Showing 3 open source projects for "device to device"

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    SDGym

    SDGym

    Benchmarking synthetic data generation methods

    ...In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device.
    Downloads: 6 This Week
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  • 2
    Stable Diffusion in Docker

    Stable Diffusion in Docker

    Run the Stable Diffusion releases in a Docker container

    ...It should take a few seconds to create one image. On less powerful GPUs you may need to modify some of the options; see the Examples section for more details. If you lack a suitable GPU you can set the options --device cpu and --onnx instead. Since it uses the model, you will need to create a user access token in your Huggingface account. Save the user access token in a file called token.txt and make sure it is available when building the container. Create an image from an existing image and a text prompt. Modify an existing image with its depth map and a text prompt.
    Downloads: 0 This Week
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  • 3
    min(DALL·E)

    min(DALL·E)

    min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch

    ...The required models will be downloaded to models_root if they are not already there. Set the dtype to torch.float16 to save GPU memory. If you have an Ampere architecture GPU you can use torch.bfloat16. Set the device to either cuda or "cpu". Once everything has finished initializing, call generate_image with some text as many times as you want. Use a positive seed for reproducible results. Higher values for supercondition_factor result in better agreement with the text but a narrower variety of generated images. Every image token is sampled from the top_k most probable tokens. ...
    Downloads: 1 This Week
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