mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-21 13:48:46 +00:00
(doc/fluxion/ld) add StableDiffusion_1
docstrings
This commit is contained in:
parent
f2bcb7f45e
commit
a1a00998ea
|
@ -1,6 +1,7 @@
|
|||
from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import SD1ControlnetAdapter
|
||||
from refiners.foundationals.latent_diffusion.stable_diffusion_1.image_prompt import SD1IPAdapter
|
||||
from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import (
|
||||
SD1Autoencoder,
|
||||
StableDiffusion_1,
|
||||
StableDiffusion_1_Inpainting,
|
||||
)
|
||||
|
@ -10,6 +11,7 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1U
|
|||
__all__ = [
|
||||
"StableDiffusion_1",
|
||||
"StableDiffusion_1_Inpainting",
|
||||
"SD1Autoencoder",
|
||||
"SD1UNet",
|
||||
"SD1ControlnetAdapter",
|
||||
"SD1IPAdapter",
|
||||
|
|
|
@ -13,10 +13,24 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1U
|
|||
|
||||
|
||||
class SD1Autoencoder(LatentDiffusionAutoencoder):
|
||||
"""Stable Diffusion 1.5 autoencoder model.
|
||||
|
||||
Attributes:
|
||||
encoder_scale: The encoder scale to use.
|
||||
"""
|
||||
|
||||
encoder_scale: float = 0.18215
|
||||
|
||||
|
||||
class StableDiffusion_1(LatentDiffusionModel):
|
||||
"""Stable Diffusion 1.5 model.
|
||||
|
||||
Attributes:
|
||||
unet: The U-Net model.
|
||||
clip_text_encoder: The text encoder.
|
||||
lda: The image autoencoder.
|
||||
"""
|
||||
|
||||
unet: SD1UNet
|
||||
clip_text_encoder: CLIPTextEncoderL
|
||||
lda: SD1Autoencoder
|
||||
|
@ -30,6 +44,16 @@ class StableDiffusion_1(LatentDiffusionModel):
|
|||
device: Device | str = "cpu",
|
||||
dtype: DType = torch.float32,
|
||||
) -> None:
|
||||
"""Initializes the model.
|
||||
|
||||
Args:
|
||||
unet: The SD1UNet U-Net model to use.
|
||||
lda: The SD1Autoencoder image autoencoder to use.
|
||||
clip_text_encoder: The CLIPTextEncoderL text encoder to use.
|
||||
solver: The solver to use.
|
||||
device: The PyTorch device to use.
|
||||
dtype: The PyTorch data type to use.
|
||||
"""
|
||||
unet = unet or SD1UNet(in_channels=4)
|
||||
lda = lda or SD1Autoencoder()
|
||||
clip_text_encoder = clip_text_encoder or CLIPTextEncoderL()
|
||||
|
@ -45,6 +69,13 @@ class StableDiffusion_1(LatentDiffusionModel):
|
|||
)
|
||||
|
||||
def compute_clip_text_embedding(self, text: str, negative_text: str = "") -> Tensor:
|
||||
"""Compute the CLIP text embedding associated with the given prompt and negative prompt.
|
||||
|
||||
Args:
|
||||
text: The prompt to compute the CLIP text embedding of.
|
||||
negative_text: The negative prompt to compute the CLIP text embedding of.
|
||||
If not provided, the negative prompt is assumed to be empty (i.e., `""`).
|
||||
"""
|
||||
conditional_embedding = self.clip_text_encoder(text)
|
||||
if text == negative_text:
|
||||
return torch.cat(tensors=(conditional_embedding, conditional_embedding), dim=0)
|
||||
|
@ -53,10 +84,22 @@ class StableDiffusion_1(LatentDiffusionModel):
|
|||
return torch.cat(tensors=(negative_embedding, conditional_embedding), dim=0)
|
||||
|
||||
def set_unet_context(self, *, timestep: Tensor, clip_text_embedding: Tensor, **_: Tensor) -> None:
|
||||
"""Set the various context parameters required by the U-Net model.
|
||||
|
||||
Args:
|
||||
timestep: The timestep tensor to use.
|
||||
clip_text_embedding: The CLIP text embedding tensor to use.
|
||||
"""
|
||||
self.unet.set_timestep(timestep=timestep)
|
||||
self.unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding)
|
||||
|
||||
def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None:
|
||||
"""Set whether to enable self-attention guidance.
|
||||
|
||||
Args:
|
||||
enable: Whether to enable self-attention guidance.
|
||||
scale: The scale to use.
|
||||
"""
|
||||
if enable:
|
||||
if sag := self._find_sag_adapter():
|
||||
sag.scale = scale
|
||||
|
@ -67,9 +110,11 @@ class StableDiffusion_1(LatentDiffusionModel):
|
|||
sag.eject()
|
||||
|
||||
def has_self_attention_guidance(self) -> bool:
|
||||
"""Whether the model has self-attention guidance or not."""
|
||||
return self._find_sag_adapter() is not None
|
||||
|
||||
def _find_sag_adapter(self) -> SD1SAGAdapter | None:
|
||||
"""Finds the self-attention guidance adapter."""
|
||||
for p in self.unet.get_parents():
|
||||
if isinstance(p, SD1SAGAdapter):
|
||||
return p
|
||||
|
@ -78,6 +123,17 @@ class StableDiffusion_1(LatentDiffusionModel):
|
|||
def compute_self_attention_guidance(
|
||||
self, x: Tensor, noise: Tensor, step: int, *, clip_text_embedding: Tensor, **kwargs: Tensor
|
||||
) -> Tensor:
|
||||
"""Compute the self-attention guidance.
|
||||
|
||||
Args:
|
||||
x: The input tensor.
|
||||
noise: The noise tensor.
|
||||
step: The step to compute the self-attention guidance at.
|
||||
clip_text_embedding: The CLIP text embedding to compute the self-attention guidance with.
|
||||
|
||||
Returns:
|
||||
The computed self-attention guidance.
|
||||
"""
|
||||
sag = self._find_sag_adapter()
|
||||
assert sag is not None
|
||||
|
||||
|
@ -106,6 +162,14 @@ class StableDiffusion_1(LatentDiffusionModel):
|
|||
|
||||
|
||||
class StableDiffusion_1_Inpainting(StableDiffusion_1):
|
||||
"""Stable Diffusion 1.5 inpainting model.
|
||||
|
||||
Attributes:
|
||||
unet: The U-Net model.
|
||||
clip_text_encoder: The text encoder.
|
||||
lda: The image autoencoder.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
unet: SD1UNet | None = None,
|
||||
|
@ -140,6 +204,16 @@ class StableDiffusion_1_Inpainting(StableDiffusion_1):
|
|||
mask: Image.Image,
|
||||
latents_size: tuple[int, int] = (64, 64),
|
||||
) -> tuple[Tensor, Tensor]:
|
||||
"""Set the inpainting conditions.
|
||||
|
||||
Args:
|
||||
target_image: The target image to inpaint.
|
||||
mask: The mask to use for inpainting.
|
||||
latents_size: The size of the latents to use.
|
||||
|
||||
Returns:
|
||||
The mask latents and the target image latents.
|
||||
"""
|
||||
target_image = target_image.convert(mode="RGB")
|
||||
mask = mask.convert(mode="L")
|
||||
|
||||
|
@ -156,6 +230,17 @@ class StableDiffusion_1_Inpainting(StableDiffusion_1):
|
|||
def compute_self_attention_guidance(
|
||||
self, x: Tensor, noise: Tensor, step: int, *, clip_text_embedding: Tensor, **kwargs: Tensor
|
||||
) -> Tensor:
|
||||
"""Compute the self-attention guidance.
|
||||
|
||||
Args:
|
||||
x: The input tensor.
|
||||
noise: The noise tensor.
|
||||
step: The step to compute the self-attention guidance at.
|
||||
clip_text_embedding: The CLIP text embedding to compute the self-attention guidance with.
|
||||
|
||||
Returns:
|
||||
The computed self-attention guidance.
|
||||
"""
|
||||
sag = self._find_sag_adapter()
|
||||
assert sag is not None
|
||||
assert self.mask_latents is not None
|
||||
|
|
|
@ -25,7 +25,7 @@ class StableDiffusion_XL(LatentDiffusionModel):
|
|||
Attributes:
|
||||
unet: The U-Net model.
|
||||
clip_text_encoder: The text encoder.
|
||||
lda (SDXLAutoencoder): The image autoencoder.
|
||||
lda: The image autoencoder.
|
||||
"""
|
||||
|
||||
unet: SDXLUNet
|
||||
|
@ -103,7 +103,7 @@ class StableDiffusion_XL(LatentDiffusionModel):
|
|||
time_ids: Tensor,
|
||||
**_: Tensor,
|
||||
) -> None:
|
||||
"""Sets the various context parameters required by the U-Net model.
|
||||
"""Set the various context parameters required by the U-Net model.
|
||||
|
||||
Args:
|
||||
timestep: The timestep to set.
|
||||
|
|
Loading…
Reference in a new issue