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Support notebook execution on smaller devices #3

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Support notebook execution on smaller devices #3

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8 changes: 7 additions & 1 deletion notebooks/T2I_sampling.ipynb
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Expand Up @@ -25,7 +25,13 @@
"you can input various texts to generate the corresponded images. \n",
"\n",
"You can certainly adjust the sampling parameters such as `temperature`, `top_k`, and `top_p` for better quality of generated images according to the given texts. \n",
"As the three parameters have lower values than the default setting, you can get more simple and confident images, and vice versa."
"As the three parameters have lower values than the default setting, you can get more simple and confident images, and vice versa.\n",
"\n",
"### Execution on local machines\n",
"If running locally on a smaller device with enough memory to load the model (e.g. RTX 3090,) first make the following changes:\n",
"1. Add `map_location='cuda'` to both `load_model` calls in step 2.\n",
"2. Reduce the `num_samples` in step 3 to meet your memory constraints.\n",
"3. Add `amp=False` to the `get_generated_images_by_texts` call in step 4."
]
},
{
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6 changes: 3 additions & 3 deletions notebooks/notebook_utils.py
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Expand Up @@ -52,16 +52,16 @@ def __call__(self, texts):
return self.encode(texts)


def load_model(path, ema=False):
def load_model(path, ema=False, map_location='cpu'):
model_config = os.path.join(os.path.dirname(path), 'config.yaml')
config = load_config(model_config)
config.arch = augment_arch_defaults(config.arch)

model, _ = create_model(config.arch, ema=False)
if ema:
ckpt = torch.load(path, map_location='cpu')['state_dict_ema']
ckpt = torch.load(path, map_location=map_location)['state_dict_ema']
else:
ckpt = torch.load(path, map_location='cpu')['state_dict']
ckpt = torch.load(path, map_location=map_location)['state_dict']
model.load_state_dict(ckpt)

return model, config
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