DALL·E mini by Craiyon - AI Image Tools Tool

Overview

Craiyon (originally released as DALL·E mini) is a lightweight, browser-based text-to-image generator that produces a 3×3 grid of creative image variations from a single text prompt. It was rebranded from DALL·E mini to Craiyon in 2022 and remains designed for fast, accessible experimentation: free users can generate Base-quality images while paid subscribers gain higher-quality “Pro” generations, priority processing, and additional aspect-ratio and download options. ([forbes.com](https://www.forbes.com/sites/qai/2022/10/21/dalle-mini-and-the-future-of-artificial-intelligence-art/?utm_source=openai)) Craiyon’s public demo runs on the web (and offers an Android app in some markets) and is backed by an open-source lineage (the original DALL·E mini project) and community tools on GitHub and Hugging Face. The service emphasizes ease-of-use for brainstorming, memes, concept sketches, and quick visual exploration rather than photoreal, production-grade output. Craiyon also provides a searchable gallery of community generations and an “Exclude” field to steer results away from unwanted elements. ([github.com](https://github.com/borisdayma/dalle-mini))

Model Statistics

• Likes: 5649

Model Details

DALL·E mini / Craiyon is a transformer-based text-to-image pipeline that uses a sequence-to-sequence approach: text prompts are encoded, then an autoregressive decoder predicts discrete image tokens that are decoded to pixels via a VQGAN-style decoder. Training drew from multiple large captioned image corpora, notably Conceptual Captions, Conceptual 12M, and a subset of YFCC100M; these datasets were used to train the Seq2Seq model and fine-tune the image encoder/decoder. The project also documents a larger DALL·E Mega variant and training journals describing distributed training strategies (TPU pods, distributed Shampoo optimizer, gradient checkpointing) for bigger models. Implementation and inference examples are available in the project’s inference pipeline notebook (Jupyter) and via the pip package. The codebase is released under an Apache-2.0 license. ([huggingface.co](https://huggingface.co/dalle-mini/dalle-mini?utm_source=openai))

Key Features

• Web-based demo that returns a 3×3 grid of nine image variations per prompt.
• Free Base-quality generation with optional paid Pro credits for higher quality.
• Four style presets: Photo, Illustration, Vector, and Raw to bias outputs.
• Exclude field to specify concepts or elements to avoid in outputs.
• Community Search and gallery to browse prompts and successful generations.
• Open-source code and model artifacts under Apache-2.0 on GitHub and Hugging Face.

Example Usage

Example (python):

# Minimal example (illustrative). See the project inference notebook for full setup and JAX/Flax device details. ([huggingface.co](https://huggingface.co/spaces/flax-community/dalle-mini/blame/4a1f007d3ccacb3ee8f3b59c153f82d462bd74cb/tools/inference/inference_pipeline.ipynb?utm_source=openai))
# Requires dalle-mini artifacts, VQGAN, and JAX/Flax runtime as used in the project's notebook.

from dalle_mini import DalleBart, DalleBartProcessor
from vqgan_jax import VQModel
import jax
import numpy as np
from PIL import Image

# Load models (actual loading requires Hugging Face model weights and JAX environment)
model = DalleBart.from_pretrained('dalle-mini/dalle-mini')
processor = DalleBartProcessor.from_pretrained('dalle-mini/dalle-mini')
vqgan = VQModel.from_pretrained('vqgan-f16-16384')

prompt = "A futuristic city skyline at sunset, cinematic lighting"
inputs = processor([prompt])

# NOTE: the real pipeline uses sharded generation across JAX devices; this is a conceptual snippet.
key = jax.random.PRNGKey(0)
encoded = model.generate(**inputs, prng_key=key, num_return_sequences=9)

# Decode tokens with VQGAN and save images (simplified)
decoded = vqgan.decode(encoded.sequences)
for i, img_arr in enumerate(np.clip((decoded*255).astype(np.uint8), 0, 255)):
    Image.fromarray(img_arr).save(f'craiyon_out_{i}.png')

# For an end-to-end runnable example and recommended generation parameters, follow
# the official inference pipeline notebook maintained in the repository. ([huggingface.co](https://huggingface.co/spaces/flax-community/dalle-mini/blame/4a1f007d3ccacb3ee8f3b59c153f82d462bd74cb/tools/inference/inference_pipeline.ipynb?utm_source=openai))

Benchmarks

GitHub stars (repository borisdayma/dalle-mini): ≈14.8k stars (Source: https://github.com/borisdayma/dalle-mini)

Hugging Face Space likes (dalle-mini/dalle-mini): ≈5.65k likes (Source: https://huggingface.co/spaces/dalle-mini/dalle-mini)

Default output per prompt: 9 image variations (3×3 grid) (Source: https://www.craiyon.com/ (Craiyon web demo and reporting))

Typical decoded image size: 256×256 pixels (model decode shape in inference pipeline) (Source: https://huggingface.co/spaces/flax-community/dalle-mini/tools/inference/inference_pipeline.ipynb)

Training data (major corpora): Conceptual Captions (3M), Conceptual12M (12M), YFCC100M subset (~15M used/subsampled) (Source: https://huggingface.co/dalle-mini/dalle-mini)

Key Information

• Category: Image Tools
• Type: AI Image Tools Tool