diff --git a/README.md b/README.md
index a8f492fee..75791a18b 100644
--- a/README.md
+++ b/README.md
@@ -13,6 +13,7 @@ There are some implement details with paper description, which maybe different w
 - We used the ResNet block and channel concatenation style like vanilla `DDPM`.
 - We used the attention mechanism in low resolution feature(16×16) like vanilla `DDPM`.
 - We encoding the $\gamma$ as `FilM` strcutrue did in `WaveGrad`, and embedding it without affine transformation.
+- We define posterior variance as $ \dfrac{1-\gamma_{t-1}}{1-\gamma_{t}}  \beta_t $  rather than $\beta_t$,  which have the similar results in vanilla paper.
 
 **If you just want to upscale `64x64px` -> `512x512px` images using the pre-trained model, check out [this google colab script](https://colab.research.google.com/drive/1G1txPI1GKueKH0cSi_DgQFKwfyJOXlhY?usp=sharing).**
 
@@ -71,9 +72,7 @@ There are some implement details with paper description, which maybe different w
 ## Usage
 ### Environment
 ```python
-# Copy the pytorch environment using the dependencies files, and you can choose the either of the following ways.
-conda env create -f core/environment.yml
-conda create  --name pytorch --file core/environment.txt
+pip install -r requirement.txt
 ```
 
 ### Pretrained Model
@@ -125,20 +124,27 @@ then you need to change the datasets config to your data path and image resoluti
 
 #### Own Data
 
-You also can use your image data by following steps.
-We have some examples in dataset folder.
+You also can use your image data by following steps, and we have some examples in dataset folder.
 
-At first, you should organize images layout like this:
+At first, you should organize images layout like this, this step can be finished by `data/prepare_data.py` automatically:
 
 ```shell
-# set the high/low resolution images, bicubic interpolation images path
+# set the high/low resolution images, bicubic interpolation images path 
 dataset/celebahq_16_128/
-├── hr_128
-├── lr_16
-└── sr_16_128
+├── hr_128 # it's same with sr_16_128 directory if you don't have ground-truth images.
+├── lr_16 # vinilla low resolution images
+└── sr_16_128 # images ready to super resolution
 ```
 
+```python
+# super resolution from 16 to 128
+python data/prepare_data.py  --path [dataset root]  --out celebahq --size 16,128 -l
+```
+
+*Note: Above script can be used whether you have the vinilla high-resolution images or not.*
+
 then you need to change the dataset config to your data path and image resolution: 
+
 ```json
 "datasets": {
     "train|val": { // train and validation part
@@ -170,7 +176,7 @@ python eval.py -p [result root]
 
 ### Inference Alone
 
-Set the HR (vanilla high resolution images), SR (images need processed) image path like step in `Own Data`. HR directory contexts can be copy from SR, and LR directory  is unnecessary. 
+Set the  image path like steps in `Own Data`, then run the script:
 
 ```python
 # run the script
diff --git a/core/environment.txt b/core/environment.txt
deleted file mode 100644
index d9d4f3985..000000000
--- a/core/environment.txt
+++ /dev/null
@@ -1,149 +0,0 @@
-# This file may be used to create an environment using:
-# $ conda create --name <env> --file <this file>
-# platform: linux-64
-@EXPLICIT
-https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda
-https://repo.anaconda.com/pkgs/main/linux-64/_tflow_select-2.3.0-eigen.conda
-https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-mkl.conda
-https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2020.12.5-ha878542_0.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/intel-openmp-2020.2-254.conda
-https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.33.1-h53a641e_7.conda
-https://repo.anaconda.com/pkgs/main/linux-64/libgfortran-ng-7.3.0-hdf63c60_0.conda
-https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-9.1.0-hdf63c60_0.conda
-https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-9.1.0-hdf63c60_0.conda
-https://repo.anaconda.com/pkgs/main/linux-64/mkl-2020.2-256.conda
-https://repo.anaconda.com/pkgs/main/linux-64/c-ares-1.17.1-h27cfd23_0.conda
-https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/linux-64/cudatoolkit-10.2.89-hfd86e86_1.conda
-https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/linux-64/expat-2.2.9-he1b5a44_2.tar.bz2
-https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/linux-64/icu-58.2-hf484d3e_1000.tar.bz2
-https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/linux-64/jpeg-9b-h024ee3a_2.conda
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-https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.3-he6710b0_2.conda
-https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/linux-64/libuuid-2.32.1-h14c3975_1000.tar.bz2
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diff --git a/core/environment.yml b/core/environment.yml
deleted file mode 100644
index 2afd4ef50..000000000
--- a/core/environment.yml
+++ /dev/null
@@ -1,188 +0,0 @@
-name: pytorch
-channels:
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
-  - conda-forge
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/menpo/
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
-  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
-  - defaults
-dependencies:
-  - _libgcc_mutex=0.1=main
-  - _tflow_select=2.3.0=eigen
-  - absl-py=0.11.0=pyhd3eb1b0_1
-  - aiohttp=3.6.3=py37h7b6447c_0
-  - arrow=1.0.3=py37h89c1867_0
-  - astunparse=1.6.3=py_0
-  - async-timeout=3.0.1=py37_0
-  - attrs=20.3.0=pyhd3eb1b0_0
-  - blas=1.0=mkl
-  - blinker=1.4=py37_0
-  - brotlipy=0.7.0=py37h27cfd23_1003
-  - c-ares=1.17.1=h27cfd23_0
-  - ca-certificates=2020.12.5=ha878542_0
-  - cachetools=4.2.0=pyhd3eb1b0_0
-  - certifi=2020.12.5=py37h89c1867_1
-  - cffi=1.14.4=py37h261ae71_0
-  - chardet=3.0.4=py37h06a4308_1003
-  - click=7.1.2=py_0
-  - cloudpickle=1.6.0=py_0
-  - cryptography=3.3.1=py37h3c74f83_0
-  - cudatoolkit=10.2.89=hfd86e86_1
-  - cycler=0.10.0=py_2
-  - cytoolz=0.11.0=py37h4abf009_1
-  - dask-core=2020.12.0=pyhd8ed1ab_0
-  - dbus=1.13.6=he372182_0
-  - decorator=4.4.2=py_0
-  - expat=2.2.9=he1b5a44_2
-  - fontconfig=2.13.1=he4413a7_1000
-  - freetype=2.10.4=h7ca028e_0
-  - gast=0.3.3=py_0
-  - glib=2.66.1=h92f7085_0
-  - google-auth=1.24.0=pyhd3eb1b0_0
-  - google-auth-oauthlib=0.4.2=pyhd3eb1b0_2
-  - google-pasta=0.2.0=py_0
-  - grpcio=1.31.0=py37hf8bcb03_0
-  - gst-plugins-base=1.14.0=hbbd80ab_1
-  - gstreamer=1.14.0=hb31296c_0
-  - h5py=2.10.0=py37hd6299e0_1
-  - hdf5=1.10.6=hb1b8bf9_0
-  - icu=58.2=hf484d3e_1000
-  - idna=2.10=py_0
-  - imageio=2.9.0=py_0
-  - importlib-metadata=2.0.0=py_1
-  - intel-openmp=2020.2=254
-  - joblib=1.0.0=pyhd8ed1ab_0
-  - jpeg=9b=h024ee3a_2
-  - keras-preprocessing=1.1.0=py_1
-  - kiwisolver=1.3.1=py37hc928c03_0
-  - lcms2=2.11=h396b838_0
-  - ld_impl_linux-64=2.33.1=h53a641e_7
-  - libblas=3.8.0=21_mkl
-  - libcblas=3.8.0=21_mkl
-  - libedit=3.1.20191231=h14c3975_1
-  - libffi=3.3=he6710b0_2
-  - libgcc-ng=9.1.0=hdf63c60_0
-  - libgfortran-ng=7.3.0=hdf63c60_0
-  - libpng=1.6.37=h21135ba_2
-  - libprotobuf=3.13.0.1=hd408876_0
-  - libstdcxx-ng=9.1.0=hdf63c60_0
-  - libtiff=4.1.0=h2733197_1
-  - libuuid=2.32.1=h14c3975_1000
-  - libuv=1.40.0=hd18ef5c_0
-  - libxcb=1.13=h14c3975_1002
-  - libxml2=2.9.10=hb55368b_3
-  - lz4-c=1.9.2=he1b5a44_3
-  - markdown=3.3.3=py37h06a4308_0
-  - matplotlib=3.3.3=py37h89c1867_0
-  - matplotlib-base=3.3.3=py37h4f6019d_0
-  - mkl=2020.2=256
-  - mkl-service=2.3.0=py37he8ac12f_0
-  - mkl_fft=1.2.0=py37h23d657b_0
-  - mkl_random=1.1.1=py37h0573a6f_0
-  - multidict=4.7.6=py37h7b6447c_1
-  - ncurses=6.2=he6710b0_1
-  - networkx=2.5=py_0
-  - ninja=1.10.2=py37hff7bd54_0
-  - numpy=1.19.2=py37h54aff64_0
-  - numpy-base=1.19.2=py37hfa32c7d_0
-  - oauthlib=3.1.0=py_0
-  - olefile=0.46=pyh9f0ad1d_1
-  - openssl=1.1.1l=h7f8727e_0
-  - opt_einsum=3.1.0=py_0
-  - patsy=0.5.1=py_0
-  - pcre=8.44=he1b5a44_0
-  - pillow=8.0.1=py37he98fc37_0
-  - pip=20.3.3=py37h06a4308_0
-  - protobuf=3.13.0.1=py37he6710b0_1
-  - pthread-stubs=0.4=h36c2ea0_1001
-  - pyasn1=0.4.8=py_0
-  - pyasn1-modules=0.2.8=py_0
-  - pycparser=2.20=py_2
-  - pyjwt=1.7.1=py37_0
-  - pyopenssl=20.0.1=pyhd3eb1b0_1
-  - pyparsing=2.4.7=pyh9f0ad1d_0
-  - pyqt=5.9.2=py37hcca6a23_4
-  - pysocks=1.7.1=py37_1
-  - python=3.7.9=h7579374_0
-  - python-dateutil=2.8.1=py_0
-  - python_abi=3.7=1_cp37m
-  - pytorch=1.7.1=py3.7_cuda10.2.89_cudnn7.6.5_0
-  - pytz=2021.1=pyhd8ed1ab_0
-  - pywavelets=1.1.1=py37h161383b_3
-  - qt=5.9.7=h5867ecd_1
-  - readline=8.0=h7b6447c_0
-  - requests=2.25.1=pyhd3eb1b0_0
-  - requests-oauthlib=1.3.0=py_0
-  - rsa=4.6=py_0
-  - scikit-image=0.16.2=py37hb3f55d8_0
-  - seaborn=0.11.1=hd8ed1ab_1
-  - seaborn-base=0.11.1=pyhd8ed1ab_1
-  - setuptools=51.0.0=py37h06a4308_2
-  - sip=4.19.8=py37hf484d3e_0
-  - six=1.15.0=py37h06a4308_0
-  - sqlite=3.33.0=h62c20be_0
-  - statsmodels=0.11.1=py37h8f50634_2
-  - tensorboard=2.4.0=pyhc547734_0
-  - tensorboard-plugin-wit=1.6.0=py_0
-  - tensorflow=2.3.0=eigen_py37h189e6a2_0
-  - tensorflow-base=2.3.0=eigen_py37h3b305d7_0
-  - tensorflow-estimator=2.3.0=pyheb71bc4_0
-  - termcolor=1.1.0=py37_1
-  - threadpoolctl=2.1.0=pyh5ca1d4c_0
-  - timm=0.4.12=pyhd8ed1ab_0
-  - tk=8.6.10=hbc83047_0
-  - toolz=0.11.1=py_0
-  - torchaudio=0.7.2=py37
-  - torchvision=0.8.2=py37_cu102
-  - tornado=6.1=py37h4abf009_0
-  - typing_extensions=3.7.4.3=py_0
-  - urllib3=1.26.2=pyhd3eb1b0_0
-  - werkzeug=1.0.1=py_0
-  - wheel=0.36.2=pyhd3eb1b0_0
-  - wrapt=1.12.1=py37h7b6447c_1
-  - xorg-libxau=1.0.9=h14c3975_0
-  - xorg-libxdmcp=1.1.3=h516909a_0
-  - xz=5.2.5=h7b6447c_0
-  - yaml=0.2.5=h516909a_0
-  - yarl=1.6.3=py37h27cfd23_0
-  - zipp=3.4.0=pyhd3eb1b0_0
-  - zlib=1.2.11=h7b6447c_3
-  - zstd=1.4.5=h6597ccf_2
-  - pip:
-    - backcall==0.2.0
-    - einops==0.3.0
-    - inplace-abn==1.1.0
-    - ipykernel==5.5.3
-    - ipython==7.22.0
-    - ipython-genutils==0.2.0
-    - jedi==0.18.0
-    - jupyter-client==6.1.12
-    - jupyter-core==4.7.1
-    - lmdb==1.0.0
-    - nltk==3.6.5
-    - opencv-python==4.4.0.46
-    - pandas==1.2.3
-    - parso==0.8.2
-    - pexpect==4.8.0
-    - pickleshare==0.7.5
-    - prompt-toolkit==3.0.18
-    - ptyprocess==0.7.0
-    - pygments==2.8.1
-    - pyyaml==5.3.1
-    - pyzmq==22.0.3
-    - regex==2021.11.10
-    - scikit-learn==0.24.0
-    - scipy==1.2.1
-    - tensorboardx==2.4.1
-    - torch-tb-profiler==0.1.0
-    - tqdm==4.55.0
-    - traitlets==5.0.5
-    - wcwidth==0.2.5
-prefix: /home/user/anaconda3/envs/pytorch
-
diff --git a/infer.py b/infer.py
index a8201fea0..edc1e0357 100755
--- a/infer.py
+++ b/infer.py
@@ -8,8 +8,6 @@
 from core.wandb_logger import WandbLogger
 from tensorboardX import SummaryWriter
 import os
-import numpy as np
-import wandb
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
diff --git a/requirement.txt b/requirement.txt
new file mode 100644
index 000000000..0aaed4d63
--- /dev/null
+++ b/requirement.txt
@@ -0,0 +1,11 @@
+torch>=1.6
+torchvision
+numpy
+pandas
+tqdm
+lmdb
+opencv-python
+pillow
+tensorboardx
+wandb
+
diff --git a/sample.py b/sample.py
index c3fc2236d..ce2b54a73 100755
--- a/sample.py
+++ b/sample.py
@@ -9,7 +9,6 @@
 from tensorboardX import SummaryWriter
 import os
 import numpy as np
-import wandb
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
diff --git a/sr.py b/sr.py
index c8d0f7b75..0657500b9 100755
--- a/sr.py
+++ b/sr.py
@@ -9,7 +9,6 @@
 from tensorboardX import SummaryWriter
 import os
 import numpy as np
-import wandb
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
@@ -42,6 +41,7 @@
 
     # Initialize WandbLogger
     if opt['enable_wandb']:
+        import wandb
         wandb_logger = WandbLogger(opt)
         wandb.define_metric('validation/val_step')
         wandb.define_metric('epoch')