init
Browse files- .gitignore +0 -1
- README.md +11 -0
- SuperResolutionAnimeDiffusion.zip +3 -0
- Waifu2x/model_check_points/CRAN_V2/CARN_model_checkpoint.pt +3 -0
- Waifu2x/model_check_points/CRAN_V2/ReadME.md +34 -0
- app.py +9 -7
- random_examples.zip +3 -0
- test.py +23 -0
.gitignore
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*.state_dict
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*.config
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*.args
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*.zip
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*.gz
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*.bin
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*.result.txt
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*.state_dict
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*.config
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*.args
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*.gz
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*.bin
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*.result.txt
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README.md
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# Super Resolution Anime Diffusion
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---
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title: Anything V3.0
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emoji: π
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colorFrom: gray
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colorTo: yellow
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sdk: gradio
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sdk_version: 3.10.1
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app_file: app.py
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pinned: false
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---
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# Super Resolution Anime Diffusion
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SuperResolutionAnimeDiffusion.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:ef0a17ab5723e783a679cab83a05411ab09bb04886a629a5181bb18df0175c76
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size 256633269
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Waifu2x/model_check_points/CRAN_V2/CARN_model_checkpoint.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:3a0ef4fdea5b0b2a91b05b7a39fe63df3e27e1d18df477065e7f268a7feaaad2
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size 4329165
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Waifu2x/model_check_points/CRAN_V2/ReadME.md
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# Resume & Use Model Check Points
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This folder contains check points for models and their weights. They are generated from [PyTorch's pickle](https://pytorch.org/docs/master/notes/serialization.html).
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Model specifications are in each folder's ReadME.
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Pickle names with "model" contain the entire models, and they can be used as an freeze module by calling the "forward_checkpoint" function to generate images.
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Example:
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```python
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import torch
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# No need to reconstruct the model
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model = torch.load("./DCSCN/DCSCN_model_387epos_L12_noise_1.pt")
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x = torch.randn((1,3,10,10)), torch.randn((1,3,20,20))
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out = model.forward_checkpoint(a)
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```
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Pickle names with "weights" are model weights, and they are named dictionaries.
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Example:
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```python
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model = DCSCN(*) # the setting must be the same to load check points weights.
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model.load_state_dict(torch.load("./DCSCN/DCSCN_weights_387epos_L12_noise_1.pt"))
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# then you can resume the model training
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```
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Model check poins in Upconv_7 and vgg_7 are from [waifu2x's repo](https://github.com/nagadomi/waifu2x/tree/master/models). To load weights into a model, please use ```load_pre_train_weights``` function.
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Example:
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```python
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model = UpConv_7()
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model.load_pre_train_weights(json_file=...)
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# then the model is ready to use
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```
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app.py
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import random
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import zipfile
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import findfile
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-
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for z_file in findfile.find_cwd_files(and_key=['.zip'],
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exclude_key=['.ignore', 'git', 'SuperResolutionAnimeDiffusion'],
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recursive=1):
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with zipfile.ZipFile(z_file, 'r') as zip_ref:
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zip_ref.extractall()
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import PIL.Image
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import autocuda
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from pyabsa.utils.pyabsa_utils import fprint
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from diffusers import (
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AutoencoderKL,
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UNet2DConditionModel,
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import random
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import zipfile
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import findfile
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import PIL.Image
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import autocuda
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from pyabsa.utils.pyabsa_utils import fprint
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# for z_file in findfile.find_cwd_files(and_key=['.zip'],
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# exclude_key=['.ignore', 'git', 'SuperResolutionAnimeDiffusion'],
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# recursive=10):
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# fprint(f"Extracting {z_file}...")
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# with zipfile.ZipFile(z_file, 'r') as zip_ref:
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# zip_ref.extractall(os.path.dirname(z_file))
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os.system('unzip random_examples.zip -d random_examples')
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from diffusers import (
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AutoencoderKL,
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UNet2DConditionModel,
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random_examples.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:0d38130425c106b715732609413675a33cd3eb551fe7eb7789ca87b5210d9f55
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size 8051179
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test.py
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# -*- coding: utf-8 -*-
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# file: test.py
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# time: 23:21 2023/1/27
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# author: yangheng <hy345@exeter.ac.uk>
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# github: https://github.com/yangheng95
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# huggingface: https://huggingface.co/yangheng
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# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en
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# Copyright (C) 2021. All Rights Reserved.
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def fixBadZipfile(zipFile):
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f = open(zipFile, 'r+b')
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data = f.read()
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pos = data.find('\x50\x4b\x05\x06') # End of central directory signature
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if (pos > 0):
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f.seek(pos + 22) # size of 'ZIP end of central directory record'
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f.truncate()
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f.close()
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else:
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pass
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if __name__ == '__main__':
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fixBadZipfile('RealESRGANv030/weights/RealESRGAN_x4plus_anime_6B.zip')
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