diff --git a/README.md b/README.md index 07e59bc..0334b97 100644 --- a/README.md +++ b/README.md @@ -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 )