stable-diffusion-tensorflow
ml-stable-diffusion
stable-diffusion-tensorflow | ml-stable-diffusion | |
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18 | 45 | |
1,569 | 16,130 | |
- | 0.8% | |
0.0 | 7.4 | |
9 months ago | about 1 month ago | |
Python | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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stable-diffusion-tensorflow
- Keras model SD or similar I can train from scratch?
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Anyone attempted to convert stablediffusion tensorflow to tf lite?
was curious if someone attempted the conversion? I tried here https://github.com/divamgupta/stable-diffusion-tensorflow/issues/58 but having some input shapes error. First time trying the conversion here, would love to run it on a edge tpu.
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Stable Diffusion Tensorflow to TF Lite
Checking here is someone tried to convert the tensorflow diffusion model into a tf lite?https://github.com/divamgupta/stable-diffusion-tensorflow/issues/58
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SD on intel arc?
Actually I was just on GitHub trying to submit issues related to me testing Intel's PyTorch and Tensorflow extensions when I saw this; it seems that someone has already ported SD over to the tensorflow framework and so you can probably start using intel's extension for tensorflow with it immediately; and according to this article you can use Intel's extension within WSL under windows as well. But unfortunately given how the guy whose issue I linked to has been facing pretty serious performance issues of inferencing taking many minutes longer than it should when using an A770 to do SD-related inferencing, you might be better off waiting for intel's extension for tensorflow versions 1.2 and greater or something like that, so that when it's your turn to use it, Intel has already ironed out most of the major bugs within the software :)
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Stable Diffusion with AMDGPU on WSL
tensorflow-stable-diffusion
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Image2Image with AMD hardware?
# clone git clone https://github.com/divamgupta/stable-diffusion-tensorflow.git cd stable-diffusion-tensorflow # create venv python -m venv --prompt sdtf-windows-directml venv venv\Scripts\activate # verify venv is installed and activated pip --version # install deps pip install -r requirements.txt pip install tensorflow-directml-plugin # you should see DML debug output and at least one GPU python -c 'import tensorflow as tf; print(tf.config.list_physical_devices())' # run (show help) python text2image.py --help python text2image.py --prompt "a fluffy kitten"
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I have no PC. Just DLed this for iOS
(Answers based on stable-diffusion open model) If you have a M1 processor: https://github.com/divamgupta/diffusionbee-stable-diffusion-ui (I've tested it) Or this claimed faster with TensorFlow: https://github.com/divamgupta/stable-diffusion-tensorflow
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Keras Inpainting Colab
Added inpainting support to the original keras implementation: https://github.com/divamgupta/stable-diffusion-tensorflow Colab: https://colab.research.google.com/drive/1Bf-bNmAdtQhPcYNyC-guu0uTu9MYYfLu Github page: https://github.com/ShaunXZ/stable-diffusion-tensorflow
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[N] Stable Diffusion reaches new record (with explanation + colab link)
I wonder if you mean 13 seconds per image because this implementation reports ~10s per image with mixed precision.
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High-performance image generation using Stable Diffusion in KerasCV
On intel MacBookPro, CPU-only, the original one[1] using pytorch only utilized one core. A tensorflow implementation[2] with oneDNN support which utilized most of the cores ran at ~11sec/iteration. Another OpenVINO based implementation[3] ran at ~6.0sec/iteration.
[1] https://github.com/CompVis/stable-diffusion/
[2] https://github.com/divamgupta/stable-diffusion-tensorflow/
[3] https://github.com/bes-dev/stable_diffusion.openvino/
ml-stable-diffusion
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Show HN: Run Stable Diffusion Directly on iPhone
Not sure how that got in here. Apple released CoreML Stable Diffusion library a little over a year ago [1]. Hugging Face released their version of the example app for the CoreML Stable Diffusion library [2].
The app should be able to run on iPhone 14 Pro, I believe the requirements is about 6-8Gb of RAM. And I was not able to run it on iPhone 13 Mini, because it has only 4Gb of RAM.
- [1] https://github.com/apple/ml-stable-diffusion
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Apple releases MLX; has working Stable Diffusion example
Where are you seeing a Stable Diffusion example? I'm familiar with Apple's CoreML Implementation of StableDiffusion, but is there something else in the SD world available for download now as part of MLX?
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Stable Diffusion XL on iPhone with Core ML
Other features and improvements to the repo https://github.com/apple/ml-stable-diffusion
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FLaNK Stack Weekly for 20 June 2023
M1! https://github.com/apple/ml-stable-diffusion
- Apple Introduces M2 Ultra with up to 192GB Unified Memory - LLM powerhouse?
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Need help choosing between two laptops
M2 MBA can run Stable Diffusion and LLaMa comfortably, which means generating your potential game/image asset locally. They're pretty much impractical in 7340.
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Speed Is All You Need: On-Device Acceleration of Large Diffusion Models
Interestingly these are OpenCL kernels so in theory some of the optimizations might run out-of-the-box on CPUs.
It would be instructive to compare their speedups on the iPhone to the Apple CoreML implementation: https://github.com/apple/ml-stable-diffusion
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Is it worth buying a used M1 Mac for stable diffusion when you have iPad M1 but Intel Mac
Stable Diffusion runs great on my M1 Macs. The Draw Things app makes it really easy to run too. You also can’t disregard that Apple’s M chips actually have dedicated neural processing for ML/AI. This actual makes a Mac more affordable in this category because you don’t need to purchase a beefy graphics card. Not to mention that Apple has even optimized their software specifically for Stable Diffusion (related GitHub). Draw Things can take advantage of this. There’s a few guides to running the web UI on M1 too. I prefer the Draw Things app because of how easy it is to use, but the web UI is also nice because of all of the plugins and workflows that the community has built over time.
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Stable diffusion for Apple silicon
LINKS: ml-stable-diffusion: https://github.com/apple/ml-stable-diffusion Diffusers (HuggingFace Mac App): https://apps.apple.com/app/diffusers/id1666309574?mt=12
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Apple: Transformer architecture optimized for Apple Silicon
So, is Stable Diffusion working finally on TPU or not? DiffusionBee uses GPU and running this https://github.com/apple/ml-stable-diffusion with CPU_AND_NE just segfaults
What are some alternatives?
fast-stable-diffusion - fast-stable-diffusion + DreamBooth
MochiDiffusion - Run Stable Diffusion on Mac natively
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
ml-ane-transformers - Reference implementation of the Transformer architecture optimized for Apple Neural Engine (ANE)
AITemplate - AITemplate is a Python framework which renders neural network into high performance CUDA/HIP C++ code. Specialized for FP16 TensorCore (NVIDIA GPU) and MatrixCore (AMD GPU) inference.
modelscope - ModelScope: bring the notion of Model-as-a-Service to life.
keras-cv - Industry-strength Computer Vision workflows with Keras
pulsar-recipes - A StreamNative library containing a collection of recipes that are implemented on top of the Pulsar client to provide higher-level functionality closer to the application domain.
intel-extension-for-tensorflow - Intel® Extension for TensorFlow*
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.
stable-diffusion-webui - Stable Diffusion web UI