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fixed bug with cuda in detokenizer

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This commit is contained in:
Brett Kuprel 2022-06-28 22:02:35 -04:00
parent 764b0bc685
commit 1fbb209623
3 changed files with 5 additions and 5 deletions
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2
README.md
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@ -6,7 +6,7 @@ This is a minimal implementation of [DALL·E Mini](https://github.com/borisdayma
### Setup ### Setup
Run `sh setup.sh` to install dependencies and download pretrained models. In the bash script, Git LFS is used to download the VQGan detokenizer from Hugging Face and the Weight & Biases python package is used to download the DALL·E Mini and DALL·E Mega transformer models. These models can also be downloaded manually: Run `sh setup.sh` to install dependencies and download pretrained models. The models can also be downloaded manually:
[VQGan](https://huggingface.co/dalle-mini/vqgan_imagenet_f16_16384), [VQGan](https://huggingface.co/dalle-mini/vqgan_imagenet_f16_16384),
[DALL·E Mini](https://wandb.ai/dalle-mini/dalle-mini/artifacts/DalleBart_model/mini-1/v0/files), [DALL·E Mini](https://wandb.ai/dalle-mini/dalle-mini/artifacts/DalleBart_model/mini-1/v0/files),
[DALL·E Mega](https://wandb.ai/dalle-mini/dalle-mini/artifacts/DalleBart_model/mega-1-fp16/v14/files) [DALL·E Mega](https://wandb.ai/dalle-mini/dalle-mini/artifacts/DalleBart_model/mega-1-fp16/v14/files)

4
min_dalle/generate_image.py
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@ -63,7 +63,7 @@ def generate_image_from_text(
image_token_count = image_token_count image_token_count = image_token_count
) )
if image_token_count == config['image_length']: if image_token_count == config['image_length']:
image = detokenize_torch(image_tokens) image = detokenize_torch(image_tokens, is_torch=True)
return Image.fromarray(image) return Image.fromarray(image)
else: else:
print(list(image_tokens.to('cpu').detach().numpy())) print(list(image_tokens.to('cpu').detach().numpy()))
@ -74,5 +74,5 @@ def generate_image_from_text(
config = config, config = config,
params = params_dalle_bart, params = params_dalle_bart,
) )
image = detokenize_torch(torch.tensor(image_tokens)) image = detokenize_torch(torch.tensor(image_tokens), is_torch=False)
return Image.fromarray(image) return Image.fromarray(image)

4
min_dalle/min_dalle_torch.py
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@ -104,13 +104,13 @@ def generate_image_tokens_torch(
return image_tokens return image_tokens
def detokenize_torch(image_tokens: LongTensor) -> numpy.ndarray: def detokenize_torch(image_tokens: LongTensor, is_torch: bool) -> numpy.ndarray:
print("detokenizing image") print("detokenizing image")
model_path = './pretrained/vqgan' model_path = './pretrained/vqgan'
params = load_vqgan_torch_params(model_path) params = load_vqgan_torch_params(model_path)
detokenizer = VQGanDetokenizer() detokenizer = VQGanDetokenizer()
detokenizer.load_state_dict(params) detokenizer.load_state_dict(params)
# if torch.cuda.is_available(): detokenizer = detokenizer.cuda() if torch.cuda.is_available() and is_torch: detokenizer = detokenizer.cuda()
image = detokenizer.forward(image_tokens).to(torch.uint8) image = detokenizer.forward(image_tokens).to(torch.uint8)
del detokenizer, params del detokenizer, params
return image.to('cpu').detach().numpy() return image.to('cpu').detach().numpy()