48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
from __future__ import annotations
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import math
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from typing import Sequence
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import numpy as np
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import torch
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from comfy import model_management
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from PIL import Image as PILImage
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def pilimgbatch_to_torch(
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imgbatch: Sequence[PILImage, ...] | torch.Tensor,
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) -> torch.Tensor:
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if isinstance(imgbatch, torch.Tensor):
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print("pibtt: skip", imgbatch.shape, imgbatch.min(), imgbatch.max())
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return imgbatch
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npi = np.stack(
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tuple(np.array(i).astype(np.float32) / 255.0 for i in imgbatch),
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axis=0,
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)
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return torch.from_numpy(npi)
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return torch.from_numpy(npi.transpose(0, 3, 1, 2))
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def torch_to_pilimgbatch(t: torch.Tensor) -> tuple[PILImage, ...]:
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return tuple(
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PILImage.fromarray(
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np.clip((255.0 * i).cpu().numpy(), 0, 255).astype(np.uint8),
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)
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for i in t
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)
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def ensure_model(model):
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mp = model.inner_model.model_patcher
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if model_management.LoadedModel(mp) in model_management.current_loaded_models:
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return
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model_management.load_models_gpu((mp,))
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def fallback(val, default, *, exclude=None, default_is_fun=False):
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return val if val is not exclude else (default() if default_is_fun else default)
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def scale_dim(n, factor=1.0, *, increment=64) -> int:
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return math.ceil((n * factor) / increment) * increment
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