safetensors_util
fastsdcpu
| safetensors_util | fastsdcpu | |
|---|---|---|
| 3 | 6 | |
| 66 | 956 | |
| - | - | |
| 8.1 | 9.5 | |
| 5 months ago | 3 days ago | |
| Python | Python | |
| - | MIT License |
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safetensors_util
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Training for concepts other than styles or a specific person
There is also this tool: https://github.com/by321/safetensors_util which can show captions used to train a LoRA. It probably works on mine. You could try it on CIVITAI models to get an idea of other peoples captions.
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Is there a way to extract the settings of a LyCORIS model?
If it's a safetensors file then you might be able to read the metadata with this, I have no idea about anything LyCORIS tho lol sorry, no idea if they're even safetensors or what. Figured I'd link the Reddit post so you can leave the person a like if you use their app and it helps you, but if you'd rather just skip to the Github for it that's your decision :) I've tried it and can confirm it works with Loras and regular models in safetensors format, so LyCORIS should be at least possible, as long as it's a safetensors file.
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Do you want to see how your favorite LoRA was trained ? You might be in luck, many files have a metadata field that records training information/parameters.
Many safetensors files, especially LoRA files, have a metadata field in the file header that records training information. This small python utility program lets you see this information, run it like this:
fastsdcpu
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FastSD CPU beta 21 - SDXL Turbo OpenVINO support (2.5 seconds on CPU)
Release : https://github.com/rupeshs/fastsdcpu/releases/tag/v1.0.0-beta.21
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Krita AI Diffusion
Too bad I don't have the Hardware to run it. Anyone had success with stable diffusion on Steam Deck ? The only thing that works for me is https://github.com/rupeshs/fastsdcpu , but it takes 1m per 512x512 image and is LCM
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$95 AMD CPU Becomes 16GB GPU to Run AI Software
> one minute and 50 seconds to generate a 512 x 512-pixel image with the default setting of 50 steps
A little over 2s/iter? That is... Not great.
It is slower than CPU diffusion: https://github.com/rupeshs/fastsdcpu
Stable Diffusion in particular doesn't need much VRAM anyway. I get that many people are stuck on lower end computers, but ~4GB is not an unreasonable requirement.
- FLaNK Stack Weekly for 30 Oct 2023
- Generate images in one second on your Mac using a latent consistency model
- rupeshs/fastsdcpu: Fast stable diffusion CPU
What are some alternatives?
kohya-sd-scripts-webui - Gradio wrapper for sd-scripts by kohya
Deep-Learning-Ultra - Open source Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in PyTorch, OpenCV (compiled for GPU), TensorFlow 2 for GPU, PyG and NVIDIA RAPIDS
xTuring - Build, customize and control you own LLMs. From data pre-processing to fine-tuning, xTuring provides an easy way to personalize open-source LLMs. Join our discord community: https://discord.gg/TgHXuSJEk6
sd-gui - Clean and simple Stable Diffusion GUI for macOS, Windows, and Linux
Dreambooth - Fine-tuning of diffusion models
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
StableDiffusionUI - Stable Diffusion UI: Diffusers (CUDA/ONNX)
latent-consistency-model - Run Latent Consistency Models on your Mac
Once-Upon-AI-Time - GPT-3 and Stable Diffusion powered short story generator
stablediffusion-infinity - Outpainting with Stable Diffusion on an infinite canvas
StableDiffusion-CheatSheet - A list of StableDiffusion styles and some notes for offline use. Pure HTML, CSS and a bit of JS.
jupyter-notebook-package - A Kurtosis package for Python data engineers, deploying a Jupyter notebook along with a configurable set of databases, and a visualization tool (Streamlit)