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Code Issues Actions 4 Packages Projects Releases Wiki Activity

#7535 Update FloatTensor type hints to Tensor (#7883)

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* 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
This commit is contained in:
Mark Van Aken
2024-05-10 12:53:31 -07:00
committed by GitHub
parent 04f4bd54ea
commit be4afa0bb4
275 changed files with 3765 additions and 3824 deletions
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102
examples/community/stable_diffusion_reference.py
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@@ -268,10 +268,10 @@ class StableDiffusionReferencePipeline(
width: int,
callback_steps: Optional[int],
negative_prompt: Optional[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,
ip_adapter_image: Optional[torch.Tensor] = None,
ip_adapter_image_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image_embeds: Optional[torch.Tensor] = None,
callback_on_step_end_tensor_inputs: Optional[List[str]] = None,
) -> None:
"""
@@ -283,10 +283,10 @@ class StableDiffusionReferencePipeline(
width (int): The width of the input image.
callback_steps (Optional[int]): The number of steps to perform the callback on.
negative_prompt (Optional[str]): The negative prompt text.
prompt_embeds (Optional[torch.FloatTensor]): The prompt embeddings.
negative_prompt_embeds (Optional[torch.FloatTensor]): The negative prompt embeddings.
prompt_embeds (Optional[torch.Tensor]): The prompt embeddings.
negative_prompt_embeds (Optional[torch.Tensor]): The negative prompt embeddings.
ip_adapter_image (Optional[torch.Tensor]): The input adapter image.
ip_adapter_image_embeds (Optional[torch.FloatTensor]): The input adapter image embeddings.
ip_adapter_image_embeds (Optional[torch.Tensor]): The input adapter image embeddings.
callback_on_step_end_tensor_inputs (Optional[List[str]]): The list of tensor inputs to perform the callback on.
Raises:
@@ -357,11 +357,11 @@ class StableDiffusionReferencePipeline(
num_images_per_prompt: int,
do_classifier_free_guidance: bool,
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,
lora_scale: Optional[float] = None,
**kwargs,
) -> torch.FloatTensor:
) -> torch.Tensor:
r"""
Encodes the prompt into embeddings.
@@ -371,13 +371,13 @@ class StableDiffusionReferencePipeline(
num_images_per_prompt (int): The number of images per prompt.
do_classifier_free_guidance (bool): Whether to use classifier-free guidance.
negative_prompt (Optional[Union[str, List[str]]], optional): The negative prompt text or a list of negative prompt texts. Defaults to None.
prompt_embeds (Optional[torch.FloatTensor], optional): The prompt embeddings. Defaults to None.
negative_prompt_embeds (Optional[torch.FloatTensor], optional): The negative prompt embeddings. Defaults to None.
prompt_embeds (Optional[torch.Tensor], optional): The prompt embeddings. Defaults to None.
negative_prompt_embeds (Optional[torch.Tensor], optional): The negative prompt embeddings. Defaults to None.
lora_scale (Optional[float], optional): The LoRA scale. Defaults to None.
**kwargs: Additional keyword arguments.
Returns:
torch.FloatTensor: The encoded prompt embeddings.
torch.Tensor: The encoded prompt embeddings.
"""
deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)
@@ -407,11 +407,11 @@ class StableDiffusionReferencePipeline(
num_images_per_prompt: int,
do_classifier_free_guidance: bool,
negative_prompt: Optional[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,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
) -> torch.FloatTensor:
) -> torch.Tensor:
r"""
Encodes the prompt into text encoder hidden states.
@@ -428,10 +428,10 @@ class StableDiffusionReferencePipeline(
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.
@@ -813,7 +813,7 @@ class StableDiffusionReferencePipeline(
def __call__(
self,
prompt: Union[str, List[str]] = None,
ref_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
ref_image: Union[torch.Tensor, PIL.Image.Image] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
@@ -822,12 +822,12 @@ class StableDiffusionReferencePipeline(
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
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,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
guidance_rescale: float = 0.0,
@@ -844,9 +844,9 @@ class StableDiffusionReferencePipeline(
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
ref_image (`torch.FloatTensor`, `PIL.Image.Image`):
ref_image (`torch.Tensor`, `PIL.Image.Image`):
The Reference Control input condition. Reference Control uses this input condition to generate guidance to Unet. If
the type is specified as `Torch.FloatTensor`, it is passed to Reference Control as is. `PIL.Image.Image` can
the type is specified as `torch.Tensor`, it is passed to Reference Control as is. `PIL.Image.Image` can
also be accepted as an image.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image.
@@ -873,14 +873,14 @@ class StableDiffusionReferencePipeline(
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`.
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.
@@ -892,7 +892,7 @@ class StableDiffusionReferencePipeline(
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.
@@ -1017,10 +1017,10 @@ class StableDiffusionReferencePipeline(
def hacked_basic_transformer_inner_forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
timestep: Optional[torch.LongTensor] = None,
cross_attention_kwargs: Dict[str, Any] = None,
class_labels: Optional[torch.LongTensor] = None,
@@ -1135,12 +1135,12 @@ class StableDiffusionReferencePipeline(
def hack_CrossAttnDownBlock2D_forward(
self,
hidden_states: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
hidden_states: torch.Tensor,
temb: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
):
eps = 1e-6
@@ -1191,10 +1191,10 @@ class StableDiffusionReferencePipeline(
def hacked_DownBlock2D_forward(
self,
hidden_states: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
hidden_states: torch.Tensor,
temb: Optional[torch.Tensor] = None,
**kwargs: Any,
) -> Tuple[torch.FloatTensor, ...]:
) -> Tuple[torch.Tensor, ...]:
eps = 1e-6
output_states = ()
@@ -1236,15 +1236,15 @@ class StableDiffusionReferencePipeline(
def hacked_CrossAttnUpBlock2D_forward(
self,
hidden_states: torch.FloatTensor,
res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
temb: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
hidden_states: torch.Tensor,
res_hidden_states_tuple: Tuple[torch.Tensor, ...],
temb: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
upsample_size: Optional[int] = None,
attention_mask: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
) -> torch.FloatTensor:
attention_mask: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
eps = 1e-6
# TODO(Patrick, William) - attention mask is not used
for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
@@ -1292,12 +1292,12 @@ class StableDiffusionReferencePipeline(
def hacked_UpBlock2D_forward(
self,
hidden_states: torch.FloatTensor,
res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
temb: Optional[torch.FloatTensor] = None,
hidden_states: torch.Tensor,
res_hidden_states_tuple: Tuple[torch.Tensor, ...],
temb: Optional[torch.Tensor] = None,
upsample_size: Optional[int] = None,
**kwargs: Any,
) -> torch.FloatTensor:
) -> torch.Tensor:
eps = 1e-6
for i, resnet in enumerate(self.resnets):
# pop res hidden states