plaidml
stable-diffusion
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plaidml | stable-diffusion | |
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14 | 382 | |
4,575 | 65,389 | |
0.1% | 2.2% | |
5.4 | 0.0 | |
9 months ago | 16 days ago | |
C++ | Jupyter Notebook | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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plaidml
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We’re Brian Retford, Jason Morton, and Ryan Cao, various researchers and developers in the ZKML (zero knowledge machine learning) space and we’ve been asked by r/privacy mods to help explain and answer questions about ZKML and why it’s important for the future of data privacy! AMA
basically agree with all of this, however I do want to highlight that there is no 'ZKML protocol plan' - the panel here are all involved in quite different projects and interested in ZKML for a variety of reasons. As one of the authors of https://github.com/plaidml/plaidml I'm not expecting any kind of standard protocol to evolve for several years; the group behind the AMA though is optimistic about the potential of ZKML and this AMA is part of the start of developing useful protocols.
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Whisper – open source speech recognition by OpenAI
It understands my Swedish attempts at English really well with the medium.en model. (Although, it gives me a funny warning: `UserWarning: medium.en is an English-only model but receipted 'English'; using English instead.`. I guess it doesn't want to be told to use English when that's all it can do.)
However, it runs very slowly. It uses the CPU on my macbook, presumably because it hasn't got a NVidia card.
Googling about that I found [plaidML](https://github.com/plaidml/plaidml) which is a project promising to run ML on many different gpu architectures. Does anyone know whether it is possible to plug them together somehow? I am not an ML researcher, and don't quite understand anything about the technical details of the domain, but I can understand and write python code in domains that I do understand, so I could do some glue work if required.
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Cloud Based training for my model?
Have you tried PlaidML https://github.com/plaidml/plaidml
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GPU computing on Apple Silicon
This doesn't answer your question, but it would be cool if we had something based on MLIR for GPU compute. From what I've read, it closes the gap between NVIDIA and other GPU vendors a lot more than pure compute shaders. e.g. ONNX-MLIR, PlaidML, and IREE.
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Image processing library? Also GUI development recommendations?
There is a library called PlaidML which is supposed to support Keras on a wide variety of GPUs, including the Iris. But it doesn't. I get the issue reported as Issue #168, which was first reported in 2018 and is still open. That's what I mean by not well supported.
- Question about the viability of AMD GPUs
- Ask HN: Will there ever be a cross platform GPU interface?
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[P] DLPrimitives - wondering about best development direction
Not really: https://github.com/plaidml/plaidml/commits/plaidml-v1
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Adventures in homelab AI: Putting the torch to an R710
There are reports on github of plaidML conking out on older CPUs with a similar "illegal instruction err.
- Machine learning on a new amd radeon gpu?
stable-diffusion
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Go is bigger than crab!
Which is a 1-click install of Stable Diffusion with an alternative web interface. You can choose a different approach but this one is pretty simple and I am new to this stuff.
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Why & How to check Invisible Watermark
an invisible watermarking of the outputs, to help viewers identify the images as machine-generated.
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How to create an Image generating AI?
It sounds like you just want to set up Stable Diffusion to run locally. I don't think your computer's specs will be able to do it. You need a graphics card with a decent amount of VRAM. Stable diffusion is in Python as is almost every AI open source project I've seen. If you can get your hands on a system with an Nvidia RTX card with as much VRAM as possible, you're in business. I have an RTX 3060 with 12 gigs of VRAM and I can run stable diffusion and a whole variety of open source LLMs as well as other projects like face swap, Roop, tortoise TTS, sadtalker, etc...
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Two video cards...one dedicated to Stable Diffusion...the other for everything else on my PC?
Use specific GPU on multi GPU systems · Issue #87 · CompVis/stable-diffusion · GitHub
- Automatic1111 - Multiple GPUs
- Ist Google inzwischen einfach unbrauchbar?
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Why are people so against compensation for artists?
I dealt with this in one of my posts. At least SD 1.1 till 1.5 are all trained on a batch size of 2048. The version pretty much everyone uses (1.5) is first pretrained at a resolution of 256x256 for 237K steps on laion2B-en, at the end of those training steps it will have seen roughly 500M images in laion2B-en. After that it is pre-trained for 194K steps on laion-high-resolution at a resolution of 512x512, which is a subset of 170M images from laion5B. Finally it is trained for 1.110K steps on LAION aesthetic v2 5+. This is easily verified by taking a glance at the model card of SD 1.5. Though that one doesn't specify for part of the training exactly which aesthetic set was used for part of the training, for that you have to look at the CompVis github repo. Thus at the end of it all both the most recent images and the majority of images will have come from LAION aesthetic v2 5+ (seeing every image approx 4 times). Realistically a lot of the weights obtained from pretraining on 2B will have been lost, and only provided a good starting point for the weights.
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Is SDXL really open-source?
stable diffusion · CompVis/stable-diffusion@2ff270f · GitHub
- I want to ask the AI to draw me as a Pokemon anime character then draw six of Pokemon of my choice next to me. What are my best free, 15$ or under and 30$ or under choices?
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how can i create my own ai image model
Here for example --> https://github.com/CompVis/stable-diffusion
What are some alternatives?
tensorflow-opencl - OpenCL support for TensorFlow
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
diffusers-uncensored - Uncensored fork of diffusers
pytorch-coriander - OpenCL build of pytorch - (in-progress, not useable)
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
onnx-mlir - Representation and Reference Lowering of ONNX Models in MLIR Compiler Infrastructure
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
dlprimitives - Deep Learning Primitives and Mini-Framework for OpenCL
onnx - Open standard for machine learning interoperability