VictorMylle/Kohya-ss-sd-scripts
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Merge branch 'sd3' into sd3_5_support

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Kohya S
2024-10-26 22:03:41 +09:00
parent 014064fd81 8549669f89
commit 150579db32
4 changed files with 32 additions and 21 deletions
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15
networks/resize_lora.py
View File

@@ -39,12 +39,7 @@ def load_state_dict(file_name, dtype):
return sd, metadata
def save_to_file(file_name, state_dict, dtype, metadata):
if dtype is not None:
for key in list(state_dict.keys()):
if type(state_dict[key]) == torch.Tensor:
state_dict[key] = state_dict[key].to(dtype)
def save_to_file(file_name, state_dict, metadata):
if model_util.is_safetensors(file_name):
save_file(state_dict, file_name, metadata)
else:
@@ -349,12 +344,18 @@ def resize(args):
metadata["ss_network_dim"] = "Dynamic"
metadata["ss_network_alpha"] = "Dynamic"
# cast to save_dtype before calculating hashes
for key in list(state_dict.keys()):
value = state_dict[key]
if type(value) == torch.Tensor and value.dtype.is_floating_point and value.dtype != save_dtype:
state_dict[key] = value.to(save_dtype)
model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
metadata["sshs_model_hash"] = model_hash
metadata["sshs_legacy_hash"] = legacy_hash
logger.info(f"saving model to: {args.save_to}")
save_to_file(args.save_to, state_dict, save_dtype, metadata)
save_to_file(args.save_to, state_dict, metadata)
def setup_parser() -> argparse.ArgumentParser:

15
networks/sdxl_merge_lora.py
View File

@@ -35,12 +35,7 @@ def load_state_dict(file_name, dtype):
return sd, metadata
def save_to_file(file_name, model, state_dict, dtype, metadata):
if dtype is not None:
for key in list(state_dict.keys()):
if type(state_dict[key]) == torch.Tensor:
state_dict[key] = state_dict[key].to(dtype)
def save_to_file(file_name, model, metadata):
if os.path.splitext(file_name)[1] == ".safetensors":
save_file(model, file_name, metadata=metadata)
else:
@@ -430,6 +425,12 @@ def merge(args):
else:
state_dict, metadata = merge_lora_models(args.models, args.ratios, args.lbws, merge_dtype, args.concat, args.shuffle)
# cast to save_dtype before calculating hashes
for key in list(state_dict.keys()):
value = state_dict[key]
if type(value) == torch.Tensor and value.dtype.is_floating_point and value.dtype != save_dtype:
state_dict[key] = value.to(save_dtype)
logger.info(f"calculating hashes and creating metadata...")
model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
@@ -445,7 +446,7 @@ def merge(args):
metadata.update(sai_metadata)
logger.info(f"saving model to: {args.save_to}")
save_to_file(args.save_to, state_dict, state_dict, save_dtype, metadata)
save_to_file(args.save_to, state_dict, metadata)
def setup_parser() -> argparse.ArgumentParser:

15
networks/svd_merge_lora.py
View File

@@ -216,12 +216,7 @@ def load_state_dict(file_name, dtype):
return sd, metadata
def save_to_file(file_name, state_dict, dtype, metadata):
if dtype is not None:
for key in list(state_dict.keys()):
if type(state_dict[key]) == torch.Tensor:
state_dict[key] = state_dict[key].to(dtype)
def save_to_file(file_name, state_dict, metadata):
if os.path.splitext(file_name)[1] == ".safetensors":
save_file(state_dict, file_name, metadata=metadata)
else:
@@ -430,6 +425,12 @@ def merge(args):
args.models, args.ratios, args.lbws, args.new_rank, new_conv_rank, args.device, merge_dtype
)
# cast to save_dtype before calculating hashes
for key in list(state_dict.keys()):
value = state_dict[key]
if type(value) == torch.Tensor and value.dtype.is_floating_point and value.dtype != save_dtype:
state_dict[key] = value.to(save_dtype)
logger.info(f"calculating hashes and creating metadata...")
model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
@@ -451,7 +452,7 @@ def merge(args):
metadata.update(sai_metadata)
logger.info(f"saving model to: {args.save_to}")
save_to_file(args.save_to, state_dict, save_dtype, metadata)
save_to_file(args.save_to, state_dict, metadata)
def setup_parser() -> argparse.ArgumentParser: