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Allow textual_inversion_flax script to use save_steps and revision flag #2075

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Merged
merged 5 commits into from Mar 6, 2023
Merged

Allow textual_inversion_flax script to use save_steps and revision flag #2075

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bc52fbc
Update textual_inversion_flax.py
Jan 23, 2023
62529b2
Update textual_inversion_flax.py
Jan 23, 2023
b163481
Typo
Jan 23, 2023
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Format source
Jan 23, 2023
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Merge branch 'main' into patch-1
Jan 25, 2023
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25 changes: 22 additions & 3 deletions examples/textual_inversion/textual_inversion_flax.py
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Original file line number Diff line number Diff line change
Expand Up @@ -121,6 +121,12 @@ def parse_args():
default=5000,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--save_steps",
type=int,
default=500,
help="Save learned_embeds.bin every X updates steps.",
)
parser.add_argument(
"--learning_rate",
type=float,
Expand All @@ -136,6 +142,13 @@ def parse_args():
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
Expand Down Expand Up @@ -420,9 +433,9 @@ def main():
placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)

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

# Create sampling rng
rng = jax.random.PRNGKey(args.seed)
Expand Down Expand Up @@ -619,6 +632,12 @@ def compute_loss(params):

if global_step >= args.max_train_steps:
break
if global_step % args.save_steps == 0:
learned_embeds = get_params_to_save(state.params)["text_model"]["embeddings"]["token_embedding"]["embedding"][
placeholder_token_id
]
learned_embeds_dict = {args.placeholder_token: learned_embeds}
jnp.save(os.path.join(args.output_dir, "learned_embeds-"+str(global_step)+".npy"), learned_embeds_dict)

train_metric = jax_utils.unreplicate(train_metric)

Expand Down