diffusers VS stable-diffusion-webui

# Step 1: Install required packages pip install transformers>=4.51.3 pip install git+https://github.com/huggingface/diffusers pip install python-pptx torch pillow # Step 2: Verify CUDA availability (for GPU users) python -c "import torch; print(torch.cuda.is_available())"

Diffusers Documentation

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 pip install einops timm pillow pip install git+https://github.com/huggingface/transformers pip install git+https://github.com/huggingface/accelerate pip install git+https://github.com/huggingface/diffusers pip install huggingface_hub pip install sentencepiece bitsandbytes protobuf decord numpy

System requirements: 24GB GPU memory and 64GB+ RAM > pip install git+https://github.com/huggingface/diffusers

pip install --upgrade pip pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 pip install git+https://github.com/huggingface/diffusers.git pip install transformers accelerate safetensors numpy pillow

I'm not a fan of Python, but it is the languange of choice for most Machine Learning code. I'm currently working on a NodeJS based project where I need to call the HuggingFace Diffusers python module. So I came up with an IPC wrapper around the the Python script.

!pip install torch torchvision torchaudio einops timm pillow !pip install git+https://github.com/huggingface/transformers !pip install git+https://github.com/huggingface/accelerate !pip install git+https://github.com/huggingface/diffusers !pip install huggingface_hub !pip install sentencepiece bitsandbytes protobuf decord ffmpeg-python imageio opencv-python

> All the self-hosted LLM and text-to-image models come with some restrictions trained into them

https://github.com/huggingface/diffusers/issues/3422

import modal image = modal.Image.debian_slim(python_version="3.10").apt_install( "libglib2.0-0", "libsm6", "libxrender1", "libxext6", "ffmpeg", "libgl1", "git" ).pip_install( "git+https://github.com/huggingface/diffusers.git", "invisible_watermark", "transformers", "accelerate", "safetensors", "sentencepiece", )

pip install torch==2.3.1 pip install git+https://github.com/huggingface/diffusers.git pip install transformers==4.43.3 sentencepiece==0.2.0 accelerate==0.33.0 protobuf==5

Link (Github): https://github.com/AUTOMATIC1111/stable-diffusion-webui

For those interested in this innovative tool, accessing the GitHub repository at https://github.com/AUTOMATIC1111/stable-diffusion-webui provides further details and instructions on how to utilize its features effectively. Embrace the future of creativity and unlock new possibilities with this enhanced web interface for Stable Diffusion.

A year and a half ago, I embarked on an open-source project that has since grown and evolved significantly. Inspired by the AUTOMATIC1111 project, which was just starting to gain traction at the time, I kept adding new features and capabilities. Today, my project integrates over 50 different neural networks, each handling a unique task. In this article, I want to share some practical tips and key takeaways from my journey. I hope they prove helpful to you and motivate you to refactor your code.

We will be looking into the image generation aspect of this generative model. We will use stable-diffusion-webui for more user-friendly interface.

* LLaVA model: I'll add more documentation. You are right Llava could not generate images. For image generation I don't have immediate plans, but checkout these projects for local image generation.

- https://diffusionbee.com/

- https://github.com/comfyanonymous/ComfyUI

- https://github.com/AUTOMATIC1111/stable-diffusion-webui

I would love to be able to have a native stable diffusion experience, my rx 580 takes 30s to generate a single image. But it does work after following https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki...

I got this up and running on my windows machine in short order and I don't even know what stable diffusion is.

But again, it would be nice to have first class support to locally participate in the fun.

In Auto1111, that just uses Image.blend. :)

https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob...