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# min(DALL·E)
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[![Open In Colab ](https://colab.research.google.com/assets/colab-badge.svg )](https://colab.research.google.com/github/kuprel/min-dalle/blob/main/min_dalle.ipynb)
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[![Replicate ](https://replicate.com/kuprel/min-dalle/badge )](https://replicate.com/kuprel/min-dalle)
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[![Join us on Discord ](https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white )](https://discord.gg/xBPBXfcFHd)
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This is a fast, minimal implementation 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.
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To generate a 4x4 grid of DALL·E Mega images it takes:
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- 89 sec with a T4 in Colab
- 48 sec with a P100 in Colab
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- 14 sec with an A100 on Replicate
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- TBD with an H100 (@NVIDIA?)
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The flax model and code for converting it to torch can be found [here ](https://github.com/kuprel/min-dalle-flax ).
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## Install
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```bash
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$ pip install min-dalle
```
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## Usage
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Load the model parameters once and reuse the model to generate multiple images.
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```python
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from min_dalle import MinDalle
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model = MinDalle(
is_mega=True,
is_reusable=True,
models_root='./pretrained'
)
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```
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The required models will be downloaded to `models_root` if they are not already there. Once everything has finished initializing, call `generate_image` with some text as many times as you want.
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```python
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image = model.generate_image(
'Dali painting of WALL·E',
seed=-1,
grid_size=4,
log2_supercondition_factor=3
)
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display(image)
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```
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< img src = "https://github.com/kuprel/min-dalle/raw/main/examples/dali_walle.jpg" alt = "min-dalle" width = "300" / >
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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.
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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 it the model is decoding. The detokenizer adds a slight delay for each intermediate image.
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```python
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image_stream = model.generate_image_stream(
text='Dali painting of WALL·E',
seed=-1,
grid_size=3,
log2_mid_count=3,
log2_supercondition_factor=3
)
is_first = True
for image in image_stream:
display_image = display if is_first else update_display
display_image(image, display_id=1)
is_first = False
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```
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< img src = "https://github.com/kuprel/min-dalle/raw/main/examples/dali_walle_animated.gif" alt = "min-dalle" width = "300" / >
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### Command Line
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Use `image_from_text.py` to generate images from the command line.
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```bash
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$ python image_from_text.py --text='artificial intelligence' --no-mega
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```
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< img src = "https://github.com/kuprel/min-dalle/raw/main/examples/artificial_intelligence.jpg" alt = "min-dalle" width = "200" / >