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lvmin
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3b2d07e9f5
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34b1e3a3c5
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@ -23,8 +23,8 @@ class KSamplerAdvanced:
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sampler = self.SAMPLERS[0]
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self.scheduler = scheduler
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self.sampler = sampler
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self.sigma_min=float(self.model_wrap.sigma_min)
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self.sigma_max=float(self.model_wrap.sigma_max)
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self.sigma_min = float(self.model_wrap.sigma_min)
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self.sigma_max = float(self.model_wrap.sigma_max)
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self.set_steps(steps, denoise)
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self.denoise = denoise
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self.model_options = model_options
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@ -40,7 +40,8 @@ class KSamplerAdvanced:
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if self.scheduler == "karras":
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sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max)
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elif self.scheduler == "exponential":
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sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max)
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sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=self.sigma_min,
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sigma_max=self.sigma_max)
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elif self.scheduler == "normal":
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sigmas = self.model_wrap.get_sigmas(steps)
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elif self.scheduler == "simple":
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@ -59,11 +60,12 @@ class KSamplerAdvanced:
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if denoise is None or denoise > 0.9999:
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self.sigmas = self.calculate_sigmas(steps).to(self.device)
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else:
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new_steps = int(steps/denoise)
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new_steps = int(steps / denoise)
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sigmas = self.calculate_sigmas(new_steps).to(self.device)
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self.sigmas = sigmas[-(steps + 1):]
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def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
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def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None,
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force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
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if sigmas is None:
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sigmas = self.sigmas
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sigma_min = self.sigma_min
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@ -92,7 +94,7 @@ class KSamplerAdvanced:
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calculate_start_end_timesteps(self.model_wrap, negative)
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calculate_start_end_timesteps(self.model_wrap, positive)
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#make sure each cond area has an opposite one with the same area
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# make sure each cond area has an opposite one with the same area
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for c in positive:
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create_cond_with_same_area_if_none(negative, c)
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for c in negative:
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@ -100,30 +102,36 @@ class KSamplerAdvanced:
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pre_run_control(self.model_wrap, negative + positive)
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apply_empty_x_to_equal_area(list(filter(lambda c: c[1].get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x])
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apply_empty_x_to_equal_area(
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list(filter(lambda c: c[1].get('control_apply_to_uncond', False) == True, positive)), negative, 'control',
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lambda cond_cnets, x: cond_cnets[x])
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apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])
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if self.model.is_adm():
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positive = encode_adm(self.model, positive, noise.shape[0], noise.shape[3], noise.shape[2], self.device, "positive")
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negative = encode_adm(self.model, negative, noise.shape[0], noise.shape[3], noise.shape[2], self.device, "negative")
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positive = encode_adm(self.model, positive, noise.shape[0], noise.shape[3], noise.shape[2], self.device,
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"positive")
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negative = encode_adm(self.model, negative, noise.shape[0], noise.shape[3], noise.shape[2], self.device,
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"negative")
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if latent_image is not None:
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latent_image = self.model.process_latent_in(latent_image)
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extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": self.model_options, "seed":seed}
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extra_args = {"cond": positive, "uncond": negative, "cond_scale": cfg, "model_options": self.model_options,
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"seed": seed}
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cond_concat = None
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if hasattr(self.model, 'concat_keys'): #inpaint
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if hasattr(self.model, 'concat_keys'): # inpaint
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cond_concat = []
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for ck in self.model.concat_keys:
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if denoise_mask is not None:
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if ck == "mask":
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cond_concat.append(denoise_mask[:,:1])
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cond_concat.append(denoise_mask[:, :1])
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elif ck == "masked_image":
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cond_concat.append(latent_image) #NOTE: the latent_image should be masked by the mask in pixel space
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cond_concat.append(
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latent_image) # NOTE: the latent_image should be masked by the mask in pixel space
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else:
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if ck == "mask":
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cond_concat.append(torch.ones_like(noise)[:,:1])
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cond_concat.append(torch.ones_like(noise)[:, :1])
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elif ck == "masked_image":
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cond_concat.append(blank_inpaint_image_like(noise))
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extra_args["cond_concat"] = cond_concat
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@ -133,11 +141,16 @@ class KSamplerAdvanced:
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else:
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max_denoise = True
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if self.sampler == "uni_pc":
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samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=max_denoise, extra_args=extra_args, noise_mask=denoise_mask, callback=callback, disable=disable_pbar)
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samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas,
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sampling_function=sampling_function, max_denoise=max_denoise,
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extra_args=extra_args, noise_mask=denoise_mask, callback=callback,
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disable=disable_pbar)
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elif self.sampler == "uni_pc_bh2":
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samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=max_denoise, extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2', disable=disable_pbar)
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samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas,
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sampling_function=sampling_function, max_denoise=max_denoise,
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extra_args=extra_args, noise_mask=denoise_mask, callback=callback,
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variant='bh2', disable=disable_pbar)
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elif self.sampler == "ddim":
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timesteps = []
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for s in range(sigmas.shape[0]):
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@ -153,24 +166,26 @@ class KSamplerAdvanced:
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sampler = DDIMSampler(self.model, device=self.device)
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sampler.make_schedule_timesteps(ddim_timesteps=timesteps, verbose=False)
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z_enc = sampler.stochastic_encode(latent_image, torch.tensor([len(timesteps) - 1] * noise.shape[0]).to(self.device), noise=noise, max_denoise=max_denoise)
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z_enc = sampler.stochastic_encode(latent_image,
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torch.tensor([len(timesteps) - 1] * noise.shape[0]).to(self.device),
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noise=noise, max_denoise=max_denoise)
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samples, _ = sampler.sample_custom(ddim_timesteps=timesteps,
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conditioning=positive,
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batch_size=noise.shape[0],
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shape=noise.shape[1:],
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verbose=False,
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unconditional_guidance_scale=cfg,
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unconditional_conditioning=negative,
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eta=0.0,
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x_T=z_enc,
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x0=latent_image,
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img_callback=ddim_callback,
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denoise_function=self.model_wrap.predict_eps_discrete_timestep,
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extra_args=extra_args,
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mask=noise_mask,
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to_zero=sigmas[-1]==0,
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end_step=sigmas.shape[0] - 1,
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disable_pbar=disable_pbar)
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conditioning=positive,
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batch_size=noise.shape[0],
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shape=noise.shape[1:],
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verbose=False,
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unconditional_guidance_scale=cfg,
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unconditional_conditioning=negative,
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eta=0.0,
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x_T=z_enc,
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x0=latent_image,
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img_callback=ddim_callback,
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denoise_function=self.model_wrap.predict_eps_discrete_timestep,
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extra_args=extra_args,
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mask=noise_mask,
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to_zero=sigmas[-1] == 0,
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end_step=sigmas.shape[0] - 1,
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disable_pbar=disable_pbar)
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else:
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extra_args["denoise_mask"] = denoise_mask
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@ -190,11 +205,17 @@ class KSamplerAdvanced:
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if latent_image is not None:
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noise += latent_image
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if self.sampler == "dpm_fast":
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samples = k_diffusion_sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=k_callback, disable=disable_pbar)
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samples = k_diffusion_sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], total_steps,
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extra_args=extra_args, callback=k_callback,
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disable=disable_pbar)
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elif self.sampler == "dpm_adaptive":
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samples = k_diffusion_sampling.sample_dpm_adaptive(self.model_k, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=k_callback, disable=disable_pbar)
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samples = k_diffusion_sampling.sample_dpm_adaptive(self.model_k, noise, sigma_min, sigmas[0],
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extra_args=extra_args, callback=k_callback,
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disable=disable_pbar)
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else:
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samples = getattr(k_diffusion_sampling, "sample_{}".format(self.sampler))(self.model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar)
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samples = getattr(k_diffusion_sampling, "sample_{}".format(self.sampler))(self.model_k, noise, sigmas,
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extra_args=extra_args,
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callback=k_callback,
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disable=disable_pbar)
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return self.model.process_latent_out(samples.to(torch.float32))
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