Loading modules/sd_samplers.py +2 −2 Original line number Diff line number Diff line Loading @@ -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) Loading @@ -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 Loading Loading
modules/sd_samplers.py +2 −2 Original line number Diff line number Diff line Loading @@ -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) Loading @@ -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 Loading
