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sayakpaul
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diffusers-qa-chatbot-artifacts

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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 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€”
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 AttentionProcessor as defined under
self.processor in
diffusers.cross_attention.
noise_level (int, optional, defaults to 250) β€”
The amount of noise to add to the upscaled image. Must be in the range [0, 1000)
clean_caption (bool, optional, defaults to True) β€”
Whether or not to clean the caption before creating embeddings. Requires beautifulsoup4 and ftfy to
be installed. If the dependencies are not installed, the embeddings will be created from the raw
prompt.
Returns
~pipelines.stable_diffusion.IFPipelineOutput or tuple
~pipelines.stable_diffusion.IFPipelineOutput 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) or watermarked ...
Function invoked when calling the pipeline for generation.
Examples:
Copied
>>> from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline
>>> from diffusers.utils import pt_to_pil
>>> import torch
>>> from PIL import Image
>>> import requests
>>> from io import BytesIO
>>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
>>> response = requests.get(url)
>>> original_image = Image.open(BytesIO(response.content)).convert("RGB")
>>> original_image = original_image.resize((768, 512))