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(doc/fluxion/ld) add SDXLAutoencoder
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@ -10,10 +10,24 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDX
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class SDXLAutoencoder(LatentDiffusionAutoencoder):
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class SDXLAutoencoder(LatentDiffusionAutoencoder):
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"""Stable Diffusion XL autoencoder model.
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Attributes:
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encoder_scale: The encoder scale to use.
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"""
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encoder_scale: float = 0.13025
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encoder_scale: float = 0.13025
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class StableDiffusion_XL(LatentDiffusionModel):
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class StableDiffusion_XL(LatentDiffusionModel):
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"""Stable Diffusion XL model.
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Attributes:
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unet: The U-Net model.
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clip_text_encoder: The text encoder.
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lda (SDXLAutoencoder): The image autoencoder.
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"""
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unet: SDXLUNet
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unet: SDXLUNet
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clip_text_encoder: DoubleTextEncoder
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clip_text_encoder: DoubleTextEncoder
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lda: SDXLAutoencoder
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lda: SDXLAutoencoder
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@ -27,6 +41,16 @@ class StableDiffusion_XL(LatentDiffusionModel):
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device: Device | str = "cpu",
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device: Device | str = "cpu",
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dtype: DType = torch.float32,
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dtype: DType = torch.float32,
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) -> None:
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) -> None:
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"""Initializes the model.
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Args:
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unet: The SDXLUNet U-Net model to use.
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lda: The SDXLAutoencoder image autoencoder to use.
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clip_text_encoder: The DoubleTextEncoder text encoder to use.
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solver: The solver to use.
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device: The PyTorch device to use.
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dtype: The PyTorch data type to use.
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"""
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unet = unet or SDXLUNet(in_channels=4)
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unet = unet or SDXLUNet(in_channels=4)
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lda = lda or SDXLAutoencoder()
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lda = lda or SDXLAutoencoder()
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clip_text_encoder = clip_text_encoder or DoubleTextEncoder()
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clip_text_encoder = clip_text_encoder or DoubleTextEncoder()
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@ -42,6 +66,13 @@ class StableDiffusion_XL(LatentDiffusionModel):
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)
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)
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def compute_clip_text_embedding(self, text: str, negative_text: str | None = None) -> tuple[Tensor, Tensor]:
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def compute_clip_text_embedding(self, text: str, negative_text: str | None = None) -> tuple[Tensor, Tensor]:
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"""Compute the CLIP text embedding associated with the given prompt and negative prompt.
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Args:
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text: The prompt to compute the CLIP text embedding of.
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negative_text: The negative prompt to compute the CLIP text embedding of.
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If not provided, the negative prompt is assumed to be empty (i.e., `""`).
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"""
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conditional_embedding, conditional_pooled_embedding = self.clip_text_encoder(text)
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conditional_embedding, conditional_pooled_embedding = self.clip_text_encoder(text)
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if text == negative_text:
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if text == negative_text:
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return torch.cat(tensors=(conditional_embedding, conditional_embedding), dim=0), torch.cat(
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return torch.cat(tensors=(conditional_embedding, conditional_embedding), dim=0), torch.cat(
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@ -57,6 +88,7 @@ class StableDiffusion_XL(LatentDiffusionModel):
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@property
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@property
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def default_time_ids(self) -> Tensor:
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def default_time_ids(self) -> Tensor:
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"""The default time IDs to use."""
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# [original_height, original_width, crop_top, crop_left, target_height, target_width]
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# [original_height, original_width, crop_top, crop_left, target_height, target_width]
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# See https://arxiv.org/abs/2307.01952 > 2.2 Micro-Conditioning
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# See https://arxiv.org/abs/2307.01952 > 2.2 Micro-Conditioning
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time_ids = torch.tensor(data=[1024, 1024, 0, 0, 1024, 1024], device=self.device)
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time_ids = torch.tensor(data=[1024, 1024, 0, 0, 1024, 1024], device=self.device)
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@ -71,6 +103,14 @@ class StableDiffusion_XL(LatentDiffusionModel):
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time_ids: Tensor,
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time_ids: Tensor,
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**_: Tensor,
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**_: Tensor,
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) -> None:
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) -> None:
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"""Sets the various context parameters required by the U-Net model.
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Args:
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timestep: The timestep to set.
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clip_text_embedding: The CLIP text embedding to set.
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pooled_text_embedding: The pooled CLIP text embedding to set.
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time_ids: The time IDs to set.
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"""
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self.unet.set_timestep(timestep=timestep)
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self.unet.set_timestep(timestep=timestep)
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self.unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding)
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self.unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding)
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self.unet.set_pooled_text_embedding(pooled_text_embedding=pooled_text_embedding)
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self.unet.set_pooled_text_embedding(pooled_text_embedding=pooled_text_embedding)
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@ -98,6 +138,12 @@ class StableDiffusion_XL(LatentDiffusionModel):
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)
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)
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def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None:
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def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None:
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"""Sets the self-attention guidance.
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Args:
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enable: Whether to enable self-attention guidance or not.
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scale: The scale to use.
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"""
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if enable:
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if enable:
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if sag := self._find_sag_adapter():
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if sag := self._find_sag_adapter():
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sag.scale = scale
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sag.scale = scale
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@ -108,9 +154,11 @@ class StableDiffusion_XL(LatentDiffusionModel):
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sag.eject()
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sag.eject()
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def has_self_attention_guidance(self) -> bool:
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def has_self_attention_guidance(self) -> bool:
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"""Whether the model has self-attention guidance or not."""
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return self._find_sag_adapter() is not None
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return self._find_sag_adapter() is not None
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def _find_sag_adapter(self) -> SDXLSAGAdapter | None:
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def _find_sag_adapter(self) -> SDXLSAGAdapter | None:
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"""Finds the self-attention guidance adapter."""
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for p in self.unet.get_parents():
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for p in self.unet.get_parents():
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if isinstance(p, SDXLSAGAdapter):
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if isinstance(p, SDXLSAGAdapter):
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return p
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return p
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@ -127,6 +175,19 @@ class StableDiffusion_XL(LatentDiffusionModel):
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time_ids: Tensor,
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time_ids: Tensor,
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**kwargs: Tensor,
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**kwargs: Tensor,
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) -> Tensor:
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) -> Tensor:
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"""Compute the self-attention guidance.
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Args:
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x: The input tensor.
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noise: The noise tensor.
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step: The step to compute the self-attention guidance at.
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clip_text_embedding: The CLIP text embedding to compute the self-attention guidance with.
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pooled_text_embedding: The pooled CLIP text embedding to compute the self-attention guidance with.
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time_ids: The time IDs to compute the self-attention guidance with.
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Returns:
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The computed self-attention guidance.
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"""
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sag = self._find_sag_adapter()
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sag = self._find_sag_adapter()
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assert sag is not None
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assert sag is not None
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