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Kohya S
@@ -1,4 +1,3 @@
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import math
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import argparse
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import os
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@@ -9,216 +8,234 @@ import lora
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def load_state_dict(file_name, dtype):
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if os.path.splitext(file_name)[1] == '.safetensors':
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sd = load_file(file_name)
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else:
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sd = torch.load(file_name, map_location='cpu')
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for key in list(sd.keys()):
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if type(sd[key]) == torch.Tensor:
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sd[key] = sd[key].to(dtype)
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return sd
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if os.path.splitext(file_name)[1] == ".safetensors":
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sd = load_file(file_name)
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else:
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sd = torch.load(file_name, map_location="cpu")
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for key in list(sd.keys()):
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if type(sd[key]) == torch.Tensor:
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sd[key] = sd[key].to(dtype)
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return sd
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def save_to_file(file_name, model, state_dict, dtype):
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if dtype is not None:
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for key in list(state_dict.keys()):
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if type(state_dict[key]) == torch.Tensor:
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state_dict[key] = state_dict[key].to(dtype)
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if dtype is not None:
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for key in list(state_dict.keys()):
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if type(state_dict[key]) == torch.Tensor:
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state_dict[key] = state_dict[key].to(dtype)
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if os.path.splitext(file_name)[1] == '.safetensors':
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save_file(model, file_name)
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else:
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torch.save(model, file_name)
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if os.path.splitext(file_name)[1] == ".safetensors":
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save_file(model, file_name)
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else:
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torch.save(model, file_name)
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def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
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text_encoder.to(merge_dtype)
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unet.to(merge_dtype)
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text_encoder.to(merge_dtype)
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unet.to(merge_dtype)
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# create module map
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name_to_module = {}
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for i, root_module in enumerate([text_encoder, unet]):
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if i == 0:
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prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER
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target_replace_modules = lora.LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE
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else:
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prefix = lora.LoRANetwork.LORA_PREFIX_UNET
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target_replace_modules = lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE
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for name, module in root_module.named_modules():
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if module.__class__.__name__ in target_replace_modules:
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for child_name, child_module in module.named_modules():
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if child_module.__class__.__name__ == "Linear" or child_module.__class__.__name__ == "Conv2d":
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lora_name = prefix + '.' + name + '.' + child_name
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lora_name = lora_name.replace('.', '_')
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name_to_module[lora_name] = child_module
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for model, ratio in zip(models, ratios):
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print(f"loading: {model}")
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lora_sd = load_state_dict(model, merge_dtype)
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print(f"merging...")
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for key in lora_sd.keys():
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if "lora_down" in key:
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up_key = key.replace("lora_down", "lora_up")
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alpha_key = key[:key.index("lora_down")] + 'alpha'
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# find original module for this lora
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module_name = '.'.join(key.split('.')[:-2]) # remove trailing ".lora_down.weight"
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if module_name not in name_to_module:
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print(f"no module found for LoRA weight: {key}")
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continue
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module = name_to_module[module_name]
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# print(f"apply {key} to {module}")
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down_weight = lora_sd[key]
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up_weight = lora_sd[up_key]
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dim = down_weight.size()[0]
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alpha = lora_sd.get(alpha_key, dim)
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scale = alpha / dim
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# W <- W + U * D
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weight = module.weight
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# print(module_name, down_weight.size(), up_weight.size())
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if len(weight.size()) == 2:
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# linear
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weight = weight + ratio * (up_weight @ down_weight) * scale
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elif down_weight.size()[2:4] == (1, 1):
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# conv2d 1x1
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weight = weight + ratio * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)
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).unsqueeze(2).unsqueeze(3) * scale
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# create module map
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name_to_module = {}
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for i, root_module in enumerate([text_encoder, unet]):
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if i == 0:
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prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER
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target_replace_modules = lora.LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE
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else:
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# conv2d 3x3
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conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
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# print(conved.size(), weight.size(), module.stride, module.padding)
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weight = weight + ratio * conved * scale
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prefix = lora.LoRANetwork.LORA_PREFIX_UNET
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target_replace_modules = lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE
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module.weight = torch.nn.Parameter(weight)
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for name, module in root_module.named_modules():
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if module.__class__.__name__ in target_replace_modules:
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for child_name, child_module in module.named_modules():
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if child_module.__class__.__name__ == "Linear" or child_module.__class__.__name__ == "Conv2d":
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lora_name = prefix + "." + name + "." + child_name
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lora_name = lora_name.replace(".", "_")
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name_to_module[lora_name] = child_module
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for model, ratio in zip(models, ratios):
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print(f"loading: {model}")
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lora_sd = load_state_dict(model, merge_dtype)
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print(f"merging...")
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for key in lora_sd.keys():
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if "lora_down" in key:
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up_key = key.replace("lora_down", "lora_up")
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alpha_key = key[: key.index("lora_down")] + "alpha"
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# find original module for this lora
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module_name = ".".join(key.split(".")[:-2]) # remove trailing ".lora_down.weight"
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if module_name not in name_to_module:
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print(f"no module found for LoRA weight: {key}")
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continue
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module = name_to_module[module_name]
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# print(f"apply {key} to {module}")
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down_weight = lora_sd[key]
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up_weight = lora_sd[up_key]
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dim = down_weight.size()[0]
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alpha = lora_sd.get(alpha_key, dim)
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scale = alpha / dim
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# W <- W + U * D
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weight = module.weight
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# print(module_name, down_weight.size(), up_weight.size())
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if len(weight.size()) == 2:
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# linear
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weight = weight + ratio * (up_weight @ down_weight) * scale
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elif down_weight.size()[2:4] == (1, 1):
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# conv2d 1x1
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weight = (
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weight
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+ ratio
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* (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
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* scale
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)
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else:
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# conv2d 3x3
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conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
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# print(conved.size(), weight.size(), module.stride, module.padding)
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weight = weight + ratio * conved * scale
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module.weight = torch.nn.Parameter(weight)
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def merge_lora_models(models, ratios, merge_dtype):
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base_alphas = {} # alpha for merged model
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base_dims = {}
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base_alphas = {} # alpha for merged model
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base_dims = {}
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merged_sd = {}
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for model, ratio in zip(models, ratios):
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print(f"loading: {model}")
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lora_sd = load_state_dict(model, merge_dtype)
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merged_sd = {}
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for model, ratio in zip(models, ratios):
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print(f"loading: {model}")
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lora_sd = load_state_dict(model, merge_dtype)
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# get alpha and dim
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alphas = {} # alpha for current model
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dims = {} # dims for current model
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for key in lora_sd.keys():
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if 'alpha' in key:
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lora_module_name = key[:key.rfind(".alpha")]
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alpha = float(lora_sd[key].detach().numpy())
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alphas[lora_module_name] = alpha
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if lora_module_name not in base_alphas:
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base_alphas[lora_module_name] = alpha
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elif "lora_down" in key:
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lora_module_name = key[:key.rfind(".lora_down")]
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dim = lora_sd[key].size()[0]
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dims[lora_module_name] = dim
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if lora_module_name not in base_dims:
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base_dims[lora_module_name] = dim
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# get alpha and dim
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alphas = {} # alpha for current model
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dims = {} # dims for current model
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for key in lora_sd.keys():
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if "alpha" in key:
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lora_module_name = key[: key.rfind(".alpha")]
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alpha = float(lora_sd[key].detach().numpy())
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alphas[lora_module_name] = alpha
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if lora_module_name not in base_alphas:
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base_alphas[lora_module_name] = alpha
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elif "lora_down" in key:
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lora_module_name = key[: key.rfind(".lora_down")]
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dim = lora_sd[key].size()[0]
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dims[lora_module_name] = dim
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if lora_module_name not in base_dims:
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base_dims[lora_module_name] = dim
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for lora_module_name in dims.keys():
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if lora_module_name not in alphas:
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alpha = dims[lora_module_name]
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alphas[lora_module_name] = alpha
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if lora_module_name not in base_alphas:
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base_alphas[lora_module_name] = alpha
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for lora_module_name in dims.keys():
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if lora_module_name not in alphas:
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alpha = dims[lora_module_name]
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alphas[lora_module_name] = alpha
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if lora_module_name not in base_alphas:
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base_alphas[lora_module_name] = alpha
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print(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}")
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print(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}")
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# merge
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print(f"merging...")
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for key in lora_sd.keys():
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if 'alpha' in key:
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continue
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# merge
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print(f"merging...")
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for key in lora_sd.keys():
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if "alpha" in key:
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continue
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lora_module_name = key[:key.rfind(".lora_")]
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lora_module_name = key[: key.rfind(".lora_")]
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base_alpha = base_alphas[lora_module_name]
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alpha = alphas[lora_module_name]
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base_alpha = base_alphas[lora_module_name]
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alpha = alphas[lora_module_name]
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scale = math.sqrt(alpha / base_alpha) * ratio
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scale = math.sqrt(alpha / base_alpha) * ratio
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if key in merged_sd:
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assert merged_sd[key].size() == lora_sd[key].size(
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), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません"
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merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
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else:
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merged_sd[key] = lora_sd[key] * scale
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if key in merged_sd:
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assert (
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merged_sd[key].size() == lora_sd[key].size()
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), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません"
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merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
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else:
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merged_sd[key] = lora_sd[key] * scale
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# set alpha to sd
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for lora_module_name, alpha in base_alphas.items():
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key = lora_module_name + ".alpha"
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merged_sd[key] = torch.tensor(alpha)
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# set alpha to sd
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for lora_module_name, alpha in base_alphas.items():
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key = lora_module_name + ".alpha"
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merged_sd[key] = torch.tensor(alpha)
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print("merged model")
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print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")
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print("merged model")
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print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")
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return merged_sd
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return merged_sd
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def merge(args):
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assert len(args.models) == len(args.ratios), f"number of models must be equal to number of ratios / モデルの数と重みの数は合わせてください"
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assert len(args.models) == len(args.ratios), f"number of models must be equal to number of ratios / モデルの数と重みの数は合わせてください"
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def str_to_dtype(p):
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if p == 'float':
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return torch.float
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if p == 'fp16':
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return torch.float16
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if p == 'bf16':
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return torch.bfloat16
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return None
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def str_to_dtype(p):
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if p == "float":
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return torch.float
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if p == "fp16":
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return torch.float16
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if p == "bf16":
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return torch.bfloat16
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return None
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merge_dtype = str_to_dtype(args.precision)
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save_dtype = str_to_dtype(args.save_precision)
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if save_dtype is None:
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save_dtype = merge_dtype
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merge_dtype = str_to_dtype(args.precision)
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save_dtype = str_to_dtype(args.save_precision)
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if save_dtype is None:
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save_dtype = merge_dtype
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if args.sd_model is not None:
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print(f"loading SD model: {args.sd_model}")
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if args.sd_model is not None:
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print(f"loading SD model: {args.sd_model}")
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text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.sd_model)
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text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.sd_model)
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merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype)
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merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype)
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print(f"saving SD model to: {args.save_to}")
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model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet,
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args.sd_model, 0, 0, save_dtype, vae)
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else:
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state_dict = merge_lora_models(args.models, args.ratios, merge_dtype)
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print(f"saving SD model to: {args.save_to}")
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model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet, args.sd_model, 0, 0, save_dtype, vae)
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else:
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state_dict = merge_lora_models(args.models, args.ratios, merge_dtype)
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print(f"saving model to: {args.save_to}")
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save_to_file(args.save_to, state_dict, state_dict, save_dtype)
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print(f"saving model to: {args.save_to}")
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save_to_file(args.save_to, state_dict, state_dict, save_dtype)
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def setup_parser() -> argparse.ArgumentParser:
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parser = argparse.ArgumentParser()
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parser.add_argument("--v2", action='store_true',
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help='load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む')
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parser.add_argument("--save_precision", type=str, default=None,
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choices=[None, "float", "fp16", "bf16"], help="precision in saving, same to merging if omitted / 保存時に精度を変更して保存する、省略時はマージ時の精度と同じ")
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parser.add_argument("--precision", type=str, default="float",
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choices=["float", "fp16", "bf16"], help="precision in merging (float is recommended) / マージの計算時の精度（floatを推奨）")
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parser.add_argument("--sd_model", type=str, default=None,
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help="Stable Diffusion model to load: ckpt or safetensors file, merge LoRA models if omitted / 読み込むモデル、ckptまたはsafetensors。省略時はLoRAモデル同士をマージする")
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parser.add_argument("--save_to", type=str, default=None,
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help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors")
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parser.add_argument("--models", type=str, nargs='*',
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help="LoRA models to merge: ckpt or safetensors file / マージするLoRAモデル、ckptまたはsafetensors")
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parser.add_argument("--ratios", type=float, nargs='*',
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help="ratios for each model / それぞれのLoRAモデルの比率")
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parser = argparse.ArgumentParser()
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parser.add_argument("--v2", action="store_true", help="load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む")
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parser.add_argument(
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"--save_precision",
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type=str,
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default=None,
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choices=[None, "float", "fp16", "bf16"],
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help="precision in saving, same to merging if omitted / 保存時に精度を変更して保存する、省略時はマージ時の精度と同じ",
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)
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parser.add_argument(
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"--precision",
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type=str,
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default="float",
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choices=["float", "fp16", "bf16"],
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help="precision in merging (float is recommended) / マージの計算時の精度（floatを推奨）",
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)
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parser.add_argument(
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"--sd_model",
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type=str,
|
||||
default=None,
|
||||
help="Stable Diffusion model to load: ckpt or safetensors file, merge LoRA models if omitted / 読み込むモデル、ckptまたはsafetensors。省略時はLoRAモデル同士をマージする",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--save_to", type=str, default=None, help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--models", type=str, nargs="*", help="LoRA models to merge: ckpt or safetensors file / マージするLoRAモデル、ckptまたはsafetensors"
|
||||
)
|
||||
parser.add_argument("--ratios", type=float, nargs="*", help="ratios for each model / それぞれのLoRAモデルの比率")
|
||||
|
||||
return parser
|
||||
return parser
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = setup_parser()
|
||||
if __name__ == "__main__":
|
||||
parser = setup_parser()
|
||||
|
||||
args = parser.parse_args()
|
||||
merge(args)
|
||||
args = parser.parse_args()
|
||||
merge(args)
|
||||
|
||||
Reference in New Issue
Block a user