File size: 7,501 Bytes
4450790 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 |
#Original code can be found on: https://github.com/black-forest-labs/flux
from dataclasses import dataclass
import torch
from torch import Tensor, nn
from .layers import (
DoubleStreamBlock,
EmbedND,
LastLayer,
MLPEmbedder,
SingleStreamBlock,
timestep_embedding,
)
from einops import rearrange, repeat
import comfy.ldm.common_dit
@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
theta: int
patch_size: int
qkv_bias: bool
guidance_embed: bool
class Flux(nn.Module):
"""
Transformer model for flow matching on sequences.
"""
def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):
super().__init__()
self.dtype = dtype
params = FluxParams(**kwargs)
self.params = params
self.patch_size = params.patch_size
self.in_channels = params.in_channels * params.patch_size * params.patch_size
self.out_channels = params.out_channels * params.patch_size * params.patch_size
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 = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
self.guidance_in = (
MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()
)
self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)
self.double_blocks = nn.ModuleList(
[
DoubleStreamBlock(
self.hidden_size,
self.num_heads,
mlp_ratio=params.mlp_ratio,
qkv_bias=params.qkv_bias,
dtype=dtype, device=device, operations=operations
)
for _ in range(params.depth)
]
)
self.single_blocks = nn.ModuleList(
[
SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)
for _ in range(params.depth_single_blocks)
]
)
if final_layer:
self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations)
def forward_orig(
self,
img: Tensor,
img_ids: Tensor,
txt: Tensor,
txt_ids: Tensor,
timesteps: Tensor,
y: Tensor,
guidance: Tensor = None,
control=None,
transformer_options={},
) -> Tensor:
patches_replace = transformer_options.get("patches_replace", {})
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).to(img.dtype))
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).to(img.dtype))
vec = vec + self.vector_in(y[:,:self.params.vec_in_dim])
txt = self.txt_in(txt)
ids = torch.cat((txt_ids, img_ids), dim=1)
pe = self.pe_embedder(ids)
blocks_replace = patches_replace.get("dit", {})
for i, block in enumerate(self.double_blocks):
if ("double_block", i) in blocks_replace:
def block_wrap(args):
out = {}
out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"])
return out
out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe}, {"original_block": block_wrap})
txt = out["txt"]
img = out["img"]
else:
img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
if control is not None: # Controlnet
control_i = control.get("input")
if i < len(control_i):
add = control_i[i]
if add is not None:
img += add
img = torch.cat((txt, img), 1)
for i, block in enumerate(self.single_blocks):
if ("single_block", i) in blocks_replace:
def block_wrap(args):
out = {}
out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"])
return out
out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe}, {"original_block": block_wrap})
img = out["img"]
else:
img = block(img, vec=vec, pe=pe)
if control is not None: # Controlnet
control_o = control.get("output")
if i < len(control_o):
add = control_o[i]
if add is not None:
img[:, txt.shape[1] :, ...] += add
img = img[:, txt.shape[1] :, ...]
img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
return img
def forward(self, x, timestep, context, y, guidance, control=None, transformer_options={}, **kwargs):
bs, c, h, w = x.shape
patch_size = self.patch_size
x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
h_len = ((h + (patch_size // 2)) // patch_size)
w_len = ((w + (patch_size // 2)) // patch_size)
img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options)
return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]
|