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weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input |
argument. |
output_type (str, optional, defaults to "pil") — |
The output format of the generate image. Choose between |
PIL: PIL.Image.Image or np.array. |
return_dict (bool, optional, defaults to True) — |
Whether or not to return a StableDiffusionPipelineOutput instead 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). |
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. |
cross_attention_kwargs (dict, optional) — |
A kwargs dictionary that if specified is passed along to the AttnProcessor as defined under |
self.processor in |
diffusers.cross_attention. |
Returns |
StableDiffusionPipelineOutput or tuple |
StableDiffusionPipelineOutput if return_dict is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of bools denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the safety_checker`. |
Function invoked when calling the pipeline for generation. |
Examples: |
Copied |
>>> import torch |
>>> from diffusers import StableDiffusionPipeline |
>>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) |
>>> pipe = pipe.to("cuda") |
>>> prompt = "a photo of an astronaut riding a horse on mars" |
>>> image = pipe(prompt).images[0] |
enable_attention_slicing |
< |
source |
> |
( |
slice_size: typing.Union[str, int, NoneType] = 'auto' |
) |
Parameters |
slice_size (str or int, optional, defaults to "auto") — |
When "auto", halves the input to the attention heads, so attention will be computed in two steps. If |
"max", maxium amount of memory will be saved by running only one slice at a time. If a number is |
provided, uses as many slices as attention_head_dim // slice_size. In this case, attention_head_dim |
must be a multiple of slice_size. |
Enable sliced attention computation. |
When this option is enabled, the attention module will split the input tensor in slices, to compute attention |
in several steps. This is useful to save some memory in exchange for a small speed decrease. |
disable_attention_slicing |
< |
source |
> |
( |
) |
Disable sliced attention computation. If enable_attention_slicing was previously invoked, this method will go |
back to computing attention in one step. |
enable_vae_slicing |
< |
source |
> |
( |
) |
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