|
|
""" Utiliy functions to load pre-trained models more easily """
|
|
|
import os
|
|
|
import pkg_resources
|
|
|
from omegaconf import OmegaConf
|
|
|
|
|
|
import torch
|
|
|
from huggingface_hub import hf_hub_download
|
|
|
|
|
|
from imagedream.ldm.util import instantiate_from_config
|
|
|
|
|
|
|
|
|
PRETRAINED_MODELS = {
|
|
|
"sd-v2.1-base-4view-ipmv": {
|
|
|
"config": "sd_v2_base_ipmv.yaml",
|
|
|
"repo_id": "Peng-Wang/ImageDream",
|
|
|
"filename": "sd-v2.1-base-4view-ipmv.pt",
|
|
|
},
|
|
|
"sd-v2.1-base-4view-ipmv-local": {
|
|
|
"config": "sd_v2_base_ipmv_local.yaml",
|
|
|
"repo_id": "Peng-Wang/ImageDream",
|
|
|
"filename": "sd-v2.1-base-4view-ipmv-local.pt",
|
|
|
},
|
|
|
}
|
|
|
|
|
|
|
|
|
def get_config_file(config_path):
|
|
|
cfg_file = pkg_resources.resource_filename(
|
|
|
"imagedream", os.path.join("configs", config_path)
|
|
|
)
|
|
|
if not os.path.exists(cfg_file):
|
|
|
raise RuntimeError(f"Config {config_path} not available!")
|
|
|
return cfg_file
|
|
|
|
|
|
|
|
|
def build_model(model_name, config_path=None, ckpt_path=None, cache_dir=None):
|
|
|
if (config_path is not None) and (ckpt_path is not None):
|
|
|
config = OmegaConf.load(config_path)
|
|
|
model = instantiate_from_config(config.model)
|
|
|
model.load_state_dict(torch.load(ckpt_path, map_location="cpu"), strict=False)
|
|
|
return model
|
|
|
|
|
|
if not model_name in PRETRAINED_MODELS:
|
|
|
raise RuntimeError(
|
|
|
f"Model name {model_name} is not a pre-trained model. Available models are:\n- "
|
|
|
+ "\n- ".join(PRETRAINED_MODELS.keys())
|
|
|
)
|
|
|
model_info = PRETRAINED_MODELS[model_name]
|
|
|
|
|
|
|
|
|
print(f"Loading model from config: {model_info['config']}")
|
|
|
config_file = get_config_file(model_info["config"])
|
|
|
config = OmegaConf.load(config_file)
|
|
|
model = instantiate_from_config(config.model)
|
|
|
|
|
|
|
|
|
if not ckpt_path:
|
|
|
ckpt_path = hf_hub_download(
|
|
|
repo_id=model_info["repo_id"],
|
|
|
filename=model_info["filename"],
|
|
|
cache_dir=cache_dir,
|
|
|
)
|
|
|
print(f"Loading model from cache file: {ckpt_path}")
|
|
|
model.load_state_dict(torch.load(ckpt_path, map_location="cpu"), strict=False)
|
|
|
return model
|
|
|
|
