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