Spaces:
Running
Running
File size: 2,617 Bytes
891b88f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
import os
from functools import lru_cache
from typing import Dict, Optional
import requests
import torch as th
from filelock import FileLock
from tqdm.auto import tqdm
MODEL_PATHS = {
"base": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/base.pt",
"upsample": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/upsample.pt",
"base-inpaint": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/base_inpaint.pt",
"upsample-inpaint": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/upsample_inpaint.pt",
"clip/image-enc": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/clip_image_enc.pt",
"clip/text-enc": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/clip_text_enc.pt",
}
@lru_cache()
def default_cache_dir() -> str:
return os.path.join(os.path.abspath(os.getcwd()), "glide_model_cache")
def fetch_file_cached(
url: str, progress: bool = True, cache_dir: Optional[str] = None, chunk_size: int = 4096
) -> str:
"""
Download the file at the given URL into a local file and return the path.
If cache_dir is specified, it will be used to download the files.
Otherwise, default_cache_dir() is used.
"""
if cache_dir is None:
cache_dir = default_cache_dir()
os.makedirs(cache_dir, exist_ok=True)
response = requests.get(url, stream=True)
size = int(response.headers.get("content-length", "0"))
local_path = os.path.join(cache_dir, url.split("/")[-1])
with FileLock(local_path + ".lock"):
if os.path.exists(local_path):
return local_path
if progress:
pbar = tqdm(total=size, unit="iB", unit_scale=True)
tmp_path = local_path + ".tmp"
with open(tmp_path, "wb") as f:
for chunk in response.iter_content(chunk_size):
if progress:
pbar.update(len(chunk))
f.write(chunk)
os.rename(tmp_path, local_path)
if progress:
pbar.close()
return local_path
def load_checkpoint(
checkpoint_name: str,
device: th.device,
progress: bool = True,
cache_dir: Optional[str] = None,
chunk_size: int = 4096,
) -> Dict[str, th.Tensor]:
if checkpoint_name not in MODEL_PATHS:
raise ValueError(
f"Unknown checkpoint name {checkpoint_name}. Known names are: {MODEL_PATHS.keys()}."
)
path = fetch_file_cached(
MODEL_PATHS[checkpoint_name], progress=progress, cache_dir=cache_dir, chunk_size=chunk_size
)
return th.load(path, map_location=device)
|