Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

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<
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(
prompt: typing.Union[str, typing.List[str]] = None
num_inference_steps: int = 100
timesteps: typing.List[int] = None
guidance_scale: float = 7.0
negative_prompt: typing.Union[str, typing.List[str], NoneType] = None
num_images_per_prompt: typing.Optional[int] = 1
height: typing.Optional[int] = None
width: typing.Optional[int] = None
eta: float = 0.0
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None
prompt_embeds: typing.Optional[torch.FloatTensor] = None
negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None
output_type: typing.Optional[str] = 'pil'
return_dict: bool = True
callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None
callback_steps: int = 1
clean_caption: bool = True
cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None
)
β†’
~pipelines.stable_diffusion.IFPipelineOutput or tuple
Parameters
prompt (str or List[str], optional) β€”
The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds.
instead.
num_inference_steps (int, optional, defaults to 50) β€”
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
timesteps (List[int], optional) β€”
Custom timesteps to use for the denoising process. If not defined, equal spaced num_inference_steps
timesteps are used. Must be in descending order.
guidance_scale (float, optional, defaults to 7.5) β€”
Guidance scale as defined in Classifier-Free Diffusion Guidance.
guidance_scale is defined as w of equation 2. of Imagen
Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt,
usually at the expense of lower image quality.
negative_prompt (str or List[str], optional) β€”
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).
num_images_per_prompt (int, optional, defaults to 1) β€”
The number of images to generate per prompt.
height (int, optional, defaults to self.unet.config.sample_size) β€”
The height in pixels of the generated image.
width (int, optional, defaults to self.unet.config.sample_size) β€”
The width in pixels of the generated image.
eta (float, optional, defaults to 0.0) β€”
Corresponds to parameter eta (Ξ·) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
schedulers.DDIMScheduler, will be ignored for others.
generator (torch.Generator or List[torch.Generator], optional) β€”
One or a list of torch generator(s)
to make generation deterministic.
prompt_embeds (torch.FloatTensor, optional) β€”
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) β€”
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.
output_type (str, optional, defaults to "pil") β€”
The output format of the generate image. Choose between
PIL: PIL.Image.Image or np.array.
return_dict (bool, optional, defaults to True) β€”
Whether or not to return a ~pipelines.stable_diffusion.IFPipelineOutput instead of a plain tuple.
callback (Callable, optional) β€”