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๐ GENERAL CONSULTING ABC 123 BY OSAROPRIME โข.
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๐ MAGENTRON โข ๐
๐ ARTIFICIAL INTELLIGENCE 2.0 โข : IMAGINATION PROXIA (LIBRARY OF DIFFUSION MODELS)
*๏ธโฃ๐ถ๐ค
REQUIREMENTS:
[*] Software Requirements: Google Colab/Jupyter Notebook, Python, Tensor Flow
[*] HARDWARE REQUIREMENTS: fast GPU Graphics Processing Unit)
[*] DEPENDENCIES: INCLUDED
CLICK ON THE LINKS BELOW FOR A JUPYTER NOTEBOOK ON IMAGINATION PROXIA:
๐ https://github.com/GCABC123/magnetron.artificial-intelligence-2.0.mincloud.proxia--IMAGINATION-A1
๐ https://github.com/GCABC123/magnetron.artificial-intelligence-2.0.mincloud.proxia--IMAGINATION-B
๐ https://github.com/GCABC123/magnetron.artificial-intelligence-2.0.mincloud.proxia--IMAGINATION-C
๐ https://github.com/GCABC123/magnetron.artificial-intelligence-2.0.mincloud.proxia--IMAGINATION-D
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Prerequisite reading:
๐ ARTIFICIAL INTELLIGENCE PRIMER โข: https://www.facebook.com/artificialintelligenceprimer
๐ ARTIFICIAL INTELLIGENCE 2.0 โข DOCUMENTATION: https://www.facebook.com/aibyabc123/
๐ MEMBER'S CLUB โข DOCUMENTATION - https://www.facebook.com/abc123membersclub/
๐ INCLUDED STICKERS/SIGN:
FIND STICKERS HERE: https://bit.ly/3B8D3lE
PROMOTIONAL MATERIAL FOR ๐ ๐๐๐ก๐๐ง๐ฅ๐ข๐ก ๐ง๐๐๐๐ก๐ข๐๐ข๐๐ฌ โข. (CUSTOM GRAPHICS BY ๐๐๐ ๐ญ๐ฎ๐ฏ ๐๐๐ฆ๐ฌ๐๐ก โข/๐ข๐ฆ๐๐ฅ๐ข ๐๐๐ฅ๐ฅ๐๐ข๐ง๐ง). THE ๐ ๐๐๐ก๐๐ง๐ฅ๐ข๐ก ๐ง๐๐๐๐ก๐ข๐๐ข๐๐ฌ โข SYMBOL/LOGO IS A TRADEMARK OF ๐ง๐๐ ๐๐๐ ๐ญ๐ฎ๐ฏ ๐๐ฅ๐ข๐จ๐ฃ โข FOR ๐ ๐๐๐ก๐๐ง๐ฅ๐ข๐ก ๐ง๐๐๐๐ก๐ข๐๐ข๐๐ฌ โข. ๐ง๐๐ ๐๐๐ ๐ญ๐ฎ๐ฏ ๐๐ฅ๐ข๐จ๐ฃ โข SYMBOL/LOGO IS A TRADEMARK OF ๐ง๐๐ ๐๐๐ ๐ญ๐ฎ๐ฏ ๐๐ฅ๐ข๐จ๐ฃ โข. *๏ธโฃ๐ถ๐ค
PROMOTIONAL MATERIAL FOR ๐๐ฅ๐ง๐๐๐๐๐๐๐ ๐๐ก๐ง๐๐๐๐๐๐๐ก๐๐ ๐ฎ.๐ฌ โข. (CUSTOM GRAPHICS BY ๐๐๐ ๐ญ๐ฎ๐ฏ ๐๐๐ฆ๐ฌ๐๐ก โข/๐ข๐ฆ๐๐ฅ๐ข ๐๐๐ฅ๐ฅ๐๐ข๐ง๐ง) THE ๐๐ฅ๐๐๐ข๐ก & ๐๐ฅ๐ข๐ช๐ก ๐ SYMBOL/LOGO IS A TRADEMARK OF ๐ง๐๐ ๐๐๐ ๐ญ๐ฎ๐ฏ ๐๐ฅ๐ข๐จ๐ฃ โข ASSOCIATED WITH TECHNOLOGY. ๐ง๐๐ ๐๐๐ ๐ญ๐ฎ๐ฏ ๐๐ฅ๐ข๐จ๐ฃ โข SYMBOL/LOGO IS A TRADEMARK OF ๐ง๐๐ ๐๐๐ ๐ญ๐ฎ๐ฏ ๐๐ฅ๐ข๐จ๐ฃ โข. You must display the included stickers/signs (so that it is clearly visible) if you are working with MAGNETRON โข TECHNOLOGY for the purposes of determining whether you want to purchase a technology license or not. This includes but is not limited to public technology displays, trade shows, technology expos, media appearances, Investor events, Computers (exterior), MINDCLOUD STORAGE (e.g server room doors, render farm room doors) etc.
NOTE: IMAGINATION PROXIA A IS DESCRIBED IN THE ๐๐ฅ๐ง๐๐๐๐๐๐๐ ๐๐ก๐ง๐๐๐๐๐๐๐ก๐๐ ๐ฎ.๐ฌ โข DOCUMENTATION.
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๐ค Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, ๐ค Diffusers is a modular toolbox that supports both. Our library is designed with a focus on usability over performance, simple over easy, and customizability over abstractions.
๐ค Diffusers offers three core components:
- State-of-the-art diffusion pipelines that can be run in inference with just a few lines of code.
- Interchangeable noise schedulers for different diffusion speeds and output quality.
- Pretrained models that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.
We recommend installing ๐ค Diffusers in a virtual environment from PyPi or Conda. For more details about installing PyTorch and Flax, please refer to their official documentation.
With pip (official package):
pip install --upgrade diffusers[torch]With conda (maintained by the community):
conda install -c conda-forge diffusersWith pip (official package):
pip install --upgrade diffusers[flax]Please refer to the How to use Stable Diffusion in Apple Silicon guide.
Generating outputs is super easy with ๐ค Diffusers. To generate an image from text, use the from_pretrained method to load any pretrained diffusion model (browse the Hub for 4000+ checkpoints):
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipeline.to("cuda")
pipeline("An image of a squirrel in Picasso style").images[0]You can also dig into the models and schedulers toolbox to build your own diffusion system:
from diffusers import DDPMScheduler, UNet2DModel
from PIL import Image
import torch
import numpy as np
scheduler = DDPMScheduler.from_pretrained("google/ddpm-cat-256")
model = UNet2DModel.from_pretrained("google/ddpm-cat-256").to("cuda")
scheduler.set_timesteps(50)
sample_size = model.config.sample_size
noise = torch.randn((1, 3, sample_size, sample_size)).to("cuda")
input = noise
for t in scheduler.timesteps:
with torch.no_grad():
noisy_residual = model(input, t).sample
prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample
input = prev_noisy_sample
image = (input / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
image = Image.fromarray((image * 255).round().astype("uint8"))
imageCheck out the Quickstart to launch your diffusion journey today!
| Documentation | What can I learn? |
|---|---|
| Tutorial | A basic crash course for learning how to use the library's most important features like using models and schedulers to build your own diffusion system, and training your own diffusion model. |
| Loading | Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers. |
| Pipelines for inference | Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library. |
| Optimization | Guides for how to optimize your diffusion model to run faster and consume less memory. |
| Training | Guides for how to train a diffusion model for different tasks with different training techniques. |
This library concretizes previous work by many different authors and would not have been possible without their great research and implementations. We'd like to thank, in particular, the following implementations which have helped us in our development and without which the API could not have been as polished today:
- @CompVis' latent diffusion models library, available here
- @hojonathanho original DDPM implementation, available here as well as the extremely useful translation into PyTorch by @pesser, available here
- @ermongroup's DDIM implementation, available here
- @yang-song's Score-VE and Score-VP implementations, available here
We also want to thank @heejkoo for the very helpful overview of papers, code and resources on diffusion models, available here as well as @crowsonkb and @rromb for useful discussions and insights.
@misc{von-platen-etal-2022-diffusers,
author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf},
title = {Diffusers: State-of-the-art diffusion models},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/huggingface/diffusers}}
}