def METHOD_NAME(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
    device = comfy.model_management.get_torch_device()
    if noise_mask is not None:
        noise_mask = prepare_mask(noise_mask, noise.shape, device)
    real_model = None
    models, inference_memory = get_additional_models(positive, negative, model.model_dtype())
    comfy.model_management.load_models_gpu([model] + models, comfy.model_management.batch_area_memory(noise.shape[0] * noise.shape[2] * noise.shape[3]) + inference_memory)
    real_model = model.model
    noise = noise.to(device)
    latent_image = latent_image.to(device)
    positive_copy = broadcast_cond(positive, noise.shape[0], device)
    negative_copy = broadcast_cond(negative, noise.shape[0], device)
    sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
    samples = sampler.METHOD_NAME(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
    samples = samples.cpu()
    cleanup_additional_models(models)
    return samples