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move image tensor normalize under fluxion's utils
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@ -4,7 +4,7 @@ from numpy import array, float32
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from pathlib import Path
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from safetensors import safe_open as _safe_open # type: ignore
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from safetensors.torch import save_file as _save_file # type: ignore
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from torch import norm as _norm, manual_seed as _manual_seed # type: ignore
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from torch import as_tensor, norm as _norm, manual_seed as _manual_seed # type: ignore
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import torch
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from torch.nn.functional import pad as _pad, interpolate as _interpolate # type: ignore
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from torch import Tensor, device as Device, dtype as DType
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@ -34,6 +34,31 @@ def interpolate(x: Tensor, factor: float | torch.Size, mode: str = "nearest") ->
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) # type: ignore
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# Adapted from https://github.com/pytorch/vision/blob/main/torchvision/transforms/_functional_tensor.py
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def normalize(tensor: Tensor, mean: list[float], std: list[float], inplace: bool = False) -> Tensor:
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assert tensor.is_floating_point()
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assert tensor.ndim >= 3
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if not inplace:
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tensor = tensor.clone()
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dtype = tensor.dtype
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mean_tensor = as_tensor(mean, dtype=tensor.dtype, device=tensor.device)
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std_tensor = as_tensor(std, dtype=tensor.dtype, device=tensor.device)
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if (std_tensor == 0).any():
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raise ValueError(f"std evaluated to zero after conversion to {dtype}, leading to division by zero.")
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if mean_tensor.ndim == 1:
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mean_tensor = mean_tensor.view(-1, 1, 1)
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if std_tensor.ndim == 1:
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std_tensor = std_tensor.view(-1, 1, 1)
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return tensor.sub_(mean_tensor).div_(std_tensor)
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def image_to_tensor(image: Image.Image, device: Device | str | None = None, dtype: DType | None = None) -> Tensor:
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return torch.tensor(array(image).astype(float32).transpose(2, 0, 1) / 255.0, device=device, dtype=dtype).unsqueeze(
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0
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@ -2,7 +2,7 @@ from enum import IntEnum
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from functools import partial
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from typing import Generic, TypeVar, Any, Callable, TYPE_CHECKING
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from torch import Tensor, as_tensor, cat, zeros_like, device as Device, dtype as DType
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from torch import Tensor, cat, zeros_like, device as Device, dtype as DType
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from PIL import Image
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from refiners.fluxion.adapters.adapter import Adapter
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@ -10,7 +10,7 @@ from refiners.fluxion.adapters.lora import Lora
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from refiners.foundationals.clip.image_encoder import CLIPImageEncoderH
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from refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock2d
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from refiners.fluxion.layers.attentions import ScaledDotProductAttention
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from refiners.fluxion.utils import image_to_tensor
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from refiners.fluxion.utils import image_to_tensor, normalize
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import refiners.fluxion.layers as fl
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if TYPE_CHECKING:
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@ -228,33 +228,8 @@ class IPAdapter(Generic[T], fl.Chain, Adapter[T]):
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std: list[float] | None = None,
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) -> Tensor:
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# Default mean and std are parameters from https://github.com/openai/CLIP
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return self._normalize(
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return normalize(
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image_to_tensor(image.resize(size), device=self.target.device, dtype=self.target.dtype),
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mean=[0.48145466, 0.4578275, 0.40821073] if mean is None else mean,
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std=[0.26862954, 0.26130258, 0.27577711] if std is None else std,
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)
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# Adapted from https://github.com/pytorch/vision/blob/main/torchvision/transforms/_functional_tensor.py
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@staticmethod
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def _normalize(tensor: Tensor, mean: list[float], std: list[float], inplace: bool = False) -> Tensor:
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assert tensor.is_floating_point()
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assert tensor.ndim >= 3
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if not inplace:
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tensor = tensor.clone()
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dtype = tensor.dtype
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mean_tensor = as_tensor(mean, dtype=tensor.dtype, device=tensor.device)
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std_tensor = as_tensor(std, dtype=tensor.dtype, device=tensor.device)
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if (std_tensor == 0).any():
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raise ValueError(f"std evaluated to zero after conversion to {dtype}, leading to division by zero.")
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if mean_tensor.ndim == 1:
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mean_tensor = mean_tensor.view(-1, 1, 1)
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if std_tensor.ndim == 1:
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std_tensor = std_tensor.view(-1, 1, 1)
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return tensor.sub_(mean_tensor).div_(std_tensor)
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