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"""
Script to push and load custom PyTorch models to/from the Hugging Face Hub.
"""
import argparse
import torch
from tokenizer.tokenizer_image.vq_model_hf import VQ_models_HF, VQModelHF
from huggingface_hub import hf_hub_download
model2ckpt = {
"GPT-XL": ("vq_ds16_c2i.pt", "c2i_XL_384.pt", 384),
"GPT-B": ("vq_ds16_c2i.pt", "c2i_B_256.pt", 256),
}
def load_model(args):
ckpt_folder = "./"
vq_ckpt, gpt_ckpt, _ = model2ckpt[args.gpt_model]
hf_hub_download(repo_id="FoundationVision/LlamaGen", filename=vq_ckpt, local_dir=ckpt_folder)
hf_hub_download(repo_id="FoundationVision/LlamaGen", filename=gpt_ckpt, local_dir=ckpt_folder)
# create and load model
vq_model = VQ_models_HF[args.vq_model](
codebook_size=args.codebook_size,
codebook_embed_dim=args.codebook_embed_dim)
vq_model.eval()
checkpoint = torch.load(f"{ckpt_folder}{vq_ckpt}", map_location="cpu")
vq_model.load_state_dict(checkpoint["model"])
del checkpoint
print(f"image tokenizer is loaded")
return vq_model
parser = argparse.ArgumentParser()
parser.add_argument("--gpt-model", type=str, default="GPT-XL")
parser.add_argument("--vq-model", type=str, choices=list(VQ_models_HF.keys()), default="VQ-16")
parser.add_argument("--codebook-size", type=int, default=16384, help="codebook size for vector quantization")
parser.add_argument("--codebook-embed-dim", type=int, default=8, help="codebook dimension for vector quantization")
args = parser.parse_args()
# load weights
vq_model = load_model(args)
# push to hub
vq_model.push_to_hub("FoundationVision/vq-ds16-c2i")
# reload
model = VQModelHF.from_pretrained("FoundationVision/vq-ds16-c2i") |