readme: ---
  tags:
  - pytorch
  license: mit
  ---
  
  # 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)
  [![Discord](https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white)](https://discord.com/channels/823813159592001537/912729332311556136)
  
  **[GitHub](https://github.com/kuprel/min-dalle)**
  
  This is a fast, minimal port of Boris Dayma's [DALL·E Mini](https://github.com/borisdayma/dalle-mini) (with mega weights).  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
  
  Here's a more detailed breakdown of performance on an A100. Credit to [@technobird22](https://github.com/technobird22) and his [NeoGen](https://github.com/technobird22/NeoGen) discord bot for the graph.
  <br />
  <img src="https://github.com/kuprel/min-dalle/raw/main/performance.png" alt="min-dalle" width="450"/>
  <br />
  
  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,
      device='cuda',
      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`.  Set the `device` to either "cuda" or "cpu".  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 `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.  The largest logit is subtracted from the logits to avoid infs.  The logits are then divided by the `temperature`.  If `is_seamless` is true, the image grid will be tiled in token space not pixel space.
  
  ```python
  image = model.generate_image(
      text='Nuclear explosion broccoli',
      seed=-1,
      grid_size=4,
      is_seamless=False,
      temperature=1,
      top_k=256,
      supercondition_factor=32,
      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 to [@hardmaru](https://twitter.com/hardmaru) for the [example](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,
      grid_size=3,
      is_seamless=False,
      temperature=1,
      top_k=256,
      supercondition_factor=16,
      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))
  ```
  
  ### Progressive Outputs
  
  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.  Set `progressive_outputs` to `True` to enable this.  An example is implemented in the colab.
  
  ```python
  image_stream = model.generate_image_stream(
      text='Dali painting of WALL·E',
      seed=-1,
      grid_size=3,
      progressive_outputs=True,
      is_seamless=False,
      temperature=1,
      top_k=256,
      supercondition_factor=16,
      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"/>
  
  **[❤️ Sponsor](https://github.com/sponsors/kuprel)**