Function view_images

sample.py:158–187

(images, save_path, base_count, num_rows=1, offset_ratio=0.02, step=None)

    return img

def view_images(images, save_path, base_count, num_rows=1, offset_ratio=0.02, step=None):
    if type(images) is list:
        num_empty = len(images) % num_rows
    elif images.ndim == 4:
        num_empty = images.shape[0] % num_rows
    else:
        images = [images]
        num_empty = 0

    empty_images = np.ones(images[0].shape, dtype=np.uint8) * 255
    images = [image.astype(np.uint8) for image in images] + [empty_images] * num_empty
    num_items = len(images)

    h, w, c = images[0].shape
    offset = int(h * offset_ratio)
    num_cols = num_items // num_rows
    image_ = np.ones((h * num_rows + offset * (num_rows - 1),
                      w * num_cols + offset * (num_cols - 1), 3), dtype=np.uint8) * 255
    for i in range(num_rows):
        for j in range(num_cols):
            image_[i * (h + offset): i * (h + offset) + h:, j * (w + offset): j * (w + offset) + w] = images[
                i * num_cols + j]

    pil_img = Image.fromarray(image_)
    base_count += 40
    if step is not None:
        pil_img.save(os.path.join(save_path, f"{base_count:05}_{step}.png"))
    else:
        pil_img.save(os.path.join(save_path, f"{base_count:05}.png"))
    # display(pil_img)

def aggregate_attention(prompts, attention_store: AttentionStore, res: int, from_where: List[str], is_cross: bool, select: int):
    out = []