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Implement callback support for DDIM sampler #167

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Implement callback support for DDIM sampler #167

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15 changes: 10 additions & 5 deletions optimizedSD/ddpm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -526,7 +526,9 @@ def sample(self,
)

elif sampler == "ddim":
samples = self.ddim_sampling(x_latent, conditioning, S, unconditional_guidance_scale=unconditional_guidance_scale,
samples = self.ddim_sampling(x_latent, conditioning, S,
callback=callback, img_callback=img_callback,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning,
mask = mask,init_latent=x_T,use_original_steps=False)

Expand Down Expand Up @@ -687,7 +689,8 @@ def add_noise(self, x0, t):

@torch.no_grad()
def ddim_sampling(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
mask = None,init_latent=None,use_original_steps=False):
mask = None,init_latent=None,use_original_steps=False,
callback=None, img_callback=None):

timesteps = self.ddim_timesteps
timesteps = timesteps[:t_start]
Expand All @@ -707,10 +710,12 @@ def ddim_sampling(self, x_latent, cond, t_start, unconditional_guidance_scale=1.
x0_noisy = x0
x_dec = x0_noisy* mask + (1. - mask) * x_dec

x_dec = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
x_dec, pred_x0 = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning)

if callback: callback(i)
if img_callback: img_callback(pred_x0, i)

if mask is not None:
return x0 * mask + (1. - mask) * x_dec

Expand Down Expand Up @@ -756,7 +761,7 @@ def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=F
if noise_dropout > 0.:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
return x_prev
return x_prev, pred_x0


def append_zero(self, x):
Expand Down