81 lines
2.8 KiB
Markdown
Vendored
81 lines
2.8 KiB
Markdown
Vendored
# min(DALL·E)
|
|
|
|
[](https://colab.research.google.com/github/kuprel/min-dalle/blob/main/min_dalle.ipynb)
|
|
|
|
[](https://replicate.com/kuprel/min-dalle)
|
|
|
|
[](https://discord.gg/xBPBXfcFHd)
|
|
|
|
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.
|
|
|
|
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
|
|
- 14 sec with an A100 on Replicate
|
|
- TBD with an H100 (@NVIDIA?)
|
|
|
|
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(
|
|
is_mega=True,
|
|
is_reusable=True,
|
|
models_root='./pretrained'
|
|
)
|
|
```
|
|
|
|
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.
|
|
|
|
```python
|
|
image = model.generate_image(
|
|
'Dali painting of WALL·E',
|
|
seed=-1,
|
|
grid_size=4,
|
|
log2_supercondition_factor=3
|
|
)
|
|
display(image)
|
|
```
|
|
<img src="https://github.com/kuprel/min-dalle/raw/main/examples/dali_walle.jpg" alt="min-dalle" width="300"/>
|
|
|
|
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.
|
|
|
|
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.
|
|
|
|
```python
|
|
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
|
|
```
|
|
<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"/>
|