84 lines
3.2 KiB
Markdown
Vendored
84 lines
3.2 KiB
Markdown
Vendored
# min(DALL·E)
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[](https://colab.research.google.com/github/kuprel/min-dalle/blob/main/min_dalle.ipynb)
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[](https://replicate.com/kuprel/min-dalle)
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[](https://discord.com/channels/823813159592001537/912729332311556136)
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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.
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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
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- 48 sec with a P100 in Colab
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- 14 sec with an A100 on Replicate
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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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```
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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(
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models_root='./pretrained',
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dtype=torch.float32,
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is_mega=True,
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is_reusable=True
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)
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```
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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.
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```python
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image = model.generate_image(
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text='Nuclear explosion broccoli',
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seed=-1,
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grid_size=4,
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log2_k=6,
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log2_supercondition_factor=5,
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is_verbose=False
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)
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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/nuclear_broccoli.jpg" alt="min-dalle" width="400"/>
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credit: https://twitter.com/hardmaru/status/1544354119527596034
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### Interactive
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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 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.
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```python
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image_stream = model.generate_image_stream(
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text='Dali painting of WALL·E',
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seed=-1,
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grid_size=3,
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log2_mid_count=3,
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log2_k=6,
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log2_supercondition_factor=3,
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is_verbose=False
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)
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for image in image_stream:
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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_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"/>
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