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https://github.com/invoke-ai/InvokeAI
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Pass device to set_timestep to avoid float64 error
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@ -291,13 +291,14 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
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scheduler_name=self.scheduler,
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)
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unet_info = context.services.model_manager.get_model(**self.unet.unet.dict(), context=context)
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num_inference_steps = self.steps
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scheduler.set_timesteps(num_inference_steps)
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scheduler.set_timesteps(num_inference_steps, device=unet_info.device)
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timesteps = scheduler.timesteps
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latents = latents * scheduler.init_noise_sigma
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unet_info = context.services.model_manager.get_model(**self.unet.unet.dict(), context=context)
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do_classifier_free_guidance = True
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cross_attention_kwargs = None
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with unet_info as unet:
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@ -537,9 +538,14 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
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scheduler_name=self.scheduler,
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)
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unet_info = context.services.model_manager.get_model(
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**self.unet.unet.dict(),
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context=context,
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)
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# apply denoising_start
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num_inference_steps = self.steps
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scheduler.set_timesteps(num_inference_steps)
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scheduler.set_timesteps(num_inference_steps, device=unet_info.device)
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t_start = int(round(self.denoising_start * num_inference_steps))
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timesteps = scheduler.timesteps[t_start * scheduler.order :]
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@ -551,10 +557,6 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
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latents = scheduler.add_noise(latents, noise, timesteps[:1])
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del noise
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unet_info = context.services.model_manager.get_model(
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**self.unet.unet.dict(),
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context=context,
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)
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do_classifier_free_guidance = True
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cross_attention_kwargs = None
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with unet_info as unet:
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