Showing 4 open source projects for "cpu"

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    Automate contact and company data extraction

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  • 1
    ComfyUI

    ComfyUI

    The most powerful and modular diffusion model GUI, api and backend

    The most powerful and modular diffusion model is GUI and backend. This UI will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart-based interface. We are a team dedicated to iterating and improving ComfyUI, supporting the ComfyUI ecosystem with tools like node manager, node registry, cli, automated testing, and public documentation. Open source AI models will win in the long run against closed models and we are only at the beginning. Our core mission...
    Downloads: 452 This Week
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  • 2
    KoboldCpp

    KoboldCpp

    Run GGUF models easily with a UI or API. One File. Zero Install.

    KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models, inspired by the original KoboldAI. It's a single self-contained distributable that builds off llama.cpp and adds many additional powerful features.
    Downloads: 460 This Week
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  • 3
    Stable Diffusion in Docker

    Stable Diffusion in Docker

    Run the Stable Diffusion releases in a Docker container

    ...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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  • 4
    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: 0 This Week
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