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import torch
from PIL import Image
from pathlib import Path
from omegaconf import OmegaConf
from scripts.demo.streamlit_helpers import (
load_model_from_config,
get_sampler,
get_batch,
do_sample,
)
def load_config_and_model(ckpt: Path):
if (ckpt.parent.parent / "configs").exists():
config_path = list((ckpt.parent.parent / "configs").glob("*-project.yaml"))[0]
else:
config_path = list(
(ckpt.parent.parent.parent / "configs").glob("*-project.yaml")
)[0]
config = OmegaConf.load(config_path)
model, msg = load_model_from_config(config, ckpt)
return config, model
def load_sampler(sampler_cfg):
return get_sampler(**sampler_cfg)
def load_batch():
pass
class DiffusionEngine:
def __init__(self, cfg) -> None:
self.cfg = cfg
def sample(self):
pass
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