In this folder, we provide an example to show that how does RAFT work on diffusion models.
The requirements are shown below.
accelerate 0.18.0
asttokens 2.2.1
backcall 0.2.0
bitsandbytes 0.37.2
certifi 2022.12.7
charset-normalizer 3.1.0
clip 1.0
cmake 3.26.1
comm 0.1.3
contourpy 1.0.7
cycler 0.11.0
debugpy 1.6.7
decorator 5.1.1
diffusers 0.14.0
executing 1.2.0
filelock 3.11.0
fonttools 4.39.3
ftfy 6.1.1
huggingface-hub 0.13.4
idna 3.4
importlib-metadata 6.2.0
importlib-resources 5.12.0
ipykernel 6.22.0
ipython 8.12.0
jedi 0.18.2
Jinja2 3.1.2
jupyter_client 8.1.0
jupyter_core 5.3.0
kiwisolver 1.4.4
lit 16.0.0
MarkupSafe 2.1.2
matplotlib 3.7.1
matplotlib-inline 0.1.6
mpmath 1.3.0
mypy-extensions 1.0.0
nest-asyncio 1.5.6
networkx 3.1
numpy 1.24.2
nvidia-cublas-cu11 11.10.3.66
nvidia-cuda-cupti-cu11 11.7.101
nvidia-cuda-nvrtc-cu11 11.7.99
nvidia-cuda-runtime-cu11 11.7.99
nvidia-cudnn-cu11 8.5.0.96
nvidia-cufft-cu11 10.9.0.58
nvidia-curand-cu11 10.2.10.91
nvidia-cusolver-cu11 11.4.0.1
nvidia-cusparse-cu11 11.7.4.91
nvidia-nccl-cu11 2.14.3
nvidia-nvtx-cu11 11.7.91
open-clip-torch 2.16.0
packaging 23.0
pandas 2.0.0
parso 0.8.3
pexpect 4.8.0
pickleshare 0.7.5
Pillow 9.5.0
pip 23.0.1
platformdirs 3.2.0
prompt-toolkit 3.0.38
protobuf 3.20.3
psutil 5.9.4
ptyprocess 0.7.0
pure-eval 0.2.2
Pygments 2.14.0
pyparsing 3.0.9
pyre-extensions 0.0.23
python-dateutil 2.8.2
pytz 2023.3
PyYAML 6.0
pyzmq 25.0.2
regex 2023.3.23
requests 2.28.2
sentencepiece 0.1.97
setuptools 65.6.3
six 1.16.0
stack-data 0.6.2
sympy 1.11.1
timm 0.6.13
tokenizers 0.13.3
torch 2.0.0
torchvision 0.15.1
tornado 6.2
tqdm 4.65.0
traitlets 5.9.0
transformers 4.27.4
triton 2.0.0
typing_extensions 4.5.0
typing-inspect 0.8.0
tzdata 2023.3
urllib3 1.26.15
wcwidth 0.2.6
wheel 0.38.4
xformers 0.0.18
zipp 3.15.0