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Unverified Commit be4afa0b authored by Mark Van Aken's avatar Mark Van Aken Committed by GitHub
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#7535 Update FloatTensor type hints to Tensor (#7883) 

* find & replace all FloatTensors to Tensor

* apply formatting

* Update torch.FloatTensor to torch.Tensor in the remaining files

* formatting

* Fix the rest of the places where FloatTensor is used as well as in documentation

* formatting

* Update new file from FloatTensor to Tensor
parent 04f4bd54
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Showing with 256 additions and 267 deletions
src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py
View file @ be4afa0b
......@@ -392,7 +392,7 @@ class TextToVideoZeroPipeline(DiffusionPipeline, StableDiffusionMixin, TextualIn
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
......@@ -529,12 +529,12 @@ class TextToVideoZeroPipeline(DiffusionPipeline, StableDiffusionMixin, TextualIn
num_videos_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
latents: Optional[torch.Tensor] = None,
motion_field_strength_x: float = 12,
motion_field_strength_y: float = 12,
output_type: Optional[str] = "tensor",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: Optional[int] = 1,
t0: int = 44,
t1: int = 47,
......@@ -569,7 +569,7 @@ class TextToVideoZeroPipeline(DiffusionPipeline, StableDiffusionMixin, TextualIn
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
......@@ -581,7 +581,7 @@ class TextToVideoZeroPipeline(DiffusionPipeline, StableDiffusionMixin, TextualIn
a plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
......@@ -795,8 +795,8 @@ class TextToVideoZeroPipeline(DiffusionPipeline, StableDiffusionMixin, TextualIn
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
):
......@@ -816,10 +816,10 @@ class TextToVideoZeroPipeline(DiffusionPipeline, StableDiffusionMixin, TextualIn
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
......
src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py
View file @ be4afa0b
......@@ -581,10 +581,10 @@ class TextToVideoZeroSDXLPipeline(
do_classifier_free_guidance: bool = True,
negative_prompt: Optional[str] = None,
negative_prompt_2: Optional[str] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
pooled_prompt_embeds: Optional[torch.Tensor] = None,
negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
):
......@@ -610,17 +610,17 @@ class TextToVideoZeroSDXLPipeline(
negative_prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
prompt_embeds (`torch.FloatTensor`, *optional*):
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument.
......@@ -861,7 +861,7 @@ class TextToVideoZeroSDXLPipeline(
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
......@@ -933,16 +933,16 @@ class TextToVideoZeroSDXLPipeline(
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
frame_ids: Optional[List[int]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
pooled_prompt_embeds: Optional[torch.Tensor] = None,
negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
latents: Optional[torch.Tensor] = None,
motion_field_strength_x: float = 12,
motion_field_strength_y: float = 12,
output_type: Optional[str] = "tensor",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
guidance_rescale: float = 0.0,
......@@ -1002,21 +1002,21 @@ class TextToVideoZeroSDXLPipeline(
frame_ids (`List[int]`, *optional*):
Indexes of the frames that are being generated. This is used when generating longer videos
chunk-by-chunk.
prompt_embeds (`torch.FloatTensor`, *optional*):
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument.
latents (`torch.FloatTensor`, *optional*):
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
......@@ -1034,7 +1034,7 @@ class TextToVideoZeroSDXLPipeline(
of a plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
......
src/diffusers/pipelines/unclip/pipeline_unclip.py
View file @ be4afa0b
......@@ -217,9 +217,9 @@ class UnCLIPPipeline(DiffusionPipeline):
decoder_num_inference_steps: int = 25,
super_res_num_inference_steps: int = 7,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prior_latents: Optional[torch.FloatTensor] = None,
decoder_latents: Optional[torch.FloatTensor] = None,
super_res_latents: Optional[torch.FloatTensor] = None,
prior_latents: Optional[torch.Tensor] = None,
decoder_latents: Optional[torch.Tensor] = None,
super_res_latents: Optional[torch.Tensor] = None,
text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
text_attention_mask: Optional[torch.Tensor] = None,
prior_guidance_scale: float = 4.0,
......@@ -248,11 +248,11 @@ class UnCLIPPipeline(DiffusionPipeline):
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
prior_latents (`torch.FloatTensor` of shape (batch size, embeddings dimension), *optional*):
prior_latents (`torch.Tensor` of shape (batch size, embeddings dimension), *optional*):
Pre-generated noisy latents to be used as inputs for the prior.
decoder_latents (`torch.FloatTensor` of shape (batch size, channels, height, width), *optional*):
decoder_latents (`torch.Tensor` of shape (batch size, channels, height, width), *optional*):
Pre-generated noisy latents to be used as inputs for the decoder.
super_res_latents (`torch.FloatTensor` of shape (batch size, channels, super res height, super res width), *optional*):
super_res_latents (`torch.Tensor` of shape (batch size, channels, super res height, super res width), *optional*):
Pre-generated noisy latents to be used as inputs for the decoder.
prior_guidance_scale (`float`, *optional*, defaults to 4.0):
A higher guidance scale value encourages the model to generate images closely linked to the text
......
src/diffusers/pipelines/unclip/pipeline_unclip_image_variation.py
View file @ be4afa0b
......@@ -199,13 +199,13 @@ class UnCLIPImageVariationPipeline(DiffusionPipeline):
@torch.no_grad()
def __call__(
self,
image: Optional[Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor]] = None,
image: Optional[Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor]] = None,
num_images_per_prompt: int = 1,
decoder_num_inference_steps: int = 25,
super_res_num_inference_steps: int = 7,
generator: Optional[torch.Generator] = None,
decoder_latents: Optional[torch.FloatTensor] = None,
super_res_latents: Optional[torch.FloatTensor] = None,
decoder_latents: Optional[torch.Tensor] = None,
super_res_latents: Optional[torch.Tensor] = None,
image_embeddings: Optional[torch.Tensor] = None,
decoder_guidance_scale: float = 8.0,
output_type: Optional[str] = "pil",
......@@ -215,7 +215,7 @@ class UnCLIPImageVariationPipeline(DiffusionPipeline):
The call function to the pipeline for generation.
Args:
image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.Tensor`):
`Image` or tensor representing an image batch to be used as the starting point. If you provide a
tensor, it needs to be compatible with the [`CLIPImageProcessor`]
[configuration](https://huggingface.co/fusing/karlo-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json).
......@@ -231,9 +231,9 @@ class UnCLIPImageVariationPipeline(DiffusionPipeline):
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
decoder_latents (`torch.FloatTensor` of shape (batch size, channels, height, width), *optional*):
decoder_latents (`torch.Tensor` of shape (batch size, channels, height, width), *optional*):
Pre-generated noisy latents to be used as inputs for the decoder.
super_res_latents (`torch.FloatTensor` of shape (batch size, channels, super res height, super res width), *optional*):
super_res_latents (`torch.Tensor` of shape (batch size, channels, super res height, super res width), *optional*):
Pre-generated noisy latents to be used as inputs for the decoder.
decoder_guidance_scale (`float`, *optional*, defaults to 4.0):
A higher guidance scale value encourages the model to generate images closely linked to the text
......
src/diffusers/pipelines/unidiffuser/modeling_text_decoder.py
View file @ be4afa0b
......@@ -220,7 +220,7 @@ class UniDiffuserTextDecoder(ModelMixin, ConfigMixin, ModuleUtilsMixin):
input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*):
Tokenizer indices of input sequence tokens in the vocabulary. One of `input_ids` and `input_embeds`
must be supplied.
input_embeds (`torch.FloatTensor` of shape `(batch_size, seq_len, hidden_size)`, *optional*):
input_embeds (`torch.Tensor` of shape `(batch_size, seq_len, hidden_size)`, *optional*):
An embedded representation to directly pass to the transformer as a prefix for beam search. One of
`input_ids` and `input_embeds` must be supplied.
device:
......
src/diffusers/pipelines/unidiffuser/modeling_uvit.py
View file @ be4afa0b
......@@ -739,8 +739,7 @@ class UTransformer2DModel(ModelMixin, ConfigMixin):
"""
Args:
hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`.
When continuous, `torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input
hidden_states
When continuous, `torch.Tensor` of shape `(batch size, channel, height, width)`): Input hidden_states
encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
self-attention.
......@@ -1038,9 +1037,9 @@ class UniDiffuserModel(ModelMixin, ConfigMixin):
def forward(
self,
latent_image_embeds: torch.FloatTensor,
image_embeds: torch.FloatTensor,
prompt_embeds: torch.FloatTensor,
latent_image_embeds: torch.Tensor,
image_embeds: torch.Tensor,
prompt_embeds: torch.Tensor,
timestep_img: Union[torch.Tensor, float, int],
timestep_text: Union[torch.Tensor, float, int],
data_type: Optional[Union[torch.Tensor, float, int]] = 1,
......@@ -1049,11 +1048,11 @@ class UniDiffuserModel(ModelMixin, ConfigMixin):
):
"""
Args:
latent_image_embeds (`torch.FloatTensor` of shape `(batch size, latent channels, height, width)`):
latent_image_embeds (`torch.Tensor` of shape `(batch size, latent channels, height, width)`):
Latent image representation from the VAE encoder.
image_embeds (`torch.FloatTensor` of shape `(batch size, 1, clip_img_dim)`):
image_embeds (`torch.Tensor` of shape `(batch size, 1, clip_img_dim)`):
CLIP-embedded image representation (unsqueezed in the first dimension).
prompt_embeds (`torch.FloatTensor` of shape `(batch size, seq_len, text_dim)`):
prompt_embeds (`torch.Tensor` of shape `(batch size, seq_len, text_dim)`):
CLIP-embedded text representation.
timestep_img (`torch.long` or `float` or `int`):
Current denoising step for the image.
......
src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py
View file @ be4afa0b
......@@ -304,7 +304,7 @@ class UniDiffuserPipeline(DiffusionPipeline):
if isinstance(image, PIL.Image.Image):
batch_size = 1
else:
# Image must be available and type either PIL.Image.Image or torch.FloatTensor.
# Image must be available and type either PIL.Image.Image or torch.Tensor.
# Not currently supporting something like image_embeds.
batch_size = image.shape[0]
multiplier = num_prompts_per_image
......@@ -353,8 +353,8 @@ class UniDiffuserPipeline(DiffusionPipeline):
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = None,
**kwargs,
):
......@@ -386,8 +386,8 @@ class UniDiffuserPipeline(DiffusionPipeline):
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
):
......@@ -407,10 +407,10 @@ class UniDiffuserPipeline(DiffusionPipeline):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
......@@ -1080,7 +1080,7 @@ class UniDiffuserPipeline(DiffusionPipeline):
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
image: Optional[Union[torch.FloatTensor, PIL.Image.Image]] = None,
image: Optional[Union[torch.Tensor, PIL.Image.Image]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
data_type: Optional[int] = 1,
......@@ -1091,15 +1091,15 @@ class UniDiffuserPipeline(DiffusionPipeline):
num_prompts_per_image: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_latents: Optional[torch.FloatTensor] = None,
vae_latents: Optional[torch.FloatTensor] = None,
clip_latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
latents: Optional[torch.Tensor] = None,
prompt_latents: Optional[torch.Tensor] = None,
vae_latents: Optional[torch.Tensor] = None,
clip_latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: int = 1,
):
r"""
......@@ -1109,7 +1109,7 @@ class UniDiffuserPipeline(DiffusionPipeline):
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
Required for text-conditioned image generation (`text2img`) mode.
image (`torch.FloatTensor` or `PIL.Image.Image`, *optional*):
image (`torch.Tensor` or `PIL.Image.Image`, *optional*):
`Image` or tensor representing an image batch. Required for image-conditioned text generation
(`img2text`) mode.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
......@@ -1144,29 +1144,29 @@ class UniDiffuserPipeline(DiffusionPipeline):
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for joint
image-text generation. Can be used to tweak the same generation with different prompts. If not
provided, a latents tensor is generated by sampling using the supplied random `generator`. This assumes
a full set of VAE, CLIP, and text latents, if supplied, overrides the value of `prompt_latents`,
`vae_latents`, and `clip_latents`.
prompt_latents (`torch.FloatTensor`, *optional*):
prompt_latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for text
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
vae_latents (`torch.FloatTensor`, *optional*):
vae_latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
clip_latents (`torch.FloatTensor`, *optional*):
clip_latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument. Used in text-conditioned
image generation (`text2img`) mode.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are be generated from the `negative_prompt` input argument. Used
in text-conditioned image generation (`text2img`) mode.
......@@ -1176,7 +1176,7 @@ class UniDiffuserPipeline(DiffusionPipeline):
Whether or not to return a [`~pipelines.ImageTextPipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
......
src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py
View file @ be4afa0b
......@@ -130,7 +130,7 @@ class PaellaVQModel(ModelMixin, ConfigMixin):
)
@apply_forward_hook
def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> VQEncoderOutput:
def encode(self, x: torch.Tensor, return_dict: bool = True) -> VQEncoderOutput:
h = self.in_block(x)
h = self.down_blocks(h)
......@@ -141,8 +141,8 @@ class PaellaVQModel(ModelMixin, ConfigMixin):
@apply_forward_hook
def decode(
self, h: torch.FloatTensor, force_not_quantize: bool = True, return_dict: bool = True
) -> Union[DecoderOutput, torch.FloatTensor]:
self, h: torch.Tensor, force_not_quantize: bool = True, return_dict: bool = True
) -> Union[DecoderOutput, torch.Tensor]:
if not force_not_quantize:
quant, _, _ = self.vquantizer(h)
else:
......@@ -155,10 +155,10 @@ class PaellaVQModel(ModelMixin, ConfigMixin):
return DecoderOutput(sample=dec)
def forward(self, sample: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
def forward(self, sample: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
r"""
Args:
sample (`torch.FloatTensor`): Input sample.
sample (`torch.Tensor`): Input sample.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
"""
......
src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py
View file @ be4afa0b
......@@ -209,7 +209,7 @@ class WuerstchenDecoderPipeline(DiffusionPipeline):
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image_embeddings: Union[torch.FloatTensor, List[torch.FloatTensor]],
image_embeddings: Union[torch.Tensor, List[torch.Tensor]],
prompt: Union[str, List[str]] = None,
num_inference_steps: int = 12,
timesteps: Optional[List[float]] = None,
......@@ -217,7 +217,7 @@ class WuerstchenDecoderPipeline(DiffusionPipeline):
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
latents: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
......@@ -228,7 +228,7 @@ class WuerstchenDecoderPipeline(DiffusionPipeline):
Function invoked when calling the pipeline for generation.
Args:
image_embedding (`torch.FloatTensor` or `List[torch.FloatTensor]`):
image_embedding (`torch.Tensor` or `List[torch.Tensor]`):
Image Embeddings either extracted from an image or generated by a Prior Model.
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
......@@ -252,7 +252,7 @@ class WuerstchenDecoderPipeline(DiffusionPipeline):
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
......
src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py
View file @ be4afa0b
......@@ -154,11 +154,11 @@ class WuerstchenCombinedPipeline(DiffusionPipeline):
decoder_timesteps: Optional[List[float]] = None,
decoder_guidance_scale: float = 0.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
num_images_per_prompt: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
latents: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
prior_callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
......@@ -176,10 +176,10 @@ class WuerstchenCombinedPipeline(DiffusionPipeline):
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings for the prior. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings for the prior. Can be used to easily tweak text inputs, *e.g.*
prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt`
input argument.
......@@ -218,7 +218,7 @@ class WuerstchenCombinedPipeline(DiffusionPipeline):
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
......
src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py
View file @ be4afa0b
......@@ -54,12 +54,12 @@ class WuerstchenPriorPipelineOutput(BaseOutput):
Output class for WuerstchenPriorPipeline.
Args:
image_embeddings (`torch.FloatTensor` or `np.ndarray`)
image_embeddings (`torch.Tensor` or `np.ndarray`)
Prior image embeddings for text prompt
"""
image_embeddings: Union[torch.FloatTensor, np.ndarray]
image_embeddings: Union[torch.Tensor, np.ndarray]
class WuerstchenPriorPipeline(DiffusionPipeline, LoraLoaderMixin):
......@@ -136,8 +136,8 @@ class WuerstchenPriorPipeline(DiffusionPipeline, LoraLoaderMixin):
do_classifier_free_guidance,
prompt=None,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
):
if prompt is not None and isinstance(prompt, str):
batch_size = 1
......@@ -288,11 +288,11 @@ class WuerstchenPriorPipeline(DiffusionPipeline, LoraLoaderMixin):
timesteps: List[float] = None,
guidance_scale: float = 8.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
num_images_per_prompt: Optional[int] = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
latents: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pt",
return_dict: bool = True,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
......@@ -324,10 +324,10 @@ class WuerstchenPriorPipeline(DiffusionPipeline, LoraLoaderMixin):
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `decoder_guidance_scale` is less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
......@@ -336,7 +336,7 @@ class WuerstchenPriorPipeline(DiffusionPipeline, LoraLoaderMixin):
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
......
src/diffusers/schedulers/deprecated/scheduling_karras_ve.py
View file @ be4afa0b
......@@ -31,19 +31,19 @@ class KarrasVeOutput(BaseOutput):
Output class for the scheduler's step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
derivative (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
derivative (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Derivative of predicted original image sample (x_0).
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample (x_{0}) based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
"""
prev_sample: torch.FloatTensor
derivative: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
prev_sample: torch.Tensor
derivative: torch.Tensor
pred_original_sample: Optional[torch.Tensor] = None
class KarrasVeScheduler(SchedulerMixin, ConfigMixin):
......@@ -94,21 +94,21 @@ class KarrasVeScheduler(SchedulerMixin, ConfigMixin):
# setable values
self.num_inference_steps: int = None
self.timesteps: np.IntTensor = None
self.schedule: torch.FloatTensor = None # sigma(t_i)
self.schedule: torch.Tensor = None # sigma(t_i)
def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.
Args:
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain.
Returns:
`torch.FloatTensor`:
`torch.Tensor`:
A scaled input sample.
"""
return sample
......@@ -136,14 +136,14 @@ class KarrasVeScheduler(SchedulerMixin, ConfigMixin):
self.schedule = torch.tensor(schedule, dtype=torch.float32, device=device)
def add_noise_to_input(
self, sample: torch.FloatTensor, sigma: float, generator: Optional[torch.Generator] = None
) -> Tuple[torch.FloatTensor, float]:
self, sample: torch.Tensor, sigma: float, generator: Optional[torch.Generator] = None
) -> Tuple[torch.Tensor, float]:
"""
Explicit Langevin-like "churn" step of adding noise to the sample according to a `gamma_i ≥ 0` to reach a
higher noise level `sigma_hat = sigma_i + gamma_i*sigma_i`.
Args:
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
The input sample.
sigma (`float`):
generator (`torch.Generator`, *optional*):
......@@ -163,10 +163,10 @@ class KarrasVeScheduler(SchedulerMixin, ConfigMixin):
def step(
self,
model_output: torch.FloatTensor,
model_output: torch.Tensor,
sigma_hat: float,
sigma_prev: float,
sample_hat: torch.FloatTensor,
sample_hat: torch.Tensor,
return_dict: bool = True,
) -> Union[KarrasVeOutput, Tuple]:
"""
......@@ -174,11 +174,11 @@ class KarrasVeScheduler(SchedulerMixin, ConfigMixin):
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor`):
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
sigma_hat (`float`):
sigma_prev (`float`):
sample_hat (`torch.FloatTensor`):
sample_hat (`torch.Tensor`):
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput`] or `tuple`.
......@@ -202,25 +202,25 @@ class KarrasVeScheduler(SchedulerMixin, ConfigMixin):
def step_correct(
self,
model_output: torch.FloatTensor,
model_output: torch.Tensor,
sigma_hat: float,
sigma_prev: float,
sample_hat: torch.FloatTensor,
sample_prev: torch.FloatTensor,
derivative: torch.FloatTensor,
sample_hat: torch.Tensor,
sample_prev: torch.Tensor,
derivative: torch.Tensor,
return_dict: bool = True,
) -> Union[KarrasVeOutput, Tuple]:
"""
Corrects the predicted sample based on the `model_output` of the network.
Args:
model_output (`torch.FloatTensor`):
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
sigma_hat (`float`): TODO
sigma_prev (`float`): TODO
sample_hat (`torch.FloatTensor`): TODO
sample_prev (`torch.FloatTensor`): TODO
derivative (`torch.FloatTensor`): TODO
sample_hat (`torch.Tensor`): TODO
sample_prev (`torch.Tensor`): TODO
derivative (`torch.Tensor`): TODO
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] or `tuple`.
......
src/diffusers/schedulers/scheduling_amused.py
View file @ be4afa0b
......@@ -29,16 +29,16 @@ class AmusedSchedulerOutput(BaseOutput):
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
"""
prev_sample: torch.FloatTensor
pred_original_sample: torch.FloatTensor = None
prev_sample: torch.Tensor
pred_original_sample: torch.Tensor = None
class AmusedScheduler(SchedulerMixin, ConfigMixin):
......@@ -70,7 +70,7 @@ class AmusedScheduler(SchedulerMixin, ConfigMixin):
def step(
self,
model_output: torch.FloatTensor,
model_output: torch.Tensor,
timestep: torch.long,
sample: torch.LongTensor,
starting_mask_ratio: int = 1,
......
src/diffusers/schedulers/scheduling_consistency_decoder.py
View file @ be4afa0b
......@@ -61,12 +61,12 @@ class ConsistencyDecoderSchedulerOutput(BaseOutput):
Output class for the scheduler's `step` function.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
"""
prev_sample: torch.FloatTensor
prev_sample: torch.Tensor
class ConsistencyDecoderScheduler(SchedulerMixin, ConfigMixin):
......@@ -113,28 +113,28 @@ class ConsistencyDecoderScheduler(SchedulerMixin, ConfigMixin):
def init_noise_sigma(self):
return self.sqrt_one_minus_alphas_cumprod[self.timesteps[0]]
def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.
Args:
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain.
Returns:
`torch.FloatTensor`:
`torch.Tensor`:
A scaled input sample.
"""
return sample * self.c_in[timestep]
def step(
self,
model_output: torch.FloatTensor,
timestep: Union[float, torch.FloatTensor],
sample: torch.FloatTensor,
model_output: torch.Tensor,
timestep: Union[float, torch.Tensor],
sample: torch.Tensor,
generator: Optional[torch.Generator] = None,
return_dict: bool = True,
) -> Union[ConsistencyDecoderSchedulerOutput, Tuple]:
......@@ -143,11 +143,11 @@ class ConsistencyDecoderScheduler(SchedulerMixin, ConfigMixin):
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor`):
model_output (`torch.Tensor`):
The direct output from the learned diffusion model.
timestep (`float`):
The current timestep in the diffusion chain.
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
generator (`torch.Generator`, *optional*):
A random number generator.
......
src/diffusers/schedulers/scheduling_consistency_models.py
View file @ be4afa0b
......@@ -33,12 +33,12 @@ class CMStochasticIterativeSchedulerOutput(BaseOutput):
Output class for the scheduler's `step` function.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
"""
prev_sample: torch.FloatTensor
prev_sample: torch.Tensor
class CMStochasticIterativeScheduler(SchedulerMixin, ConfigMixin):
......@@ -126,20 +126,18 @@ class CMStochasticIterativeScheduler(SchedulerMixin, ConfigMixin):
"""
self._begin_index = begin_index
def scale_model_input(
self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor]
) -> torch.FloatTensor:
def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:
"""
Scales the consistency model input by `(sigma**2 + sigma_data**2) ** 0.5`.
Args:
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
The input sample.
timestep (`float` or `torch.FloatTensor`):
timestep (`float` or `torch.Tensor`):
The current timestep in the diffusion chain.
Returns:
`torch.FloatTensor`:
`torch.Tensor`:
A scaled input sample.
"""
# Get sigma corresponding to timestep
......@@ -278,7 +276,7 @@ class CMStochasticIterativeScheduler(SchedulerMixin, ConfigMixin):
</Tip>
Args:
sigma (`torch.FloatTensor`):
sigma (`torch.Tensor`):
The current sigma in the Karras sigma schedule.
Returns:
......@@ -319,9 +317,9 @@ class CMStochasticIterativeScheduler(SchedulerMixin, ConfigMixin):
def step(
self,
model_output: torch.FloatTensor,
timestep: Union[float, torch.FloatTensor],
sample: torch.FloatTensor,
model_output: torch.Tensor,
timestep: Union[float, torch.Tensor],
sample: torch.Tensor,
generator: Optional[torch.Generator] = None,
return_dict: bool = True,
) -> Union[CMStochasticIterativeSchedulerOutput, Tuple]:
......@@ -330,11 +328,11 @@ class CMStochasticIterativeScheduler(SchedulerMixin, ConfigMixin):
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor`):
model_output (`torch.Tensor`):
The direct output from the learned diffusion model.
timestep (`float`):
The current timestep in the diffusion chain.
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
generator (`torch.Generator`, *optional*):
A random number generator.
......@@ -417,10 +415,10 @@ class CMStochasticIterativeScheduler(SchedulerMixin, ConfigMixin):
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise
def add_noise(
self,
original_samples: torch.FloatTensor,
noise: torch.FloatTensor,
timesteps: torch.FloatTensor,
) -> torch.FloatTensor:
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.Tensor,
) -> torch.Tensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):
......
src/diffusers/schedulers/scheduling_ddim.py
View file @ be4afa0b
......@@ -35,16 +35,16 @@ class DDIMSchedulerOutput(BaseOutput):
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
prev_sample: torch.Tensor
pred_original_sample: Optional[torch.Tensor] = None
# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
......@@ -98,11 +98,11 @@ def rescale_zero_terminal_snr(betas):
Args:
betas (`torch.FloatTensor`):
betas (`torch.Tensor`):
the betas that the scheduler is being initialized with.
Returns:
`torch.FloatTensor`: rescaled betas with zero terminal SNR
`torch.Tensor`: rescaled betas with zero terminal SNR
"""
# Convert betas to alphas_bar_sqrt
alphas = 1.0 - betas
......@@ -233,19 +233,19 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
self.num_inference_steps = None
self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))
def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.
Args:
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain.
Returns:
`torch.FloatTensor`:
`torch.Tensor`:
A scaled input sample.
"""
return sample
......@@ -261,7 +261,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
return variance
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
"""
"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
......@@ -341,13 +341,13 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
def step(
self,
model_output: torch.FloatTensor,
model_output: torch.Tensor,
timestep: int,
sample: torch.FloatTensor,
sample: torch.Tensor,
eta: float = 0.0,
use_clipped_model_output: bool = False,
generator=None,
variance_noise: Optional[torch.FloatTensor] = None,
variance_noise: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[DDIMSchedulerOutput, Tuple]:
"""
......@@ -355,11 +355,11 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor`):
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
timestep (`float`):
The current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
eta (`float`):
The weight of noise for added noise in diffusion step.
......@@ -370,7 +370,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
`use_clipped_model_output` has no effect.
generator (`torch.Generator`, *optional*):
A random number generator.
variance_noise (`torch.FloatTensor`):
variance_noise (`torch.Tensor`):
Alternative to generating noise with `generator` by directly providing the noise for the variance
itself. Useful for methods such as [`CycleDiffusion`].
return_dict (`bool`, *optional*, defaults to `True`):
......@@ -470,10 +470,10 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
def add_noise(
self,
original_samples: torch.FloatTensor,
noise: torch.FloatTensor,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.IntTensor,
) -> torch.FloatTensor:
) -> torch.Tensor:
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
# Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement
# for the subsequent add_noise calls
......@@ -495,9 +495,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
return noisy_samples
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
def get_velocity(
self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor
) -> torch.FloatTensor:
def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:
# Make sure alphas_cumprod and timestep have same device and dtype as sample
self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device)
alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype)
......
src/diffusers/schedulers/scheduling_ddim_inverse.py
View file @ be4afa0b
......@@ -33,16 +33,16 @@ class DDIMSchedulerOutput(BaseOutput):
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
prev_sample: torch.Tensor
pred_original_sample: Optional[torch.Tensor] = None
# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
......@@ -97,11 +97,11 @@ def rescale_zero_terminal_snr(betas):
Args:
betas (`torch.FloatTensor`):
betas (`torch.Tensor`):
the betas that the scheduler is being initialized with.
Returns:
`torch.FloatTensor`: rescaled betas with zero terminal SNR
`torch.Tensor`: rescaled betas with zero terminal SNR
"""
# Convert betas to alphas_bar_sqrt
alphas = 1.0 - betas
......@@ -231,19 +231,19 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps).copy().astype(np.int64))
# Copied from diffusers.schedulers.scheduling_ddim.DDIMScheduler.scale_model_input
def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.
Args:
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain.
Returns:
`torch.FloatTensor`:
`torch.Tensor`:
A scaled input sample.
"""
return sample
......@@ -288,9 +288,9 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
def step(
self,
model_output: torch.FloatTensor,
model_output: torch.Tensor,
timestep: int,
sample: torch.FloatTensor,
sample: torch.Tensor,
return_dict: bool = True,
) -> Union[DDIMSchedulerOutput, Tuple]:
"""
......@@ -298,11 +298,11 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor`):
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
timestep (`float`):
The current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
eta (`float`):
The weight of noise for added noise in diffusion step.
......@@ -311,7 +311,7 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no
clipping has happened, "corrected" `model_output` would coincide with the one provided as input and
`use_clipped_model_output` has no effect.
variance_noise (`torch.FloatTensor`):
variance_noise (`torch.Tensor`):
Alternative to generating noise with `generator` by directly providing the noise for the variance
itself. Useful for methods such as [`CycleDiffusion`].
return_dict (`bool`, *optional*, defaults to `True`):
......
src/diffusers/schedulers/scheduling_ddim_parallel.py
View file @ be4afa0b
......@@ -35,16 +35,16 @@ class DDIMParallelSchedulerOutput(BaseOutput):
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
prev_sample: torch.Tensor
pred_original_sample: Optional[torch.Tensor] = None
# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
......@@ -99,11 +99,11 @@ def rescale_zero_terminal_snr(betas):
Args:
betas (`torch.FloatTensor`):
betas (`torch.Tensor`):
the betas that the scheduler is being initialized with.
Returns:
`torch.FloatTensor`: rescaled betas with zero terminal SNR
`torch.Tensor`: rescaled betas with zero terminal SNR
"""
# Convert betas to alphas_bar_sqrt
alphas = 1.0 - betas
......@@ -241,19 +241,19 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))
# Copied from diffusers.schedulers.scheduling_ddim.DDIMScheduler.scale_model_input
def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.
Args:
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain.
Returns:
`torch.FloatTensor`:
`torch.Tensor`:
A scaled input sample.
"""
return sample
......@@ -283,7 +283,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
return variance
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
"""
"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
......@@ -364,13 +364,13 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
def step(
self,
model_output: torch.FloatTensor,
model_output: torch.Tensor,
timestep: int,
sample: torch.FloatTensor,
sample: torch.Tensor,
eta: float = 0.0,
use_clipped_model_output: bool = False,
generator=None,
variance_noise: Optional[torch.FloatTensor] = None,
variance_noise: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[DDIMParallelSchedulerOutput, Tuple]:
"""
......@@ -378,9 +378,9 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor`): direct output from learned diffusion model.
model_output (`torch.Tensor`): direct output from learned diffusion model.
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
current instance of sample being created by diffusion process.
eta (`float`): weight of noise for added noise in diffusion step.
use_clipped_model_output (`bool`): if `True`, compute "corrected" `model_output` from the clipped
......@@ -388,7 +388,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
`self.config.clip_sample` is `True`. If no clipping has happened, "corrected" `model_output` would
coincide with the one provided as input and `use_clipped_model_output` will have not effect.
generator: random number generator.
variance_noise (`torch.FloatTensor`): instead of generating noise for the variance using `generator`, we
variance_noise (`torch.Tensor`): instead of generating noise for the variance using `generator`, we
can directly provide the noise for the variance itself. This is useful for methods such as
CycleDiffusion. (https://arxiv.org/abs/2210.05559)
return_dict (`bool`): option for returning tuple rather than DDIMParallelSchedulerOutput class
......@@ -486,12 +486,12 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
def batch_step_no_noise(
self,
model_output: torch.FloatTensor,
model_output: torch.Tensor,
timesteps: List[int],
sample: torch.FloatTensor,
sample: torch.Tensor,
eta: float = 0.0,
use_clipped_model_output: bool = False,
) -> torch.FloatTensor:
) -> torch.Tensor:
"""
Batched version of the `step` function, to be able to reverse the SDE for multiple samples/timesteps at once.
Also, does not add any noise to the predicted sample, which is necessary for parallel sampling where the noise
......@@ -501,10 +501,10 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor`): direct output from learned diffusion model.
model_output (`torch.Tensor`): direct output from learned diffusion model.
timesteps (`List[int]`):
current discrete timesteps in the diffusion chain. This is now a list of integers.
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
current instance of sample being created by diffusion process.
eta (`float`): weight of noise for added noise in diffusion step.
use_clipped_model_output (`bool`): if `True`, compute "corrected" `model_output` from the clipped
......@@ -513,7 +513,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
coincide with the one provided as input and `use_clipped_model_output` will have not effect.
Returns:
`torch.FloatTensor`: sample tensor at previous timestep.
`torch.Tensor`: sample tensor at previous timestep.
"""
if self.num_inference_steps is None:
......@@ -595,10 +595,10 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
def add_noise(
self,
original_samples: torch.FloatTensor,
noise: torch.FloatTensor,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.IntTensor,
) -> torch.FloatTensor:
) -> torch.Tensor:
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
# Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement
# for the subsequent add_noise calls
......@@ -620,9 +620,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
return noisy_samples
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
def get_velocity(
self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor
) -> torch.FloatTensor:
def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:
# Make sure alphas_cumprod and timestep have same device and dtype as sample
self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device)
alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype)
......
src/diffusers/schedulers/scheduling_ddpm.py
View file @ be4afa0b
......@@ -33,16 +33,16 @@ class DDPMSchedulerOutput(BaseOutput):
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
prev_sample: torch.Tensor
pred_original_sample: Optional[torch.Tensor] = None
def betas_for_alpha_bar(
......@@ -96,11 +96,11 @@ def rescale_zero_terminal_snr(betas):
Args:
betas (`torch.FloatTensor`):
betas (`torch.Tensor`):
the betas that the scheduler is being initialized with.
Returns:
`torch.FloatTensor`: rescaled betas with zero terminal SNR
`torch.Tensor`: rescaled betas with zero terminal SNR
"""
# Convert betas to alphas_bar_sqrt
alphas = 1.0 - betas
......@@ -231,19 +231,19 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
self.variance_type = variance_type
def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.
Args:
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain.
Returns:
`torch.FloatTensor`:
`torch.Tensor`:
A scaled input sample.
"""
return sample
......@@ -363,7 +363,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
return variance
def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
"""
"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
......@@ -398,9 +398,9 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
def step(
self,
model_output: torch.FloatTensor,
model_output: torch.Tensor,
timestep: int,
sample: torch.FloatTensor,
sample: torch.Tensor,
generator=None,
return_dict: bool = True,
) -> Union[DDPMSchedulerOutput, Tuple]:
......@@ -409,11 +409,11 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor`):
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
timestep (`float`):
The current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
generator (`torch.Generator`, *optional*):
A random number generator.
......@@ -498,10 +498,10 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
def add_noise(
self,
original_samples: torch.FloatTensor,
noise: torch.FloatTensor,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.IntTensor,
) -> torch.FloatTensor:
) -> torch.Tensor:
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
# Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement
# for the subsequent add_noise calls
......@@ -522,9 +522,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def get_velocity(
self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor
) -> torch.FloatTensor:
def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:
# Make sure alphas_cumprod and timestep have same device and dtype as sample
self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device)
alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype)
......
src/diffusers/schedulers/scheduling_ddpm_parallel.py
View file @ be4afa0b
......@@ -34,16 +34,16 @@ class DDPMParallelSchedulerOutput(BaseOutput):
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
prev_sample: torch.Tensor
pred_original_sample: Optional[torch.Tensor] = None
# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
......@@ -98,11 +98,11 @@ def rescale_zero_terminal_snr(betas):
Args:
betas (`torch.FloatTensor`):
betas (`torch.Tensor`):
the betas that the scheduler is being initialized with.
Returns:
`torch.FloatTensor`: rescaled betas with zero terminal SNR
`torch.Tensor`: rescaled betas with zero terminal SNR
"""
# Convert betas to alphas_bar_sqrt
alphas = 1.0 - betas
......@@ -240,19 +240,19 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
self.variance_type = variance_type
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.scale_model_input
def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.
Args:
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain.
Returns:
`torch.FloatTensor`:
`torch.Tensor`:
A scaled input sample.
"""
return sample
......@@ -375,7 +375,7 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
return variance
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
"""
"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
......@@ -410,9 +410,9 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
def step(
self,
model_output: torch.FloatTensor,
model_output: torch.Tensor,
timestep: int,
sample: torch.FloatTensor,
sample: torch.Tensor,
generator=None,
return_dict: bool = True,
) -> Union[DDPMParallelSchedulerOutput, Tuple]:
......@@ -421,9 +421,9 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor`): direct output from learned diffusion model.
model_output (`torch.Tensor`): direct output from learned diffusion model.
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
current instance of sample being created by diffusion process.
generator: random number generator.
return_dict (`bool`): option for returning tuple rather than DDPMParallelSchedulerOutput class
......@@ -506,10 +506,10 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
def batch_step_no_noise(
self,
model_output: torch.FloatTensor,
model_output: torch.Tensor,
timesteps: List[int],
sample: torch.FloatTensor,
) -> torch.FloatTensor:
sample: torch.Tensor,
) -> torch.Tensor:
"""
Batched version of the `step` function, to be able to reverse the SDE for multiple samples/timesteps at once.
Also, does not add any noise to the predicted sample, which is necessary for parallel sampling where the noise
......@@ -519,14 +519,14 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor`): direct output from learned diffusion model.
model_output (`torch.Tensor`): direct output from learned diffusion model.
timesteps (`List[int]`):
current discrete timesteps in the diffusion chain. This is now a list of integers.
sample (`torch.FloatTensor`):
sample (`torch.Tensor`):
current instance of sample being created by diffusion process.
Returns:
`torch.FloatTensor`: sample tensor at previous timestep.
`torch.Tensor`: sample tensor at previous timestep.
"""
t = timesteps
num_inference_steps = self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps
......@@ -587,10 +587,10 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
def add_noise(
self,
original_samples: torch.FloatTensor,
noise: torch.FloatTensor,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.IntTensor,
) -> torch.FloatTensor:
) -> torch.Tensor:
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
# Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement
# for the subsequent add_noise calls
......@@ -612,9 +612,7 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
return noisy_samples
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
def get_velocity(
self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor
) -> torch.FloatTensor:
def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:
# Make sure alphas_cumprod and timestep have same device and dtype as sample
self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device)
alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype)
......
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