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https://github.com/invoke-ai/InvokeAI
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Fix avoid storing extra conditioning info in two places.
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@ -360,7 +360,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
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) -> ConditioningData:
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positive_cond_data = context.conditioning.load(self.positive_conditioning.conditioning_name)
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c = positive_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
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extra_conditioning_info = c.extra_conditioning
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negative_cond_data = context.conditioning.load(self.negative_conditioning.conditioning_name)
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uc = negative_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
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@ -370,7 +369,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
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text_embeddings=c,
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guidance_scale=self.cfg_scale,
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guidance_rescale_multiplier=self.cfg_rescale_multiplier,
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extra=extra_conditioning_info,
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postprocessing_settings=PostprocessingSettings(
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threshold=0.0, # threshold,
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warmup=0.2, # warmup,
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@ -427,10 +427,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
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return latents, attention_map_saver
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ip_adapter_unet_patcher = None
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if conditioning_data.extra is not None and conditioning_data.extra.wants_cross_attention_control:
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extra_conditioning_info = conditioning_data.text_embeddings.extra_conditioning
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if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
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attn_ctx = self.invokeai_diffuser.custom_attention_context(
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self.invokeai_diffuser.model,
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extra_conditioning_info=conditioning_data.extra,
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extra_conditioning_info=extra_conditioning_info,
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step_count=len(self.scheduler.timesteps),
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)
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self.use_ip_adapter = False
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@ -21,11 +21,7 @@ class ExtraConditioningInfo:
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@dataclass
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class BasicConditioningInfo:
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embeds: torch.Tensor
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# TODO(ryand): Right now we awkwardly copy the extra conditioning info from here up to `ConditioningData`. This
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# should only be stored in one place.
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extra_conditioning: Optional[ExtraConditioningInfo]
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# weight: float
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# mode: ConditioningAlgo
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def to(self, device, dtype=None):
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self.embeds = self.embeds.to(device=device, dtype=dtype)
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@ -83,7 +79,6 @@ class ConditioningData:
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ref [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf)
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"""
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guidance_rescale_multiplier: float = 0
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extra: Optional[ExtraConditioningInfo] = None
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scheduler_args: dict[str, Any] = field(default_factory=dict)
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"""
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Additional arguments to pass to invokeai_diffuser.do_latent_postprocessing().
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