| | import hashlib
|
| | import torch
|
| |
|
| | from comfy.cli_args import args
|
| |
|
| | from PIL import ImageFile, UnidentifiedImageError
|
| |
|
| | def conditioning_set_values(conditioning, values={}):
|
| | c = []
|
| | for t in conditioning:
|
| | n = [t[0], t[1].copy()]
|
| | for k in values:
|
| | n[1][k] = values[k]
|
| | c.append(n)
|
| |
|
| | return c
|
| |
|
| | def pillow(fn, arg):
|
| | prev_value = None
|
| | try:
|
| | x = fn(arg)
|
| | except (OSError, UnidentifiedImageError, ValueError):
|
| | prev_value = ImageFile.LOAD_TRUNCATED_IMAGES
|
| | ImageFile.LOAD_TRUNCATED_IMAGES = True
|
| | x = fn(arg)
|
| | finally:
|
| | if prev_value is not None:
|
| | ImageFile.LOAD_TRUNCATED_IMAGES = prev_value
|
| | return x
|
| |
|
| | def hasher():
|
| | hashfuncs = {
|
| | "md5": hashlib.md5,
|
| | "sha1": hashlib.sha1,
|
| | "sha256": hashlib.sha256,
|
| | "sha512": hashlib.sha512
|
| | }
|
| | return hashfuncs[args.default_hashing_function]
|
| |
|
| | def string_to_torch_dtype(string):
|
| | if string == "fp32":
|
| | return torch.float32
|
| | if string == "fp16":
|
| | return torch.float16
|
| | if string == "bf16":
|
| | return torch.bfloat16
|
| |
|
| | def image_alpha_fix(destination, source):
|
| | if destination.shape[-1] < source.shape[-1]:
|
| | source = source[...,:destination.shape[-1]]
|
| | elif destination.shape[-1] > source.shape[-1]:
|
| | destination = torch.nn.functional.pad(destination, (0, 1))
|
| | destination[..., -1] = 1.0
|
| | return destination, source
|
| |
|
