Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

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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.
image (torch.FloatTensor or PIL.Image.Image) β€”
Image, or tensor representing an image batch, that will be used as the starting point for the
process.
strength (float, optional, defaults to 0.8) β€”
Conceptually, indicates how much to transform the reference image. Must be between 0 and 1. image
will be used as a starting point, adding more noise to it the larger the strength. The number of
denoising steps depends on the amount of noise initially added. When strength is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
num_inference_steps. A value of 1, therefore, essentially ignores 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. This parameter will be modulated by strength.
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.
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, 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.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.
Returns
~pipelines.stable_diffusion.AltDiffusionPipelineOutput or tuple
~pipelines.stable_diffusion.AltDiffusionPipelineOutput 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 requests
>>> import torch
>>> from PIL import Image