Function sample_hacked

modules/sample_hijack.py:89–159

(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None)

@torch.no_grad()
@torch.inference_mode()
def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
    global current_refiner

    positive = positive[:]
    negative = negative[:]

    resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], device)
    resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], device)

    model_wrap = wrap_model(model)

    calculate_start_end_timesteps(model, negative)
    calculate_start_end_timesteps(model, positive)

    if latent_image is not None:
        latent_image = model.process_latent_in(latent_image)

    if hasattr(model, 'extra_conds'):
        positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask)
        negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask)

    #make sure each cond area has an opposite one with the same area
    for c in positive:
        create_cond_with_same_area_if_none(negative, c)
    for c in negative:
        create_cond_with_same_area_if_none(positive, c)

    # pre_run_control(model, negative + positive)
    pre_run_control(model, positive)  # negative is not necessary in Fooocus, 0.5s faster.

    apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x])
    apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])

    extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed}

    if current_refiner is not None and hasattr(current_refiner.model, 'extra_conds'):
        positive_refiner = clip_separate_after_preparation(positive, target_model=current_refiner.model)
        negative_refiner = clip_separate_after_preparation(negative, target_model=current_refiner.model)

        positive_refiner = encode_model_conds(current_refiner.model.extra_conds, positive_refiner, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask)
        negative_refiner = encode_model_conds(current_refiner.model.extra_conds, negative_refiner, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask)

    def refiner_switch():
        cleanup_additional_models(set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control")))

        extra_args["cond"] = positive_refiner
        extra_args["uncond"] = negative_refiner

        # clear ip-adapter for refiner
        extra_args['model_options'] = {k: {} if k == 'transformer_options' else v for k, v in extra_args['model_options'].items()}

        models, inference_memory = get_additional_models(positive_refiner, negative_refiner, current_refiner.model_dtype())
        ldm_patched.modules.model_management.load_models_gpu(
            [current_refiner] + models,
            model.memory_required([noise.shape[0] * 2] + list(noise.shape[1:])) + inference_memory)

        model_wrap.inner_model = current_refiner.model
        print('Refiner Swapped')