Function create_infotext

modules/processing.py:600–793

this function is used to generate the infotext that is stored in the generated images, it contains the parameters that are required to generate the image Args: p: StableDiffusionProcessing all_prompts: list[str] all_seeds: list[int] all_subseeds: list[i

(p, all_prompts, all_seeds, all_subseeds, iteration=0, position_in_batch=0, use_main_prompt=False, index=None, all_negative_prompts=None)

        iteration: int
        position_in_batch: int
        use_main_prompt: bool
        index: int
        all_negative_prompts: list[str]

    Returns: str

    Extra generation params
    p.extra_generation_params dictionary allows for additional parameters to be added to the infotext
    this can be use by the base webui or extensions.
    To add a new entry, add a new key value pair, the dictionary key will be used as the key of the parameter in the infotext
    the value generation_params can be defined as:
        - str | None
        - List[str|None]
        - callable func(**kwargs) -> str | None

    When defined as a string, it will be used as without extra processing; this is this most common use case.

    Defining as a list allows for parameter that changes across images in the job, for example, the 'Seed' parameter.
    The list should have the same length as the total number of images in the entire job.

    Defining as a callable function allows parameter cannot be generated earlier or when extra logic is required.
    For example 'HiRes prompt', due to reasons the hr_prompt might be changed by process in the pipeline or extensions
    and may vary across different images, defining as a static string or list would not work.

    The function takes locals() as **kwargs, as such will have access to variables like 'p' and 'index'.
    the base signature of the function should be:
        func(**kwargs) -> str | None
    optionally it can have additional arguments that will be used in the function:
        func(p, index, **kwargs) -> str | None
    note: for better future compatibility even though this function will have access to all variables in the locals(),
        it is recommended to only use the arguments present in the function signature of create_infotext.
    For actual implementation examples, see StableDiffusionProcessingTxt2Img.init > get_hr_prompt.
    """

    if use_main_prompt:
        index = 0
    elif index is None:
        index = position_in_batch + iteration * p.batch_size

    if all_negative_prompts is None:
        all_negative_prompts = p.all_negative_prompts

    clip_skip = int(getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers))
    enable_hr = getattr(p, 'enable_hr', False)
    token_merging_ratio = p.get_token_merging_ratio()
    token_merging_ratio_hr = p.get_token_merging_ratio(for_hr=True)

    prompt_text = p.main_prompt if use_main_prompt else all_prompts[index]
    negative_prompt = p.main_negative_prompt if use_main_prompt else all_negative_prompts[index]

    uses_ensd = opts.eta_noise_seed_delta != 0
    if uses_ensd:
        uses_ensd = sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p)

    timesteps_samplers = ["DDIM", "DDIM CFG++", "PLMS", "UniPC"]

    generation_params = {
        "Steps": p.steps,