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bc8c4af | 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 | import torch
def FluxDiTStateDictConverter(state_dict):
is_nexus_gen = sum([key.startswith("pipe.dit.") for key in state_dict]) > 0
if is_nexus_gen:
dit_state_dict = {}
for key in state_dict:
if key.startswith('pipe.dit.'):
param = state_dict[key]
new_key = key.replace("pipe.dit.", "")
if new_key.startswith("final_norm_out.linear."):
param = torch.concat([param[3072:], param[:3072]], dim=0)
dit_state_dict[new_key] = param
return dit_state_dict
rename_dict = {
"time_in.in_layer.bias": "time_embedder.timestep_embedder.0.bias",
"time_in.in_layer.weight": "time_embedder.timestep_embedder.0.weight",
"time_in.out_layer.bias": "time_embedder.timestep_embedder.2.bias",
"time_in.out_layer.weight": "time_embedder.timestep_embedder.2.weight",
"txt_in.bias": "context_embedder.bias",
"txt_in.weight": "context_embedder.weight",
"vector_in.in_layer.bias": "pooled_text_embedder.0.bias",
"vector_in.in_layer.weight": "pooled_text_embedder.0.weight",
"vector_in.out_layer.bias": "pooled_text_embedder.2.bias",
"vector_in.out_layer.weight": "pooled_text_embedder.2.weight",
"final_layer.linear.bias": "final_proj_out.bias",
"final_layer.linear.weight": "final_proj_out.weight",
"guidance_in.in_layer.bias": "guidance_embedder.timestep_embedder.0.bias",
"guidance_in.in_layer.weight": "guidance_embedder.timestep_embedder.0.weight",
"guidance_in.out_layer.bias": "guidance_embedder.timestep_embedder.2.bias",
"guidance_in.out_layer.weight": "guidance_embedder.timestep_embedder.2.weight",
"img_in.bias": "x_embedder.bias",
"img_in.weight": "x_embedder.weight",
"final_layer.adaLN_modulation.1.weight": "final_norm_out.linear.weight",
"final_layer.adaLN_modulation.1.bias": "final_norm_out.linear.bias",
}
suffix_rename_dict = {
"img_attn.norm.key_norm.scale": "attn.norm_k_a.weight",
"img_attn.norm.query_norm.scale": "attn.norm_q_a.weight",
"img_attn.proj.bias": "attn.a_to_out.bias",
"img_attn.proj.weight": "attn.a_to_out.weight",
"img_attn.qkv.bias": "attn.a_to_qkv.bias",
"img_attn.qkv.weight": "attn.a_to_qkv.weight",
"img_mlp.0.bias": "ff_a.0.bias",
"img_mlp.0.weight": "ff_a.0.weight",
"img_mlp.2.bias": "ff_a.2.bias",
"img_mlp.2.weight": "ff_a.2.weight",
"img_mod.lin.bias": "norm1_a.linear.bias",
"img_mod.lin.weight": "norm1_a.linear.weight",
"txt_attn.norm.key_norm.scale": "attn.norm_k_b.weight",
"txt_attn.norm.query_norm.scale": "attn.norm_q_b.weight",
"txt_attn.proj.bias": "attn.b_to_out.bias",
"txt_attn.proj.weight": "attn.b_to_out.weight",
"txt_attn.qkv.bias": "attn.b_to_qkv.bias",
"txt_attn.qkv.weight": "attn.b_to_qkv.weight",
"txt_mlp.0.bias": "ff_b.0.bias",
"txt_mlp.0.weight": "ff_b.0.weight",
"txt_mlp.2.bias": "ff_b.2.bias",
"txt_mlp.2.weight": "ff_b.2.weight",
"txt_mod.lin.bias": "norm1_b.linear.bias",
"txt_mod.lin.weight": "norm1_b.linear.weight",
"linear1.bias": "to_qkv_mlp.bias",
"linear1.weight": "to_qkv_mlp.weight",
"linear2.bias": "proj_out.bias",
"linear2.weight": "proj_out.weight",
"modulation.lin.bias": "norm.linear.bias",
"modulation.lin.weight": "norm.linear.weight",
"norm.key_norm.scale": "norm_k_a.weight",
"norm.query_norm.scale": "norm_q_a.weight",
}
state_dict_ = {}
for name in state_dict:
original_name = name
if name.startswith("model.diffusion_model."):
name = name[len("model.diffusion_model."):]
names = name.split(".")
if name in rename_dict:
rename = rename_dict[name]
state_dict_[rename] = state_dict[original_name]
elif names[0] == "double_blocks":
rename = f"blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])]
state_dict_[rename] = state_dict[original_name]
elif names[0] == "single_blocks":
if ".".join(names[2:]) in suffix_rename_dict:
rename = f"single_blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])]
state_dict_[rename] = state_dict[original_name]
else:
pass
return state_dict_
def FluxDiTStateDictConverterFromDiffusers(state_dict):
global_rename_dict = {
"context_embedder": "context_embedder",
"x_embedder": "x_embedder",
"time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0",
"time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2",
"time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0",
"time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2",
"time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0",
"time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2",
"norm_out.linear": "final_norm_out.linear",
"proj_out": "final_proj_out",
}
rename_dict = {
"proj_out": "proj_out",
"norm1.linear": "norm1_a.linear",
"norm1_context.linear": "norm1_b.linear",
"attn.to_q": "attn.a_to_q",
"attn.to_k": "attn.a_to_k",
"attn.to_v": "attn.a_to_v",
"attn.to_out.0": "attn.a_to_out",
"attn.add_q_proj": "attn.b_to_q",
"attn.add_k_proj": "attn.b_to_k",
"attn.add_v_proj": "attn.b_to_v",
"attn.to_add_out": "attn.b_to_out",
"ff.net.0.proj": "ff_a.0",
"ff.net.2": "ff_a.2",
"ff_context.net.0.proj": "ff_b.0",
"ff_context.net.2": "ff_b.2",
"attn.norm_q": "attn.norm_q_a",
"attn.norm_k": "attn.norm_k_a",
"attn.norm_added_q": "attn.norm_q_b",
"attn.norm_added_k": "attn.norm_k_b",
}
rename_dict_single = {
"attn.to_q": "a_to_q",
"attn.to_k": "a_to_k",
"attn.to_v": "a_to_v",
"attn.norm_q": "norm_q_a",
"attn.norm_k": "norm_k_a",
"norm.linear": "norm.linear",
"proj_mlp": "proj_in_besides_attn",
"proj_out": "proj_out",
}
state_dict_ = {}
for name in state_dict:
param = state_dict[name]
if name.endswith(".weight") or name.endswith(".bias"):
suffix = ".weight" if name.endswith(".weight") else ".bias"
prefix = name[:-len(suffix)]
if prefix in global_rename_dict:
if global_rename_dict[prefix] == "final_norm_out.linear":
param = torch.concat([param[3072:], param[:3072]], dim=0)
state_dict_[global_rename_dict[prefix] + suffix] = param
elif prefix.startswith("transformer_blocks."):
names = prefix.split(".")
names[0] = "blocks"
middle = ".".join(names[2:])
if middle in rename_dict:
name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]])
state_dict_[name_] = param
elif prefix.startswith("single_transformer_blocks."):
names = prefix.split(".")
names[0] = "single_blocks"
middle = ".".join(names[2:])
if middle in rename_dict_single:
name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]])
state_dict_[name_] = param
else:
pass
else:
pass
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:
mlp = torch.zeros(4 * 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."))
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.")
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_ |