【深度学习 AIGC】stablediffusion-infinity 在无界限画布中输出绘画 Outpainting

代码:https://github.com/lkwq007/stablediffusion-infinity/tree/master

启动环境:

shell 复制代码
git clone --recurse-submodules https://github.com/lkwq007/stablediffusion-infinity
cd stablediffusion-infinity
conda env create -f environment.yml
conda activate sd-inf

# 一定更新一下!
conda install -c conda-forge diffusers transformers ftfy accelerate
conda update -c conda-forge diffusers transformers ftfy accelerate
pip install -U gradio

python app.py

修改了一下app.py的东西,最后面修改了ip和端口:

css 复制代码
launch_extra_kwargs = {
    "show_error": True,
    # "favicon_path": ""
}
launch_kwargs = vars(args)
launch_kwargs = {k: v for k, v in launch_kwargs.items() if v is not None}
print(launch_kwargs)
launch_kwargs.pop("remote_model", None)
launch_kwargs.pop("local_model", None)
launch_kwargs.pop("fp32", None)
launch_kwargs.pop("lowvram", None)
launch_kwargs.update(launch_extra_kwargs)
try:
    import google.colab

    launch_kwargs["debug"] = True
except:
    pass

if RUN_IN_SPACE:
    print("run in space")
    demo.launch()
elif args.debug:
    print(111111111)
    launch_kwargs["share"]=True
    launch_kwargs["server_name"] = "0.0.0.0"
    launch_kwargs["server_port"] = 8000
    demo.queue().launch(**launch_kwargs)
else:
    print(222222222)
    launch_kwargs["share"]=True
    launch_kwargs["server_name"] = "0.0.0.0"
    launch_kwargs["server_port"] = 8000
    demo.queue().launch(**launch_kwargs)

可以对照一下环境:

shell 复制代码
(sd-inf)   Thu Sep 14    20:59:37    /ssd/xiedong/stablediffusion-infinity  pip list
Package                       Version
----------------------------- ---------
absl-py                       1.3.0
accelerate                    0.22.0
aiofiles                      23.2.1
aiohttp                       3.8.1
aiosignal                     1.3.1
altair                        5.1.1
antlr4-python3-runtime        4.9.3
anyio                         3.6.2
async-timeout                 4.0.2
attrs                         23.1.0
backports.functools-lru-cache 1.6.4
bcrypt                        4.0.1
brotlipy                      0.7.0
cachetools                    5.2.0
certifi                       2023.7.22
cffi                          1.15.1
charset-normalizer            2.0.4
click                         8.1.3
cloudpickle                   2.0.0
cmake                         3.25.0
colorama                      0.4.6
commonmark                    0.9.1
contourpy                     1.0.6
cryptography                  38.0.1
cycler                        0.11.0
cytoolz                       0.12.0
dask                          2022.7.0
dataclasses                   0.8
datasets                      2.7.0
diffusers                     0.14.0
dill                          0.3.6
einops                        0.4.1
fastapi                       0.87.0
ffmpy                         0.3.0
filelock                      3.8.0
fonttools                     4.38.0
fpie                          0.2.4
frozenlist                    1.3.0
fsspec                        2022.10.0
ftfy                          6.1.1
google-auth                   2.14.1
google-auth-oauthlib          0.4.6
gradio                        3.44.2
gradio_client                 0.5.0
grpcio                        1.51.0
h11                           0.12.0
httpcore                      0.15.0
httpx                         0.23.1
huggingface-hub               0.17.1
idna                          3.4
imagecodecs                   2021.8.26
imageio                       2.19.3
importlib-metadata            5.0.0
importlib-resources           6.0.1
Jinja2                        3.1.2
joblib                        1.2.0
jsonschema                    4.19.0
jsonschema-specifications     2023.7.1
kiwisolver                    1.4.4
linkify-it-py                 1.0.3
llvmlite                      0.39.1
locket                        1.0.0
Markdown                      3.4.1
markdown-it-py                2.1.0
MarkupSafe                    2.1.1
matplotlib                    3.6.2
mdit-py-plugins               0.3.1
mdurl                         0.1.2
mkl-fft                       1.3.1
mkl-random                    1.2.2
mkl-service                   2.4.0
multidict                     6.0.2
multiprocess                  0.70.12.2
networkx                      2.8.4
numba                         0.56.4
numpy                         1.23.4
oauthlib                      3.2.2
omegaconf                     2.2.3
opencv-python                 4.6.0.66
opencv-python-headless        4.6.0.66
orjson                        3.8.2
packaging                     21.3
pandas                        1.4.2
paramiko                      2.12.0
partd                         1.2.0
Pillow                        9.2.0
pip                           22.2.2
protobuf                      3.20.3
psutil                        5.9.1
pyarrow                       8.0.0
pyasn1                        0.4.8
pyasn1-modules                0.2.8
pycparser                     2.21
pycryptodome                  3.15.0
pydantic                      1.10.2
pyDeprecate                   0.3.2
pydub                         0.25.1
Pygments                      2.13.0
PyNaCl                        1.5.0
pyOpenSSL                     22.0.0
pyparsing                     3.0.9
PySocks                       1.7.1
python-dateutil               2.8.2
python-multipart              0.0.5
pytorch-lightning             1.7.7
pytz                          2022.6
PyWavelets                    1.3.0
PyYAML                        6.0
referencing                   0.30.2
regex                         2022.4.24
requests                      2.28.1
requests-oauthlib             1.3.1
responses                     0.18.0
rfc3986                       1.5.0
rich                          12.6.0
rpds-py                       0.10.3
rsa                           4.9
sacremoses                    0.0.53
safetensors                   0.3.2
scikit-image                  0.19.2
scipy                         1.9.3
semantic-version              2.10.0
setuptools                    65.5.0
six                           1.16.0
sniffio                       1.3.0
sourceinspect                 0.0.4
starlette                     0.21.0
taichi                        1.2.2
tensorboard                   2.11.0
tensorboard-data-server       0.6.1
tensorboard-plugin-wit        1.8.1
tifffile                      2021.7.2
timm                          0.6.11
tokenizers                    0.11.4
toolz                         0.12.0
torch                         1.13.0
torchaudio                    0.13.0
torchmetrics                  0.10.3
torchvision                   0.14.0
tqdm                          4.64.1
transformers                  4.33.1
typing_extensions             4.3.0
uc-micro-py                   1.0.1
urllib3                       1.26.12
uvicorn                       0.20.0
wcwidth                       0.2.5
websockets                    10.4
Werkzeug                      2.2.2
wheel                         0.37.1
xxhash                        0.0.0
yarl                          1.7.2
zipp                          3.10.0

路径下建立一个stabilityai,然后下载stable-diffusion-2-inpainting放进去,sd-vae-ft-mse是stable-diffusion-2-inpainting/vae里的东西复制了一遍。

shell 复制代码
(sd-inf)   Thu Sep 14    21:00:31    /ssd/xiedong/stablediffusion-infinity  tree stabilityai/
stabilityai/
├── sd-vae-ft-mse
│   ├── config.json
│   ├── diffusion_pytorch_model.bin
│   ├── diffusion_pytorch_model.fp16.bin
│   ├── diffusion_pytorch_model.fp16.safetensors
│   └── diffusion_pytorch_model.safetensors
└── stable-diffusion-2-inpainting
    ├── 512-inpainting-ema.ckpt
    ├── 512-inpainting-ema.safetensors
    ├── feature_extractor
    │   └── preprocessor_config.json
    ├── merged-leopards.png
    ├── model_index.json
    ├── README.md
    ├── scheduler
    │   └── scheduler_config.json
    ├── sd-vae-ft-mse-original
    │   ├── README.md
    │   ├── vae-ft-mse-840000-ema-pruned.ckpt
    │   └── vae-ft-mse-840000-ema-pruned.safetensors
    ├── text_encoder
    │   ├── config.json
    │   ├── model.fp16.safetensors
    │   ├── model.safetensors
    │   ├── pytorch_model.bin
    │   └── pytorch_model.fp16.bin
    ├── tokenizer
    │   ├── merges.txt
    │   ├── special_tokens_map.json
    │   ├── tokenizer_config.json
    │   └── vocab.json
    ├── unet
    │   ├── config.json
    │   ├── diffusion_pytorch_model.bin
    │   ├── diffusion_pytorch_model.fp16.bin
    │   ├── diffusion_pytorch_model.fp16.safetensors
    │   └── diffusion_pytorch_model.safetensors
    └── vae
        ├── config.json
        ├── diffusion_pytorch_model.bin
        ├── diffusion_pytorch_model.fp16.bin
        ├── diffusion_pytorch_model.fp16.safetensors
        └── diffusion_pytorch_model.safetensors

然后就可以用了:



相关推荐
shayudiandian1 小时前
用深度学习实现语音识别系统
人工智能·深度学习·语音识别
铅笔侠_小龙虾8 小时前
深度学习理论推导--梯度下降法
人工智能·深度学习
墨风如雪8 小时前
Mistral 3 炸场:欧洲 AI 巨头用 Apache 2.0 给闭源模型上了一课
aigc
&&Citrus9 小时前
【杂谈】SNNU公共计算平台:深度学习服务器配置与远程开发指北
服务器·人工智能·vscode·深度学习·snnu
STLearner9 小时前
AI论文速读 | U-Cast:学习高维时间序列预测的层次结构
大数据·论文阅读·人工智能·深度学习·学习·机器学习·数据挖掘
oden9 小时前
Prompt工程实战:让AI输出质量提升10倍的技巧
aigc·ai编程
黑客思维者10 小时前
LLM底层原理学习笔记:Adam优化器为何能征服巨型模型成为深度学习的“速度与稳定之王”
笔记·深度学习·学习·llm·adam优化器
云雾J视界10 小时前
多Stream并发实战:用流水线技术将AIGC服务P99延迟压降63%
aigc·api·cpu·stream·gpu·cuda·多并发
oden12 小时前
Claude用不好浪费钱?10个高级技巧让效率翻3倍
aigc·ai编程·claude