VictorMylle/Kohya-ss-sd-scripts
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Replace print with logger if they are logs (#905)

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* Add get_my_logger()

* Use logger instead of print

* Fix log level

* Removed line-breaks for readability

* Use setup_logging()

* Add rich to requirements.txt

* Make simple

* Use logger instead of print

---------

Co-authored-by: Kohya S <52813779+kohya-ss@users.noreply.github.com>
This commit is contained in:
Yuta Hayashibe
2024-02-04 16:14:34 +07:00
committed by GitHub
parent 7f948db158
commit 5f6bf29e52
62 changed files with 1195 additions and 961 deletions
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37
networks/merge_lora.py
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@@ -7,7 +7,10 @@ from safetensors.torch import load_file, save_file
from library import sai_model_spec, train_util
import library.model_util as model_util
import lora
from library.utils import setup_logging
setup_logging()
import logging
logger = logging.getLogger(__name__)
def load_state_dict(file_name, dtype):
if os.path.splitext(file_name)[1] == ".safetensors":
@@ -61,10 +64,10 @@ def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
name_to_module[lora_name] = child_module
for model, ratio in zip(models, ratios):
print(f"loading: {model}")
logger.info(f"loading: {model}")
lora_sd, _ = load_state_dict(model, merge_dtype)
print(f"merging...")
logger.info(f"merging...")
for key in lora_sd.keys():
if "lora_down" in key:
up_key = key.replace("lora_down", "lora_up")
@@ -73,10 +76,10 @@ def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
# find original module for this lora
module_name = ".".join(key.split(".")[:-2]) # remove trailing ".lora_down.weight"
if module_name not in name_to_module:
print(f"no module found for LoRA weight: {key}")
logger.info(f"no module found for LoRA weight: {key}")
continue
module = name_to_module[module_name]
# print(f"apply {key} to {module}")
# logger.info(f"apply {key} to {module}")
down_weight = lora_sd[key]
up_weight = lora_sd[up_key]
@@ -104,7 +107,7 @@ def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
else:
# conv2d 3x3
conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
# print(conved.size(), weight.size(), module.stride, module.padding)
# logger.info(conved.size(), weight.size(), module.stride, module.padding)
weight = weight + ratio * conved * scale
module.weight = torch.nn.Parameter(weight)
@@ -118,7 +121,7 @@ def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False):
v2 = None
base_model = None
for model, ratio in zip(models, ratios):
print(f"loading: {model}")
logger.info(f"loading: {model}")
lora_sd, lora_metadata = load_state_dict(model, merge_dtype)
if lora_metadata is not None:
@@ -151,10 +154,10 @@ def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False):
if lora_module_name not in base_alphas:
base_alphas[lora_module_name] = alpha
print(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}")
logger.info(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}")
# merge
print(f"merging...")
logger.info(f"merging...")
for key in lora_sd.keys():
if "alpha" in key:
continue
@@ -196,8 +199,8 @@ def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False):
merged_sd[key_down] = merged_sd[key_down][perm]
merged_sd[key_up] = merged_sd[key_up][:,perm]
print("merged model")
print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")
logger.info("merged model")
logger.info(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")
# check all dims are same
dims_list = list(set(base_dims.values()))
@@ -239,7 +242,7 @@ def merge(args):
save_dtype = merge_dtype
if args.sd_model is not None:
print(f"loading SD model: {args.sd_model}")
logger.info(f"loading SD model: {args.sd_model}")
text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.sd_model)
@@ -264,18 +267,18 @@ def merge(args):
)
if args.v2:
# TODO read sai modelspec
print(
logger.warning(
"Cannot determine if model is for v-prediction, so save metadata as v-prediction / modelがv-prediction用か否か不明なため、仮にv-prediction用としてmetadataを保存します"
)
print(f"saving SD model to: {args.save_to}")
logger.info(f"saving SD model to: {args.save_to}")
model_util.save_stable_diffusion_checkpoint(
args.v2, args.save_to, text_encoder, unet, args.sd_model, 0, 0, sai_metadata, save_dtype, vae
)
else:
state_dict, metadata, v2 = merge_lora_models(args.models, args.ratios, merge_dtype, args.concat, args.shuffle)
print(f"calculating hashes and creating metadata...")
logger.info(f"calculating hashes and creating metadata...")
model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
metadata["sshs_model_hash"] = model_hash
@@ -289,12 +292,12 @@ def merge(args):
)
if v2:
# TODO read sai modelspec
print(
logger.warning(
"Cannot determine if LoRA is for v-prediction, so save metadata as v-prediction / LoRAがv-prediction用か否か不明なため、仮にv-prediction用としてmetadataを保存します"
)
metadata.update(sai_metadata)
print(f"saving model to: {args.save_to}")
logger.info(f"saving model to: {args.save_to}")
save_to_file(args.save_to, state_dict, state_dict, save_dtype, metadata)