Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,855 Bytes
319886d af44a4b 319886d af44a4b 319886d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 |
from dataclasses import dataclass
from typing import List
import torch
from torch import Tensor, nn
from models.modules.layers import (
DoubleStreamBlock,
EmbedND,
LastLayer,
MLPEmbedder,
SingleStreamBlock,
timestep_embedding,
)
from models.modules.lora import LinearLora, replace_linear_with_lora
@dataclass
class FluxParams:
in_channels: int
out_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):
super().__init__()
self.params = params
self.in_channels = params.in_channels
self.out_channels = params.out_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(
[
DoubleStreamBlock(
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(
[
SingleStreamBlock(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,
txt_mask: Tensor = None,
img_mask: Tensor = None,
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_mask=img_mask, txt_mask=txt_mask)
img = torch.cat((txt, img), 1)
attn_mask = torch.cat((txt_mask, img_mask), 1)
for block in self.single_blocks:
img = block(img, vec=vec, pe=pe, attn_mask=attn_mask)
img = img[:, txt.shape[1] :, ...]
img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
# print(f'flux out {img.shape} {img.mean()}')
return img
def forward_with_cfg(
self,
img: Tensor,
img_ids: Tensor,
txt: Tensor,
txt_ids: Tensor,
timesteps: Tensor,
y: Tensor,
txt_mask: Tensor = None,
img_mask: Tensor = None,
guidance: Tensor | None = None,
cfg_scale: float = 1.0,
) -> Tensor:
half = img[: len(img) // 2]
combined = torch.cat([half, half], dim=0)
model_out = self.forward(img, img_ids, txt, txt_ids, timesteps, y, txt_mask, img_mask, guidance)
cond_v, uncond_v = torch.split(model_out, len(model_out) // 2, dim=0)
cond_v = uncond_v + cfg_scale * (cond_v - uncond_v)
img = torch.cat([cond_v, uncond_v], dim=0)
return img
def get_fsdp_wrap_module_list(self) -> List[nn.Module]:
return list(self.double_blocks) + list(self.single_blocks) + [self.final_layer] + [self.img_in, self.vector_in, self.guidance_in, self.txt_in, self.time_in]
def get_checkpointing_wrap_module_list(self) -> List[nn.Module]:
return list(self.double_blocks) + list(self.single_blocks) + [self.final_layer] + [self.img_in, self.vector_in, self.guidance_in, self.txt_in, self.time_in]
class FluxLoraWrapper(Flux):
def __init__(
self,
lora_rank: int = 128,
lora_scale: float = 1.0,
*args,
**kwargs,
) -> None:
super().__init__(*args, **kwargs)
self.lora_rank = lora_rank
replace_linear_with_lora(
self,
max_rank=lora_rank,
scale=lora_scale,
)
def set_lora_scale(self, scale: float) -> None:
for module in self.modules():
if isinstance(module, LinearLora):
module.set_scale(scale=scale)
|