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allow empty pretrains

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SayanoAI
2024-10-02 11:48:41 -04:00
parent 34a79dc9cd
commit ef94c1e722
1 changed files with 9 additions and 7 deletions
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custom_nodes/rvc_nodes.py
+9 -7
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@@ -393,13 +393,13 @@ class RVCTrainModelNode:
name_filters=["G"],
filter_func=lambda x: os.stat(x).st_size/1024**2<600, #generators are 400+mb
format_func=lambda x: f"pretrained_v2/{os.path.basename(x)}")
PRETRAINED_G = list(set(PRETRAINED_G)) # dedupe
PRETRAINED_G = [" "]+list(set(PRETRAINED_G)) # dedupe
PRETRAINED_D = PRETRAINED_MODELS_D + get_filenames(
root=BASE_MODELS_DIR,folder="pretrained_v2",
name_filters=["D"],
filter_func=lambda x: os.stat(x).st_size/1024**2>600, #discriminators are 800+mb
format_func=lambda x: f"pretrained_v2/{os.path.basename(x)}")
PRETRAINED_D = list(set(PRETRAINED_D)) # dedupe
PRETRAINED_D = [" "]+list(set(PRETRAINED_D)) # dedupe
return {
"required": {
@@ -408,7 +408,7 @@ class RVCTrainModelNode:
},
"optional": dict(
gpu=(DEVICES, {"default": DEVICES[0]}),
total_epoch=("INT",dict(default=100,min=10,max=1000,step=10)),
total_epoch=("INT",dict(default=100,min=1,max=1000,step=1)),
save_every_epoch=("INT",dict(default=0,min=0,max=100)),
pretrained_G=(PRETRAINED_G,{"default": PRETRAINED_G[0]}),
pretrained_D=(PRETRAINED_D,{"default": PRETRAINED_D[0]}),
@@ -439,8 +439,8 @@ class RVCTrainModelNode:
gpu="0",
total_epoch=100,
save_every_epoch=0,
pretrained_G="",
pretrained_D="",
pretrained_G=" ",
pretrained_D=" ",
if_save_latest=True,
if_cache_gpu=True,
if_save_every_weights=False,
@@ -451,6 +451,8 @@ class RVCTrainModelNode:
log_every_epoch=1.,
num_workers=1):
pretrained_G = pretrained_G.strip()
pretrained_D = pretrained_D.strip()
sample_rate = rvc_dataset_pipe["sample_rate"]
name = rvc_dataset_pipe["name"]
dataset_dir = rvc_dataset_pipe["dataset_dir"]
@@ -468,8 +470,8 @@ class RVCTrainModelNode:
hparams.save_every_epoch = save_every_epoch
hparams.name = name
hparams.total_epoch = total_epoch
hparams.pretrainG = model_downloader(pretrained_G)
hparams.pretrainD = model_downloader(pretrained_D)
hparams.pretrainG = model_downloader(pretrained_G) if pretrained_G else ""
hparams.pretrainD = model_downloader(pretrained_D) if pretrained_D else ""
hparams.version = "v2"
hparams.gpus = gpu
hparams.sample_rate = sample_rate