init

.gitignore

@@ -10,7 +10,6 @@ integrated_datasets/
 *.state_dict
 *.config
 *.args
-*.zip
 *.gz
 *.bin
 *.result.txt

README.md

@@ -1,3 +1,14 @@
+---
+title: Anything V3.0
+emoji: 🏃
+colorFrom: gray
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.10.1
+app_file: app.py
+pinned: false
+---
+
 # Super Resolution Anime Diffusion 
 
 

SuperResolutionAnimeDiffusion.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ef0a17ab5723e783a679cab83a05411ab09bb04886a629a5181bb18df0175c76
+size 256633269

Waifu2x/model_check_points/CRAN_V2/CARN_model_checkpoint.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3a0ef4fdea5b0b2a91b05b7a39fe63df3e27e1d18df477065e7f268a7feaaad2
+size 4329165

Waifu2x/model_check_points/CRAN_V2/ReadME.md

@@ -0,0 +1,34 @@
+# Resume & Use Model Check Points 
+
+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). 
+
+Model specifications are in each folder's ReadME.  
+
+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. 
+
+Example:
+```python
+import torch
+# No need to reconstruct the model 
+model = torch.load("./DCSCN/DCSCN_model_387epos_L12_noise_1.pt")
+x = torch.randn((1,3,10,10)), torch.randn((1,3,20,20))
+out = model.forward_checkpoint(a)
+```
+
+Pickle names with "weights" are model weights, and they are named dictionaries. 
+
+Example:
+```python
+model = DCSCN(*)    # the setting must be the same to load check points weights. 
+model.load_state_dict(torch.load("./DCSCN/DCSCN_weights_387epos_L12_noise_1.pt"))
+# then you can resume the model training 
+```
+
+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. 
+
+Example: 
+```python
+model = UpConv_7()
+model.load_pre_train_weights(json_file=...)
+# then the model is ready to use 
+```

app.py

@@ -2,17 +2,19 @@ import os
 import random
 import zipfile
 import findfile
-
-for z_file in findfile.find_cwd_files(and_key=['.zip'],
-                                      exclude_key=['.ignore', 'git', 'SuperResolutionAnimeDiffusion'],
-                                      recursive=1):
-    with zipfile.ZipFile(z_file, 'r') as zip_ref:
-        zip_ref.extractall()
-
 import PIL.Image
 import autocuda
 from pyabsa.utils.pyabsa_utils import fprint
 
+# for z_file in findfile.find_cwd_files(and_key=['.zip'],
+#                                       exclude_key=['.ignore', 'git', 'SuperResolutionAnimeDiffusion'],
+#                                       recursive=10):
+#     fprint(f"Extracting {z_file}...")
+#     with zipfile.ZipFile(z_file, 'r') as zip_ref:
+#         zip_ref.extractall(os.path.dirname(z_file))
+
+os.system('unzip random_examples.zip -d random_examples')
+
 from diffusers import (
     AutoencoderKL,
     UNet2DConditionModel,

random_examples.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0d38130425c106b715732609413675a33cd3eb551fe7eb7789ca87b5210d9f55
+size 8051179

test.py

@@ -0,0 +1,23 @@
+# -*- coding: utf-8 -*-
+# file: test.py
+# time: 23:21 2023/1/27
+# author: yangheng <[email protected]>
+# github: https://github.com/yangheng95
+# huggingface: https://huggingface.co/yangheng
+# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en
+# Copyright (C) 2021. All Rights Reserved.
+
+def fixBadZipfile(zipFile):
+ f = open(zipFile, 'r+b')
+ data = f.read()
+ pos = data.find('\x50\x4b\x05\x06') # End of central directory signature
+ if (pos > 0):
+     f.seek(pos + 22)   # size of 'ZIP end of central directory record'
+     f.truncate()
+     f.close()
+ else:
+     pass
+
+
+if __name__ == '__main__':
+    fixBadZipfile('RealESRGANv030/weights/RealESRGAN_x4plus_anime_6B.zip')