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https://github.com/invoke-ai/InvokeAI
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Fix unet_info location, can have no device prop
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@ -291,17 +291,17 @@ 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, 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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scheduler.set_timesteps(num_inference_steps, device=unet.device)
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timesteps = scheduler.timesteps
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extra_step_kwargs = dict()
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if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
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extra_step_kwargs.update(
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@ -543,9 +543,12 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
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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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# apply denoising_start
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num_inference_steps = self.steps
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scheduler.set_timesteps(num_inference_steps, device=unet_info.device)
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scheduler.set_timesteps(num_inference_steps, device=self.scheduler.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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@ -557,9 +560,6 @@ 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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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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# apply scheduler extra args
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extra_step_kwargs = dict()
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if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
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