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b910c09 | 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 | import torch
import torch.nn as nn
from einops import repeat
from functools import partial
from .dit import DiTBlock, DiT, FinalLayer
COMPILE = True
if torch.cuda.is_available():
compile_fn = partial(
torch.compile, fullgraph=True, backend="inductor" if torch.cuda.get_device_capability()[0] >= 7 else "aot_eager"
)
else:
compile_fn = lambda f: f
# ===================================================================================================
def pf_modulate(x, shift, scale):
return x * (1 + scale) + shift
class PatchForcingDiTBlock(DiTBlock):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if COMPILE:
self.forward = compile_fn(self.forward)
def forward(self, x, c):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=-1)
x = x + gate_msa * self.attn(pf_modulate(self.norm1(x), shift_msa, scale_msa))
x = x + gate_mlp * self.mlp(pf_modulate(self.norm2(x), shift_mlp, scale_mlp))
return x
class PatchForcingFinalLayer(FinalLayer):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if COMPILE:
self.forward = compile_fn(self.forward)
def forward(self, x, c):
shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1)
x = pf_modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class PatchForcingDiT(DiT):
def __init__(
self,
*args,
patch_size=2,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio: float = 4.0,
predict_uncertainty: bool = True,
compile: bool = False,
**kwargs,
):
super().__init__(
*args, patch_size=patch_size, hidden_size=hidden_size, depth=depth, num_heads=num_heads, **kwargs
)
global COMPILE
COMPILE = compile
# predict uncertainty per patch (replace dit blocks and last layer)
self.predict_uncertainty = predict_uncertainty
if self.predict_uncertainty:
assert self.learn_sigma is False, "cannot use both learn_sigma and predict_uncertainty!"
assert self.return_sigma is False, "cannot use both return_sigma and predict_uncertainty!"
# replace DiT blocks
self.blocks = nn.ModuleList(
[PatchForcingDiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio) for _ in range(depth)]
)
# replace final layer
self.out_channels = self.out_channels + 1
self.final_layer = PatchForcingFinalLayer(hidden_size, patch_size, self.out_channels)
self.initialize_weights()
def forward(self, x, t, y=None, return_uncertainty: bool = False):
"""
Forward pass of DiT.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N, num_patches) tensor of diffusion timesteps
y: (N,) tensor of class labels
"""
x = self.x_embedder(x) + self.pos_embed # (N, T, D), where T = H * W / patch_size ** 2
# patch-level t's
if self.predict_uncertainty:
assert x.shape[1] == t.shape[1], f"x: {x.shape}, t: {t.shape}: require patch-level t's!"
t = t[..., None] # (N, T) -> (N, T, 1)
t = self.t_embedder(t) # (N, 1, T, D)
t = t.squeeze(1) # (N, T, D) one embedding per patch
else:
t = self.t_embedder(t) # (N, D)
cond = t
if self.y_embedder is not None:
y = self.y_embedder(y, self.training) # (N, D)
if self.predict_uncertainty:
y = repeat(y, "b c -> b n c", n=x.shape[1]) # (N, D) -> (N, T, D)
cond = cond + y # (N, T, D)
for block in self.blocks:
x = block(x, cond) # (N, T, D)
x = self.final_layer(x, cond) # (N, T, patch_size ** 2 * out_channels)
x = self.unpatchify(x) # (N, out_channels, H, W)
# split uncertainty
if self.predict_uncertainty:
logvar_theta = x[:, -1:, :, :] # (b, 1, h, w)
x = x[:, :-1, :, :] # (b, c, h, w)
if return_uncertainty:
return x, logvar_theta
if self.learn_sigma and not self.return_sigma: # LEGACY
x, _ = x.chunk(2, dim=1)
return x
# ===================================================================================================
def PF_XL_2(**kwargs):
return PatchForcingDiT(depth=28, hidden_size=1152, patch_size=2, num_heads=16, **kwargs)
def PF_L_2(**kwargs):
return PatchForcingDiT(depth=24, hidden_size=1024, patch_size=2, num_heads=16, **kwargs)
def PF_B_2(**kwargs):
return PatchForcingDiT(depth=12, hidden_size=768, patch_size=2, num_heads=12, **kwargs)
PF_models = {
"PF-XL/2": PF_XL_2,
"PF-L/2": PF_L_2,
"PF-B/2": PF_B_2,
}
if __name__ == "__main__":
DEV = "cuda" if torch.cuda.is_available() else "cpu"
model = PF_models["PF-XL/2"]().to(DEV)
print(f"{sum([p.numel() for p in model.parameters() if p.requires_grad]):,}")
inp = dict(
x=torch.randn((2, 4, 32, 32)).to(DEV),
t=torch.rand((2,)).to(DEV),
y=torch.randint(0, 1000, (2,)).to(DEV),
)
with torch.no_grad():
out = model(**inp)
print(out.shape)
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