Skip to content

Support revision in Flax text-to-image training #2567

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Mar 7, 2023
Merged

Support revision in Flax text-to-image training #2567

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
17 changes: 13 additions & 4 deletions examples/text_to_image/train_text_to_image_flax.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -48,6 +48,13 @@ def parse_args():
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--dataset_name",
type=str,
Expand Down Expand Up @@ -386,15 +393,17 @@ def collate_fn(examples):
weight_dtype = jnp.bfloat16

# Load models and create wrapper for stable diffusion
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
tokenizer = CLIPTokenizer.from_pretrained(
args.pretrained_model_name_or_path, revision=args.revision, subfolder="tokenizer"
)
text_encoder = FlaxCLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", dtype=weight_dtype
args.pretrained_model_name_or_path, revision=args.revision, subfolder="text_encoder", dtype=weight_dtype
)
vae, vae_params = FlaxAutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path, subfolder="vae", dtype=weight_dtype
args.pretrained_model_name_or_path, revision=args.revision, subfolder="vae", dtype=weight_dtype
)
unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", dtype=weight_dtype
args.pretrained_model_name_or_path, revision=args.revision, subfolder="unet", dtype=weight_dtype
)

# Optimization
Expand Down