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fix broken DDIM with img2img
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@ -136,6 +136,8 @@ class VanillaStableDiffusionSampler:
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def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
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steps, t_enc = setup_img2img_steps(p, steps)
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self.initialize(p)
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# existing code fails with cetain step counts, like 9
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try:
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self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
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@ -144,8 +146,6 @@ class VanillaStableDiffusionSampler:
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x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
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self.initialize(p)
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self.init_latent = x
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self.step = 0
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