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0.3.13, simplified code, specify device when initializing MinDalle

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Brett Kuprel
2022-07-15 17:18:23 -04:00
parent a2dca41623
commit 3c28b1059b
9 changed files with 139 additions and 188 deletions
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README.md
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@@ -36,12 +36,13 @@ from min_dalle import MinDalle
model = MinDalle(
models_root='./pretrained',
dtype=torch.float32,
device='cuda',
is_mega=True,
is_reusable=True
)
```
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`.
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`. Set the `device` to either "cuda" or "cpu". 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(
@@ -88,7 +89,7 @@ image.save('image_{}.png'.format(i))
### Progressive Outputs
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.
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. Set `progressive_outputs` to `True` to enable this. An example is implemented in the colab.
```python
image_stream = model.generate_image_stream(