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fix code format style

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This commit is contained in:
CrazyBoyM 2024-08-07 18:38:14 +08:00
parent 2b68ab0a42
commit bda7c82134
1 changed files with 153 additions and 82 deletions
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229
comfy/ldm/hydit/controlnet.py
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@ -6,7 +6,12 @@ import torch.nn.functional as F
from torch.utils import checkpoint
from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed, RMSNorm
from comfy.ldm.modules.diffusionmodules.mmdit import (
Mlp,
TimestepEmbedder,
PatchEmbed,
RMSNorm,
)
from comfy.ldm.modules.diffusionmodules.util import timestep_embedding
from .poolers import AttentionPool
@ -15,11 +20,13 @@ from .models import HunYuanDiTBlock
from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop
def zero_module(module):
for p in module.parameters():
nn.init.zeros_(p)
return module
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
@ -60,29 +67,31 @@ class HunYuanControlNet(nn.Module):
log_fn: callable
The logging function.
"""
def __init__(self,
input_size: tuple = 128,
patch_size: int = 2,
in_channels: int = 4,
hidden_size: int = 1408,
depth: int = 40,
num_heads: int = 16,
mlp_ratio: float = 4.3637,
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,
def __init__(
self,
input_size: tuple = 128,
patch_size: int = 2,
in_channels: int = 4,
hidden_size: int = 1408,
depth: int = 40,
num_heads: int = 16,
mlp_ratio: float = 4.3637,
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
@ -103,19 +112,44 @@ class HunYuanControlNet(nn.Module):
self.dtype = dtype
self.latent_format = comfy.latent_formats.SDXL
self.mlp_t5 = nn.Sequential(
nn.Linear(self.text_states_dim_t5, self.text_states_dim_t5 * 4, bias=True, dtype=dtype, device=device),
nn.Linear(
self.text_states_dim_t5,
self.text_states_dim_t5 * 4,
bias=True,
dtype=dtype,
device=device,
),
nn.SiLU(),
nn.Linear(self.text_states_dim_t5 * 4, self.text_states_dim, bias=True, dtype=dtype, device=device),
nn.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.randn(self.text_len + self.text_len_t5, self.text_states_dim, dtype=dtype, device=device))
torch.randn(
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)
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
@ -126,60 +160,89 @@ class HunYuanControlNet(nn.Module):
if self.use_style_cond:
# Here we use a default learned embedder layer for future extension.
self.style_embedder = nn.Embedding(1, hidden_size, dtype=dtype, device=device)
self.style_embedder = nn.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.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),
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),
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=False,
attn_precision=attn_precision,
dtype=dtype,
device=device,
operations=operations,
)
for _ in range(19)
])
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=False,
attn_precision=attn_precision,
dtype=dtype,
device=device,
operations=operations,
)
for _ in range(19)
]
)
# Input zero linear for the first block
self.before_proj = zero_module(nn.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device))
self.before_proj = zero_module(
nn.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
)
# Output zero linear for the every block
self.after_proj_list = nn.ModuleList(
[zero_module(nn.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)) for _ in range(len(self.blocks))]
[
zero_module(
nn.Linear(
self.hidden_size, self.hidden_size, dtype=dtype, device=device
)
)
for _ in range(len(self.blocks))
]
)
def forward(self,
x: torch.Tensor,
t: torch.Tensor = None,
condition = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
text_embedding_mask=None,
encoder_hidden_states_t5=None,
text_embedding_mask_t5=None,
image_meta_size=None,
style=None,
transformer_options=None,
**kwarg
):
def forward(
self,
x: torch.Tensor,
t: torch.Tensor = None,
condition=None,
encoder_hidden_states: Optional[torch.Tensor] = None,
text_embedding_mask=None,
encoder_hidden_states_t5=None,
text_embedding_mask_t5=None,
image_meta_size=None,
style=None,
transformer_options=None,
**kwarg,
):
"""
Forward pass of the encoder.
@ -209,31 +272,40 @@ class HunYuanControlNet(nn.Module):
if condition.shape[0] == 1:
condition = torch.repeat_interleave(condition, x.shape[0], dim=0)
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
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[:, -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
_, _, oh, ow = x.shape
th, tw = oh // self.patch_size, ow // self.patch_size
# _, _, oh, ow = x.shape
# th, tw = oh // self.patch_size, ow // 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)
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=self.dtype)
x = self.x_embedder(x)
# ========================= Concatenate all extra vectors =========================
# Build text tokens with pooling
extra_vec = self.pooler(encoder_hidden_states_t5)
@ -259,10 +331,9 @@ class HunYuanControlNet(nn.Module):
# ========================= Forward pass through HunYuanDiT blocks =========================
controls = []
x = x + self.before_proj(condition) # add condition
x = x + self.before_proj(condition) # add condition
for layer, block in enumerate(self.blocks):
x = block(x, c, text_states, freqs_cis_img)
controls.append(self.after_proj_list[layer](x)) # zero linear for output
return {'output': controls}
controls.append(self.after_proj_list[layer](x)) # zero linear for output
return {"output": controls}