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use Half precision for reduced memory usage & faster speed

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This allows users with 6 & 8gb cards to run 512x512 and for even larger resolutions for bigger GPUs
I compared the output in Beyond Compare and there are minor differences detected at tolerance 3, but side by side the differences are not perceptible.
This commit is contained in:
xra 2022-08-19 17:23:43 +09:00
parent 9eaef0c5a8
commit a5fb8469ed
1 changed files with 5 additions and 2 deletions

7
ldm/simplet2i.py
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@ -256,6 +256,8 @@ class T2I:
model = self.load_model() # will instantiate the model or return it from cache
precision_scope = autocast if self.precision=="autocast" else nullcontext
# grid and individual are mutually exclusive, with individual taking priority.
# not necessary, but needed for compatability with dream bot
if (grid is None):
@ -279,7 +281,8 @@ class T2I:
assert os.path.isfile(init_img)
init_image = self._load_img(init_img).to(self.device)
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
with precision_scope("cuda"):
init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
@ -292,7 +295,6 @@ class T2I:
t_enc = int(strength * steps)
print(f"target t_enc is {t_enc} steps")
precision_scope = autocast if self.precision=="autocast" else nullcontext
images = list()
seeds = list()
@ -401,6 +403,7 @@ class T2I:
m, u = model.load_state_dict(sd, strict=False)
model.cuda()
model.eval()
model.half()
return model
def _load_img(self,path):