ComfyUI/comfy/ldm/omnigen/omnigen2.py
2025-06-25 19:35:57 -04:00

470 lines
22 KiB
Python

# Original code: https://github.com/VectorSpaceLab/OmniGen2
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from comfy.ldm.lightricks.model import Timesteps
from comfy.ldm.flux.layers import EmbedND
from comfy.ldm.modules.attention import optimized_attention_masked
import comfy.model_management
import comfy.ldm.common_dit
def apply_rotary_emb(x, freqs_cis):
if x.shape[1] == 0:
return x
t_ = x.reshape(*x.shape[:-1], -1, 1, 2)
t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1]
return t_out.reshape(*x.shape).to(dtype=x.dtype)
def swiglu(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
return F.silu(x) * y
class TimestepEmbedding(nn.Module):
def __init__(self, in_channels: int, time_embed_dim: int, dtype=None, device=None, operations=None):
super().__init__()
self.linear_1 = operations.Linear(in_channels, time_embed_dim, dtype=dtype, device=device)
self.act = nn.SiLU()
self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim, dtype=dtype, device=device)
def forward(self, sample: torch.Tensor) -> torch.Tensor:
sample = self.linear_1(sample)
sample = self.act(sample)
sample = self.linear_2(sample)
return sample
class LuminaRMSNormZero(nn.Module):
def __init__(self, embedding_dim: int, norm_eps: float = 1e-5, dtype=None, device=None, operations=None):
super().__init__()
self.silu = nn.SiLU()
self.linear = operations.Linear(min(embedding_dim, 1024), 4 * embedding_dim, dtype=dtype, device=device)
self.norm = operations.RMSNorm(embedding_dim, eps=norm_eps, dtype=dtype, device=device)
def forward(self, x: torch.Tensor, emb: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
emb = self.linear(self.silu(emb))
scale_msa, gate_msa, scale_mlp, gate_mlp = emb.chunk(4, dim=1)
x = self.norm(x) * (1 + scale_msa[:, None])
return x, gate_msa, scale_mlp, gate_mlp
class LuminaLayerNormContinuous(nn.Module):
def __init__(self, embedding_dim: int, conditioning_embedding_dim: int, elementwise_affine: bool = False, eps: float = 1e-6, out_dim: Optional[int] = None, dtype=None, device=None, operations=None):
super().__init__()
self.silu = nn.SiLU()
self.linear_1 = operations.Linear(conditioning_embedding_dim, embedding_dim, dtype=dtype, device=device)
self.norm = operations.LayerNorm(embedding_dim, eps, elementwise_affine, dtype=dtype, device=device)
self.linear_2 = operations.Linear(embedding_dim, out_dim, bias=True, dtype=dtype, device=device) if out_dim is not None else None
def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:
emb = self.linear_1(self.silu(conditioning_embedding).to(x.dtype))
x = self.norm(x) * (1 + emb)[:, None, :]
if self.linear_2 is not None:
x = self.linear_2(x)
return x
class LuminaFeedForward(nn.Module):
def __init__(self, dim: int, inner_dim: int, multiple_of: int = 256, dtype=None, device=None, operations=None):
super().__init__()
inner_dim = multiple_of * ((inner_dim + multiple_of - 1) // multiple_of)
self.linear_1 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device)
self.linear_2 = operations.Linear(inner_dim, dim, bias=False, dtype=dtype, device=device)
self.linear_3 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device)
def forward(self, x: torch.Tensor) -> torch.Tensor:
h1, h2 = self.linear_1(x), self.linear_3(x)
return self.linear_2(swiglu(h1, h2))
class Lumina2CombinedTimestepCaptionEmbedding(nn.Module):
def __init__(self, hidden_size: int = 4096, text_feat_dim: int = 2048, frequency_embedding_size: int = 256, norm_eps: float = 1e-5, timestep_scale: float = 1.0, dtype=None, device=None, operations=None):
super().__init__()
self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0.0, scale=timestep_scale)
self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=min(hidden_size, 1024), dtype=dtype, device=device, operations=operations)
self.caption_embedder = nn.Sequential(
operations.RMSNorm(text_feat_dim, eps=norm_eps, dtype=dtype, device=device),
operations.Linear(text_feat_dim, hidden_size, bias=True, dtype=dtype, device=device),
)
def forward(self, timestep: torch.Tensor, text_hidden_states: torch.Tensor, dtype: torch.dtype) -> Tuple[torch.Tensor, torch.Tensor]:
timestep_proj = self.time_proj(timestep).to(dtype=dtype)
time_embed = self.timestep_embedder(timestep_proj)
caption_embed = self.caption_embedder(text_hidden_states)
return time_embed, caption_embed
class Attention(nn.Module):
def __init__(self, query_dim: int, dim_head: int, heads: int, kv_heads: int, eps: float = 1e-5, bias: bool = False, dtype=None, device=None, operations=None):
super().__init__()
self.heads = heads
self.kv_heads = kv_heads
self.dim_head = dim_head
self.scale = dim_head ** -0.5
self.to_q = operations.Linear(query_dim, heads * dim_head, bias=bias, dtype=dtype, device=device)
self.to_k = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device)
self.to_v = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device)
self.norm_q = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)
self.norm_k = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)
self.to_out = nn.Sequential(
operations.Linear(heads * dim_head, query_dim, bias=bias, dtype=dtype, device=device),
nn.Dropout(0.0)
)
def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, image_rotary_emb: Optional[torch.Tensor] = None) -> torch.Tensor:
batch_size, sequence_length, _ = hidden_states.shape
query = self.to_q(hidden_states)
key = self.to_k(encoder_hidden_states)
value = self.to_v(encoder_hidden_states)
query = query.view(batch_size, -1, self.heads, self.dim_head)
key = key.view(batch_size, -1, self.kv_heads, self.dim_head)
value = value.view(batch_size, -1, self.kv_heads, self.dim_head)
query = self.norm_q(query)
key = self.norm_k(key)
if image_rotary_emb is not None:
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb)
query = query.transpose(1, 2)
key = key.transpose(1, 2)
value = value.transpose(1, 2)
if self.kv_heads < self.heads:
key = key.repeat_interleave(self.heads // self.kv_heads, dim=1)
value = value.repeat_interleave(self.heads // self.kv_heads, dim=1)
hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True)
hidden_states = self.to_out[0](hidden_states)
return hidden_states
class OmniGen2TransformerBlock(nn.Module):
def __init__(self, dim: int, num_attention_heads: int, num_kv_heads: int, multiple_of: int, ffn_dim_multiplier: float, norm_eps: float, modulation: bool = True, dtype=None, device=None, operations=None):
super().__init__()
self.modulation = modulation
self.attn = Attention(
query_dim=dim,
dim_head=dim // num_attention_heads,
heads=num_attention_heads,
kv_heads=num_kv_heads,
eps=1e-5,
bias=False,
dtype=dtype, device=device, operations=operations,
)
self.feed_forward = LuminaFeedForward(
dim=dim,
inner_dim=4 * dim,
multiple_of=multiple_of,
dtype=dtype, device=device, operations=operations
)
if modulation:
self.norm1 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations)
else:
self.norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
self.ffn_norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
self.norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
self.ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
if self.modulation:
norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb)
attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb)
hidden_states = hidden_states + gate_msa.unsqueeze(1).tanh() * self.norm2(attn_output)
mlp_output = self.feed_forward(self.ffn_norm1(hidden_states) * (1 + scale_mlp.unsqueeze(1)))
hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output)
else:
norm_hidden_states = self.norm1(hidden_states)
attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb)
hidden_states = hidden_states + self.norm2(attn_output)
mlp_output = self.feed_forward(self.ffn_norm1(hidden_states))
hidden_states = hidden_states + self.ffn_norm2(mlp_output)
return hidden_states
class OmniGen2RotaryPosEmbed(nn.Module):
def __init__(self, theta: int, axes_dim: Tuple[int, int, int], axes_lens: Tuple[int, int, int] = (300, 512, 512), patch_size: int = 2):
super().__init__()
self.theta = theta
self.axes_dim = axes_dim
self.axes_lens = axes_lens
self.patch_size = patch_size
self.rope_embedder = EmbedND(dim=sum(axes_dim), theta=self.theta, axes_dim=axes_dim)
def forward(self, batch_size, encoder_seq_len, l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, ref_img_sizes, img_sizes, device):
p = self.patch_size
seq_lengths = [cap_len + sum(ref_img_len) + img_len for cap_len, ref_img_len, img_len in zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len)]
max_seq_len = max(seq_lengths)
max_ref_img_len = max([sum(ref_img_len) for ref_img_len in l_effective_ref_img_len])
max_img_len = max(l_effective_img_len)
position_ids = torch.zeros(batch_size, max_seq_len, 3, dtype=torch.int32, device=device)
for i, (cap_seq_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)):
position_ids[i, :cap_seq_len] = repeat(torch.arange(cap_seq_len, dtype=torch.int32, device=device), "l -> l 3")
pe_shift = cap_seq_len
pe_shift_len = cap_seq_len
if ref_img_sizes[i] is not None:
for ref_img_size, ref_img_len in zip(ref_img_sizes[i], l_effective_ref_img_len[i]):
H, W = ref_img_size
ref_H_tokens, ref_W_tokens = H // p, W // p
row_ids = repeat(torch.arange(ref_H_tokens, dtype=torch.int32, device=device), "h -> h w", w=ref_W_tokens).flatten()
col_ids = repeat(torch.arange(ref_W_tokens, dtype=torch.int32, device=device), "w -> h w", h=ref_H_tokens).flatten()
position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 0] = pe_shift
position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 1] = row_ids
position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 2] = col_ids
pe_shift += max(ref_H_tokens, ref_W_tokens)
pe_shift_len += ref_img_len
H, W = img_sizes[i]
H_tokens, W_tokens = H // p, W // p
row_ids = repeat(torch.arange(H_tokens, dtype=torch.int32, device=device), "h -> h w", w=W_tokens).flatten()
col_ids = repeat(torch.arange(W_tokens, dtype=torch.int32, device=device), "w -> h w", h=H_tokens).flatten()
position_ids[i, pe_shift_len: seq_len, 0] = pe_shift
position_ids[i, pe_shift_len: seq_len, 1] = row_ids
position_ids[i, pe_shift_len: seq_len, 2] = col_ids
freqs_cis = self.rope_embedder(position_ids).movedim(1, 2)
cap_freqs_cis_shape = list(freqs_cis.shape)
cap_freqs_cis_shape[1] = encoder_seq_len
cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
ref_img_freqs_cis_shape = list(freqs_cis.shape)
ref_img_freqs_cis_shape[1] = max_ref_img_len
ref_img_freqs_cis = torch.zeros(*ref_img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
img_freqs_cis_shape = list(freqs_cis.shape)
img_freqs_cis_shape[1] = max_img_len
img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
for i, (cap_seq_len, ref_img_len, img_len, seq_len) in enumerate(zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, seq_lengths)):
cap_freqs_cis[i, :cap_seq_len] = freqs_cis[i, :cap_seq_len]
ref_img_freqs_cis[i, :sum(ref_img_len)] = freqs_cis[i, cap_seq_len:cap_seq_len + sum(ref_img_len)]
img_freqs_cis[i, :img_len] = freqs_cis[i, cap_seq_len + sum(ref_img_len):cap_seq_len + sum(ref_img_len) + img_len]
return cap_freqs_cis, ref_img_freqs_cis, img_freqs_cis, freqs_cis, l_effective_cap_len, seq_lengths
class OmniGen2Transformer2DModel(nn.Module):
def __init__(
self,
patch_size: int = 2,
in_channels: int = 16,
out_channels: Optional[int] = None,
hidden_size: int = 2304,
num_layers: int = 26,
num_refiner_layers: int = 2,
num_attention_heads: int = 24,
num_kv_heads: int = 8,
multiple_of: int = 256,
ffn_dim_multiplier: Optional[float] = None,
norm_eps: float = 1e-5,
axes_dim_rope: Tuple[int, int, int] = (32, 32, 32),
axes_lens: Tuple[int, int, int] = (300, 512, 512),
text_feat_dim: int = 1024,
timestep_scale: float = 1.0,
image_model=None,
device=None,
dtype=None,
operations=None,
):
super().__init__()
self.patch_size = patch_size
self.out_channels = out_channels or in_channels
self.hidden_size = hidden_size
self.dtype = dtype
self.rope_embedder = OmniGen2RotaryPosEmbed(
theta=10000,
axes_dim=axes_dim_rope,
axes_lens=axes_lens,
patch_size=patch_size,
)
self.x_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device)
self.ref_image_patch_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device)
self.time_caption_embed = Lumina2CombinedTimestepCaptionEmbedding(
hidden_size=hidden_size,
text_feat_dim=text_feat_dim,
norm_eps=norm_eps,
timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations
)
self.noise_refiner = nn.ModuleList([
OmniGen2TransformerBlock(
hidden_size, num_attention_heads, num_kv_heads,
multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations
) for _ in range(num_refiner_layers)
])
self.ref_image_refiner = nn.ModuleList([
OmniGen2TransformerBlock(
hidden_size, num_attention_heads, num_kv_heads,
multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations
) for _ in range(num_refiner_layers)
])
self.context_refiner = nn.ModuleList([
OmniGen2TransformerBlock(
hidden_size, num_attention_heads, num_kv_heads,
multiple_of, ffn_dim_multiplier, norm_eps, modulation=False, dtype=dtype, device=device, operations=operations
) for _ in range(num_refiner_layers)
])
self.layers = nn.ModuleList([
OmniGen2TransformerBlock(
hidden_size, num_attention_heads, num_kv_heads,
multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations
) for _ in range(num_layers)
])
self.norm_out = LuminaLayerNormContinuous(
embedding_dim=hidden_size,
conditioning_embedding_dim=min(hidden_size, 1024),
elementwise_affine=False,
eps=1e-6,
out_dim=patch_size * patch_size * self.out_channels, dtype=dtype, device=device, operations=operations
)
self.image_index_embedding = nn.Parameter(torch.empty(5, hidden_size, device=device, dtype=dtype))
def flat_and_pad_to_seq(self, hidden_states, ref_image_hidden_states):
batch_size = len(hidden_states)
p = self.patch_size
img_sizes = [(img.size(1), img.size(2)) for img in hidden_states]
l_effective_img_len = [(H // p) * (W // p) for (H, W) in img_sizes]
if ref_image_hidden_states is not None:
ref_image_hidden_states = list(map(lambda ref: comfy.ldm.common_dit.pad_to_patch_size(ref, (p, p)), ref_image_hidden_states))
ref_img_sizes = [[(imgs.size(2), imgs.size(3)) if imgs is not None else None for imgs in ref_image_hidden_states]] * batch_size
l_effective_ref_img_len = [[(ref_img_size[0] // p) * (ref_img_size[1] // p) for ref_img_size in _ref_img_sizes] if _ref_img_sizes is not None else [0] for _ref_img_sizes in ref_img_sizes]
else:
ref_img_sizes = [None for _ in range(batch_size)]
l_effective_ref_img_len = [[0] for _ in range(batch_size)]
flat_ref_img_hidden_states = None
if ref_image_hidden_states is not None:
imgs = []
for ref_img in ref_image_hidden_states:
B, C, H, W = ref_img.size()
ref_img = rearrange(ref_img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p)
imgs.append(ref_img)
flat_ref_img_hidden_states = torch.cat(imgs, dim=1)
img = hidden_states
B, C, H, W = img.size()
flat_hidden_states = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p)
return (
flat_hidden_states, flat_ref_img_hidden_states,
None, None,
l_effective_ref_img_len, l_effective_img_len,
ref_img_sizes, img_sizes,
)
def img_patch_embed_and_refine(self, hidden_states, ref_image_hidden_states, padded_img_mask, padded_ref_img_mask, noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb):
batch_size = len(hidden_states)
hidden_states = self.x_embedder(hidden_states)
if ref_image_hidden_states is not None:
ref_image_hidden_states = self.ref_image_patch_embedder(ref_image_hidden_states)
image_index_embedding = comfy.model_management.cast_to(self.image_index_embedding, dtype=hidden_states.dtype, device=hidden_states.device)
for i in range(batch_size):
shift = 0
for j, ref_img_len in enumerate(l_effective_ref_img_len[i]):
ref_image_hidden_states[i, shift:shift + ref_img_len, :] = ref_image_hidden_states[i, shift:shift + ref_img_len, :] + image_index_embedding[j]
shift += ref_img_len
for layer in self.noise_refiner:
hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb)
if ref_image_hidden_states is not None:
for layer in self.ref_image_refiner:
ref_image_hidden_states = layer(ref_image_hidden_states, padded_ref_img_mask, ref_img_rotary_emb, temb)
hidden_states = torch.cat([ref_image_hidden_states, hidden_states], dim=1)
return hidden_states
def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, **kwargs):
B, C, H, W = x.shape
hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
_, _, H_padded, W_padded = hidden_states.shape
timestep = 1.0 - timesteps
text_hidden_states = context
text_attention_mask = attention_mask
ref_image_hidden_states = ref_latents
device = hidden_states.device
temb, text_hidden_states = self.time_caption_embed(timestep, text_hidden_states, hidden_states[0].dtype)
(
hidden_states, ref_image_hidden_states,
img_mask, ref_img_mask,
l_effective_ref_img_len, l_effective_img_len,
ref_img_sizes, img_sizes,
) = self.flat_and_pad_to_seq(hidden_states, ref_image_hidden_states)
(
context_rotary_emb, ref_img_rotary_emb, noise_rotary_emb,
rotary_emb, encoder_seq_lengths, seq_lengths,
) = self.rope_embedder(
hidden_states.shape[0], text_hidden_states.shape[1], [num_tokens] * text_hidden_states.shape[0],
l_effective_ref_img_len, l_effective_img_len,
ref_img_sizes, img_sizes, device,
)
for layer in self.context_refiner:
text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb)
img_len = hidden_states.shape[1]
combined_img_hidden_states = self.img_patch_embed_and_refine(
hidden_states, ref_image_hidden_states,
img_mask, ref_img_mask,
noise_rotary_emb, ref_img_rotary_emb,
l_effective_ref_img_len, l_effective_img_len,
temb,
)
hidden_states = torch.cat([text_hidden_states, combined_img_hidden_states], dim=1)
attention_mask = None
for layer in self.layers:
hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb)
hidden_states = self.norm_out(hidden_states, temb)
p = self.patch_size
output = rearrange(hidden_states[:, -img_len:], 'b (h w) (p1 p2 c) -> b c (h p1) (w p2)', h=H_padded // p, w=W_padded// p, p1=p, p2=p)[:, :, :H, :W]
return -output