|
|
|
|
@ -136,6 +136,8 @@ class VanillaStableDiffusionSampler:
|
|
|
|
|
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
|
|
|
|
|
steps, t_enc = setup_img2img_steps(p, steps)
|
|
|
|
|
|
|
|
|
|
self.initialize(p)
|
|
|
|
|
|
|
|
|
|
# existing code fails with cetain step counts, like 9
|
|
|
|
|
try:
|
|
|
|
|
self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
|
|
|
|
|
@ -144,8 +146,6 @@ class VanillaStableDiffusionSampler:
|
|
|
|
|
|
|
|
|
|
x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
|
|
|
|
|
|
|
|
|
|
self.initialize(p)
|
|
|
|
|
|
|
|
|
|
self.init_latent = x
|
|
|
|
|
self.step = 0
|
|
|
|
|
|
|
|
|
|
|