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import json
import numpy as np
import torch
from huggan.pytorch.lightweight_gan.lightweight_gan import LightweightGAN
from huggingface_hub import hf_hub_download
CONFIG_NAME = "config.json"
revision = None
cache_dir = None
force_download = False
proxies = None
resume_download = False
local_files_only = False
token = None
def carga_modelo(nombre_modelo="ceyda/butterfly_cropped_uniq1K_512", model_version=None):
# Load the config
config_file = hf_hub_download(
repo_id=str(nombre_modelo),
filename=CONFIG_NAME,
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
token=token,
local_files_only=local_files_only,
)
with open(config_file, "r", encoding="utf-8") as f:
config = json.load(f)
gan = LightweightGAN(latent_dim=256, image_size=512)
gan = gan._from_pretrained(
model_id=str(nombre_modelo),
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
token=token,
use_auth_token=False,
config=config, # usually in **model_kwargs
)
gan.eval()
return gan
def genera(gan, batch_size=1):
with torch.no_grad():
ims = gan.G(torch.randn(batch_size, gan.latent_dim)).clamp(0.0, 1.0) * 255
ims = ims.permute(0,2,3,1).detach().cpu().numpy().astype(np.uint8)
return ims |