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from dataclasses import dataclass
import torch
from torch import Tensor, nn
from flux.modules.layers import (DoubleStreamBlock, EmbedND, LastLayer,
MLPEmbedder, SingleStreamBlock,
SingleStreamBlock_kv,DoubleStreamBlock_kv,
timestep_embedding)
@dataclass
class FluxParams:
in_channels: int
vec_in_dim: int
context_in_dim: int
hidden_size: int
mlp_ratio: float
num_heads: int
depth: int
depth_single_blocks: int
axes_dim: list[int]
theta: int
qkv_bias: bool
guidance_embed: bool
class Flux(nn.Module):
"""
Transformer model for flow matching on sequences.
"""
def __init__(self, params: FluxParams,double_block_cls=DoubleStreamBlock,single_block_cls=SingleStreamBlock):
super().__init__()
self.params = params
self.in_channels = params.in_channels
self.out_channels = self.in_channels
if params.hidden_size % params.num_heads != 0:
raise ValueError(
f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
)
pe_dim = params.hidden_size // params.num_heads
if sum(params.axes_dim) != pe_dim:
raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
self.hidden_size = params.hidden_size
self.num_heads = params.num_heads
self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
self.guidance_in = (
MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
)
self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
self.double_blocks = nn.ModuleList(
[
double_block_cls(
self.hidden_size,
self.num_heads,
mlp_ratio=params.mlp_ratio,
qkv_bias=params.qkv_bias,
)
for _ in range(params.depth)
]
)
self.single_blocks = nn.ModuleList(
[
single_block_cls(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio)
for _ in range(params.depth_single_blocks)
]
)
self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
def forward(
self,
img: Tensor,
img_ids: Tensor,
txt: Tensor,
txt_ids: Tensor,
timesteps: Tensor,
y: Tensor,
guidance: Tensor | None = None,
) -> Tensor:
if img.ndim != 3 or txt.ndim != 3:
raise ValueError("Input img and txt tensors must have 3 dimensions.")
# running on sequences img
img = self.img_in(img)
vec = self.time_in(timestep_embedding(timesteps, 256))
if self.params.guidance_embed:
if guidance is None:
raise ValueError("Didn't get guidance strength for guidance distilled model.")
vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
vec = vec + self.vector_in(y)
txt = self.txt_in(txt)
ids = torch.cat((txt_ids, img_ids), dim=1)
pe = self.pe_embedder(ids)
for block in self.double_blocks:
img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
img = torch.cat((txt, img), 1)
for block in self.single_blocks:
img = block(img, vec=vec, pe=pe)
img = img[:, txt.shape[1] :, ...]
img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
return img
class Flux_kv(Flux):
"""
继承Flux类，重写forward方法
"""
def __init__(self, params: FluxParams,double_block_cls=DoubleStreamBlock_kv,single_block_cls=SingleStreamBlock_kv):
super().__init__(params,double_block_cls,single_block_cls)
def forward(
self,
img: Tensor, # （B,x,x） (1,4080,64)
img_ids: Tensor,
txt: Tensor, # torch.Size([1, 512, 4096])
txt_ids: Tensor,
timesteps: Tensor, # torch.Size([1])
y: Tensor, # torch.Size([1, 768])
guidance: Tensor | None = None, # torch.Size([1])
info: dict = {},
) -> Tensor:
if img.ndim != 3 or txt.ndim != 3:
raise ValueError("Input img and txt tensors must have 3 dimensions.")
# running on sequences img
img = self.img_in(img)
vec = self.time_in(timestep_embedding(timesteps, 256)) # torch.Size([1, 3072])
if self.params.guidance_embed:
if guidance is None:
raise ValueError("Didn't get guidance strength for guidance distilled model.")
vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) # torch.Size([1, 3072])
vec = vec + self.vector_in(y)# torch.Size([1, 3072])
txt = self.txt_in(txt) # ([1, 512, 4096]) -> torch.Size([1, 512, 3072])
ids = torch.cat((txt_ids, img_ids), dim=1) # torch.Size([1, 512, 3]) torch.Size([1, 4080, 3]) -> torch.Size([1, 4592, 3])
pe = self.pe_embedder(ids) # torch.Size([1, 1, 4592, 64, 2, 2])
if not info['inverse']:
mask_indices = info['mask_indices'] # 图片seq坐标下的
info['pe_mask'] = torch.cat((pe[:, :, :512, ...],pe[:, :, mask_indices+512, ...]),dim=2)
cnt = 0
for block in self.double_blocks:
info['id'] = cnt
img, txt = block(img=img, txt=txt, vec=vec, pe=pe, info=info)
cnt += 1
cnt = 0
x = torch.cat((txt, img), 1)
for block in self.single_blocks:
info['id'] = cnt
x = block(x, vec=vec, pe=pe, info=info)
cnt += 1
img = x[:, txt.shape[1] :, ...]
img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
return img
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