Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

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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 StableDiffusionPipelineOutput 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
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
>>> import requests
>>> from PIL import Image
>>> from diffusers import StableDiffusionDepth2ImgPipeline
>>> pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
... "stabilityai/stable-diffusion-2-depth",
... torch_dtype=torch.float16,
... )
>>> pipe.to("cuda")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> init_image = Image.open(requests.get(url, stream=True).raw)
>>> prompt = "two tigers"
>>> n_propmt = "bad, deformed, ugly, bad anotomy"
>>> image = pipe(prompt=prompt, image=init_image, negative_prompt=n_propmt, strength=0.7).images[0]
enable_attention_slicing
<
source
>
(
slice_size: typing.Union[str, int, NoneType] = 'auto'
)
Parameters
slice_size (str or int, optional, defaults to "auto") β€”
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.