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min(DALL·E)

This is a fast, minimal port of DALL·E Mega. It has been stripped down for inference and converted to PyTorch. The only third party dependencies are numpy, requests, pillow and torch.

To generate a 4x4 grid of DALL·E Mega images it takes:

89 sec with a T4 in Colab
48 sec with a P100 in Colab
13 sec with an A100 on Replicate

The flax model and code for converting it to torch can be found here.

Install

$ pip install min-dalle

Usage

Load the model parameters once and reuse the model to generate multiple images.

from min_dalle import MinDalle

model = MinDalle(
    models_root='./pretrained',
    dtype=torch.float32,
    is_mega=True, 
    is_reusable=True
)

The required models will be downloaded to models_root if they are not already there. Set the dtype to torch.float16 to save GPU memory. If you have an Ampere architecture GPU you can use torch.bfloat16. Once everything has finished initializing, call generate_image with some text as many times as you want. Use a positive seed for reproducible results. Higher values for log2_supercondition_factor result in better agreement with the text but a narrower variety of generated images. Every image token is sampled from the top-$k$ most probable tokens.

image = model.generate_image(
    text='Nuclear explosion broccoli',
    seed=-1,
    grid_size=4,
    log2_k=6,
    log2_supercondition_factor=5,
    is_verbose=False
)

display(image)

credit: https://twitter.com/hardmaru/status/1544354119527596034

Saving Individual Images

The images can also be generated as a FloatTensor in case you want to process them manually.

images = model.generate_images(
    text='Nuclear explosion broccoli',
    seed=-1,
    image_count=7,
    log2_k=6,
    log2_supercondition_factor=5,
    is_verbose=False
)

To get an image into PIL format you will have to first move the images to the CPU and convert the tensor to a numpy array.

images = images.to('cpu').numpy()

Then image $i$ can be coverted to a PIL.Image and saved

image = Image.fromarray(images[i])
image.save('image_{}.png'.format(i))

Interactive

If the model is being used interactively (e.g. in a notebook) generate_image_stream can be used to generate a stream of images as the model is decoding. The detokenizer adds a slight delay for each image. Setting log2_mid_count to 3 results in a total of 2 ** 3 = 8 generated images. The only valid values for log2_mid_count are 0, 1, 2, 3, and 4. This is implemented in the colab.

image_stream = model.generate_image_stream(
    text='Dali painting of WALL·E',
    seed=-1,
    grid_size=3,
    log2_mid_count=3,
    log2_k=6,
    log2_supercondition_factor=3,
    is_verbose=False
)

for image in image_stream:
    display(image)

Command Line

Use image_from_text.py to generate images from the command line.

$ python image_from_text.py --text='artificial intelligence' --no-mega

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