| 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 |
|
|