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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 |
cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None |
) |
β |
~pipelines.stable_diffusion.AltDiffusionPipelineOutput 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. |
height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) β |
The height in pixels of the generated image. |
width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) β |
The width in pixels of the generated image. |
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. |
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. 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. |
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. |
latents (torch.FloatTensor, optional) β |
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) 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.AltDiffusionPipelineOutput instead of a |
plain tuple. |
callback (Callable, optional) β |
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) β |
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) β |
A kwargs dictionary that if specified is passed along to the AttnProcessor as defined under |
self.processor in |
diffusers.cross_attention. |
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