fix img2img
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0c7bdcc1b1
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@ -73,3 +73,6 @@ class SD3Inferencer(torch.nn.Module):
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(self, 'cond_stage_model'),
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(self, 'model'),
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]
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def add_noise_to_latent(self, x, noise, amount):
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return x * (1 - amount) + noise * amount
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@ -1737,10 +1737,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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latmask = latmask[0]
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if self.mask_round:
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latmask = np.around(latmask)
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latmask = np.tile(latmask[None], (4, 1, 1))
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latmask = np.tile(latmask[None], (self.init_latent.shape[1], 1, 1))
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self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)
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self.nmask = torch.asarray(latmask).to(shared.device).type(self.sd_model.dtype)
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self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(devices.dtype)
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self.nmask = torch.asarray(latmask).to(shared.device).type(devices.dtype)
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# this needs to be fixed to be done in sample() using actual seeds for batches
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if self.inpainting_fill == 2:
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@ -133,7 +133,10 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
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sigmas = self.get_sigmas(p, steps)
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sigma_sched = sigmas[steps - t_enc - 1:]
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xi = x + noise * sigma_sched[0]
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if hasattr(shared.sd_model, 'add_noise_to_latent'):
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xi = shared.sd_model.add_noise_to_latent(x, noise, sigma_sched[0])
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else:
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xi = x + noise * sigma_sched[0]
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if opts.img2img_extra_noise > 0:
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p.extra_generation_params["Extra noise"] = opts.img2img_extra_noise
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