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Brett Kuprel 2022-07-07 21:14:06 -04:00
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@ -53,6 +53,21 @@ display(image)
<img src="https://github.com/kuprel/min-dalle/raw/main/examples/nuclear_broccoli.jpg" alt="min-dalle" width="400"/>
credit: https://twitter.com/hardmaru/status/1544354119527596034
The images can also be generated as a `FloatTensor` in case you want to process them manually (e.g. save individual images).
```python
images = model.generate_images(
text='Nuclear explosion broccoli',
seed=-1,
image_count=7,
log2_k=6,
log2_supercondition_factor=5,
is_verbose=False
)
```
Note: you will have to move the images to the cpu and convert to numpy, e.g. `images = images.to('cpu').detach().numpy()`. Then image $i$ can be coverted to a PIL.Image `image = Image.fromarray(images[i])`, and saved with its `save` method `image.save('image.png')`.
### Interactive
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.