75 lines
2.2 KiB
Python
75 lines
2.2 KiB
Python
import importlib
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import torch
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import yaml
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from omegaconf import OmegaConf
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from taming.models.vqgan import VQModel
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def load_config(config_path, display=False):
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config = OmegaConf.load(config_path)
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if display:
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print(yaml.dump(OmegaConf.to_container(config)))
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return config
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def load_vqgan(device, conf_path=None, ckpt_path=None):
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if conf_path is None:
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conf_path = "./model_checkpoints/vqgan_only.yaml"
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config = load_config(conf_path, display=False)
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model = VQModel(**config.model.params)
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if ckpt_path is None:
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ckpt_path = "./model_checkpoints/vqgan_only.pt"
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sd = torch.load(ckpt_path, map_location=device)
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if ".ckpt" in ckpt_path:
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sd = sd["state_dict"]
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model.load_state_dict(sd, strict=True)
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model.to(device)
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del sd
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return model
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def reconstruct_with_vqgan(x, model):
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z, _, [_, _, indices] = model.encode(x)
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print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}")
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xrec = model.decode(z)
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return xrec
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def get_obj_from_str(string, reload=False):
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module, cls = string.rsplit(".", 1)
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if reload:
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module_imp = importlib.import_module(module)
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importlib.reload(module_imp)
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return getattr(importlib.import_module(module, package=None), cls)
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def instantiate_from_config(config):
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if "target" not in config:
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raise KeyError("Expected key `target` to instantiate.")
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return get_obj_from_str(config["target"])(**config.get("params", {}))
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def load_model_from_config(config, sd, gpu=True, eval_mode=True):
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model = instantiate_from_config(config)
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if sd is not None:
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model.load_state_dict(sd)
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if gpu:
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model.cuda()
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if eval_mode:
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model.eval()
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return {"model": model}
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def load_model(config, ckpt, gpu, eval_mode):
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# load the specified checkpoint
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if ckpt:
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pl_sd = torch.load(ckpt, map_location="cpu")
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global_step = pl_sd["global_step"]
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print(f"loaded model from global step {global_step}.")
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else:
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pl_sd = {"state_dict": None}
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global_step = None
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model = load_model_from_config(config.model, pl_sd["state_dict"], gpu=gpu, eval_mode=eval_mode)["model"]
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return model, global_step
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