File size: 21,290 Bytes
ff53311 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 | from .general import GeneralLoRALoader
import torch, math
class FluxLoRALoader(GeneralLoRALoader):
def __init__(self, device="cpu", torch_dtype=torch.float32):
super().__init__(device=device, torch_dtype=torch_dtype)
self.diffusers_rename_dict = {
"transformer.single_transformer_blocks.blockid.attn.to_k.lora_A.weight":"single_blocks.blockid.a_to_k.lora_A.weight",
"transformer.single_transformer_blocks.blockid.attn.to_k.lora_B.weight":"single_blocks.blockid.a_to_k.lora_B.weight",
"transformer.single_transformer_blocks.blockid.attn.to_q.lora_A.weight":"single_blocks.blockid.a_to_q.lora_A.weight",
"transformer.single_transformer_blocks.blockid.attn.to_q.lora_B.weight":"single_blocks.blockid.a_to_q.lora_B.weight",
"transformer.single_transformer_blocks.blockid.attn.to_v.lora_A.weight":"single_blocks.blockid.a_to_v.lora_A.weight",
"transformer.single_transformer_blocks.blockid.attn.to_v.lora_B.weight":"single_blocks.blockid.a_to_v.lora_B.weight",
"transformer.single_transformer_blocks.blockid.norm.linear.lora_A.weight":"single_blocks.blockid.norm.linear.lora_A.weight",
"transformer.single_transformer_blocks.blockid.norm.linear.lora_B.weight":"single_blocks.blockid.norm.linear.lora_B.weight",
"transformer.single_transformer_blocks.blockid.proj_mlp.lora_A.weight":"single_blocks.blockid.proj_in_besides_attn.lora_A.weight",
"transformer.single_transformer_blocks.blockid.proj_mlp.lora_B.weight":"single_blocks.blockid.proj_in_besides_attn.lora_B.weight",
"transformer.single_transformer_blocks.blockid.proj_out.lora_A.weight":"single_blocks.blockid.proj_out.lora_A.weight",
"transformer.single_transformer_blocks.blockid.proj_out.lora_B.weight":"single_blocks.blockid.proj_out.lora_B.weight",
"transformer.transformer_blocks.blockid.attn.add_k_proj.lora_A.weight":"blocks.blockid.attn.b_to_k.lora_A.weight",
"transformer.transformer_blocks.blockid.attn.add_k_proj.lora_B.weight":"blocks.blockid.attn.b_to_k.lora_B.weight",
"transformer.transformer_blocks.blockid.attn.add_q_proj.lora_A.weight":"blocks.blockid.attn.b_to_q.lora_A.weight",
"transformer.transformer_blocks.blockid.attn.add_q_proj.lora_B.weight":"blocks.blockid.attn.b_to_q.lora_B.weight",
"transformer.transformer_blocks.blockid.attn.add_v_proj.lora_A.weight":"blocks.blockid.attn.b_to_v.lora_A.weight",
"transformer.transformer_blocks.blockid.attn.add_v_proj.lora_B.weight":"blocks.blockid.attn.b_to_v.lora_B.weight",
"transformer.transformer_blocks.blockid.attn.to_add_out.lora_A.weight":"blocks.blockid.attn.b_to_out.lora_A.weight",
"transformer.transformer_blocks.blockid.attn.to_add_out.lora_B.weight":"blocks.blockid.attn.b_to_out.lora_B.weight",
"transformer.transformer_blocks.blockid.attn.to_k.lora_A.weight":"blocks.blockid.attn.a_to_k.lora_A.weight",
"transformer.transformer_blocks.blockid.attn.to_k.lora_B.weight":"blocks.blockid.attn.a_to_k.lora_B.weight",
"transformer.transformer_blocks.blockid.attn.to_out.0.lora_A.weight":"blocks.blockid.attn.a_to_out.lora_A.weight",
"transformer.transformer_blocks.blockid.attn.to_out.0.lora_B.weight":"blocks.blockid.attn.a_to_out.lora_B.weight",
"transformer.transformer_blocks.blockid.attn.to_q.lora_A.weight":"blocks.blockid.attn.a_to_q.lora_A.weight",
"transformer.transformer_blocks.blockid.attn.to_q.lora_B.weight":"blocks.blockid.attn.a_to_q.lora_B.weight",
"transformer.transformer_blocks.blockid.attn.to_v.lora_A.weight":"blocks.blockid.attn.a_to_v.lora_A.weight",
"transformer.transformer_blocks.blockid.attn.to_v.lora_B.weight":"blocks.blockid.attn.a_to_v.lora_B.weight",
"transformer.transformer_blocks.blockid.ff.net.0.proj.lora_A.weight":"blocks.blockid.ff_a.0.lora_A.weight",
"transformer.transformer_blocks.blockid.ff.net.0.proj.lora_B.weight":"blocks.blockid.ff_a.0.lora_B.weight",
"transformer.transformer_blocks.blockid.ff.net.2.lora_A.weight":"blocks.blockid.ff_a.2.lora_A.weight",
"transformer.transformer_blocks.blockid.ff.net.2.lora_B.weight":"blocks.blockid.ff_a.2.lora_B.weight",
"transformer.transformer_blocks.blockid.ff_context.net.0.proj.lora_A.weight":"blocks.blockid.ff_b.0.lora_A.weight",
"transformer.transformer_blocks.blockid.ff_context.net.0.proj.lora_B.weight":"blocks.blockid.ff_b.0.lora_B.weight",
"transformer.transformer_blocks.blockid.ff_context.net.2.lora_A.weight":"blocks.blockid.ff_b.2.lora_A.weight",
"transformer.transformer_blocks.blockid.ff_context.net.2.lora_B.weight":"blocks.blockid.ff_b.2.lora_B.weight",
"transformer.transformer_blocks.blockid.norm1.linear.lora_A.weight":"blocks.blockid.norm1_a.linear.lora_A.weight",
"transformer.transformer_blocks.blockid.norm1.linear.lora_B.weight":"blocks.blockid.norm1_a.linear.lora_B.weight",
"transformer.transformer_blocks.blockid.norm1_context.linear.lora_A.weight":"blocks.blockid.norm1_b.linear.lora_A.weight",
"transformer.transformer_blocks.blockid.norm1_context.linear.lora_B.weight":"blocks.blockid.norm1_b.linear.lora_B.weight",
}
self.civitai_rename_dict = {
"lora_unet_double_blocks_blockid_img_mod_lin.lora_down.weight": "blocks.blockid.norm1_a.linear.lora_A.weight",
"lora_unet_double_blocks_blockid_img_mod_lin.lora_up.weight": "blocks.blockid.norm1_a.linear.lora_B.weight",
"lora_unet_double_blocks_blockid_txt_mod_lin.lora_down.weight": "blocks.blockid.norm1_b.linear.lora_A.weight",
"lora_unet_double_blocks_blockid_txt_mod_lin.lora_up.weight": "blocks.blockid.norm1_b.linear.lora_B.weight",
"lora_unet_double_blocks_blockid_img_attn_qkv.lora_down.weight": "blocks.blockid.attn.a_to_qkv.lora_A.weight",
"lora_unet_double_blocks_blockid_img_attn_qkv.lora_up.weight": "blocks.blockid.attn.a_to_qkv.lora_B.weight",
"lora_unet_double_blocks_blockid_txt_attn_qkv.lora_down.weight": "blocks.blockid.attn.b_to_qkv.lora_A.weight",
"lora_unet_double_blocks_blockid_txt_attn_qkv.lora_up.weight": "blocks.blockid.attn.b_to_qkv.lora_B.weight",
"lora_unet_double_blocks_blockid_img_attn_proj.lora_down.weight": "blocks.blockid.attn.a_to_out.lora_A.weight",
"lora_unet_double_blocks_blockid_img_attn_proj.lora_up.weight": "blocks.blockid.attn.a_to_out.lora_B.weight",
"lora_unet_double_blocks_blockid_txt_attn_proj.lora_down.weight": "blocks.blockid.attn.b_to_out.lora_A.weight",
"lora_unet_double_blocks_blockid_txt_attn_proj.lora_up.weight": "blocks.blockid.attn.b_to_out.lora_B.weight",
"lora_unet_double_blocks_blockid_img_mlp_0.lora_down.weight": "blocks.blockid.ff_a.0.lora_A.weight",
"lora_unet_double_blocks_blockid_img_mlp_0.lora_up.weight": "blocks.blockid.ff_a.0.lora_B.weight",
"lora_unet_double_blocks_blockid_img_mlp_2.lora_down.weight": "blocks.blockid.ff_a.2.lora_A.weight",
"lora_unet_double_blocks_blockid_img_mlp_2.lora_up.weight": "blocks.blockid.ff_a.2.lora_B.weight",
"lora_unet_double_blocks_blockid_txt_mlp_0.lora_down.weight": "blocks.blockid.ff_b.0.lora_A.weight",
"lora_unet_double_blocks_blockid_txt_mlp_0.lora_up.weight": "blocks.blockid.ff_b.0.lora_B.weight",
"lora_unet_double_blocks_blockid_txt_mlp_2.lora_down.weight": "blocks.blockid.ff_b.2.lora_A.weight",
"lora_unet_double_blocks_blockid_txt_mlp_2.lora_up.weight": "blocks.blockid.ff_b.2.lora_B.weight",
"lora_unet_single_blocks_blockid_modulation_lin.lora_down.weight": "single_blocks.blockid.norm.linear.lora_A.weight",
"lora_unet_single_blocks_blockid_modulation_lin.lora_up.weight": "single_blocks.blockid.norm.linear.lora_B.weight",
"lora_unet_single_blocks_blockid_linear1.lora_down.weight": "single_blocks.blockid.to_qkv_mlp.lora_A.weight",
"lora_unet_single_blocks_blockid_linear1.lora_up.weight": "single_blocks.blockid.to_qkv_mlp.lora_B.weight",
"lora_unet_single_blocks_blockid_linear2.lora_down.weight": "single_blocks.blockid.proj_out.lora_A.weight",
"lora_unet_single_blocks_blockid_linear2.lora_up.weight": "single_blocks.blockid.proj_out.lora_B.weight",
}
def fuse_lora_to_base_model(self, model: torch.nn.Module, state_dict_lora, alpha=1.0):
super().fuse_lora_to_base_model(model, state_dict_lora, alpha)
def convert_state_dict(self, state_dict):
def guess_block_id(name,model_resource):
if model_resource == 'civitai':
names = name.split("_")
for i in names:
if i.isdigit():
return i, name.replace(f"_{i}_", "_blockid_")
if model_resource == 'diffusers':
names = name.split(".")
for i in names:
if i.isdigit():
return i, name.replace(f"transformer_blocks.{i}.", "transformer_blocks.blockid.")
return None, None
def guess_resource(state_dict):
for k in state_dict:
if "lora_unet_" in k:
return 'civitai'
elif k.startswith("transformer."):
return 'diffusers'
else:
None
model_resource = guess_resource(state_dict)
if model_resource is None:
return state_dict
rename_dict = self.diffusers_rename_dict if model_resource == 'diffusers' else self.civitai_rename_dict
def guess_alpha(state_dict):
for name, param in state_dict.items():
if ".alpha" in name:
for suffix in [".lora_down.weight", ".lora_A.weight"]:
name_ = name.replace(".alpha", suffix)
if name_ in state_dict:
lora_alpha = param.item() / state_dict[name_].shape[0]
lora_alpha = math.sqrt(lora_alpha)
return lora_alpha
return 1
alpha = guess_alpha(state_dict)
state_dict_ = {}
for name, param in state_dict.items():
block_id, source_name = guess_block_id(name,model_resource)
if alpha != 1:
param *= alpha
if source_name in rename_dict:
target_name = rename_dict[source_name]
target_name = target_name.replace(".blockid.", f".{block_id}.")
state_dict_[target_name] = param
else:
state_dict_[name] = param
if model_resource == 'diffusers':
for name in list(state_dict_.keys()):
if "single_blocks." in name and ".a_to_q." in name:
mlp = state_dict_.get(name.replace(".a_to_q.", ".proj_in_besides_attn."), None)
if mlp is None:
dim = 4
if 'lora_A' in name:
dim = 1
mlp = torch.zeros(dim * state_dict_[name].shape[0],
*state_dict_[name].shape[1:],
dtype=state_dict_[name].dtype)
else:
state_dict_.pop(name.replace(".a_to_q.", ".proj_in_besides_attn."))
if 'lora_A' in name:
param = torch.concat([
state_dict_.pop(name),
state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")),
state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")),
mlp,
], dim=0)
elif 'lora_B' in name:
d, r = state_dict_[name].shape
param = torch.zeros((3*d+mlp.shape[0], 3*r+mlp.shape[1]), dtype=state_dict_[name].dtype, device=state_dict_[name].device)
param[:d, :r] = state_dict_.pop(name)
param[d:2*d, r:2*r] = state_dict_.pop(name.replace(".a_to_q.", ".a_to_k."))
param[2*d:3*d, 2*r:3*r] = state_dict_.pop(name.replace(".a_to_q.", ".a_to_v."))
param[3*d:, 3*r:] = mlp
else:
param = torch.concat([
state_dict_.pop(name),
state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")),
state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")),
mlp,
], dim=0)
name_ = name.replace(".a_to_q.", ".to_qkv_mlp.")
state_dict_[name_] = param
for name in list(state_dict_.keys()):
for component in ["a", "b"]:
if f".{component}_to_q." in name:
name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.")
concat_dim = 0
if 'lora_A' in name:
param = torch.concat([
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
], dim=0)
elif 'lora_B' in name:
origin = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")]
d, r = origin.shape
# print(d, r)
param = torch.zeros((3*d, 3*r), dtype=origin.dtype, device=origin.device)
param[:d, :r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")]
param[d:2*d, r:2*r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")]
param[2*d:3*d, 2*r:3*r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")]
else:
param = torch.concat([
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
], dim=0)
state_dict_[name_] = param
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q."))
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k."))
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v."))
return state_dict_
class FluxLoRAConverter:
def __init__(self):
pass
@staticmethod
def align_to_opensource_format(state_dict, alpha=None):
prefix_rename_dict = {
"single_blocks": "lora_unet_single_blocks",
"blocks": "lora_unet_double_blocks",
}
middle_rename_dict = {
"norm.linear": "modulation_lin",
"to_qkv_mlp": "linear1",
"proj_out": "linear2",
"norm1_a.linear": "img_mod_lin",
"norm1_b.linear": "txt_mod_lin",
"attn.a_to_qkv": "img_attn_qkv",
"attn.b_to_qkv": "txt_attn_qkv",
"attn.a_to_out": "img_attn_proj",
"attn.b_to_out": "txt_attn_proj",
"ff_a.0": "img_mlp_0",
"ff_a.2": "img_mlp_2",
"ff_b.0": "txt_mlp_0",
"ff_b.2": "txt_mlp_2",
}
suffix_rename_dict = {
"lora_B.weight": "lora_up.weight",
"lora_A.weight": "lora_down.weight",
}
state_dict_ = {}
for name, param in state_dict.items():
names = name.split(".")
if names[-2] != "lora_A" and names[-2] != "lora_B":
names.pop(-2)
prefix = names[0]
middle = ".".join(names[2:-2])
suffix = ".".join(names[-2:])
block_id = names[1]
if middle not in middle_rename_dict:
continue
rename = prefix_rename_dict[prefix] + "_" + block_id + "_" + middle_rename_dict[middle] + "." + suffix_rename_dict[suffix]
state_dict_[rename] = param
if rename.endswith("lora_up.weight"):
lora_alpha = alpha if alpha is not None else param.shape[-1]
state_dict_[rename.replace("lora_up.weight", "alpha")] = torch.tensor((lora_alpha,))[0]
return state_dict_
@staticmethod
def align_to_diffsynth_format(state_dict):
rename_dict = {
"lora_unet_double_blocks_blockid_img_mod_lin.lora_down.weight": "blocks.blockid.norm1_a.linear.lora_A.default.weight",
"lora_unet_double_blocks_blockid_img_mod_lin.lora_up.weight": "blocks.blockid.norm1_a.linear.lora_B.default.weight",
"lora_unet_double_blocks_blockid_txt_mod_lin.lora_down.weight": "blocks.blockid.norm1_b.linear.lora_A.default.weight",
"lora_unet_double_blocks_blockid_txt_mod_lin.lora_up.weight": "blocks.blockid.norm1_b.linear.lora_B.default.weight",
"lora_unet_double_blocks_blockid_img_attn_qkv.lora_down.weight": "blocks.blockid.attn.a_to_qkv.lora_A.default.weight",
"lora_unet_double_blocks_blockid_img_attn_qkv.lora_up.weight": "blocks.blockid.attn.a_to_qkv.lora_B.default.weight",
"lora_unet_double_blocks_blockid_txt_attn_qkv.lora_down.weight": "blocks.blockid.attn.b_to_qkv.lora_A.default.weight",
"lora_unet_double_blocks_blockid_txt_attn_qkv.lora_up.weight": "blocks.blockid.attn.b_to_qkv.lora_B.default.weight",
"lora_unet_double_blocks_blockid_img_attn_proj.lora_down.weight": "blocks.blockid.attn.a_to_out.lora_A.default.weight",
"lora_unet_double_blocks_blockid_img_attn_proj.lora_up.weight": "blocks.blockid.attn.a_to_out.lora_B.default.weight",
"lora_unet_double_blocks_blockid_txt_attn_proj.lora_down.weight": "blocks.blockid.attn.b_to_out.lora_A.default.weight",
"lora_unet_double_blocks_blockid_txt_attn_proj.lora_up.weight": "blocks.blockid.attn.b_to_out.lora_B.default.weight",
"lora_unet_double_blocks_blockid_img_mlp_0.lora_down.weight": "blocks.blockid.ff_a.0.lora_A.default.weight",
"lora_unet_double_blocks_blockid_img_mlp_0.lora_up.weight": "blocks.blockid.ff_a.0.lora_B.default.weight",
"lora_unet_double_blocks_blockid_img_mlp_2.lora_down.weight": "blocks.blockid.ff_a.2.lora_A.default.weight",
"lora_unet_double_blocks_blockid_img_mlp_2.lora_up.weight": "blocks.blockid.ff_a.2.lora_B.default.weight",
"lora_unet_double_blocks_blockid_txt_mlp_0.lora_down.weight": "blocks.blockid.ff_b.0.lora_A.default.weight",
"lora_unet_double_blocks_blockid_txt_mlp_0.lora_up.weight": "blocks.blockid.ff_b.0.lora_B.default.weight",
"lora_unet_double_blocks_blockid_txt_mlp_2.lora_down.weight": "blocks.blockid.ff_b.2.lora_A.default.weight",
"lora_unet_double_blocks_blockid_txt_mlp_2.lora_up.weight": "blocks.blockid.ff_b.2.lora_B.default.weight",
"lora_unet_single_blocks_blockid_modulation_lin.lora_down.weight": "single_blocks.blockid.norm.linear.lora_A.default.weight",
"lora_unet_single_blocks_blockid_modulation_lin.lora_up.weight": "single_blocks.blockid.norm.linear.lora_B.default.weight",
"lora_unet_single_blocks_blockid_linear1.lora_down.weight": "single_blocks.blockid.to_qkv_mlp.lora_A.default.weight",
"lora_unet_single_blocks_blockid_linear1.lora_up.weight": "single_blocks.blockid.to_qkv_mlp.lora_B.default.weight",
"lora_unet_single_blocks_blockid_linear2.lora_down.weight": "single_blocks.blockid.proj_out.lora_A.default.weight",
"lora_unet_single_blocks_blockid_linear2.lora_up.weight": "single_blocks.blockid.proj_out.lora_B.default.weight",
}
def guess_block_id(name):
names = name.split("_")
for i in names:
if i.isdigit():
return i, name.replace(f"_{i}_", "_blockid_")
return None, None
state_dict_ = {}
for name, param in state_dict.items():
block_id, source_name = guess_block_id(name)
if source_name in rename_dict:
target_name = rename_dict[source_name]
target_name = target_name.replace(".blockid.", f".{block_id}.")
state_dict_[target_name] = param
else:
state_dict_[name] = param
return state_dict_
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