Mirrors
/
diffusers
mirror of https://github.com/ShivamShrirao/diffusers.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

[dreambooth] low precision guard (#1916) 

* [dreambooth] low precision guard

* fix

* add docs to cli args

* Update examples/dreambooth/train_dreambooth.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* style

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
This commit is contained in:
Will Berman 2023-01-05 07:54:56 -08:00 committed by GitHub
parent 7101c7316b
commit 247b5feea1
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 20 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

22
examples/dreambooth/train_dreambooth.py
View File

@ -70,7 +70,10 @@ def parse_args(input_args=None):
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
help=(
"Revision of pretrained model identifier from huggingface.co/models. Trainable model components should be"
" float32 precision."
),
)
parser.add_argument(
"--tokenizer_name",
@ -140,7 +143,11 @@ def parse_args(input_args=None):
parser.add_argument(
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
)
parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
parser.add_argument(
"--train_text_encoder",
action="store_true",
help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
@ -671,6 +678,17 @@ def main(args):
if not args.train_text_encoder:
text_encoder.to(accelerator.device, dtype=weight_dtype)
low_precision_error_string = (
"Please make sure to always have all model weights in full float32 precision when starting training - even if"
" doing mixed precision training. copy of the weights should still be float32."
)
if unet.dtype != torch.float32:
raise ValueError(f"Unet loaded as datatype {unet.dtype}. {low_precision_error_string}")
if args.train_text_encoder and text_encoder.dtype != torch.float32:
raise ValueError(f"Text encoder loaded as datatype {text_encoder.dtype}. {low_precision_error_string}")
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps: