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143 lines
7.7 KiB
Python
143 lines
7.7 KiB
Python
import torch, math
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from diffsynth.lora import GeneralLoRALoader
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from diffsynth.models.lora import FluxLoRAFromCivitai
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class FluxLoRALoader(GeneralLoRALoader):
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def __init__(self, device="cpu", torch_dtype=torch.float32):
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super().__init__(device=device, torch_dtype=torch_dtype)
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def load(self, model: torch.nn.Module, state_dict_lora, alpha=1.0):
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super().load(model, state_dict_lora, alpha)
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def convert_state_dict(self, state_dict):
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# TODO: support other lora format
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rename_dict = {
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"lora_unet_double_blocks_blockid_img_mod_lin.lora_down.weight": "blocks.blockid.norm1_a.linear.lora_A.default.weight",
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"lora_unet_double_blocks_blockid_img_mod_lin.lora_up.weight": "blocks.blockid.norm1_a.linear.lora_B.default.weight",
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"lora_unet_double_blocks_blockid_txt_mod_lin.lora_down.weight": "blocks.blockid.norm1_b.linear.lora_A.default.weight",
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"lora_unet_double_blocks_blockid_txt_mod_lin.lora_up.weight": "blocks.blockid.norm1_b.linear.lora_B.default.weight",
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"lora_unet_double_blocks_blockid_img_attn_qkv.lora_down.weight": "blocks.blockid.attn.a_to_qkv.lora_A.default.weight",
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"lora_unet_double_blocks_blockid_img_attn_qkv.lora_up.weight": "blocks.blockid.attn.a_to_qkv.lora_B.default.weight",
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"lora_unet_double_blocks_blockid_txt_attn_qkv.lora_down.weight": "blocks.blockid.attn.b_to_qkv.lora_A.default.weight",
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"lora_unet_double_blocks_blockid_txt_attn_qkv.lora_up.weight": "blocks.blockid.attn.b_to_qkv.lora_B.default.weight",
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"lora_unet_double_blocks_blockid_img_attn_proj.lora_down.weight": "blocks.blockid.attn.a_to_out.lora_A.default.weight",
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"lora_unet_double_blocks_blockid_img_attn_proj.lora_up.weight": "blocks.blockid.attn.a_to_out.lora_B.default.weight",
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"lora_unet_double_blocks_blockid_txt_attn_proj.lora_down.weight": "blocks.blockid.attn.b_to_out.lora_A.default.weight",
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"lora_unet_double_blocks_blockid_txt_attn_proj.lora_up.weight": "blocks.blockid.attn.b_to_out.lora_B.default.weight",
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"lora_unet_double_blocks_blockid_img_mlp_0.lora_down.weight": "blocks.blockid.ff_a.0.lora_A.default.weight",
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"lora_unet_double_blocks_blockid_img_mlp_0.lora_up.weight": "blocks.blockid.ff_a.0.lora_B.default.weight",
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"lora_unet_double_blocks_blockid_img_mlp_2.lora_down.weight": "blocks.blockid.ff_a.2.lora_A.default.weight",
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"lora_unet_double_blocks_blockid_img_mlp_2.lora_up.weight": "blocks.blockid.ff_a.2.lora_B.default.weight",
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"lora_unet_double_blocks_blockid_txt_mlp_0.lora_down.weight": "blocks.blockid.ff_b.0.lora_A.default.weight",
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"lora_unet_double_blocks_blockid_txt_mlp_0.lora_up.weight": "blocks.blockid.ff_b.0.lora_B.default.weight",
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"lora_unet_double_blocks_blockid_txt_mlp_2.lora_down.weight": "blocks.blockid.ff_b.2.lora_A.default.weight",
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"lora_unet_double_blocks_blockid_txt_mlp_2.lora_up.weight": "blocks.blockid.ff_b.2.lora_B.default.weight",
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"lora_unet_single_blocks_blockid_modulation_lin.lora_down.weight": "single_blocks.blockid.norm.linear.lora_A.default.weight",
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"lora_unet_single_blocks_blockid_modulation_lin.lora_up.weight": "single_blocks.blockid.norm.linear.lora_B.default.weight",
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"lora_unet_single_blocks_blockid_linear1.lora_down.weight": "single_blocks.blockid.to_qkv_mlp.lora_A.default.weight",
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"lora_unet_single_blocks_blockid_linear1.lora_up.weight": "single_blocks.blockid.to_qkv_mlp.lora_B.default.weight",
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"lora_unet_single_blocks_blockid_linear2.lora_down.weight": "single_blocks.blockid.proj_out.lora_A.default.weight",
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"lora_unet_single_blocks_blockid_linear2.lora_up.weight": "single_blocks.blockid.proj_out.lora_B.default.weight",
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}
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def guess_block_id(name):
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names = name.split("_")
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for i in names:
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if i.isdigit():
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return i, name.replace(f"_{i}_", "_blockid_")
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return None, None
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def guess_alpha(state_dict):
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for name, param in state_dict.items():
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if ".alpha" in name:
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name_ = name.replace(".alpha", ".lora_down.weight")
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if name_ in state_dict:
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lora_alpha = param.item() / state_dict[name_].shape[0]
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lora_alpha = math.sqrt(lora_alpha)
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return lora_alpha
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return 1
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alpha = guess_alpha(state_dict)
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state_dict_ = {}
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for name, param in state_dict.items():
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block_id, source_name = guess_block_id(name)
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if alpha != 1:
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param *= alpha
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if source_name in rename_dict:
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target_name = rename_dict[source_name]
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target_name = target_name.replace(".blockid.", f".{block_id}.")
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state_dict_[target_name] = param
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else:
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state_dict_[name] = param
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return state_dict_
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class LoraMerger(torch.nn.Module):
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def __init__(self, dim):
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super().__init__()
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self.weight_base = torch.nn.Parameter(torch.randn((dim,)))
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self.weight_lora = torch.nn.Parameter(torch.randn((dim,)))
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self.weight_cross = torch.nn.Parameter(torch.randn((dim,)))
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self.weight_out = torch.nn.Parameter(torch.ones((dim,)))
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self.bias = torch.nn.Parameter(torch.randn((dim,)))
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self.activation = torch.nn.Sigmoid()
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self.norm_base = torch.nn.LayerNorm(dim, eps=1e-5)
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self.norm_lora = torch.nn.LayerNorm(dim, eps=1e-5)
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def forward(self, base_output, lora_outputs):
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norm_base_output = self.norm_base(base_output)
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norm_lora_outputs = self.norm_lora(lora_outputs)
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gate = self.activation(
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norm_base_output * self.weight_base \
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+ norm_lora_outputs * self.weight_lora \
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+ norm_base_output * norm_lora_outputs * self.weight_cross + self.bias
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)
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output = base_output + (self.weight_out * gate * lora_outputs).sum(dim=0)
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return output
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class FluxLoraPatcher(torch.nn.Module):
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def __init__(self, lora_patterns=None):
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super().__init__()
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if lora_patterns is None:
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lora_patterns = self.default_lora_patterns()
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model_dict = {}
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for lora_pattern in lora_patterns:
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name, dim = lora_pattern["name"], lora_pattern["dim"]
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model_dict[name.replace(".", "___")] = LoraMerger(dim)
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self.model_dict = torch.nn.ModuleDict(model_dict)
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def default_lora_patterns(self):
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lora_patterns = []
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lora_dict = {
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"attn.a_to_qkv": 9216, "attn.a_to_out": 3072, "ff_a.0": 12288, "ff_a.2": 3072, "norm1_a.linear": 18432,
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"attn.b_to_qkv": 9216, "attn.b_to_out": 3072, "ff_b.0": 12288, "ff_b.2": 3072, "norm1_b.linear": 18432,
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}
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for i in range(19):
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for suffix in lora_dict:
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lora_patterns.append({
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"name": f"blocks.{i}.{suffix}",
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"dim": lora_dict[suffix]
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})
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lora_dict = {"to_qkv_mlp": 21504, "proj_out": 3072, "norm.linear": 9216}
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for i in range(38):
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for suffix in lora_dict:
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lora_patterns.append({
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"name": f"single_blocks.{i}.{suffix}",
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"dim": lora_dict[suffix]
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})
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return lora_patterns
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def forward(self, base_output, lora_outputs, name):
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return self.model_dict[name.replace(".", "___")](base_output, lora_outputs)
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@staticmethod
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def state_dict_converter():
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return FluxLoraPatcherStateDictConverter()
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class FluxLoraPatcherStateDictConverter:
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def __init__(self):
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pass
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def from_civitai(self, state_dict):
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return state_dict
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