jacobmarks / text-to-image Goto Github PK

• 2023-12-19: Added support for Kandinsky-2.2 and Playground V2 models
• 2023-11-30: Version 1.2.0
  • adds local model running via diffusers (>=0.24.0)
  • adds calling from the Python SDK!
  • ⚠️ BREAKING CHANGE: the plugin and operator URIs have been changed from ai_art_gallery to text_to_image. If you have any saved pipelines that use the plugin, you will need to update the URIs.
• 2023-11-08: Version 1.1.0 adds support for DALLE-3 Model — upgrade to openai>=1.1.0 to use 😄
• 2023-10-30: Added support for Segmind Stable Diffusion (SSD-1B) Model
• 2023-10-23: Added support for Latent Consistency Model
• 2023-10-18: Added support for SDXL, operator icon, and download location selection

This plugin is a Python plugin that allows you to generate images from text prompts and add them directly into your dataset.

⚠️ This plugin is only verified to work for local datasets. It may not work for remote datasets.

This version of the plugin supports the following models:

• DALL-E2
• DALL-E3
• Kandinsky-2.2
• Latent Consistency Model
• Playground V2
• SDXL
• SDXL-Lighting
• Segmind Stable Diffusion (SSD-1B)
• Stable Diffusion
• VQGAN-CLIP

It is straightforward to add support for other models!

fiftyone plugins download https://github.com/jacobmarks/text-to-image

If you want to use Replicate models, you will need to pip install replicate and set the environment variable REPLICATE_API_TOKEN with your API token.

If you want to use DALL-E2 or DALL-E3, you will need to pip install openai and set the environment variable OPENAI_API_KEY with your API key.

To run the Latency Consistency model locally with Hugging Face's diffusers library, you will need diffusers>=0.24.0. If you need to, you can install it with pip install diffusers>=0.24.0.

Refer to the main README for more information about managing downloaded plugins and developing plugins locally.

• Generates an image from a text prompt and adds it to the dataset

You can also use the txt2img operators from the Python SDK!

⚠️ Due to the way Jupyter Notebooks interact with asyncio, this will not work in a Jupyter Notebook. You will need to run this code in a Python script or in a Python console.

import fiftyone as fo
import fiftyone.operators as foo
import fiftyone.zoo as foz

dataset = fo.load_dataset("quickstart")

## Access the operator via its URI (plugin name + operator name)
t2i = foo.get_operator("@jacobmarks/text_to_image/txt2img")

## Run the operator

prompt = "A dog sitting in a field"
t2i(dataset, prompt=prompt, model_name="latent-consistency", delegate=False)

## Pass in model-specific arguments
t2i(
    dataset,
    prompt=prompt,
    model_name="latent-consistency",
    delegate=False,
    width=768,
    height=768,
    num_inference_steps=8,
)