@ -12,7 +12,6 @@ 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).
@ -30,13 +29,14 @@ Load the model parameters once and reuse the model to generate multiple images.
from min_dalle import MinDalle
model = MinDalle(
models_root='./pretrained',
dtype=torch.float32,
is_mega=True,
is_reusable=True,
models_root='./pretrained'
is_reusable=True
)
```
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. Every image token is sampled from the top$k$ most probable tokens.
The required models will be downloaded to `models_root` if they are not already there. If you have an Ampere architecture GPU you can set the `dtype=torch.bfloat16` and save GPU memory. There is still an issue with `dtype=torch.float16` that needs to be sorted out. 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.