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我已经使用 Google Colab 中的自定义乳房 X 线照片数据集训练了 StyleGAN 2 ADA 的 ffqh1024 模型（链接到他们的仓库）。我训练的模型 .pkl 文件已在驱动器文件夹中准备就绪，我想使用该 .pkl 文件生成图像。我试过了：

!python generate.py --outdir='/content/drive/MyDrive/TFM/Generated' --trunc=1 --seeds=85,265,297,849 \ --network='/content/drive/MyDrive/TFM/colab-sg2-ada/stylegan2-ada/results/00025-ddsm-auto1-bg-resumecustom/network-snapshot-000096.pkl'

按照 GitHub 上的建议，但我收到此错误：

usage: generate.py [-h] {generate-images,truncation-traversal,generate-latent-walk,generate-neighbors,lerp-video} ... generate.py: error: unrecognized arguments: --outdir='/content/drive/MyDrive/TFM/Generated' --trunc=1 --seeds=85,265,297,849 --network='/content/drive/MyDrive/TFM/colab-sg2-ada/stylegan2-ada/results/00025-ddsm-auto1-bg-resumecustom/network-snapshot-000096.pkl'

真的不知道为什么 generate.py 无法识别参数...我必须 !pip install opensimplex 才能运行 generate.py，不知道它是否与问题有关...

在 StyleGAN 2 ADA 存储库中，有使用经过训练的模型生成图像的示例：

# Generate curated MetFaces images without truncation (Fig.10 left)
python generate.py --outdir=out --trunc=1 --seeds=85,265,297,849 \
    --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/metfaces.pkl

这是执行 !python generate.py -h 的结果：

usage: generate.py [-h]
                   {generate-images,truncation-traversal,generate-latent-walk,generate-neighbors,lerp-video}
                   ...

Generate images using pretrained network pickle.

positional arguments:
  {generate-images,truncation-traversal,generate-latent-walk,generate-neighbors,lerp-video}
                        Sub-commands
    generate-images     Generate images
    truncation-traversal
                        Generate truncation walk
    generate-latent-walk
                        Generate latent walk
    generate-neighbors  Generate random neighbors of a seed
    lerp-video          Generate interpolation video (lerp) between random
                        vectors

optional arguments:
  -h, --help            show this help message and exit

examples:

  # Generate curated MetFaces images without truncation (Fig.10 left)
  python generate.py --outdir=out --trunc=1 --seeds=85,265,297,849 \
      --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/metfaces.pkl

  # Generate uncurated MetFaces images with truncation (Fig.12 upper left)
  python generate.py --outdir=out --trunc=0.7 --seeds=600-605 \
      --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/metfaces.pkl

  # Generate class conditional CIFAR-10 images (Fig.17 left, Car)
  python generate.py --outdir=out --trunc=1 --seeds=0-35 --class=1 \
      --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/cifar10.pkl

  # Render image from projected latent vector
  python generate.py --outdir=out --dlatents=out/dlatents.npz \
      --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/ffhq.pkl
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1 回答 1

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尝试\从命令行中删除。我不确定它会起作用

于 2021-07-22T15:57:43.450 回答