stable-diffusion-webui
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stable-diffusion-webui | onnx | |
---|---|---|
75 | 38 | |
2,208 | 16,858 | |
- | 2.0% | |
9.8 | 9.5 | |
over 1 year ago | 2 days ago | |
Python | Python | |
GNU Affero General Public License v3.0 | Apache License 2.0 |
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stable-diffusion-webui
- [Stablediffusion] Interface utilisateur Web Diffusion stable
- Generating game concept art
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../../workspace/imgs/txt2img
I am using this one : https://github.com/hlky/stable-diffusion-webui
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How to generate a similar images to an input image *without* a prompt?
Not sure about the script but you can try using this web-ui's img2img tab.
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Enhancing local detail and cohesion by mosaicing
https://github.com/hlky/stable-diffusion-webui now redirects to /sd-webui/stable-diffusion-webui, as though they're the "true" sd-webui.
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reintalled new Hlky update & img2img returns errors (not just where you have to click on mask & back on crop)
As an update, in case anyone else has the issue, after getting some help (thanks u/vedroboev) I installed from here not sure what the difference is, but I got it working.
- Is anyone else unable to use the site?
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Fixing SD images with img2img, am I misunderstanding the concept?
I would pick a version on the github from 8/31 in the stable diffusion repo and then follow step 2a in this guide https://rentry.org/GUItard to transfer the files from this https://github.com/hlky/stable-diffusion-webui/tree/96aba4b36d59803f3817ee60e96a097f54962ae4
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Can't seem to get img2img up and running
This is a bug with the newest UI version. See this.
- Stable Diffusion Img2Img Help
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?
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
waifu-diffusion - stable diffusion finetuned on weeb stuff
stable-diffusion-webui - Stable Diffusion web UI
diffusers-uncensored - Uncensored fork of diffusers
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
txt2imghd - A port of GOBIG for Stable Diffusion
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
taming-transformers - Taming Transformers for High-Resolution Image Synthesis
stable-diffusion - A latent text-to-image diffusion model
iree - A retargetable MLIR-based machine learning compiler and runtime toolkit.