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fix a bug for DDIM inversion

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fix a bug of the incorrect timestep in DDIMSampler.encode() for DDIM inversion
This commit is contained in:
Shuai Yang
2023-03-28 23:33:06 +08:00
committed by GitHub
parent 16ea3b5379
commit 2d12a1bc31
1 changed files with 4 additions and 3 deletions
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7
cldm/ddim_hacked.py
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@@ -233,7 +233,8 @@ class DDIMSampler(object):
@torch.no_grad()
def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):
num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]
timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
num_reference_steps = timesteps.shape[0]
assert t_enc <= num_reference_steps
num_steps = t_enc
@@ -249,7 +250,7 @@ class DDIMSampler(object):
intermediates = []
inter_steps = []
for i in tqdm(range(num_steps), desc='Encoding Image'):
t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)
t = torch.full((x0.shape[0],), timesteps[i], device=self.model.device, dtype=torch.long)
if unconditional_guidance_scale == 1.:
noise_pred = self.model.apply_model(x_next, t, c)
else:
@@ -313,4 +314,4 @@ class DDIMSampler(object):
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning)
if callback: callback(i)
return x_dec
return x_dec