
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The size of the input image.
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patch_size: int
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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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hidden_size: int
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The hidden size of the transformer backbone.
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depth: int
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The number of transformer blocks.
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num_heads: int
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The number of attention heads.
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mlp_ratio: float
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The ratio of the hidden size of the MLP in the transformer block.
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log_fn: callable
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The logging function.
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"""
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#@register_to_config
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def __init__(self,
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input_size: tuple = 32,
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patch_size: int = 2,
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in_channels: int = 4,
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hidden_size: int = 1152,
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depth: int = 28,
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num_heads: int = 16,
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mlp_ratio: float = 4.0,
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text_states_dim = 1024,
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text_states_dim_t5 = 2048,
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text_len = 77,
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text_len_t5 = 256,
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qk_norm = True,# See http://arxiv.org/abs/2302.05442 for details.
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size_cond = False,
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use_style_cond = False,
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learn_sigma = True,
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norm = "layer",
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log_fn: callable = print,
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attn_precision=None,
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dtype=None,
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device=None,
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operations=None,
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**kwargs,
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):
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super().__init__()
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self.log_fn = log_fn
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self.depth = depth
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self.learn_sigma = learn_sigma
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self.in_channels = in_channels
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self.out_channels = in_channels * 2 if learn_sigma else in_channels
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self.patch_size = patch_size
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self.num_heads = num_heads
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self.hidden_size = hidden_size
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self.text_states_dim = text_states_dim
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self.text_states_dim_t5 = text_states_dim_t5
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self.text_len = text_len
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self.text_len_t5 = text_len_t5
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self.size_cond = size_cond
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self.use_style_cond = use_style_cond
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self.norm = norm
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self.dtype = dtype
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#import pdb
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#pdb.set_trace()
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self.mlp_t5 = nn.Sequential(
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operations.Linear(self.text_states_dim_t5, self.text_states_dim_t5 * 4, bias=True, dtype=dtype, device=device),
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nn.SiLU(),
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operations.Linear(self.text_states_dim_t5 * 4, self.text_states_dim, bias=True, dtype=dtype, device=device),
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)
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# learnable replace
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self.text_embedding_padding = nn.Parameter(
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torch.empty(self.text_len + self.text_len_t5, self.text_states_dim, dtype=dtype, device=device))
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# Attention pooling
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pooler_out_dim = 1024
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self.pooler = AttentionPool(self.text_len_t5, self.text_states_dim_t5, num_heads=8, output_dim=pooler_out_dim, dtype=dtype, device=device, operations=operations)
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# Dimension of the extra input vectors
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self.extra_in_dim = pooler_out_dim
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if self.size_cond:
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# Image size and crop size conditions
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self.extra_in_dim += 6 * 256
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if self.use_style_cond:
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# Here we use a default learned embedder layer for future extension.
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self.style_embedder = operations.Embedding(1, hidden_size, dtype=dtype, device=device)
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self.extra_in_dim += hidden_size
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# Text embedding for `add`
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self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, dtype=dtype, device=device, operations=operations)
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self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device, operations=operations)
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self.extra_embedder = nn.Sequential(
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operations.Linear(self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device),
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nn.SiLU(),
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operations.Linear(hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device),
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)
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# Image embedding
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num_patches = self.x_embedder.num_patches
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# HUnYuanDiT Blocks
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self.blocks = nn.ModuleList([
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HunYuanDiTBlock(hidden_size=hidden_size,
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c_emb_size=hidden_size,
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num_heads=num_heads,
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mlp_ratio=mlp_ratio,
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text_states_dim=self.text_states_dim,
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qk_norm=qk_norm,
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norm_type=self.norm,
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skip=layer > depth // 2,
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attn_precision=attn_precision,
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dtype=dtype,
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device=device,
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operations=operations,
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)
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for layer in range(depth)
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])
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self.final_layer = FinalLayer(hidden_size, hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
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self.unpatchify_channels = self.out_channels
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def forward(self,
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x,
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t,
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context,#encoder_hidden_states=None,
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text_embedding_mask=None,
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encoder_hidden_states_t5=None,
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text_embedding_mask_t5=None,
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image_meta_size=None,
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style=None,
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return_dict=False,
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control=None,
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transformer_options=None,
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):
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"""
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Forward pass of the encoder.
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Parameters
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----------
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x: torch.Tensor
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(B, D, H, W)
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t: torch.Tensor
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(B)
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encoder_hidden_states: torch.Tensor
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CLIP text embedding, (B, L_clip, D)
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text_embedding_mask: torch.Tensor
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CLIP text embedding mask, (B, L_clip)
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encoder_hidden_states_t5: torch.Tensor
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T5 text embedding, (B, L_t5, D)
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text_embedding_mask_t5: torch.Tensor
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T5 text embedding mask, (B, L_t5)
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image_meta_size: torch.Tensor
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(B, 6)
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style: torch.Tensor
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(B)
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cos_cis_img: torch.Tensor
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sin_cis_img: torch.Tensor
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return_dict: bool
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Whether to return a dictionary.
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"""
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#import pdb
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#pdb.set_trace()
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encoder_hidden_states = context
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text_states = encoder_hidden_states # 2,77,1024
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text_states_t5 = encoder_hidden_states_t5 # 2,256,2048
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text_states_mask = text_embedding_mask.bool() # 2,77
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text_states_t5_mask = text_embedding_mask_t5.bool() # 2,256
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b_t5, l_t5, c_t5 = text_states_t5.shape
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text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1)
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padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states)
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text_states[:,-self.text_len:] = torch.where(text_states_mask[:,-self.text_len:].unsqueeze(2), text_states[:,-self.text_len:], padding[:self.text_len])
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text_states_t5[:,-self.text_len_t5:] = torch.where(text_states_t5_mask[:,-self.text_len_t5:].unsqueeze(2), text_states_t5[:,-self.text_len_t5:], padding[self.text_len:])
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text_states = torch.cat([text_states, text_states_t5], dim=1) # 2,205，1024
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# clip_t5_mask = torch.cat([text_states_mask, text_states_t5_mask], dim=-1)
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_, _, oh, ow = x.shape
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th, tw = (oh + (self.patch_size // 2)) // self.patch_size, (ow + (self.patch_size // 2)) // self.patch_size
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# Get image RoPE embedding according to `reso`lution.
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freqs_cis_img = calc_rope(x, self.patch_size, self.hidden_size // self.num_heads) #(cos_cis_img, sin_cis_img)
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# ========================= Build time and image embedding =========================
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t = self.t_embedder(t, dtype=x.dtype)
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x = self.x_embedder(x)
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# ========================= Concatenate all extra vectors =========================
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# Build text tokens with pooling
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extra_vec = self.pooler(encoder_hidden_states_t5)
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# Build image meta size tokens if applicable
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if self.size_cond:
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image_meta_size = timestep_embedding(image_meta_size.view(-1), 256).to(x.dtype) # [B * 6, 256]
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image_meta_size = image_meta_size.view(-1, 6 * 256)
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extra_vec = torch.cat([extra_vec, image_meta_size], dim=1) # [B, D + 6 * 256]
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# Build style tokens
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if self.use_style_cond:
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if style is None:
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style = torch.zeros((extra_vec.shape[0],), device=x.device, dtype=torch.int)
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style_embedding = self.style_embedder(style, out_dtype=x.dtype)
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extra_vec = torch.cat([extra_vec, style_embedding], dim=1)
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# Concatenate all extra vectors
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c = t + self.extra_embedder(extra_vec) # [B, D]
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controls = None
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if control:
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controls = control.get("output", None)
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# ========================= Forward pass through HunYuanDiT blocks =========================
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skips = []
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for layer, block in enumerate(self.blocks):
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if layer > self.depth // 2:
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if controls is not None:
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skip = skips.pop() + controls.pop()
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else:
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skip = skips.pop()
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x = block(x, c, text_states, freqs_cis_img, skip) # (N, L, D)
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else:
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x = block(x, c, text_states, freqs_cis_img) # (N, L, D)
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if layer < (self.depth // 2 - 1):
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skips.append(x)
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if controls is not None and len(controls) != 0:
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raise ValueError("The number of controls is not equal to the number of skip connections.")
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# ========================= Final layer =========================
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x = self.final_layer(x, c) # (N, L, patch_size ** 2 * out_channels)
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x = self.unpatchify(x, th, tw) # (N, out_channels, H, W)
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if return_dict:
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return {'x': x}
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if self.learn_sigma:
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return x[:,:self.out_channels // 2,:oh,:ow]
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return x[:,:,:oh,:ow]
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def unpatchify(self, x, h, w):
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"""
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x: (N, T, patch_size**2 * C)
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imgs: (N, H, W, C)
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"""
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c = self.unpatchify_channels
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p = self.x_embedder.patch_size[0]
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# h = w = int(x.shape[1] ** 0.5)
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assert h * w == x.shape[1]
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x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
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x = torch.einsum('nhwpqc->nchpwq', x)
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imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
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return imgs
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