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(doc/fluxion/ld) add StableDiffusion_1
docstrings
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@ -1,6 +1,7 @@
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import SD1ControlnetAdapter
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.image_prompt import SD1IPAdapter
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import (
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SD1Autoencoder,
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StableDiffusion_1,
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StableDiffusion_1_Inpainting,
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)
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@ -10,6 +11,7 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1U
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__all__ = [
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"StableDiffusion_1",
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"StableDiffusion_1_Inpainting",
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"SD1Autoencoder",
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"SD1UNet",
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"SD1ControlnetAdapter",
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"SD1IPAdapter",
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@ -13,10 +13,24 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1U
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class SD1Autoencoder(LatentDiffusionAutoencoder):
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"""Stable Diffusion 1.5 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 = 0.18215
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class StableDiffusion_1(LatentDiffusionModel):
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"""Stable Diffusion 1.5 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: The image autoencoder.
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"""
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unet: SD1UNet
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clip_text_encoder: CLIPTextEncoderL
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lda: SD1Autoencoder
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@ -30,6 +44,16 @@ class StableDiffusion_1(LatentDiffusionModel):
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device: Device | str = "cpu",
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dtype: DType = torch.float32,
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) -> None:
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"""Initializes the model.
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Args:
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unet: The SD1UNet U-Net model to use.
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lda: The SD1Autoencoder image autoencoder to use.
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clip_text_encoder: The CLIPTextEncoderL 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 SD1UNet(in_channels=4)
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lda = lda or SD1Autoencoder()
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clip_text_encoder = clip_text_encoder or CLIPTextEncoderL()
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@ -45,6 +69,13 @@ class StableDiffusion_1(LatentDiffusionModel):
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)
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def compute_clip_text_embedding(self, text: str, negative_text: str = "") -> 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 = self.clip_text_encoder(text)
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if text == negative_text:
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return torch.cat(tensors=(conditional_embedding, conditional_embedding), dim=0)
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@ -53,10 +84,22 @@ class StableDiffusion_1(LatentDiffusionModel):
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return torch.cat(tensors=(negative_embedding, conditional_embedding), dim=0)
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def set_unet_context(self, *, timestep: Tensor, clip_text_embedding: Tensor, **_: Tensor) -> None:
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"""Set the various context parameters required by the U-Net model.
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Args:
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timestep: The timestep tensor to use.
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clip_text_embedding: The CLIP text embedding tensor to use.
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"""
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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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def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None:
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"""Set whether to enable self-attention guidance.
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Args:
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enable: Whether to enable self-attention guidance.
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scale: The scale to use.
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"""
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if enable:
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if sag := self._find_sag_adapter():
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sag.scale = scale
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@ -67,9 +110,11 @@ class StableDiffusion_1(LatentDiffusionModel):
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sag.eject()
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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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def _find_sag_adapter(self) -> SD1SAGAdapter | 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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if isinstance(p, SD1SAGAdapter):
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return p
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@ -78,6 +123,17 @@ class StableDiffusion_1(LatentDiffusionModel):
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def compute_self_attention_guidance(
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self, x: Tensor, noise: Tensor, step: int, *, clip_text_embedding: Tensor, **kwargs: 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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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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assert sag is not None
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@ -106,6 +162,14 @@ class StableDiffusion_1(LatentDiffusionModel):
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class StableDiffusion_1_Inpainting(StableDiffusion_1):
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"""Stable Diffusion 1.5 inpainting 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: The image autoencoder.
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"""
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def __init__(
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self,
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unet: SD1UNet | None = None,
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@ -140,6 +204,16 @@ class StableDiffusion_1_Inpainting(StableDiffusion_1):
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mask: Image.Image,
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latents_size: tuple[int, int] = (64, 64),
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) -> tuple[Tensor, Tensor]:
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"""Set the inpainting conditions.
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Args:
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target_image: The target image to inpaint.
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mask: The mask to use for inpainting.
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latents_size: The size of the latents to use.
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Returns:
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The mask latents and the target image latents.
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"""
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target_image = target_image.convert(mode="RGB")
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mask = mask.convert(mode="L")
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@ -156,6 +230,17 @@ class StableDiffusion_1_Inpainting(StableDiffusion_1):
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def compute_self_attention_guidance(
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self, x: Tensor, noise: Tensor, step: int, *, clip_text_embedding: Tensor, **kwargs: 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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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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assert sag is not None
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assert self.mask_latents is not None
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@ -25,7 +25,7 @@ class StableDiffusion_XL(LatentDiffusionModel):
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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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lda: The image autoencoder.
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"""
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unet: SDXLUNet
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@ -103,7 +103,7 @@ class StableDiffusion_XL(LatentDiffusionModel):
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time_ids: Tensor,
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**_: Tensor,
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) -> None:
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"""Sets the various context parameters required by the U-Net model.
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"""Set 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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