stable-diffusion
tvm
stable-diffusion | tvm | |
---|---|---|
111 | 16 | |
1,749 | 11,186 | |
- | 1.3% | |
10.0 | 9.9 | |
over 1 year ago | 7 days ago | |
Jupyter Notebook | Python | |
GNU Affero General Public License v3.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
stable-diffusion
-
PSA: You can run your GPU's at 80% power and get the same rendering speeds while saving heat/fan noise/electricity
use or update this one : https://github.com/hlky/stable-diffusion it has all the samplers, and if you want perfect faces, try k_euler_a
-
"a software developer after fixing a bug", by DALL-E 2
try this one https://github.com/hlky/stable-diffusion you need at least a 1050 to run it tho
- Which is the best fork out there ?
-
At the end of my rope on hlky fork, can anyone recommend any alternative GUI forks I could switch to?
https://github.com/hlky/stable-diffusion/issues/153 With 36 comments and tons of before and after comparisons, which are now deleted
-
CUDA memory error with hlky repo, (4GB Nvidia) - any ideas?
I wanted to try hlky version (https://github.com/hlky/stable-diffusion) , due to the WebUI and integration with upscaling models. It should also have the option to be optimized for low VRAM. To avoid getting a green square I have to add the parameters "--precision full --no-half". When I run a prompt, even with the smallest image size, I immediately get a CUDA memory error. Interestingly, without these parameters there isn't any memory error (but, of course, the result is a green square)
-
Fallout 5: Toronto (created with AI)
Made using https://github.com/hlky/stable-diffusion
-
Just released a Colab notebook that combines Craiyon+Stable Diffusion
Any chance to get this integrated into something like hlky's web ui?
-
AI Tekst til bilde: Elg og stavkirke med nordlys over Norsk flagg i bakgrunnen [OC] Mer detaljer i posten
Linux Guide her. Jeg har også Linux, men jeg valgte å sette det opp på Windows boksen min fordi driverne til Nvidia kortet på Linux ikke er helt sammarbeidsvillig når det kommer til å justere viftene etter sensorene i kortet (så jeg må sette det manuelt).
-
Using GFPGAN for only the eyes?
I'm seeing GFPGAN essentially remove all texture from faces, and I only want to use it on the eyes. Any thoughts on how to do this? I am using hlky/stable-diffusion now but I have no issues running a different repo/fork if needed and using command line.
- What's the best install of Stable Diffusion right now?
tvm
-
Show HN: I built a free in-browser Llama 3 chatbot powered by WebGPU
Yes. Web-llm is a wrapper of tvmjs: https://github.com/apache/tvm
Just wrappers all the way down
-
Making AMD GPUs competitive for LLM inference
Yes, this is coming! Myself and others at OctoML and in the TVM community are actively working on multi-gpu support in the compiler and runtime. Here are some of the merged and active PRs on the multi-GPU (multi-device) roadmap:
Support in TVM’s graph IR (Relax) - https://github.com/apache/tvm/pull/15447
-
VSL; Vlang's Scientific Library
Would it make sense to have a backend support for OpenXLA, Apache TVM, Jittor or other similar to get free GPU, TPU and other accelerators for free ?
- Apache TVM
-
MLC LLM - "MLC LLM is a universal solution that allows any language model to be deployed natively on a diverse set of hardware backends and native applications, plus a productive framework for everyone to further optimize model performance for their own use cases."
I have tried the iPhone app. It's fast. They're using Apache TVM which should allow better use of native accelerators on different devices. Like using metal on Apple and Vulcan or CUDA or whatever instead of just running the thing on the CPU like llama.cpp.
-
ONNX Runtime merges WebGPU back end
I was going to answer the same, I find the approach of machine learning compilers that directly compile models to host and device code better than having to bring a huge runtime. There are exciting projects in this area like TVM Unity, IREE [2], or torch.export [3]
[1] https://github.com/apache/tvm/tree/unity
[2] https://pytorch.org/get-started/pytorch-2.0/#inference-and-e...
[3] https://pytorch.org/get-started/pytorch-2.0/#inference-and-e...
-
Esp32 tensorflow lite
Apache TVM home page: https://tvm.apache.org/
-
Decompiling x86 Deep Neural Network Executables
It's pretty clear its referring to the output of Apache TVM and Meta's Glow
-
Run Stable Diffusion on Your M1 Mac’s GPU
As mentioned in sibling comments, Torch is indeed the glue in this implementation. Other glues are TVM[0] and ONNX[1]
These just cover the neural net though, and there is lots of surrounding code and pre-/post-processing that isn't covered by these systems.
For models on Replicate, we use Docker, packaged with Cog for this stuff.[2] Unfortunately Docker doesn't run natively on Mac, so if we want to use the Mac's GPU, we can't use Docker.
I wish there was a good container system for Mac. Even better if it were something that spanned both Mac and Linux. (Not as far-fetched as it seems... I used to work at Docker and spent a bit of time looking into this...)
[0] https://tvm.apache.org/
-
How to get started with machine learning.
Or use TVM, the idea is to compile your model into code that you can load at runtime. Similar to onnxruntime, it only does DNN inference; so you need domain-specific code.
What are some alternatives?
diffusers-uncensored - Uncensored fork of diffusers
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
stable-diffusion-krita-plugin
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
stable-diffusion - Optimized Stable Diffusion modified to run on lower GPU VRAM
stable-diffusion - This version of CompVis/stable-diffusion features an interactive command-line script that combines text2img and img2img functionality in a "dream bot" style interface, a WebGUI, and multiple features and other enhancements. [Moved to: https://github.com/invoke-ai/InvokeAI]
stable_diffusion.openvino
nebuly - The user analytics platform for LLMs
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/sd-webui/stable-diffusion-webui]
stable-diffusion