Function run

app.py:22–58

(*args)

def run(*args):
    id_image = args[0]
    supp_images = args[1:4]
    prompt, neg_prompt, scale, n_samples, seed, steps, H, W, id_scale, mode, id_mix = args[4:]

    pipeline.debug_img_list = []
    if mode == 'fidelity':
        attention.NUM_ZERO = 8
        attention.ORTHO = False
        attention.ORTHO_v2 = True
    elif mode == 'extremely style':
        attention.NUM_ZERO = 16
        attention.ORTHO = True
        attention.ORTHO_v2 = False
    else:
        raise ValueError

    if id_image is not None:
        id_image = resize_numpy_image_long(id_image, 1024)
        id_embeddings = pipeline.get_id_embedding(id_image)
        for supp_id_image in supp_images:
            if supp_id_image is not None:
                supp_id_image = resize_numpy_image_long(supp_id_image, 1024)
                supp_id_embeddings = pipeline.get_id_embedding(supp_id_image)
                id_embeddings = torch.cat(
                    (id_embeddings, supp_id_embeddings if id_mix else supp_id_embeddings[:, :5]), dim=1
                )
    else:
        id_embeddings = None

    seed_everything(seed)
    ims = []
    for _ in range(n_samples):
        img = pipeline.inference(prompt, (1, H, W), neg_prompt, id_embeddings, id_scale, scale, steps)[0]
        ims.append(np.array(img))

    return ims, pipeline.debug_img_list


_HEADER_ = '''