Function ksampler

modules/core.py:265–325

(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu',
             scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None,
             force_full_denoise=False, callback_function=None, refiner=None, refiner_switch=-1,
             previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None, disable_preview=False)

@torch.no_grad()
@torch.inference_mode()
def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu',
             scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None,
             force_full_denoise=False, callback_function=None, refiner=None, refiner_switch=-1,
             previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None, disable_preview=False):

    if sigmas is not None:
        sigmas = sigmas.clone().to(ldm_patched.modules.model_management.get_torch_device())

    latent_image = latent["samples"]

    if disable_noise:
        noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
    else:
        batch_inds = latent["batch_index"] if "batch_index" in latent else None
        noise = ldm_patched.modules.sample.prepare_noise(latent_image, seed, batch_inds)

    if isinstance(noise_mean, torch.Tensor):
        noise = noise + noise_mean - torch.mean(noise, dim=1, keepdim=True)

    noise_mask = None
    if "noise_mask" in latent:
        noise_mask = latent["noise_mask"]

    previewer = get_previewer(model)

    if previewer_start is None:
        previewer_start = 0

    if previewer_end is None:
        previewer_end = steps

    def callback(step, x0, x, total_steps):
        ldm_patched.modules.model_management.throw_exception_if_processing_interrupted()
        y = None
        if previewer is not None and not disable_preview:
            y = previewer(x0, previewer_start + step, previewer_end)
        if callback_function is not None:
            callback_function(previewer_start + step, x0, x, previewer_end, y)

    disable_pbar = False
    modules.sample_hijack.current_refiner = refiner
    modules.sample_hijack.refiner_switch_step = refiner_switch
    ldm_patched.modules.samplers.sample = modules.sample_hijack.sample_hacked

    try:
        samples = ldm_patched.modules.sample.sample(model,
                                                    noise, steps, cfg, sampler_name, scheduler,
                                                    positive, negative, latent_image,
                                                    denoise=denoise, disable_noise=disable_noise,
                                                    start_step=start_step,
                                                    last_step=last_step,
                                                    force_full_denoise=force_full_denoise, noise_mask=noise_mask,
                                                    callback=callback,
                                                    disable_pbar=disable_pbar, seed=seed, sigmas=sigmas)

        out = latent.copy()
        out["samples"] = samples
    finally: