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updated readme

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Brett Kuprel 2022-06-30 16:50:04 -04:00
parent 2311a1af7b
commit 08b158d580
3 changed files with 4 additions and 4 deletions
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2
README.md vendored
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@ -3,7 +3,7 @@
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/kuprel/min-dalle/blob/main/min_dalle.ipynb)   [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/kuprel/min-dalle/blob/main/min_dalle.ipynb)  
[![Replicate](https://replicate.com/kuprel/min-dalle/badge)](https://replicate.com/kuprel/min-dalle) [![Replicate](https://replicate.com/kuprel/min-dalle/badge)](https://replicate.com/kuprel/min-dalle)
This is a minimal implementation of Boris Dayma's [DALL·E Mini](https://github.com/borisdayma/dalle-mini). It has been stripped to the bare essentials necessary for doing inference, and converted to PyTorch. To run the torch model, the only third party dependencies are numpy and torch. Flax is used to convert the weights (which can be saved with `torch.save` once the model is loaded), and wandb is only used to download the models. This is a minimal implementation of Boris Dayma's [DALL·E Mini](https://github.com/borisdayma/dalle-mini). It has been stripped to the bare essentials necessary for doing inference, and converted to PyTorch. To run the torch model, the only third party dependencies are numpy and torch. Flax is used to convert the weights (which are saved with `torch.save` the first time the model is loaded), and wandb is only used to download the models.
It currently takes about 10 seconds to generate an avocado armchair with DALL·E Mega in PyTorch on Colab with a reusable model and high-RAM GPU runtime. It currently takes about 10 seconds to generate an avocado armchair with DALL·E Mega in PyTorch on Colab with a reusable model and high-RAM GPU runtime.

2
min_dalle/load_params.py
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@ -107,7 +107,7 @@ def convert_dalle_bart_torch_from_flax_params(
return P return P
def convert_and_save_mega_torch_params(is_mega: bool, model_path: str): def convert_and_save_torch_params(is_mega: bool, model_path: str):
print("converting params to torch") print("converting params to torch")
layer_count = 24 if is_mega else 12 layer_count = 24 if is_mega else 12
flax_params = load_dalle_bart_flax_params(model_path) flax_params = load_dalle_bart_flax_params(model_path)

4
min_dalle/min_dalle_torch.py
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@ -7,7 +7,7 @@ torch.set_grad_enabled(False)
torch.set_num_threads(os.cpu_count()) torch.set_num_threads(os.cpu_count())
from .load_params import ( from .load_params import (
convert_and_save_mega_torch_params, convert_and_save_torch_params,
load_dalle_bart_flax_params load_dalle_bart_flax_params
) )
from .min_dalle_base import MinDalleBase from .min_dalle_base import MinDalleBase
@ -36,7 +36,7 @@ class MinDalleTorch(MinDalleBase):
is_converted = os.path.exists(self.encoder_params_path) is_converted = os.path.exists(self.encoder_params_path)
is_converted &= os.path.exists(self.decoder_params_path) is_converted &= os.path.exists(self.decoder_params_path)
if not is_converted: if not is_converted:
convert_and_save_mega_torch_params(is_mega, self.model_path) convert_and_save_torch_params(is_mega, self.model_path)
if is_reusable: if is_reusable:
self.init_encoder() self.init_encoder()