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Brett Kuprel 2022-07-11 13:31:18 -04:00
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@ -42,15 +42,16 @@ model = MinDalle(
)
```
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.
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 `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 largest logit is subtracted from the logits to avoid infs. The logits are then divided by the `temperature`.
```python
image = model.generate_image(
text='Nuclear explosion broccoli',
seed=-1,
grid_size=4,
log2_k=6,
log2_supercondition_factor=5,
temperature=1,
top_k=256,
supercondition_factor=32,
is_verbose=False
)
@ -68,8 +69,9 @@ images = model.generate_images(
text='Nuclear explosion broccoli',
seed=-1,
image_count=7,
log2_k=6,
log2_supercondition_factor=5,
temperature=1,
top_k=256,
supercondition_factor=16,
is_verbose=False
)
```
@ -94,8 +96,9 @@ image_stream = model.generate_image_stream(
seed=-1,
grid_size=3,
log2_mid_count=3,
log2_k=6,
log2_supercondition_factor=3,
temperature=1,
top_k=256,
supercondition_factor=16,
is_verbose=False
)