hon 3.12 scribt that upscales all images with waifu2x. Use spandrel to implement waifu2x. The programm should go through D:\Bilder\Python\1. Input and its subfolders and save all processed images in D:\Bilder\Python\2. Output keeping the same folder structure. Use pillow and numpy. Model Location: D:\Bilder\Python\0. Code\Requirements\Models\Scale2x_Noise0.pth Information about spandrel:
Project description
Spandrel

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Spandrel is a library for loading and running pre-trained PyTorch models. It automatically detects the model architecture and hyperparameters from model files, and provides a unified interface for running models.

After seeing many projects extract out chaiNNer's model support into their own projects, I decided to create this PyPi package for the architecture support and model loading functionality. I'm also hoping that by having a central package anyone can use, the community will be encouraged to help add support for more models.

This package does not yet have easy inference code, but porting that code is planned as well.
Installation

Spandrel is available through pip:

pip install spandrel

Basic Usage

While Spandrel supports different kinds of models, this is how you would run a super resolution model (e.g. ESRGAN, SwinIR, HAT, etc.):

from spandrel import ImageModelDescriptor, ModelLoader
import torch
load a model from disk

model = ModelLoader().load_from_file(r"path/to/model.pth")
make sure it's an image to image model

assert isinstance(model, ImageModelDescriptor)
send it to the GPU and put it in inference mode

model.cuda().eval()
use the model

def process(image: torch.Tensor) -> torch.Tensor:
with torch.no_grad():
return model(image)

Note that model is a ModelDescriptor object, which is a wrapper around the actual PyTorch model. This wrapper provides a unified interface for running models, and also contains metadata about the model. See ImageModelDescriptor for more details about the metadata contained and how to call the model.

NOTE: ImageModelDescriptor will NOT convert an image to a tensor for you. It is purely making the forward passes of these models more convenient to use, since the actual forward passes are not always as simple as image in/image out.

More architectures

If you are working on a non-commercial open-source project or a private project, you should use spandrel and spandrel_extra_arches to get everything spandrel has to offer. The spandrel package only contains architectures with permissive and public domain licenses (MIT, Apache 2.0, public domain), so it is fit for every use case. Architectures with restrictive licenses (e.g. non-commercial) are implemented in the spandrel_extra_arches package.

import spandrel
import spandrel_extra_arches
add extra architectures before ModelLoader is used

spandrel_extra_arches.install()
load a model from disk

model = spandrel.ModelLoader().load_from_file(r"path/to/model.pth")

... # use model

Supported File Types

Spandrel mainly supports loading .pth files for all supported architectures. This is what you will typically find from official repos and community trained models. However, Spandrel also supports loading TorchScript traced models (.pt), certain types of .ckpt files, and .safetensors files for any supported architecture saved in one of these formats.
Model Architecture Support

NOTE: By its very nature, Spandrel will never be able to support every model architecture. The goal is just to support as many as is realistically possible.

Spandrel currently supports a limited amount of network architectures. If the architecture you need is not supported, feel free to request it or try adding it.
Single Image Super Resolution

ESRGAN (RRDBNet)
    This includes regular ESRGAN, ESRGAN+, "new-arch ESRGAN" (RealSR, BSRGAN), and Real-ESRGAN
    Models: Community ESRGAN | ESRGAN+ | BSRGAN | RealSR | Real-ESRGAN
Real-ESRGAN Compact (SRVGGNet) | Models
Swift-SRGAN | Models
SwinIR | Models
Swin2SR | Models
HAT | Models
Omni-SR | Models
SRFormer (+) | Models
DAT | Models
FeMaSR (+) | Models
GRL | Models
DITN | Models
MM-RealSR | Models
SPAN | Models
Real-CUGAN | Models, Pro Models
CRAFT | Models
SAFMN | Models, JPEG model
RGT | RGT Models, RGT-S Models
DCTLSA | Models
ATD | Models
AdaCode | Models
DRCT
PLKSR and RealPLKSR | Models
SeemoRe | Models
MoSR | Models

Face Restoration

GFPGAN | 1.2, 1.3, 1.4
RestoreFormer | Model
CodeFormer (+) | Model

Inpainting

LaMa | Model
MAT (+) | Model

Denoising

SCUNet | GAN Model | PSNR Model
Uformer | Denoise SIDD Model | Deblur GoPro Model
KBNet | Models
NAFNet | Models
Restormer (+) | Models
FFTformer | Models
M3SNet (+) | Models
MPRNet (+) | Deblurring, Deraining, Denoising
MIRNet2 (+) | Models (SR not supported)
DnCNN, FDnCNN | Models
DRUNet | Models
IPT | Models

DeJPEG

FBCNN | Models

Colorization

DDColor (+) | Models

Dehazing

MixDehazeNet | Models

Low-light Enhancement

RetinexFormer | Models
HVI-CIDNet | Models

(All architectures marked with a + are only part of spandrel_extra_arches.)
Security

Use .safetensors files for guaranteed security.

As you may know, loading .pth files poses a security risk due to python's pickle module being inherently unsafe and vulnerable to arbitrary code execution (ACE). To mitigate this, Spandrel only allows deserializing certain types of data. This helps to improve security, but it still doesn't fully solve the issue of ACE.
Used By

Here are some cool projects that use Spandrel:

AUTOMATIC1111's SD WebUI
ComfyUI
InvokeAI
chaiNNer
imaginAIry
dgenerate

License

This repo is bounded by the MIT license. However, the code of implemented architectures (everything inside an __arch/ directory) is bound by their original respective licenses (which are included in their respective __arch/ directories).