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apply @deltheil suggestions
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@ -154,7 +154,7 @@ class CLIPTextEncoderL(CLIPTextEncoder):
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Note:
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We replace the GeLU activation function with an approximate GeLU to comply with the original CLIP implementation
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of OpenAI (https://github.com/openai/CLIP/blob/main/clip/model.py#L166)
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of OpenAI (https://github.com/openai/CLIP/blob/a1d0717/clip/model.py#L166)
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See [[arXiv:2103.00020] Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020)
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for more details.
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@ -238,8 +238,6 @@ class LatentDiffusionAutoencoder(Chain):
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x = decoder(x / self.encoder_scale)
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return x
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# backward-compatibility alias
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# TODO: deprecate this method
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def image_to_latents(self, image: Image.Image) -> Tensor:
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return self.images_to_latents([image])
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@ -261,7 +259,6 @@ class LatentDiffusionAutoencoder(Chain):
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def decode_latents(self, x: Tensor) -> Image.Image:
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return self.latents_to_image(x)
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# TODO: deprecated this method ?
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def latents_to_image(self, x: Tensor) -> Image.Image:
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if x.shape[0] != 1:
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raise ValueError(f"Expected batch size of 1, got {x.shape[0]}")
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@ -121,6 +121,10 @@ class Solver(fl.Module, ABC):
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def remove_noise(self, x: Tensor, noise: Tensor, step: int) -> Tensor:
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"""Remove noise from the input tensor using the current step of the diffusion process.
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Note:
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See [[arXiv:2006.11239] Denoising Diffusion Probabilistic Models, Equation 15](https://arxiv.org/abs/2006.11239)
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and [[arXiv:2210.00939] Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://arxiv.org/abs/2210.00939).
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Args:
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x: The input tensor to remove noise from.
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noise: The noise tensor to remove from the input tensor.
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@ -132,9 +136,6 @@ class Solver(fl.Module, ABC):
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timestep = self.timesteps[step]
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cumulative_scale_factors = self.cumulative_scale_factors[timestep]
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noise_stds = self.noise_std[timestep]
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# See equation (15) from https://arxiv.org/pdf/2006.11239.pdf.
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# Useful to preview progress or for guidance
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# See also https://arxiv.org/pdf/2210.00939.pdf (self-attention guidance)
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denoised_x = (x - noise_stds * noise) / cumulative_scale_factors
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return denoised_x
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@ -196,6 +197,9 @@ class Solver(fl.Module, ABC):
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This method should only be overridden by solvers that
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need to scale the input according to the current timestep.
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By default, this method does not scale the input.
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(scale=1)
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Args:
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x: The input tensor to scale.
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step: The current step of the diffusion process.
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@ -96,6 +96,9 @@ class StableDiffusion_1(LatentDiffusionModel):
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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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See [[arXiv:2210.00939] Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://arxiv.org/abs/2210.00939)
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for more details.
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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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@ -114,7 +117,7 @@ class StableDiffusion_1(LatentDiffusionModel):
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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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"""Finds the self-attention guidance adapter, if any."""
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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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@ -140,6 +140,9 @@ class StableDiffusion_XL(LatentDiffusionModel):
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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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See [[arXiv:2210.00939] Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://arxiv.org/abs/2210.00939)
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for more details.
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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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@ -158,7 +161,7 @@ class StableDiffusion_XL(LatentDiffusionModel):
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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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"""Finds the self-attention guidance adapter."""
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"""Finds the self-attention guidance adapter, if any."""
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for p in self.unet.get_parents():
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if isinstance(p, SDXLSAGAdapter):
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return p
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@ -154,10 +154,10 @@ class SegmentAnything(fl.Module):
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return w
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def compute_target_size(self, size: tuple[int, int]) -> tuple[int, int]:
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"""Compute the target size for a given size.
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"""Compute the target size as expected by the image encoder.
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Args:
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size: The size of the image.
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size: The size of the input image.
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Returns:
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The target height.
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@ -171,7 +171,7 @@ class SegmentAnything(fl.Module):
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return (newh, neww)
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def preprocess_image(self, image: Image.Image, target_size: tuple[int, int]) -> Tensor:
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"""Preprocess an image.
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"""Preprocess an image without distorting its aspect ratio.
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Args:
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image: The image to preprocess.
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