Spaces:
Runtime error
Runtime error
File size: 4,417 Bytes
a4d7b31 |
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 |
import fire
from diffusers import StableDiffusionPipeline
import torch
import torch.nn as nn
from .lora import (
save_all,
_find_modules,
LoraInjectedConv2d,
LoraInjectedLinear,
inject_trainable_lora,
inject_trainable_lora_extended,
)
def _iter_lora(model):
for module in model.modules():
if isinstance(module, LoraInjectedConv2d) or isinstance(
module, LoraInjectedLinear
):
yield module
def overwrite_base(base_model, tuned_model, rank, clamp_quantile):
device = base_model.device
dtype = base_model.dtype
for lor_base, lor_tune in zip(_iter_lora(base_model), _iter_lora(tuned_model)):
if isinstance(lor_base, LoraInjectedLinear):
residual = lor_tune.linear.weight.data - lor_base.linear.weight.data
# SVD on residual
print("Distill Linear shape ", residual.shape)
residual = residual.float()
U, S, Vh = torch.linalg.svd(residual)
U = U[:, :rank]
S = S[:rank]
U = U @ torch.diag(S)
Vh = Vh[:rank, :]
dist = torch.cat([U.flatten(), Vh.flatten()])
hi_val = torch.quantile(dist, clamp_quantile)
low_val = -hi_val
U = U.clamp(low_val, hi_val)
Vh = Vh.clamp(low_val, hi_val)
assert lor_base.lora_up.weight.shape == U.shape
assert lor_base.lora_down.weight.shape == Vh.shape
lor_base.lora_up.weight.data = U.to(device=device, dtype=dtype)
lor_base.lora_down.weight.data = Vh.to(device=device, dtype=dtype)
if isinstance(lor_base, LoraInjectedConv2d):
residual = lor_tune.conv.weight.data - lor_base.conv.weight.data
print("Distill Conv shape ", residual.shape)
residual = residual.float()
residual = residual.flatten(start_dim=1)
# SVD on residual
U, S, Vh = torch.linalg.svd(residual)
U = U[:, :rank]
S = S[:rank]
U = U @ torch.diag(S)
Vh = Vh[:rank, :]
dist = torch.cat([U.flatten(), Vh.flatten()])
hi_val = torch.quantile(dist, clamp_quantile)
low_val = -hi_val
U = U.clamp(low_val, hi_val)
Vh = Vh.clamp(low_val, hi_val)
# U is (out_channels, rank) with 1x1 conv. So,
U = U.reshape(U.shape[0], U.shape[1], 1, 1)
# V is (rank, in_channels * kernel_size1 * kernel_size2)
# now reshape:
Vh = Vh.reshape(
Vh.shape[0],
lor_base.conv.in_channels,
lor_base.conv.kernel_size[0],
lor_base.conv.kernel_size[1],
)
assert lor_base.lora_up.weight.shape == U.shape
assert lor_base.lora_down.weight.shape == Vh.shape
lor_base.lora_up.weight.data = U.to(device=device, dtype=dtype)
lor_base.lora_down.weight.data = Vh.to(device=device, dtype=dtype)
def svd_distill(
target_model: str,
base_model: str,
rank: int = 4,
clamp_quantile: float = 0.99,
device: str = "cuda:0",
save_path: str = "svd_distill.safetensors",
):
pipe_base = StableDiffusionPipeline.from_pretrained(
base_model, torch_dtype=torch.float16
).to(device)
pipe_tuned = StableDiffusionPipeline.from_pretrained(
target_model, torch_dtype=torch.float16
).to(device)
# Inject unet
_ = inject_trainable_lora_extended(pipe_base.unet, r=rank)
_ = inject_trainable_lora_extended(pipe_tuned.unet, r=rank)
overwrite_base(
pipe_base.unet, pipe_tuned.unet, rank=rank, clamp_quantile=clamp_quantile
)
# Inject text encoder
_ = inject_trainable_lora(
pipe_base.text_encoder, r=rank, target_replace_module={"CLIPAttention"}
)
_ = inject_trainable_lora(
pipe_tuned.text_encoder, r=rank, target_replace_module={"CLIPAttention"}
)
overwrite_base(
pipe_base.text_encoder,
pipe_tuned.text_encoder,
rank=rank,
clamp_quantile=clamp_quantile,
)
save_all(
unet=pipe_base.unet,
text_encoder=pipe_base.text_encoder,
placeholder_token_ids=None,
placeholder_tokens=None,
save_path=save_path,
save_lora=True,
save_ti=False,
)
def main():
fire.Fire(svd_distill)
|