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Running on Zero
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abd08dc | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | import torch
from .qwen_image_image2lora import ImageEmbeddingToLoraMatrix, SequencialMLP
class LoRATrainerBlock(torch.nn.Module):
def __init__(self, lora_patterns, in_dim=1536+4096, compress_dim=128, rank=4, block_id=0, use_residual=True, residual_length=64+7, residual_dim=3584, residual_mid_dim=1024, prefix="transformer_blocks"):
super().__init__()
self.prefix = prefix
self.lora_patterns = lora_patterns
self.block_id = block_id
self.layers = []
for name, lora_a_dim, lora_b_dim in self.lora_patterns:
self.layers.append(ImageEmbeddingToLoraMatrix(in_dim, compress_dim, lora_a_dim, lora_b_dim, rank))
self.layers = torch.nn.ModuleList(self.layers)
if use_residual:
self.proj_residual = SequencialMLP(residual_length, residual_dim, residual_mid_dim, compress_dim)
else:
self.proj_residual = None
def forward(self, x, residual=None):
lora = {}
if self.proj_residual is not None: residual = self.proj_residual(residual)
for lora_pattern, layer in zip(self.lora_patterns, self.layers):
name = lora_pattern[0]
lora_a, lora_b = layer(x, residual=residual)
lora[f"{self.prefix}.{self.block_id}.{name}.lora_A.default.weight"] = lora_a
lora[f"{self.prefix}.{self.block_id}.{name}.lora_B.default.weight"] = lora_b
return lora
class ZImageImage2LoRAComponent(torch.nn.Module):
def __init__(self, lora_patterns, prefix, num_blocks=60, use_residual=True, compress_dim=128, rank=4, residual_length=64+7, residual_mid_dim=1024):
super().__init__()
self.lora_patterns = lora_patterns
self.num_blocks = num_blocks
self.blocks = []
for lora_patterns in self.lora_patterns:
for block_id in range(self.num_blocks):
self.blocks.append(LoRATrainerBlock(lora_patterns, block_id=block_id, use_residual=use_residual, compress_dim=compress_dim, rank=rank, residual_length=residual_length, residual_mid_dim=residual_mid_dim, prefix=prefix))
self.blocks = torch.nn.ModuleList(self.blocks)
self.residual_scale = 0.05
self.use_residual = use_residual
def forward(self, x, residual=None):
if residual is not None:
if self.use_residual:
residual = residual * self.residual_scale
else:
residual = None
lora = {}
for block in self.blocks:
lora.update(block(x, residual))
return lora
class ZImageImage2LoRAModel(torch.nn.Module):
def __init__(self, use_residual=False, compress_dim=64, rank=4, residual_length=64+7, residual_mid_dim=1024):
super().__init__()
lora_patterns = [
[
("attention.to_q", 3840, 3840),
("attention.to_k", 3840, 3840),
("attention.to_v", 3840, 3840),
("attention.to_out.0", 3840, 3840),
],
[
("feed_forward.w1", 3840, 10240),
("feed_forward.w2", 10240, 3840),
("feed_forward.w3", 3840, 10240),
],
]
config = {
"lora_patterns": lora_patterns,
"use_residual": use_residual,
"compress_dim": compress_dim,
"rank": rank,
"residual_length": residual_length,
"residual_mid_dim": residual_mid_dim,
}
self.layers_lora = ZImageImage2LoRAComponent(
prefix="layers",
num_blocks=30,
**config,
)
self.context_refiner_lora = ZImageImage2LoRAComponent(
prefix="context_refiner",
num_blocks=2,
**config,
)
self.noise_refiner_lora = ZImageImage2LoRAComponent(
prefix="noise_refiner",
num_blocks=2,
**config,
)
def forward(self, x, residual=None):
lora = {}
lora.update(self.layers_lora(x, residual=residual))
lora.update(self.context_refiner_lora(x, residual=residual))
lora.update(self.noise_refiner_lora(x, residual=residual))
return lora
def initialize_weights(self):
state_dict = self.state_dict()
for name in state_dict:
if ".proj_a." in name:
state_dict[name] = state_dict[name] * 0.3
elif ".proj_b.proj_out." in name:
state_dict[name] = state_dict[name] * 0
elif ".proj_residual.proj_out." in name:
state_dict[name] = state_dict[name] * 0.3
self.load_state_dict(state_dict)
class ImageEmb2LoRAWeightCompressed(torch.nn.Module):
def __init__(self, in_dim, out_dim, emb_dim, rank):
super().__init__()
self.lora_a = torch.nn.Parameter(torch.randn((rank, in_dim)))
self.lora_b = torch.nn.Parameter(torch.randn((out_dim, rank)))
self.proj = torch.nn.Linear(emb_dim, rank * rank, bias=True)
self.rank = rank
def forward(self, x):
x = self.proj(x).view(self.rank, self.rank)
lora_a = x @ self.lora_a
lora_b = self.lora_b
return lora_a, lora_b
class ZImageImage2LoRAModelCompressed(torch.nn.Module):
def __init__(self, emb_dim=1536+4096, rank=32):
super().__init__()
target_layers = [
("attention.to_q", 3840, 3840),
("attention.to_k", 3840, 3840),
("attention.to_v", 3840, 3840),
("attention.to_out.0", 3840, 3840),
("feed_forward.w1", 3840, 10240),
("feed_forward.w2", 10240, 3840),
("feed_forward.w3", 3840, 10240),
]
self.lora_patterns = [
{
"prefix": "layers",
"num_layers": 30,
"target_layers": target_layers,
},
{
"prefix": "context_refiner",
"num_layers": 2,
"target_layers": target_layers,
},
{
"prefix": "noise_refiner",
"num_layers": 2,
"target_layers": target_layers,
},
]
module_dict = {}
for lora_pattern in self.lora_patterns:
prefix, num_layers, target_layers = lora_pattern["prefix"], lora_pattern["num_layers"], lora_pattern["target_layers"]
for layer_id in range(num_layers):
for layer_name, in_dim, out_dim in target_layers:
name = f"{prefix}.{layer_id}.{layer_name}".replace(".", "___")
model = ImageEmb2LoRAWeightCompressed(in_dim, out_dim, emb_dim, rank)
module_dict[name] = model
self.module_dict = torch.nn.ModuleDict(module_dict)
def forward(self, x, residual=None):
lora = {}
for name, module in self.module_dict.items():
name = name.replace("___", ".")
name_a, name_b = f"{name}.lora_A.default.weight", f"{name}.lora_B.default.weight"
lora_a, lora_b = module(x)
lora[name_a] = lora_a
lora[name_b] = lora_b
return lora
def initialize_weights(self):
state_dict = self.state_dict()
for name in state_dict:
if "lora_b" in name:
state_dict[name] = state_dict[name] * 0
elif "lora_a" in name:
state_dict[name] = state_dict[name] * 0.2
elif "proj.weight" in name:
print(name)
state_dict[name] = state_dict[name] * 0.2
self.load_state_dict(state_dict)
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