openvino-ai-plugins-gimp
openvino-ai-plugins-gimp | stable_diffusion.openvino | |
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5 | 47 | |
380 | 1,529 | |
11.6% | - | |
8.8 | 0.8 | |
13 days ago | 8 months ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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openvino-ai-plugins-gimp
- FLaNK Stack Weekly 19 Feb 2024
- FLaNK Stack 05 Feb 2024
- Intel slaps forehead, says I got it: AI PCs. Sell them AI PCs
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Has anyone tried training SD models on the A770?
Apparently, you still have to use OpenVino as a plugin in GIMP for access to Stable Diffusion with the ARC A770. https://github.com/intel/openvino-ai-plugins-gimp
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We really need an Automatic1111 Gimp extension
It seems that Intel already has something similar, a plugin for GIMP called openvino-ai-plugins-gimp. Stable diffusion included.
stable_diffusion.openvino
- FLaNK Stack 05 Feb 2024
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Installing A1111 Stable Diffusion Error
it might be the --xformers flag, try getting rid of that since your not using cuda you wouldn't be able to run it with xformers and you could also try --use-cpu all ... you can also check this out .. https://github.com/bes-dev/stable_diffusion.openvino .. it's probably your best option if your using CPU, which if your PC Graphics are using Intel UHD 620 then you don't have a GPU and an optimized CPU inference would be best to run
- 4 Reasons to Switch to Intel Arc GPUs
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why is SD not actually using the GPU?
SD can be run on a CPU without a GPU. I know for certain it can be done with OpenVINO. In fact, on some i7s, it will run at around 3 seconds per iteration. There was a reddit SD thread a while back saying it can be done with Automatic111. Also, soe recent threads on problems with AMD GPUs suggest Automatic1111 is using the CPU rather than the intended GPU. (Fortuanely, I have a GPU, so I don't have to deal with it myself!)
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Slow Performance on RX 6800 XT; Am I Doing Something Wrong or is ROCm Just this Slow?
I'm not actually entirely convinced that it's even using the GPU. Radeontop shows 0% utilization while the images are generating. Additionally, the listed iteration speed should be impossibly slow for any GPU; it says 26.58s/it, which is slower than just running on a CPU.
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How can i fix it?
iGPU's are in short not supported. There's this repo that may or may not help you, but even if it did I wouldn't expect much.
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Stable Diffusion Web UI for Intel Arc
You can also run it in windows native with openvino, there is a barebones webui for it as well in one of the forks.Requires setting cpu to gpu in one the files. https://github.com/bes-dev/stable_diffusion.openvino
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Intel Arc A770 is underperforming in Tom's Hardware Review
In https://github.com/bes-dev/stable_diffusion.openvino/blob/master/stable_diffusion_engine.py
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So a new benchmark was done for Stable Diffusion on GPU's
" We ended up using three different Stable Diffusion projects for our testing, mostly because no single package worked on every GPU. For Nvidia, we opted for Automatic 1111's webui version(opens in new tab). AMD GPUs were tested using Nod.ai's Shark version(opens in new tab), while for Intel's Arc GPUs we used Stable Diffusion OpenVINO(opens in new tab). "
- Anyone here using Mac?
What are some alternatives?
stable-gimpfusion - A Gimp plugin that brings StableDiffusion functionality through Automatic1111's API
stable-diffusion
axodox-machinelearning - This repository contains a pure C++ ONNX implementation of multiple offline AI models, such as StableDiffusion (1.5 and XL), ControlNet, Midas, HED and OpenPose.
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
WhisperFusion - WhisperFusion builds upon the capabilities of WhisperLive and WhisperSpeech to provide a seamless conversations with an AI.
stable-diffusion
jepa - PyTorch code and models for V-JEPA self-supervised learning from video.
stable-diffusion-rocm
ragas - Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines
diffusionbee-stable-diffusion-ui - Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
stable-diffusion - A latent text-to-image diffusion model