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80 lines
3.0 KiB
80 lines
3.0 KiB
# 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 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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- 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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``` |
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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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is_mega=True, |
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is_reusable=True, |
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models_root='./pretrained' |
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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. 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. |
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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_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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### 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_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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