nvitop
stable-diffusion-webui-directml
| nvitop | stable-diffusion-webui-directml | |
|---|---|---|
| 5 | 74 | |
| 3,965 | 1,551 | |
| - | - | |
| 7.3 | 9.9 | |
| 12 days ago | 10 days ago | |
| Python | Python | |
| Apache License 2.0 | GNU Affero General Public License v3.0 |
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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.
nvitop
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Nvtop: Linux Task Monitor for Nvidia, AMD and Intel GPUs
That's why the authors recommend pipx for installing nvitop. I am not a sysadmin, but I prefer pipx over relying on the (often outdated) distro sources.
https://github.com/XuehaiPan/nvitop?tab=readme-ov-file#insta...
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Why does stable diffusion hold onto my vram even when it’s doing nothing. It works great for a few images and then it racks up so much vram usage it just won’t do anything anymore and errors out. Is there a way to free up VRAM every so often? RX 6700XT 12GB VRAM
Hey, thanks for that. I think I found what you mentioned: https://github.com/XuehaiPan/nvitop
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How to make a deep learning engineer happy in one command? `nvidia-smi`:
nvitop
- Prédiction de séries chronologiques avec une carte GPU dans Paperspace Gradient et le langage Julia…
stable-diffusion-webui-directml
- stable diffusion compliant with amd gpu or not?
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RuntimeError: Could not allocate tensor with 4915840 bytes. There is not enough GPU video memory available!
I'm getting this error using (https://github.com/lshqqytiger/stable-diffusion-webui-directml/issues), freshly installed. I'm running it on an AMD RX 6700 XT with 12gb of vram. Generating a single image at default settings (512x512, 20 steps, etc.) I can do simple prompts (i.e. "kitty cat") but as soon as I add a couple more tags, I get the aforementioned error message, usually 20-30% into generating an image. I went through this thread (https://github.com/lshqqytiger/stable-diffusion-webui-directml/issues/38) and tried every solution I saw, most of them being variations of adding --medvram --precision full --no-half --no-half-vae --opt-split-attention-v1 --opt-sub-quad-attention --disable-nan-check to the commandline arguments. What else might I be able to try? Thanks.
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Best AMD SD Guide for 2023?
i use automatic 1111 you can find the installation on the github , this branch https://github.com/lshqqytiger/stable-diffusion-webui-directml and it works fine although the speed is what it is, i also have a old GPU.
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Just how much VRAM do I need? It keeps saying I don't have enough with a 7900xt.
I'm using this one: https://github.com/lshqqytiger/stable-diffusion-webui-directml
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I am confused regarding same seed = same picture. Any explanations or insights? The journey for this in comments.
- https://github.com/lshqqytiger/stable-diffusion-webui-directm - starting webuser-ui with COMMANDLINE_ARGS=--opt-sub-quad-attention –disable-nan-check - AMD 8GB Radeon Pro WX7100
- ¿Quién fue a la marcha contra la IA en el Obelisco? Cuenten cómo estuvo
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StableDiffusion will only use my CPU?
I'm running this fork (https://github.com/lshqqytiger/stable-diffusion-webui-directml) on a pc with a Ryzen 5700x and a Radeon RX 6700 XT 12 GB Video Card.
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(AMD) Random Running Out of Memory Error After Generation
I am using direct-ml fork
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Stable Diffusion on AMD 6900XT is Super Slow
Im running Stable diffusion on my 6900XT, and I feel like its way slower than normal. Using the updated Webui https://github.com/lshqqytiger/stable-diffusion-webui-directml.
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Stable Diffusion DirectML on AMD APU only (no external GPU) - Ram Usage?
This refers to the use of iGPUs (example: Ryzen 5 5600G). No graphic card, only an APU. The DirectML Fork of Stable Diffusion (SD in short from now on) works pretty good with AMD. But not only with the GPUs, but also with only-APUs without GPUs.
What are some alternatives?
jetson_stats - 📊 Simple package for monitoring and control your NVIDIA Jetson [Orin, Xavier, Nano, TX] series
SHARK - SHARK - High Performance Machine Learning Distribution
gpustat - 📊 A simple command-line utility for querying and monitoring GPU status
StableDiffusionUI - Stable Diffusion UI: Diffusers (CUDA/ONNX)
tmu - Implements the Tsetlin Machine, Coalesced Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features, drop clause, Type III Feedback, focused negative sampling, multi-task classifier, autoencoder, literal budget, and one-vs-one multi-class classifier. TMU is written in Python with wrappers for C and CUDA-based clause evaluation and updating.
sd-webui-controlnet - WebUI extension for ControlNet
pg-counter-metrics - PG Counter Metrics ( PGCM ) is a tool for publishing PostgreSQL performance data to CloudWatch. By publishing to CloudWatch, dashboards and alarming can be used on the collected data.
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
gpu-operator - NVIDIA GPU Operator creates/configures/manages GPUs atop Kubernetes
stable-diffusion-webui - Stable Diffusion web UI
pg_activity - pg_activity is a top like application for PostgreSQL server activity monitoring.
OnnxDiffusersUI - UI for ONNX based diffusers