| import hashlib
|
| import torch
|
|
|
| from comfy.cli_args import args
|
|
|
| from PIL import ImageFile, UnidentifiedImageError
|
|
|
| def conditioning_set_values(conditioning, values={}, append=False):
|
| c = []
|
| for t in conditioning:
|
| n = [t[0], t[1].copy()]
|
| for k in values:
|
| val = values[k]
|
| if append:
|
| old_val = n[1].get(k, None)
|
| if old_val is not None:
|
| val = old_val + val
|
|
|
| n[1][k] = val
|
| 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
|
|
|
