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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.
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.
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.
editing_prompt (str or List[str], optional) β€”
The prompt or prompts to use for Semantic guidance. Semantic guidance is disabled by setting
editing_prompt = None. Guidance direction of prompt should be specified via
reverse_editing_direction.
editing_prompt_embeddings (torch.Tensor>, optional) β€”
Pre-computed embeddings to use for semantic guidance. Guidance direction of embedding should be
specified via reverse_editing_direction.
reverse_editing_direction (bool or List[bool], optional, defaults to False) β€”
Whether the corresponding prompt in editing_prompt should be increased or decreased.
edit_guidance_scale (float or List[float], optional, defaults to 5) β€”
Guidance scale for semantic guidance. If provided as list values should correspond to editing_prompt.
edit_guidance_scale is defined as s_e of equation 6 of SEGA
Paper.
edit_warmup_steps (float or List[float], optional, defaults to 10) β€”
Number of diffusion steps (for each prompt) for which semantic guidance will not be applied. Momentum
will still be calculated for those steps and applied once all warmup periods are over.
edit_warmup_steps is defined as delta (Ξ΄) of SEGA Paper.
edit_cooldown_steps (float or List[float], optional, defaults to None) β€”
Number of diffusion steps (for each prompt) after which semantic guidance will no longer be applied.
edit_threshold (float or List[float], optional, defaults to 0.9) β€”
Threshold of semantic guidance.
edit_momentum_scale (float, optional, defaults to 0.1) β€”
Scale of the momentum to be added to the semantic guidance at each diffusion step. If set to 0.0
momentum will be disabled. Momentum is already built up during warmup, i.e. for diffusion steps smaller
than sld_warmup_steps. Momentum will only be added to latent guidance once all warmup periods are
finished. edit_momentum_scale is defined as s_m of equation 7 of SEGA
Paper.
edit_mom_beta (float, optional, defaults to 0.4) β€”
Defines how semantic guidance momentum builds up. edit_mom_beta indicates how much of the previous
momentum will be kept. Momentum is already built up during warmup, i.e. for diffusion steps smaller
than edit_warmup_steps. edit_mom_beta is defined as beta_m (Ξ²) of equation 8 of SEGA
Paper.
edit_weights (List[float], optional, defaults to None) β€”
Indicates how much each individual concept should influence the overall guidance. If no weights are
provided all concepts are applied equally. edit_mom_beta is defined as g_i of equation 9 of SEGA
Paper.
sem_guidance (List[torch.Tensor], optional) β€”
List of pre-generated guidance vectors to be applied at generation. Length of the list has to
correspond to num_inference_steps.