Generates images from input text. These models can be used to generate and modify images based on text prompts.


A city above clouds, pastel colors, Victorian style

Text-to-Image Model

About Text-to-Image

Use Cases

Data Generation

Businesses can generate data for their their use cases by inputting text and getting image outputs.

Immersive Conversational Chatbots

Chatbots can be made more immersive if they provide contextual images based on the input provided by the user.

Creative Ideas for Fashion Industry

Different patterns can be generated to obtain unique pieces of fashion. Text-to-image models make creations easier for designers to conceptualize their design before actually implementing it.

Architecture Industry

Architects can utilise the models to construct an environment based out on the requirements of the floor plan. This can also include the furniture that has to be placed in that environment.

Task Variants

You can contribute variants of this task here.


You can use diffusers pipelines to infer with text-to-image models.

from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler

model_id = "stabilityai/stable-diffusion-2"
scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
pipe = pipe.to("cuda")

prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]

You can use huggingface.js to infer text-to-image models on Hugging Face Hub.

import { HfInference } from "@huggingface/inference";

const inference = new HfInference(HF_TOKEN);
await inference.textToImage({
    model: "stabilityai/stable-diffusion-2",
    inputs: "award winning high resolution photo of a giant tortoise/((ladybird)) hybrid, [trending on artstation]",
    parameters: {
        negative_prompt: "blurry",

Useful Resources

This page was made possible thanks to the efforts of Ishan Dutta, Enrique Elias Ubaldo and Oğuz Akif.

Compatible libraries

Text-to-Image demo
Models for Text-to-Image
Browse Models (13,300)

Note A latent text-to-image diffusion model capable of generating photo-realistic images given any text input.

Note A model that can be used to generate images based on text prompts. The DALL·E Mega model is the largest version of DALLE Mini.

Note A text-to-image model that can generate coherent text inside image.

Datasets for Text-to-Image
Browse Datasets (2,687)

Note RedCaps is a large-scale dataset of 12M image-text pairs collected from Reddit.

Note Conceptual Captions is a dataset consisting of ~3.3M images annotated with captions.

Spaces using Text-to-Image

Note A powerful text-to-image application.

Note An text-to-image application that can generate coherent text inside the image.

Note An powerful text-to-image application that can generate images.

Note An powerful text-to-image application that can generates 3D representations.

Metrics for Text-to-Image
The Inception Score (IS) measure assesses diversity and meaningfulness. It uses a generated image sample to predict its label. A higher score signifies more diverse and meaningful images.
The Fréchet Inception Distance (FID) calculates the distance between distributions between synthetic and real samples. A lower FID score indicates better similarity between the distributions of real and generated images.
R-precision assesses how the generated image aligns with the provided text description. It uses the generated images as queries to retrieve relevant text descriptions. The top 'r' relevant descriptions are selected and used to calculate R-precision as r/R, where 'R' is the number of ground truth descriptions associated with the generated images. A higher R-precision value indicates a better model.