Search Results for "cpu-x"
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Showing 8 open source projects for "cpu-x"
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x-unet
Implementation of a U-net complete with efficient attention
Implementation of a U-net complete with efficient attention as well as the latest research findings. For 3d (video or CT / MRI scans). -
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marqo
Tensor search for humans
A tensor-based search and analytics engine that seamlessly integrates with your applications, websites, and workflows. Marqo is a versatile and robust search and analytics engine that can be integrated into any website or application. Due to horizontal scalability, Marqo provides lightning-fast query times, even with millions of documents. Marqo helps you configure deep-learning models like CLIP to pull semantic meaning from images. It can seamlessly handle image-to-image, image-to-text and... -
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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. -
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DALL-E 2 - Pytorch
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis
...Specifically, this repository will only build out the diffusion prior network, as it is the best performing variant (but which incidentally involves a causal transformer as the denoising network) To train DALLE-2 is a 3 step process, with the training of CLIP being the most important. To train CLIP, you can either use x-clip package, or join the LAION discord, where a lot of replication efforts are already underway. Then, you will need to train the decoder, which learns to generate images based on the image embedding coming from the trained CLIP. -
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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. -
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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. ... -
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gpt-2-simple
Python package to easily retrain OpenAI's GPT-2 text-generating model
...Additionally, this package allows easier generation of text, generating to a file for easy curation, allowing for prefixes to force the text to start with a given phrase. For finetuning, it is strongly recommended to use a GPU, although you can generate using a CPU (albeit much more slowly). If you are training in the cloud, using a Colaboratory notebook or a Google Compute Engine VM w/ the TensorFlow Deep Learning image is strongly recommended. (as the GPT-2 model is hosted on GCP) You can use gpt-2-simple to retrain a model using a GPU for free in this Colaboratory notebook, which also demos additional features of the package. ...
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