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Pollinations are an effort to make generative art more approachable.

  • A frontend hosting a set of curated notebooks that allow creating and experimenting with generative art.
  • The Interplanetary Filesystem (IPFS) for decentralized censorship-resistant storage of models, code and generated content
  • Pollinations are run on Google Colab at the moment (for the free cloud GPUs)

Instructions on using Pollinations.AI.

🐍 Python Package

# Usage Example

import pollinations as ai

model: object = ai.Model()

image: object = model.generate(
    prompt=f'Golden retriever puppy playing in the rain {ai.realistic}',
    model=ai.turbo,
    height=512,
    seed=57184
)
image.save('image-output.jpg')

print(image.url)
// >>> https://image.pollinations.ai/prompt/Golden%20retriever%20puppy%20playing%20in%20the%20rain%20realistic,%20realism,%20real%20life,%20ultra%20realistic,%20high%20quality,%20real?model=turbo&width=1024&height=512&seed=57184

image

�👩‍💻 Development (outdated)

Development of Pollinations.AI Web App
`make dev` to start the development server of web app.% 

Enable logging:
`localStorage.debug = "*"`

If logs don't appear in Chrome enable logging "All Aevels" (including Verbose)


## Optionally it is possible to develop with a local IPFS node

Enable connect to local IPFS:
`localStorage.localIFS = true

`make up` to start the releant services. See next section for details
Development of Notebooks

All notebooks at pollinations/hive are automatically deployed to Pollinations.

We will add an easy way to include custom notebooks. For now, to use an external notebook it needs to be uploaded to IPFS wrapped in a folder in the form input/notebook.ipynb. The resulting IPFS hash can be used directly like so: https://pollinations.ai/p/[hash]

Development with Local IPFS node
Development environment requires `docker` & `docker-compose` for running a loca IPFS node. For docker installation, please navigate to https://docs.docker.com/get-docker/.

After docker is setup, `make` is used for managing the IPFS and development environment.

## 🟡 Initialization

To run pollinations development environment first time,

- Run `make init`, this will initialize start the IPFS docker image and fill `tmp/ipfs` folder by migrating IPFS.

## 🟢 Running

After IPFS migrated, to start development environment,
- Run `make up`, this will start the dockerized IPFS instance and detach.
- Run `make dev` to start the react application living under `/app`

## 🔴 Stopping

- Run `make down` to stop running IPFS instance.
- Run `make clean` to remove the `tmp` folder and its contents.

## ⚙️ Configuration

IPFS configuration can be found and updated in `docker/ipfs/config.json`. Every time the docker containers are started, the config file under `tmp/ipfs/config` is overwritten with this json file.
Architecture Diagram

The following diagram has an editable copy embedded. Use https://draw.io/#Hpollinations/pollinations/master/pollinations_architecture.png to edit the file.

Export the results as PNG with "Include a copy of my diagram" option selected and replace the current diagram.

Architecture Diagram

🔗 Links

Pollinations.AI's Projects

adampi icon adampi

Single-View View Synthesis in the Wild with Learned Adaptive Multiplane Images, accepted by SIGGRAPH 2022.

audioclip icon audioclip

Source code for models described in the paper "AudioCLIP: Extending CLIP to Image, Text and Audio" (https://arxiv.org/abs/2106.13043)

audiocraft icon audiocraft

Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable music generation LM with textual and melodic conditioning.

audioldm icon audioldm

AudioLDM: Generate speech, sound effects, music and beyond, with text.

avatarclip icon avatarclip

[SIGGRAPH 2022 Journal Track] AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars

bark icon bark

🔊 Text-Prompted Generative Audio Model

blip icon blip

PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

clip-actor icon clip-actor

[ECCV'22] Official PyTorch Implementation of "CLIP-Actor: Text-Driven Recommendation and Stylization for Animating Human Meshes"

clipmesh icon clipmesh

Official implementation of CLIP-Mesh: Generating textured meshes from text using pretrained image-text models

cliptranslate icon cliptranslate

Our attempt to use AudioCLIP to translate any modality into sound or images. Inspired by deep-daze, big-sleep, latent2visions etc

ddsp icon ddsp

DDSP: Differentiable Digital Signal Processing

deep-daze icon deep-daze

Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). Technique was originally created by https://twitter.com/advadnoun

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