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