Open-Sora/opensora/utils/inference_utils.py

import json
import os
import re

import torch

from opensora.datasets import IMG_FPS
from opensora.datasets.utils import read_from_path


def prepare_multi_resolution_info(info_type, batch_size, image_size, num_frames, fps, device, dtype):
    if info_type is None:
        return dict()
    elif info_type == "PixArtMS":
        hw = torch.tensor([image_size], device=device, dtype=dtype).repeat(batch_size, 1)
        ar = torch.tensor([[image_size[0] / image_size[1]]], device=device, dtype=dtype).repeat(batch_size, 1)
        return dict(ar=ar, hw=hw)
    elif info_type in ["STDiT2", "OpenSora"]:
        fps = fps if num_frames > 1 else IMG_FPS
        fps = torch.tensor([fps], device=device, dtype=dtype).repeat(batch_size)
        height = torch.tensor([image_size[0]], device=device, dtype=dtype).repeat(batch_size)
        width = torch.tensor([image_size[1]], device=device, dtype=dtype).repeat(batch_size)
        num_frames = torch.tensor([num_frames], device=device, dtype=dtype).repeat(batch_size)
        ar = torch.tensor([image_size[0] / image_size[1]], device=device, dtype=dtype).repeat(batch_size)
        return dict(height=height, width=width, num_frames=num_frames, ar=ar, fps=fps)
    else:
        raise NotImplementedError


def load_prompts(prompt_path, start_idx=None, end_idx=None):
    with open(prompt_path, "r") as f:
        prompts = [line.strip() for line in f.readlines()]
    prompts = prompts[start_idx:end_idx]
    return prompts


def get_save_path_name(
    save_dir,
    sample_name=None,  # prefix
    sample_idx=None,  # sample index
    prompt=None,  # used prompt
    prompt_as_path=False,  # use prompt as path
    num_sample=1,  # number of samples to generate for one prompt
    k=None,  # kth sample
):
    if sample_name is None:
        sample_name = "" if prompt_as_path else "sample"
    sample_name_suffix = prompt if prompt_as_path else f"_{sample_idx:04d}"
    save_path = os.path.join(save_dir, f"{sample_name}{sample_name_suffix}")
    if num_sample != 1:
        save_path = f"{save_path}-{k}"
    return save_path


def append_score_to_prompts(prompts, aes=None, flow=None, camera_motion=None):
    new_prompts = []
    for prompt in prompts:
        new_prompt = prompt
        if aes is not None and "aesthetic score:" not in prompt:
            new_prompt = f"{new_prompt} aesthetic score: {aes:.1f}."
        if flow is not None and "motion score:" not in prompt:
            new_prompt = f"{new_prompt} motion score: {flow:.1f}."
        if camera_motion is not None and "camera motion:" not in prompt:
            new_prompt = f"{new_prompt} camera motion: {camera_motion}."
        new_prompts.append(new_prompt)
    return new_prompts


def extract_json_from_prompts(prompts, reference, mask_strategy):
    ret_prompts = []
    for i, prompt in enumerate(prompts):
        parts = re.split(r"(?=[{])", prompt)
        assert len(parts) <= 2, f"Invalid prompt: {prompt}"
        ret_prompts.append(parts[0])
        if len(parts) > 1:
            additional_info = json.loads(parts[1])
            for key in additional_info:
                assert key in ["reference_path", "mask_strategy"], f"Invalid key: {key}"
                if key == "reference_path":
                    reference[i] = additional_info[key]
                elif key == "mask_strategy":
                    mask_strategy[i] = additional_info[key]
    return ret_prompts, reference, mask_strategy


def collect_references_batch(reference_paths, vae, image_size):
    refs_x = []  # refs_x: [batch, ref_num, C, T, H, W]
    for reference_path in reference_paths:
        if reference_path == "":
            refs_x.append([])
            continue
        ref_path = reference_path.split(";")
        ref = []
        for r_path in ref_path:
            r = read_from_path(r_path, image_size, transform_name="resize_crop")
            r_x = vae.encode(r.unsqueeze(0).to(vae.device, vae.dtype))
            r_x = r_x.squeeze(0)
            ref.append(r_x)
        refs_x.append(ref)
    return refs_x


def extract_prompts_loop(prompts, num_loop):
    ret_prompts = []
    for prompt in prompts:
        if prompt.startswith("|0|"):
            prompt_list = prompt.split("|")[1:]
            text_list = []
            for i in range(0, len(prompt_list), 2):
                start_loop = int(prompt_list[i])
                text = prompt_list[i + 1]
                end_loop = int(prompt_list[i + 2]) if i + 2 < len(prompt_list) else num_loop + 1
                text_list.extend([text] * (end_loop - start_loop))
            prompt = text_list[num_loop]
        ret_prompts.append(prompt)
    return ret_prompts


def split_prompt(prompt_text):
    if prompt_text.startswith("|0|"):
        # this is for prompts which look like
        # |0| a beautiful day |1| a sunny day |2| a rainy day
        # we want to parse it into a list of prompts with the loop index
        prompt_list = prompt_text.split("|")[1:]
        text_list = []
        loop_idx = []
        for i in range(0, len(prompt_list), 2):
            start_loop = int(prompt_list[i])
            text = prompt_list[i + 1].strip()
            text_list.append(text)
            loop_idx.append(start_loop)
        return text_list, loop_idx
    else:
        return [prompt_text], None


def merge_prompt(text_list, loop_idx_list=None):
    if loop_idx_list is None:
        return text_list[0]
    else:
        prompt = ""
        for i, text in enumerate(text_list):
            prompt += f"|{loop_idx_list[i]}|{text}"
        return prompt


MASK_DEFAULT = ["0", "0", "0", "0", "1", "0"]


def parse_mask_strategy(mask_strategy):
    mask_batch = []
    if mask_strategy == "" or mask_strategy is None:
        return mask_batch

    mask_strategy = mask_strategy.split(";")
    for mask in mask_strategy:
        mask_group = mask.split(",")
        num_group = len(mask_group)
        assert num_group >= 1 and num_group <= 6, f"Invalid mask strategy: {mask}"
        mask_group.extend(MASK_DEFAULT[num_group:])
        for i in range(5):
            mask_group[i] = int(mask_group[i])
        mask_group[5] = float(mask_group[5])
        mask_batch.append(mask_group)
    return mask_batch


def find_nearest_point(value, point, max_value):
    t = value // point
    if value % point > point / 2 and t < max_value // point - 1:
        t += 1
    return t * point


def apply_mask_strategy(z, refs_x, mask_strategys, loop_i, align=None):
    masks = []
    no_mask = True
    for i, mask_strategy in enumerate(mask_strategys):
        no_mask = False
        mask = torch.ones(z.shape[2], dtype=torch.float, device=z.device)
        mask_strategy = parse_mask_strategy(mask_strategy)
        for mst in mask_strategy:
            loop_id, m_id, m_ref_start, m_target_start, m_length, edit_ratio = mst
            if loop_id != loop_i:
                continue
            ref = refs_x[i][m_id]

            if m_ref_start < 0:
                # ref: [C, T, H, W]
                m_ref_start = ref.shape[1] + m_ref_start
            if m_target_start < 0:
                # z: [B, C, T, H, W]
                m_target_start = z.shape[2] + m_target_start
            if align is not None:
                m_ref_start = find_nearest_point(m_ref_start, align, ref.shape[1])
                m_target_start = find_nearest_point(m_target_start, align, z.shape[2])
            m_length = min(m_length, z.shape[2] - m_target_start, ref.shape[1] - m_ref_start)
            z[i, :, m_target_start : m_target_start + m_length] = ref[:, m_ref_start : m_ref_start + m_length]
            mask[m_target_start : m_target_start + m_length] = edit_ratio
        masks.append(mask)
    if no_mask:
        return None
    masks = torch.stack(masks)
    return masks


def append_generated(vae, generated_video, refs_x, mask_strategy, loop_i, condition_frame_length, condition_frame_edit):
    ref_x = vae.encode(generated_video)
    for j, refs in enumerate(refs_x):
        if refs is None:
            refs_x[j] = [ref_x[j]]
        else:
            refs.append(ref_x[j])
        if mask_strategy[j] is None or mask_strategy[j] == "":
            mask_strategy[j] = ""
        else:
            mask_strategy[j] += ";"
        mask_strategy[
            j
        ] += f"{loop_i},{len(refs)-1},-{condition_frame_length},0,{condition_frame_length},{condition_frame_edit}"
    return refs_x, mask_strategy


def dframe_to_frame(num):
    assert num % 5 == 0, f"Invalid num: {num}"
    return num // 5 * 17


OPENAI_CLIENT = None
REFINE_PROMPTS = None
REFINE_PROMPTS_PATH = "assets/texts/t2v_pllava.txt"
REFINE_PROMPTS_TEMPLATE = """
You need to refine user's input prompt. The user's input prompt is used for video generation task. You need to refine the user's prompt to make it more suitable for the task. Here are some examples of refined prompts:
{}

The refined prompt should pay attention to all objects in the video. The description should be useful for AI to re-generate the video. The description should be no more than six sentences. The refined prompt should be in English.
"""
RANDOM_PROMPTS = None
RANDOM_PROMPTS_TEMPLATE = """
You need to generate one input prompt for video generation task. The prompt should be suitable for the task. Here are some examples of refined prompts:
{}

The prompt should pay attention to all objects in the video. The description should be useful for AI to re-generate the video. The description should be no more than six sentences. The prompt should be in English.
"""


def get_openai_response(sys_prompt, usr_prompt, model="gpt-4o"):
    global OPENAI_CLIENT
    if OPENAI_CLIENT is None:
        from openai import OpenAI

        OPENAI_CLIENT = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))

    completion = OPENAI_CLIENT.chat.completions.create(
        model=model,
        messages=[
            {
                "role": "system",
                "content": sys_prompt,
            },  # <-- This is the system message that provides context to the model
            {
                "role": "user",
                "content": usr_prompt,
            },  # <-- This is the user message for which the model will generate a response
        ],
    )

    return completion.choices[0].message.content


def get_random_prompt_by_openai():
    global RANDOM_PROMPTS
    if RANDOM_PROMPTS is None:
        examples = load_prompts(REFINE_PROMPTS_PATH)
        RANDOM_PROMPTS = RANDOM_PROMPTS_TEMPLATE.format("\n".join(examples))

    response = get_openai_response(RANDOM_PROMPTS, "Generate one example.")
    return response


def refine_prompt_by_openai(prompt):
    global REFINE_PROMPTS
    if REFINE_PROMPTS is None:
        examples = load_prompts(REFINE_PROMPTS_PATH)
        REFINE_PROMPTS = REFINE_PROMPTS_TEMPLATE.format("\n".join(examples))

    response = get_openai_response(REFINE_PROMPTS, prompt)
    return response


def has_openai_key():
    return "OPENAI_API_KEY" in os.environ


def refine_prompts_by_openai(prompts):
    new_prompts = []
    for prompt in prompts:
        try:
            if prompt.strip() == "":
                new_prompt = get_random_prompt_by_openai()
                print(f"[Info] Empty prompt detected, generate random prompt: {new_prompt}")
            else:
                new_prompt = refine_prompt_by_openai(prompt)
                print(f"[Info] Refine prompt: {prompt} -> {new_prompt}")
            new_prompts.append(new_prompt)
        except Exception as e:
            print(f"[Warning] Failed to refine prompt: {prompt} due to {e}")
            new_prompts.append(prompt)
    return new_prompts


def add_watermark(
    input_video_path, watermark_image_path="./assets/images/watermark/watermark.png", output_video_path=None
):
    # execute this command in terminal with subprocess
    # return if the process is successful
    if output_video_path is None:
        output_video_path = input_video_path.replace(".mp4", "_watermark.mp4")
    cmd = f'ffmpeg -y -i {input_video_path} -i {watermark_image_path} -filter_complex "[1][0]scale2ref=oh*mdar:ih*0.1[logo][video];[video][logo]overlay" {output_video_path}'
    exit_code = os.system(cmd)
    is_success = exit_code == 0
    return is_success