text2image-gui
onnx
text2image-gui | onnx | |
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23 | 38 | |
903 | 16,858 | |
- | 1.0% | |
9.3 | 9.5 | |
4 months ago | 6 days ago | |
C# | Python | |
GNU General Public License v3.0 only | Apache License 2.0 |
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text2image-gui
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Why does Stable diffusion ""nmkd"" not see .safetensors format?
I read github
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I mad a python script the lets you scribble with SD in realtime
With the AMD guide
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'Everyone and Their Dog is Buying GPUs,' Musk Says as AI Startup Details Emerge
You can find NMKD here, and the readme should be quite simple to get it to work on your own machine for a basic SD setup: https://github.com/n00mkrad/text2image-gui
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I get ".safetensors" instead of ".ckpt" when downloading models?
So assuming you are suing the "NMKD" GUI i found an existing issue on the github page: The Issue.
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ELI5: Can Someone Give Me Some Simple Steps To Get Started On A Local Install?
Source code is available here if you want to check that out, the download for the precompiled program is on itch.io.
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HELPP sorry losing my mind here (i have a mx150 gpu which IS cuda compatible) also after that last line nothing happens!
The 1024x572 was from a comment talking about NMKD ,that mentions using OptimiseSD may run on less than 4GB.
- Looking for download link to NMKD 1.7.* for a friend anyone have one?
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So i wanted to ask it this a good requirements to download SD or I can't
Be sure to read the system requirements and the special AMD GPU info page.
- Update 1.7.0 of my Windows SD GUI is out! Supports VAE selection, prompt wildcards, even easier DreamBooth training, and tons of quality-of-life improvements. Details in comments.
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Dreambooth in Automatic1111 or locally?
Easiest way I have seen so far, working well for me. https://github.com/n00mkrad/text2image-gui/blob/main/DreamBooth.md
onnx
- Onyx, a new programming language powered by WebAssembly
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From Lab to Live: Implementing Open-Source AI Models for Real-Time Unsupervised Anomaly Detection in Images
Once your model has been trained and validated using Anomalib, the next step is to prepare it for real-time implementation. This is where ONNX (Open Neural Network Exchange) or OpenVINO (Open Visual Inference and Neural network Optimization) comes into play.
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Object detection with ONNX, Pipeless and a YOLO model
ONNX is an open format from the Linux Foundation to represent machine learning models. It is becoming extensively adopted by the Machine Learning community and is compatible with most of the machine learning frameworks like PyTorch, TensorFlow, etc. Converting a model between any of those formats and ONNX is really simple and can be done in most cases with a single command.
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38TB of data accidentally exposed by Microsoft AI researchers
ONNX[0], model-as-protosbufs, continuing to gain adoption will hopefully solve this issue.
[0] https://github.com/onnx/onnx
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Reddit’s LLM text model for Ads Safety
Running inference for large models on CPU is not a new problem and fortunately there has been great development in many different optimization frameworks for speeding up matrix and tensor computations on CPU. We explored multiple optimization frameworks and methods to improve latency, namely TorchScript, BetterTransformer and ONNX.
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Operationalize TensorFlow Models With ML.NET
ONNX is a format for representing machine learning models in a portable way. Additionally, ONNX models can be easily optimized and thus become smaller and faster.
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Onnx Runtime: “Cross-Platform Accelerated Machine Learning”
I would say onnx.ai [0] provides more information about ONNX for those who aren’t working with ML/DL.
[0] https://onnx.ai
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Does ONNX Runtime not support Double/float64?
It's not clear why you thing this sub is appropriate for some third party system with a Python interface. Why don't you try their discussion group: https://github.com/onnx/onnx/discussions
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Async behaviour in python web frameworks
This kind of indirection through standardisation is pretty common to make compatibility between different kinds of software components easier. Some other good examples are the LSP project from Microsoft and ONNX to represent machine learning models. The first provides a standard so that IDEs don't have to re-invent the weel for every programming language. The latter decouples training frameworks from inference frameworks. Going back to WSGI, you can find a pretty extensive rationale for the WSGI standard here if interested.
- Pickle safety in Python
What are some alternatives?
dreambooth-gui
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
ai-notes - notes for software engineers getting up to speed on new AI developments. Serves as datastore for https://latent.space writing, and product brainstorming, but has cleaned up canonical references under the /Resources folder.
stable-diffusion-webui - Stable Diffusion web UI
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
gimp-stable-diffusion
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
stable-diffusion - A latent text-to-image diffusion model
stable-diffusion
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/sd-webui/stable-diffusion-webui]