README.md

# min(DALL·E)

[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/kuprel/min-dalle/blob/main/min_dalle.ipynb)

This is a fast, minimal port of Boris Dayma's [DALL·E Mega](https://github.com/borisdayma/dalle-mini).  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](https://github.com/kuprel/min-dalle-flax).

## Install

```bash
$ pip install min-dalle
```  

## Usage

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

```python
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.

```python
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)
```
<img src="https://github.com/kuprel/min-dalle/raw/main/examples/nuclear_broccoli.jpg" alt="min-dalle" width="400"/>
credit: [https://twitter.com/hardmaru/status/1544354119527596034](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.

```python
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.
```python
images = images.to('cpu').numpy()
```
Then image $i$ can be coverted to a PIL.Image and saved
```python
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.

```python
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)
```
<img src="https://github.com/kuprel/min-dalle/raw/main/examples/dali_walle_animated.gif" alt="min-dalle" width="300"/>

### Command Line

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

```bash
$ python image_from_text.py --text='artificial intelligence' --no-mega
```
<img src="https://github.com/kuprel/min-dalle/raw/main/examples/artificial_intelligence.jpg" alt="min-dalle" width="200"/>

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