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robomaster/api/api.py

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+import io
+import gc
+import base64
+import torch
+import gradio as gr
+import tempfile
+import hashlib
+import os
+
+from fastapi import FastAPI
+from io import BytesIO
+from PIL import Image
+
+# Function to encode a file to Base64
+def encode_file_to_base64(file_path):
+    with open(file_path, "rb") as file:
+        # Encode the data to Base64
+        file_base64 = base64.b64encode(file.read())
+        return file_base64
+
+def update_edition_api(_: gr.Blocks, app: FastAPI, controller):
+    @app.post("/cogvideox_fun/update_edition")
+    def _update_edition_api(
+        datas: dict,
+    ):
+        edition = datas.get('edition', 'v2')
+
+        try:
+            controller.update_edition(
+                edition
+            )
+            comment = "Success"
+        except Exception as e:
+            torch.cuda.empty_cache()
+            comment = f"Error. error information is {str(e)}"
+
+        return {"message": comment}
+
+def update_diffusion_transformer_api(_: gr.Blocks, app: FastAPI, controller):
+    @app.post("/cogvideox_fun/update_diffusion_transformer")
+    def _update_diffusion_transformer_api(
+        datas: dict,
+    ):
+        diffusion_transformer_path = datas.get('diffusion_transformer_path', 'none')
+
+        try:
+            controller.update_diffusion_transformer(
+                diffusion_transformer_path
+            )
+            comment = "Success"
+        except Exception as e:
+            torch.cuda.empty_cache()
+            comment = f"Error. error information is {str(e)}"
+
+        return {"message": comment}
+
+def save_base64_video(base64_string):
+    video_data = base64.b64decode(base64_string)
+
+    md5_hash = hashlib.md5(video_data).hexdigest()
+    filename = f"{md5_hash}.mp4"  
+    
+    temp_dir = tempfile.gettempdir()
+    file_path = os.path.join(temp_dir, filename)
+
+    with open(file_path, 'wb') as video_file:
+        video_file.write(video_data)
+
+    return file_path
+
+def save_base64_image(base64_string):
+    video_data = base64.b64decode(base64_string)
+
+    md5_hash = hashlib.md5(video_data).hexdigest()
+    filename = f"{md5_hash}.jpg"  
+    
+    temp_dir = tempfile.gettempdir()
+    file_path = os.path.join(temp_dir, filename)
+
+    with open(file_path, 'wb') as video_file:
+        video_file.write(video_data)
+
+    return file_path
+
+def infer_forward_api(_: gr.Blocks, app: FastAPI, controller):
+    @app.post("/cogvideox_fun/infer_forward")
+    def _infer_forward_api(
+        datas: dict,
+    ):
+        base_model_path = datas.get('base_model_path', 'none')
+        lora_model_path = datas.get('lora_model_path', 'none')
+        lora_alpha_slider = datas.get('lora_alpha_slider', 0.55)
+        prompt_textbox = datas.get('prompt_textbox', None)
+        negative_prompt_textbox = datas.get('negative_prompt_textbox', 'The video is not of a high quality, it has a low resolution. Watermark present in each frame. The background is solid. Strange body and strange trajectory. Distortion. ')
+        sampler_dropdown = datas.get('sampler_dropdown', 'Euler')
+        sample_step_slider = datas.get('sample_step_slider', 30)
+        resize_method = datas.get('resize_method', "Generate by")
+        width_slider = datas.get('width_slider', 672)
+        height_slider = datas.get('height_slider', 384)
+        base_resolution = datas.get('base_resolution', 512)
+        is_image = datas.get('is_image', False)
+        generation_method = datas.get('generation_method', False)
+        length_slider = datas.get('length_slider', 49)
+        overlap_video_length = datas.get('overlap_video_length', 4)
+        partial_video_length = datas.get('partial_video_length', 72)
+        cfg_scale_slider = datas.get('cfg_scale_slider', 6)
+        start_image = datas.get('start_image', None)
+        end_image = datas.get('end_image', None)
+        validation_video = datas.get('validation_video', None)
+        validation_video_mask = datas.get('validation_video_mask', None)
+        control_video = datas.get('control_video', None)
+        denoise_strength = datas.get('denoise_strength', 0.70)
+        seed_textbox = datas.get("seed_textbox", 43)
+
+        generation_method = "Image Generation" if is_image else generation_method
+
+        if start_image is not None:
+            start_image = base64.b64decode(start_image)
+            start_image = [Image.open(BytesIO(start_image))]
+        
+        if end_image is not None:
+            end_image = base64.b64decode(end_image)
+            end_image = [Image.open(BytesIO(end_image))]
+
+        if validation_video is not None:
+            validation_video = save_base64_video(validation_video)
+
+        if validation_video_mask is not None:
+            validation_video_mask = save_base64_image(validation_video_mask)
+
+        if control_video is not None:
+            control_video = save_base64_video(control_video)
+        
+        try:
+            save_sample_path, comment = controller.generate(
+                "",
+                base_model_path,
+                lora_model_path, 
+                lora_alpha_slider,
+                prompt_textbox, 
+                negative_prompt_textbox, 
+                sampler_dropdown, 
+                sample_step_slider, 
+                resize_method,
+                width_slider, 
+                height_slider, 
+                base_resolution,
+                generation_method,
+                length_slider, 
+                overlap_video_length, 
+                partial_video_length, 
+                cfg_scale_slider, 
+                start_image,
+                end_image,
+                validation_video,
+                validation_video_mask, 
+                control_video, 
+                denoise_strength,
+                seed_textbox,
+                is_api = True,
+            )
+        except Exception as e:
+            gc.collect()
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
+            save_sample_path = ""
+            comment = f"Error. error information is {str(e)}"
+            return {"message": comment}
+        
+        if save_sample_path != "":
+            return {"message": comment, "save_sample_path": save_sample_path, "base64_encoding": encode_file_to_base64(save_sample_path)}
+        else:
+            return {"message": comment, "save_sample_path": save_sample_path}

robomaster/api/post_infer.py

@@ -0,0 +1,89 @@
+import base64
+import json
+import sys
+import time
+from datetime import datetime
+from io import BytesIO
+
+import cv2
+import requests
+import base64
+
+
+def post_diffusion_transformer(diffusion_transformer_path, url='http://127.0.0.1:7860'):
+    datas = json.dumps({
+        "diffusion_transformer_path": diffusion_transformer_path
+    })
+    r = requests.post(f'{url}/cogvideox_fun/update_diffusion_transformer', data=datas, timeout=1500)
+    data = r.content.decode('utf-8')
+    return data
+
+def post_update_edition(edition, url='http://0.0.0.0:7860'):
+    datas = json.dumps({
+        "edition": edition
+    })
+    r = requests.post(f'{url}/cogvideox_fun/update_edition', data=datas, timeout=1500)
+    data = r.content.decode('utf-8')
+    return data
+
+def post_infer(generation_method, length_slider, url='http://127.0.0.1:7860'):
+    datas = json.dumps({
+        "base_model_path": "none",
+        "motion_module_path": "none",
+        "lora_model_path": "none", 
+        "lora_alpha_slider": 0.55, 
+        "prompt_textbox": "A young woman with beautiful and clear eyes and blonde hair standing and white dress in a forest wearing a crown. She seems to be lost in thought, and the camera focuses on her face. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.", 
+        "negative_prompt_textbox": "The video is not of a high quality, it has a low resolution. Watermark present in each frame. The background is solid. Strange body and strange trajectory. Distortion. ", 
+        "sampler_dropdown": "Euler", 
+        "sample_step_slider": 50, 
+        "width_slider": 672, 
+        "height_slider": 384, 
+        "generation_method": "Video Generation",
+        "length_slider": length_slider,
+        "cfg_scale_slider": 6,
+        "seed_textbox": 43,
+    })
+    r = requests.post(f'{url}/cogvideox_fun/infer_forward', data=datas, timeout=1500)
+    data = r.content.decode('utf-8')
+    return data
+
+if __name__ == '__main__':
+    # initiate time
+    now_date    = datetime.now()
+    time_start  = time.time()  
+    
+    # -------------------------- #
+    #  Step 1: update edition
+    # -------------------------- #
+    diffusion_transformer_path = "models/Diffusion_Transformer/CogVideoX-Fun-2b-InP"
+    outputs = post_diffusion_transformer(diffusion_transformer_path)
+    print('Output update edition: ', outputs)
+
+    # -------------------------- #
+    #  Step 2: infer
+    # -------------------------- #
+    # "Video Generation" and "Image Generation"
+    generation_method = "Video Generation"
+    length_slider = 49
+    outputs = post_infer(generation_method, length_slider)
+    
+    # Get decoded data
+    outputs = json.loads(outputs)
+    base64_encoding = outputs["base64_encoding"]
+    decoded_data = base64.b64decode(base64_encoding)
+
+    is_image = True if generation_method == "Image Generation" else False
+    if is_image or length_slider == 1:
+        file_path = "1.png"
+    else:
+        file_path = "1.mp4"
+    with open(file_path, "wb") as file:
+        file.write(decoded_data)
+        
+    # End of record time
+    # The calculated time difference is the execution time of the program, expressed in seconds / s
+    time_end = time.time()  
+    time_sum = (time_end - time_start) % 60 
+    print('# --------------------------------------------------------- #')
+    print(f'#   Total expenditure: {time_sum}s')
+    print('# --------------------------------------------------------- #')

robomaster/data/bucket_sampler.py

@@ -0,0 +1,379 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+from typing import (Generic, Iterable, Iterator, List, Optional, Sequence,
+                    Sized, TypeVar, Union)
+
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+from torch.utils.data import BatchSampler, Dataset, Sampler
+
+ASPECT_RATIO_512 = {
+    '0.25': [256.0, 1024.0], '0.26': [256.0, 992.0], '0.27': [256.0, 960.0], '0.28': [256.0, 928.0],
+    '0.32': [288.0, 896.0], '0.33': [288.0, 864.0], '0.35': [288.0, 832.0], '0.4': [320.0, 800.0],
+    '0.42': [320.0, 768.0], '0.48': [352.0, 736.0], '0.5': [352.0, 704.0], '0.52': [352.0, 672.0],
+    '0.57': [384.0, 672.0], '0.6': [384.0, 640.0], '0.68': [416.0, 608.0], '0.72': [416.0, 576.0],
+    '0.78': [448.0, 576.0], '0.82': [448.0, 544.0], '0.88': [480.0, 544.0], '0.94': [480.0, 512.0],
+    '1.0': [512.0, 512.0], '1.07': [512.0, 480.0], '1.13': [544.0, 480.0], '1.21': [544.0, 448.0],
+    '1.29': [576.0, 448.0], '1.38': [576.0, 416.0], '1.46': [608.0, 416.0], '1.67': [640.0, 384.0],
+    '1.75': [672.0, 384.0], '2.0': [704.0, 352.0], '2.09': [736.0, 352.0], '2.4': [768.0, 320.0],
+    '2.5': [800.0, 320.0], '2.89': [832.0, 288.0], '3.0': [864.0, 288.0], '3.11': [896.0, 288.0],
+    '3.62': [928.0, 256.0], '3.75': [960.0, 256.0], '3.88': [992.0, 256.0], '4.0': [1024.0, 256.0]
+}
+ASPECT_RATIO_RANDOM_CROP_512 = {
+    '0.42': [320.0, 768.0], '0.5': [352.0, 704.0], 
+    '0.57': [384.0, 672.0], '0.68': [416.0, 608.0], '0.78': [448.0, 576.0], '0.88': [480.0, 544.0], 
+    '0.94': [480.0, 512.0], '1.0': [512.0, 512.0], '1.07': [512.0, 480.0], 
+    '1.13': [544.0, 480.0], '1.29': [576.0, 448.0], '1.46': [608.0, 416.0], '1.75': [672.0, 384.0], 
+    '2.0': [704.0, 352.0],  '2.4': [768.0, 320.0]
+}
+ASPECT_RATIO_RANDOM_CROP_PROB = [
+    1, 2,
+    4, 4, 4, 4,
+    8, 8, 8,
+    4, 4, 4, 4,
+    2, 1
+]
+ASPECT_RATIO_RANDOM_CROP_PROB = np.array(ASPECT_RATIO_RANDOM_CROP_PROB) / sum(ASPECT_RATIO_RANDOM_CROP_PROB)
+
+def get_closest_ratio(height: float, width: float, ratios: dict = ASPECT_RATIO_512):
+    aspect_ratio = height / width
+    closest_ratio = min(ratios.keys(), key=lambda ratio: abs(float(ratio) - aspect_ratio))
+    return ratios[closest_ratio], float(closest_ratio)
+
+def get_image_size_without_loading(path):
+    with Image.open(path) as img:
+        return img.size  # (width, height)
+
+class RandomSampler(Sampler[int]):
+    r"""Samples elements randomly. If without replacement, then sample from a shuffled dataset.
+
+    If with replacement, then user can specify :attr:`num_samples` to draw.
+
+    Args:
+        data_source (Dataset): dataset to sample from
+        replacement (bool): samples are drawn on-demand with replacement if ``True``, default=``False``
+        num_samples (int): number of samples to draw, default=`len(dataset)`.
+        generator (Generator): Generator used in sampling.
+    """
+
+    data_source: Sized
+    replacement: bool
+
+    def __init__(self, data_source: Sized, replacement: bool = False,
+                 num_samples: Optional[int] = None, generator=None) -> None:
+        self.data_source = data_source
+        self.replacement = replacement
+        self._num_samples = num_samples
+        self.generator = generator
+        self._pos_start = 0
+
+        if not isinstance(self.replacement, bool):
+            raise TypeError(f"replacement should be a boolean value, but got replacement={self.replacement}")
+
+        if not isinstance(self.num_samples, int) or self.num_samples <= 0:
+            raise ValueError(f"num_samples should be a positive integer value, but got num_samples={self.num_samples}")
+
+    @property
+    def num_samples(self) -> int:
+        # dataset size might change at runtime
+        if self._num_samples is None:
+            return len(self.data_source)
+        return self._num_samples
+
+    def __iter__(self) -> Iterator[int]:
+        n = len(self.data_source)
+        if self.generator is None:
+            seed = int(torch.empty((), dtype=torch.int64).random_().item())
+            generator = torch.Generator()
+            generator.manual_seed(seed)
+        else:
+            generator = self.generator
+
+        if self.replacement:
+            for _ in range(self.num_samples // 32):
+                yield from torch.randint(high=n, size=(32,), dtype=torch.int64, generator=generator).tolist()
+            yield from torch.randint(high=n, size=(self.num_samples % 32,), dtype=torch.int64, generator=generator).tolist()
+        else:
+            for _ in range(self.num_samples // n):
+                xx = torch.randperm(n, generator=generator).tolist()
+                if self._pos_start >= n:
+                    self._pos_start = 0
+                print("xx top 10", xx[:10], self._pos_start)
+                for idx in range(self._pos_start, n):
+                    yield xx[idx]
+                    self._pos_start = (self._pos_start + 1) % n
+                self._pos_start = 0
+            yield from torch.randperm(n, generator=generator).tolist()[:self.num_samples % n]
+
+    def __len__(self) -> int:
+        return self.num_samples
+
+class AspectRatioBatchImageSampler(BatchSampler):
+    """A sampler wrapper for grouping images with similar aspect ratio into a same batch.
+
+    Args:
+        sampler (Sampler): Base sampler.
+        dataset (Dataset): Dataset providing data information.
+        batch_size (int): Size of mini-batch.
+        drop_last (bool): If ``True``, the sampler will drop the last batch if
+            its size would be less than ``batch_size``.
+        aspect_ratios (dict): The predefined aspect ratios.
+    """
+    def __init__(
+        self,
+        sampler: Sampler,
+        dataset: Dataset,
+        batch_size: int,
+        train_folder: str = None,
+        aspect_ratios: dict = ASPECT_RATIO_512,
+        drop_last: bool = False,
+        config=None,
+        **kwargs
+    ) -> None:
+        if not isinstance(sampler, Sampler):
+            raise TypeError('sampler should be an instance of ``Sampler``, '
+                            f'but got {sampler}')
+        if not isinstance(batch_size, int) or batch_size <= 0:
+            raise ValueError('batch_size should be a positive integer value, '
+                             f'but got batch_size={batch_size}')
+        self.sampler = sampler
+        self.dataset = dataset
+        self.train_folder = train_folder
+        self.batch_size = batch_size
+        self.aspect_ratios = aspect_ratios
+        self.drop_last = drop_last
+        self.config = config
+        # buckets for each aspect ratio 
+        self._aspect_ratio_buckets = {ratio: [] for ratio in aspect_ratios}
+        # [str(k) for k, v in aspect_ratios] 
+        self.current_available_bucket_keys = list(aspect_ratios.keys())
+
+    def __iter__(self):
+        for idx in self.sampler:
+            try:
+                image_dict = self.dataset[idx]
+
+                width, height = image_dict.get("width", None), image_dict.get("height", None)
+                if width is None or height is None:
+                    image_id, name = image_dict['file_path'], image_dict['text']
+                    if self.train_folder is None:
+                        image_dir = image_id
+                    else:
+                        image_dir = os.path.join(self.train_folder, image_id)
+
+                    width, height = get_image_size_without_loading(image_dir)
+
+                    ratio = height / width # self.dataset[idx]
+                else:
+                    height = int(height)
+                    width = int(width)
+                    ratio = height / width # self.dataset[idx]
+            except Exception as e:
+                print(e)
+                continue
+            # find the closest aspect ratio
+            closest_ratio = min(self.aspect_ratios.keys(), key=lambda r: abs(float(r) - ratio))
+            if closest_ratio not in self.current_available_bucket_keys:
+                continue
+            bucket = self._aspect_ratio_buckets[closest_ratio]
+            bucket.append(idx)
+            # yield a batch of indices in the same aspect ratio group
+            if len(bucket) == self.batch_size:
+                yield bucket[:]
+                del bucket[:]
+
+class AspectRatioBatchSampler(BatchSampler):
+    """A sampler wrapper for grouping images with similar aspect ratio into a same batch.
+
+    Args:
+        sampler (Sampler): Base sampler.
+        dataset (Dataset): Dataset providing data information.
+        batch_size (int): Size of mini-batch.
+        drop_last (bool): If ``True``, the sampler will drop the last batch if
+            its size would be less than ``batch_size``.
+        aspect_ratios (dict): The predefined aspect ratios.
+    """
+    def __init__(
+        self,
+        sampler: Sampler,
+        dataset: Dataset,
+        batch_size: int,
+        video_folder: str = None,
+        train_data_format: str = "webvid",
+        aspect_ratios: dict = ASPECT_RATIO_512,
+        drop_last: bool = False,
+        config=None,
+        **kwargs
+    ) -> None:
+        if not isinstance(sampler, Sampler):
+            raise TypeError('sampler should be an instance of ``Sampler``, '
+                            f'but got {sampler}')
+        if not isinstance(batch_size, int) or batch_size <= 0:
+            raise ValueError('batch_size should be a positive integer value, '
+                             f'but got batch_size={batch_size}')
+        self.sampler = sampler
+        self.dataset = dataset
+        self.video_folder = video_folder
+        self.train_data_format = train_data_format
+        self.batch_size = batch_size
+        self.aspect_ratios = aspect_ratios
+        self.drop_last = drop_last
+        self.config = config
+        # buckets for each aspect ratio 
+        self._aspect_ratio_buckets = {ratio: [] for ratio in aspect_ratios}
+        # [str(k) for k, v in aspect_ratios] 
+        self.current_available_bucket_keys = list(aspect_ratios.keys())
+
+    def __iter__(self):
+        for idx in self.sampler:
+            try:
+                video_dict = self.dataset[idx]
+                width, more = video_dict.get("width", None), video_dict.get("height", None)
+
+                if width is None or height is None:
+                    if self.train_data_format == "normal":
+                        video_id, name = video_dict['file_path'], video_dict['text']
+                        if self.video_folder is None:
+                            video_dir = video_id
+                        else:
+                            video_dir = os.path.join(self.video_folder, video_id)
+                    else:
+                        videoid, name, page_dir = video_dict['videoid'], video_dict['name'], video_dict['page_dir']
+                        video_dir = os.path.join(self.video_folder, f"{videoid}.mp4")
+                    cap = cv2.VideoCapture(video_dir)
+
+                    # 获取视频尺寸
+                    width  = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))   # 浮点数转换为整数
+                    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))  # 浮点数转换为整数
+                    
+                    ratio = height / width # self.dataset[idx]
+                else:
+                    height = int(height)
+                    width = int(width)
+                    ratio = height / width # self.dataset[idx]
+            except Exception as e:
+                print(e)
+                continue
+            # find the closest aspect ratio
+            closest_ratio = min(self.aspect_ratios.keys(), key=lambda r: abs(float(r) - ratio))
+            if closest_ratio not in self.current_available_bucket_keys:
+                continue
+            bucket = self._aspect_ratio_buckets[closest_ratio]
+            bucket.append(idx)
+            # yield a batch of indices in the same aspect ratio group
+            if len(bucket) == self.batch_size:
+                yield bucket[:]
+                del bucket[:]
+
+class AspectRatioBatchImageVideoSampler(BatchSampler):
+    """A sampler wrapper for grouping images with similar aspect ratio into a same batch.
+
+    Args:
+        sampler (Sampler): Base sampler.
+        dataset (Dataset): Dataset providing data information.
+        batch_size (int): Size of mini-batch.
+        drop_last (bool): If ``True``, the sampler will drop the last batch if
+            its size would be less than ``batch_size``.
+        aspect_ratios (dict): The predefined aspect ratios.
+    """
+
+    def __init__(self,
+                 sampler: Sampler,
+                 dataset: Dataset,
+                 batch_size: int,
+                 train_folder: str = None,
+                 aspect_ratios: dict = ASPECT_RATIO_512,
+                 drop_last: bool = False
+                ) -> None:
+        if not isinstance(sampler, Sampler):
+            raise TypeError('sampler should be an instance of ``Sampler``, '
+                            f'but got {sampler}')
+        if not isinstance(batch_size, int) or batch_size <= 0:
+            raise ValueError('batch_size should be a positive integer value, '
+                             f'but got batch_size={batch_size}')
+        self.sampler = sampler
+        self.dataset = dataset
+        self.train_folder = train_folder
+        self.batch_size = batch_size
+        self.aspect_ratios = aspect_ratios
+        self.drop_last = drop_last
+
+        # buckets for each aspect ratio
+        self.current_available_bucket_keys = list(aspect_ratios.keys())
+        self.bucket = {
+            'image':{ratio: [] for ratio in aspect_ratios}, 
+            'video':{ratio: [] for ratio in aspect_ratios}
+        }
+
+    def __iter__(self):
+        for idx in self.sampler:
+            content_type = self.dataset[idx].get('type', 'image')
+            if content_type == 'image':
+                try:
+                    image_dict = self.dataset[idx]
+
+                    width, height = image_dict.get("width", None), image_dict.get("height", None)
+                    if width is None or height is None:
+                        image_id, name = image_dict['file_path'], image_dict['text']
+                        if self.train_folder is None:
+                            image_dir = image_id
+                        else:
+                            image_dir = os.path.join(self.train_folder, image_id)
+
+                        width, height = get_image_size_without_loading(image_dir)
+
+                        ratio = height / width # self.dataset[idx]
+                    else:
+                        height = int(height)
+                        width = int(width)
+                        ratio = height / width # self.dataset[idx]
+                except Exception as e:
+                    print(e)
+                    continue
+                # find the closest aspect ratio
+                closest_ratio = min(self.aspect_ratios.keys(), key=lambda r: abs(float(r) - ratio))
+                if closest_ratio not in self.current_available_bucket_keys:
+                    continue
+                bucket = self.bucket['image'][closest_ratio]
+                bucket.append(idx)
+                # yield a batch of indices in the same aspect ratio group
+                if len(bucket) == self.batch_size:
+                    yield bucket[:]
+                    del bucket[:]
+            else:
+                try:
+                    video_dict = self.dataset[idx]
+                    width, height = video_dict.get("width", None), video_dict.get("height", None)
+
+                    if width is None or height is None:
+                        video_id, name = video_dict['file_path'], video_dict['text']
+                        if self.train_folder is None:
+                            video_dir = video_id
+                        else:
+                            video_dir = os.path.join(self.train_folder, video_id)
+                        cap = cv2.VideoCapture(video_dir)
+
+                        # 获取视频尺寸
+                        width  = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))   # 浮点数转换为整数
+                        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))  # 浮点数转换为整数
+                        
+                        ratio = height / width # self.dataset[idx]
+                    else:
+                        height = int(height)
+                        width = int(width)
+                        ratio = height / width # self.dataset[idx]
+                except Exception as e:
+                    print(e)
+                    continue
+                # find the closest aspect ratio
+                closest_ratio = min(self.aspect_ratios.keys(), key=lambda r: abs(float(r) - ratio))
+                if closest_ratio not in self.current_available_bucket_keys:
+                    continue
+                bucket = self.bucket['video'][closest_ratio]
+                bucket.append(idx)
+                # yield a batch of indices in the same aspect ratio group
+                if len(bucket) == self.batch_size:
+                    yield bucket[:]
+                    del bucket[:]

robomaster/data/dataset_image.py

@@ -0,0 +1,76 @@
+import json
+import os
+import random
+
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+from torch.utils.data.dataset import Dataset
+
+
+class CC15M(Dataset):
+    def __init__(
+            self,
+            json_path, 
+            video_folder=None,
+            resolution=512,
+            enable_bucket=False,
+        ):
+        print(f"loading annotations from {json_path} ...")
+        self.dataset = json.load(open(json_path, 'r'))
+        self.length = len(self.dataset)
+        print(f"data scale: {self.length}")
+        
+        self.enable_bucket = enable_bucket
+        self.video_folder = video_folder
+
+        resolution = tuple(resolution) if not isinstance(resolution, int) else (resolution, resolution)
+        self.pixel_transforms = transforms.Compose([
+            transforms.Resize(resolution[0]),
+            transforms.CenterCrop(resolution),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
+        ])
+    
+    def get_batch(self, idx):
+        video_dict = self.dataset[idx]
+        video_id, name = video_dict['file_path'], video_dict['text']
+
+        if self.video_folder is None:
+            video_dir = video_id
+        else:
+            video_dir = os.path.join(self.video_folder, video_id)
+
+        pixel_values = Image.open(video_dir).convert("RGB")
+        return pixel_values, name
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, idx):
+        while True:
+            try:
+                pixel_values, name = self.get_batch(idx)
+                break
+            except Exception as e:
+                print(e)
+                idx = random.randint(0, self.length-1)
+
+        if not self.enable_bucket:
+            pixel_values = self.pixel_transforms(pixel_values)
+        else:
+            pixel_values = np.array(pixel_values)
+
+        sample = dict(pixel_values=pixel_values, text=name)
+        return sample
+
+if __name__ == "__main__":
+    dataset = CC15M(
+        csv_path="/mnt_wg/zhoumo.xjq/CCUtils/cc15m_add_index.json",
+        resolution=512,
+    )
+    
+    dataloader = torch.utils.data.DataLoader(dataset, batch_size=4, num_workers=0,)
+    for idx, batch in enumerate(dataloader):
+        print(batch["pixel_values"].shape, len(batch["text"]))

robomaster/data/dataset_image_video.py

@@ -0,0 +1,357 @@
+import csv
+import io
+import json
+import math
+import os
+import random
+from threading import Thread
+
+import albumentations
+import cv2
+import gc
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+
+from func_timeout import func_timeout, FunctionTimedOut
+from decord import VideoReader
+from PIL import Image
+from torch.utils.data import BatchSampler, Sampler
+from torch.utils.data.dataset import Dataset
+from contextlib import contextmanager
+
+import tensorflow as tf
+import tensorflow_datasets as tfds
+from PIL import Image
+from IPython import display
+import tqdm
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+VIDEO_READER_TIMEOUT = 20
+
+def dataset2path(dataset_name):
+    if dataset_name == 'robo_net':
+        version = '1.0.0'
+    elif dataset_name == 'language_table':
+        version = '0.0.1'
+    else:
+        version = '0.1.0'
+    return f'/m2v_intern/fuxiao/Open-X-Embodiement/dataset/{dataset_name}/{version}'
+
+def get_random_mask(shape):
+    f, c, h, w = shape
+
+    if f != 1:
+        mask_index = np.random.choice([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], p=[0.05, 0.2, 0.2, 0.2, 0.05, 0.05, 0.05, 0.1, 0.05, 0.05]) 
+    else:
+        mask_index = np.random.choice([0, 1], p = [0.2, 0.8])
+    mask = torch.zeros((f, 1, h, w), dtype=torch.uint8)
+
+    if mask_index == 0:
+        center_x = torch.randint(0, w, (1,)).item()
+        center_y = torch.randint(0, h, (1,)).item()
+        block_size_x = torch.randint(w // 4, w // 4 * 3, (1,)).item()  # 方块的宽度范围
+        block_size_y = torch.randint(h // 4, h // 4 * 3, (1,)).item()  # 方块的高度范围
+
+        start_x = max(center_x - block_size_x // 2, 0)
+        end_x = min(center_x + block_size_x // 2, w)
+        start_y = max(center_y - block_size_y // 2, 0)
+        end_y = min(center_y + block_size_y // 2, h)
+        mask[:, :, start_y:end_y, start_x:end_x] = 1
+    elif mask_index == 1:
+        mask[:, :, :, :] = 1
+    elif mask_index == 2:
+        mask_frame_index = np.random.randint(1, 5)
+        mask[mask_frame_index:, :, :, :] = 1
+    elif mask_index == 3:
+        mask_frame_index = np.random.randint(1, 5)
+        mask[mask_frame_index:-mask_frame_index, :, :, :] = 1
+    elif mask_index == 4:
+        center_x = torch.randint(0, w, (1,)).item()
+        center_y = torch.randint(0, h, (1,)).item()
+        block_size_x = torch.randint(w // 4, w // 4 * 3, (1,)).item()  # 方块的宽度范围
+        block_size_y = torch.randint(h // 4, h // 4 * 3, (1,)).item()  # 方块的高度范围
+
+        start_x = max(center_x - block_size_x // 2, 0)
+        end_x = min(center_x + block_size_x // 2, w)
+        start_y = max(center_y - block_size_y // 2, 0)
+        end_y = min(center_y + block_size_y // 2, h)
+
+        mask_frame_before = np.random.randint(0, f // 2)
+        mask_frame_after = np.random.randint(f // 2, f)
+        mask[mask_frame_before:mask_frame_after, :, start_y:end_y, start_x:end_x] = 1
+    elif mask_index == 5:
+        mask = torch.randint(0, 2, (f, 1, h, w), dtype=torch.uint8)
+    elif mask_index == 6:
+        num_frames_to_mask = random.randint(1, max(f // 2, 1))
+        frames_to_mask = random.sample(range(f), num_frames_to_mask)
+
+        for i in frames_to_mask:
+            block_height = random.randint(1, h // 4)
+            block_width = random.randint(1, w // 4)
+            top_left_y = random.randint(0, h - block_height)
+            top_left_x = random.randint(0, w - block_width)
+            mask[i, 0, top_left_y:top_left_y + block_height, top_left_x:top_left_x + block_width] = 1
+    elif mask_index == 7:
+        center_x = torch.randint(0, w, (1,)).item()
+        center_y = torch.randint(0, h, (1,)).item()
+        a = torch.randint(min(w, h) // 8, min(w, h) // 4, (1,)).item()  # 长半轴
+        b = torch.randint(min(h, w) // 8, min(h, w) // 4, (1,)).item()  # 短半轴
+
+        for i in range(h):
+            for j in range(w):
+                if ((i - center_y) ** 2) / (b ** 2) + ((j - center_x) ** 2) / (a ** 2) < 1:
+                    mask[:, :, i, j] = 1
+    elif mask_index == 8:
+        center_x = torch.randint(0, w, (1,)).item()
+        center_y = torch.randint(0, h, (1,)).item()
+        radius = torch.randint(min(h, w) // 8, min(h, w) // 4, (1,)).item()
+        for i in range(h):
+            for j in range(w):
+                if (i - center_y) ** 2 + (j - center_x) ** 2 < radius ** 2:
+                    mask[:, :, i, j] = 1
+    elif mask_index == 9:
+        for idx in range(f):
+            if np.random.rand() > 0.5:
+                mask[idx, :, :, :] = 1
+    else:
+        raise ValueError(f"The mask_index {mask_index} is not define")
+    return mask
+
+class ImageVideoSampler(BatchSampler):
+    """A sampler wrapper for grouping images with similar aspect ratio into a same batch.
+
+    Args:
+        sampler (Sampler): Base sampler.
+        dataset (Dataset): Dataset providing data information.
+        batch_size (int): Size of mini-batch.
+        drop_last (bool): If ``True``, the sampler will drop the last batch if
+            its size would be less than ``batch_size``.
+        aspect_ratios (dict): The predefined aspect ratios.
+    """
+
+    def __init__(self,
+                sampler: Sampler,
+                dataset: Dataset,
+                batch_size: int,
+                drop_last: bool = False
+                ) -> None:
+        if not isinstance(sampler, Sampler):
+            raise TypeError('sampler should be an instance of ``Sampler``, '
+                            f'but got {sampler}')
+        if not isinstance(batch_size, int) or batch_size <= 0:
+            raise ValueError('batch_size should be a positive integer value, '
+                             f'but got batch_size={batch_size}')
+        self.sampler = sampler
+        self.dataset = dataset
+        self.batch_size = batch_size
+        self.drop_last = drop_last
+
+        # buckets for each aspect ratio
+        self.bucket = {'image':[], 'video':[]}
+
+    def __iter__(self):
+        for idx in self.sampler:
+            content_type = self.dataset.dataset[idx].get('type', 'image')
+            self.bucket[content_type].append(idx)
+
+            # yield a batch of indices in the same aspect ratio group
+            if len(self.bucket['video']) == self.batch_size:
+                bucket = self.bucket['video']
+                yield bucket[:]
+                del bucket[:]
+            elif len(self.bucket['image']) == self.batch_size:
+                bucket = self.bucket['image']
+                yield bucket[:]
+                del bucket[:]
+
+@contextmanager
+def VideoReader_contextmanager(*args, **kwargs):
+    vr = VideoReader(*args, **kwargs)
+    try:
+        yield vr
+    finally:
+        del vr
+        gc.collect()
+
+def get_video_reader_batch(video_reader, batch_index):
+    frames = video_reader.get_batch(batch_index).asnumpy()
+    return frames
+
+def resize_frame(frame, target_short_side):
+    h, w, _ = frame.shape
+    if h < w:
+        if target_short_side > h:
+            return frame
+        new_h = target_short_side
+        new_w = int(target_short_side * w / h)
+    else:
+        if target_short_side > w:
+            return frame
+        new_w = target_short_side
+        new_h = int(target_short_side * h / w)
+    
+    resized_frame = cv2.resize(frame, (new_w, new_h))
+    return resized_frame
+
+class ImageVideoDataset(Dataset):
+    def __init__(
+            self,
+            data_root=None,
+            video_sample_size_h=256, 
+            video_sample_size_w=320, 
+            video_sample_stride=4, 
+            video_sample_n_frames=16,
+            image_sample_size=512,
+            text_drop_ratio=0.1,
+            enable_bucket=False,
+            video_length_drop_start=0.0, 
+            video_length_drop_end=1.0,
+            enable_inpaint=False,
+        ):
+        # Loading annotations from files
+        print(f"loading dataset from {data_root} ...")
+        self.data_root = data_root
+        self.dataset = []
+
+        b = tfds.builder_from_directory(builder_dir=dataset2path('fractal20220817_data'))
+        ds = b.as_dataset(split='train')
+
+        for i, batch in tqdm.tqdm(enumerate(ds), desc="Loading Open-X-Embodiement dataset"):
+            episode = batch['steps']
+
+        del dataset
+
+        self.length = len(self.dataset)
+        print(f"data scale: {self.length}")
+        # TODO: enable bucket training
+        self.enable_bucket   = enable_bucket
+        self.text_drop_ratio = text_drop_ratio
+        self.enable_inpaint  = enable_inpaint
+
+        self.video_length_drop_start = video_length_drop_start
+        self.video_length_drop_end = video_length_drop_end
+
+        # Video params
+        self.video_sample_stride    = video_sample_stride
+        self.video_sample_n_frames  = video_sample_n_frames
+        self.video_sample_size = (video_sample_size_h, video_sample_size_w)
+        self.video_transforms = transforms.Compose(
+            [
+                transforms.Resize(min(self.video_sample_size)),
+                transforms.CenterCrop(self.video_sample_size),
+                transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
+            ]
+        )
+
+    def get_batch(self, idx):
+        data_info = self.dataset[idx % len(self.dataset)]
+        
+        if data_info.get('type', 'image')=='video':
+            video_id, text = data_info['file_path'], data_info['text']
+
+            if self.data_root is None:
+                video_dir = video_id
+            else:
+                video_dir = os.path.join(self.data_root, video_id)
+
+            with VideoReader_contextmanager(video_dir, num_threads=2) as video_reader:
+                min_sample_n_frames = min(
+                    self.video_sample_n_frames, 
+                    int(len(video_reader) * (self.video_length_drop_end - self.video_length_drop_start) // self.video_sample_stride)
+                )
+                if min_sample_n_frames == 0:
+                    raise ValueError(f"No Frames in video.")
+
+                video_length = int(self.video_length_drop_end * len(video_reader))
+                clip_length = min(video_length, (min_sample_n_frames - 1) * self.video_sample_stride + 1)
+                start_idx   = random.randint(int(self.video_length_drop_start * video_length), video_length - clip_length) if video_length != clip_length else 0
+                batch_index = np.linspace(start_idx, start_idx + clip_length - 1, min_sample_n_frames, dtype=int)
+
+                try:
+                    sample_args = (video_reader, batch_index)
+                    pixel_values = func_timeout(
+                        VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
+                    )
+                    resized_frames = []
+                    for i in range(len(pixel_values)):
+                        frame = pixel_values[i]
+                        resized_frame = resize_frame(frame, self.larger_side_of_image_and_video)
+                        resized_frames.append(resized_frame)
+                    pixel_values = np.array(resized_frames)
+                except FunctionTimedOut:
+                    raise ValueError(f"Read {idx} timeout.")
+                except Exception as e:
+                    raise ValueError(f"Failed to extract frames from video. Error is {e}.")
+
+                if not self.enable_bucket:
+                    pixel_values = torch.from_numpy(pixel_values).permute(0, 3, 1, 2).contiguous()
+                    pixel_values = pixel_values / 255.
+                    del video_reader
+                else:
+                    pixel_values = pixel_values
+
+                if not self.enable_bucket:
+                    pixel_values = self.video_transforms(pixel_values)
+                
+                # Random use no text generation
+                if random.random() < self.text_drop_ratio:
+                    text = ''
+            return pixel_values, text, 'video'
+        else:
+            image_path, text = data_info['file_path'], data_info['text']
+            if self.data_root is not None:
+                image_path = os.path.join(self.data_root, image_path)
+            image = Image.open(image_path).convert('RGB')
+            if not self.enable_bucket:
+                image = self.image_transforms(image).unsqueeze(0)
+            else:
+                image = np.expand_dims(np.array(image), 0)
+            if random.random() < self.text_drop_ratio:
+                text = ''
+            return image, text, 'image'
+
+    def __len__(self):
+        return self.length
+    
+    def __getitem__(self, idx):
+        data_info = self.dataset[idx % len(self.dataset)]
+        data_type = data_info.get('type', 'image')
+        while True:
+            sample = {}
+            try:
+                data_info_local = self.dataset[idx % len(self.dataset)]
+                data_type_local = data_info_local.get('type', 'image')
+                if data_type_local != data_type:
+                    raise ValueError("data_type_local != data_type")
+
+                pixel_values, name, data_type = self.get_batch(idx)
+                sample["pixel_values"] = pixel_values
+                sample["text"] = name
+                sample["data_type"] = data_type
+                sample["idx"] = idx
+                
+                if len(sample) > 0:
+                    break
+            except Exception as e:
+                print(e, self.dataset[idx % len(self.dataset)])
+                idx = random.randint(0, self.length-1)
+
+        if self.enable_inpaint and not self.enable_bucket:
+            mask = get_random_mask(pixel_values.size())
+            mask_pixel_values = pixel_values * (1 - mask) + torch.ones_like(pixel_values) * -1 * mask
+            sample["mask_pixel_values"] = mask_pixel_values
+            sample["mask"] = mask
+
+            clip_pixel_values = sample["pixel_values"][0].permute(1, 2, 0).contiguous()
+            clip_pixel_values = (clip_pixel_values * 0.5 + 0.5) * 255
+            sample["clip_pixel_values"] = clip_pixel_values
+
+            ref_pixel_values = sample["pixel_values"][0].unsqueeze(0)
+            if (mask == 1).all():
+                ref_pixel_values = torch.ones_like(ref_pixel_values) * -1
+            sample["ref_pixel_values"] = ref_pixel_values
+
+        return sample
+
+

robomaster/data/dataset_video.py

@@ -0,0 +1,262 @@
+import csv
+import gc
+import io
+import json
+import math
+import os
+import random
+from contextlib import contextmanager
+from threading import Thread
+
+import albumentations
+import cv2
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+from decord import VideoReader
+from einops import rearrange
+from func_timeout import FunctionTimedOut, func_timeout
+from PIL import Image
+from torch.utils.data import BatchSampler, Sampler
+from torch.utils.data.dataset import Dataset
+
+VIDEO_READER_TIMEOUT = 20
+
+def get_random_mask(shape):
+    f, c, h, w = shape
+    
+    mask_index = np.random.randint(0, 4)
+    mask = torch.zeros((f, 1, h, w), dtype=torch.uint8)
+    if mask_index == 0:
+        mask[1:, :, :, :] = 1
+    elif mask_index == 1:
+        mask_frame_index = 1
+        mask[mask_frame_index:-mask_frame_index, :, :, :] = 1
+    elif mask_index == 2:
+        center_x = torch.randint(0, w, (1,)).item()
+        center_y = torch.randint(0, h, (1,)).item()
+        block_size_x = torch.randint(w // 4, w // 4 * 3, (1,)).item()  # 方块的宽度范围
+        block_size_y = torch.randint(h // 4, h // 4 * 3, (1,)).item()  # 方块的高度范围
+
+        start_x = max(center_x - block_size_x // 2, 0)
+        end_x = min(center_x + block_size_x // 2, w)
+        start_y = max(center_y - block_size_y // 2, 0)
+        end_y = min(center_y + block_size_y // 2, h)
+        mask[:, :, start_y:end_y, start_x:end_x] = 1
+    elif mask_index == 3:
+        center_x = torch.randint(0, w, (1,)).item()
+        center_y = torch.randint(0, h, (1,)).item()
+        block_size_x = torch.randint(w // 4, w // 4 * 3, (1,)).item()  # 方块的宽度范围
+        block_size_y = torch.randint(h // 4, h // 4 * 3, (1,)).item()  # 方块的高度范围
+
+        start_x = max(center_x - block_size_x // 2, 0)
+        end_x = min(center_x + block_size_x // 2, w)
+        start_y = max(center_y - block_size_y // 2, 0)
+        end_y = min(center_y + block_size_y // 2, h)
+
+        mask_frame_before = np.random.randint(0, f // 2)
+        mask_frame_after = np.random.randint(f // 2, f)
+        mask[mask_frame_before:mask_frame_after, :, start_y:end_y, start_x:end_x] = 1
+    else:
+        raise ValueError(f"The mask_index {mask_index} is not define")
+    return mask
+
+
+@contextmanager
+def VideoReader_contextmanager(*args, **kwargs):
+    vr = VideoReader(*args, **kwargs)
+    try:
+        yield vr
+    finally:
+        del vr
+        gc.collect()
+
+
+def get_video_reader_batch(video_reader, batch_index):
+    frames = video_reader.get_batch(batch_index).asnumpy()
+    return frames
+
+
+class WebVid10M(Dataset):
+    def __init__(
+            self,
+            csv_path, video_folder,
+            sample_size=256, sample_stride=4, sample_n_frames=16,
+            enable_bucket=False, enable_inpaint=False, is_image=False,
+        ):
+        print(f"loading annotations from {csv_path} ...")
+        with open(csv_path, 'r') as csvfile:
+            self.dataset = list(csv.DictReader(csvfile))
+        self.length = len(self.dataset)
+        print(f"data scale: {self.length}")
+
+        self.video_folder    = video_folder
+        self.sample_stride   = sample_stride
+        self.sample_n_frames = sample_n_frames
+        self.enable_bucket   = enable_bucket
+        self.enable_inpaint  = enable_inpaint
+        self.is_image        = is_image
+        
+        sample_size = tuple(sample_size) if not isinstance(sample_size, int) else (sample_size, sample_size)
+        self.pixel_transforms = transforms.Compose([
+            transforms.Resize(sample_size[0]),
+            transforms.CenterCrop(sample_size),
+            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
+        ])
+    
+    def get_batch(self, idx):
+        video_dict = self.dataset[idx]
+        videoid, name, page_dir = video_dict['videoid'], video_dict['name'], video_dict['page_dir']
+        
+        video_dir    = os.path.join(self.video_folder, f"{videoid}.mp4")
+        video_reader = VideoReader(video_dir)
+        video_length = len(video_reader)
+        
+        if not self.is_image:
+            clip_length = min(video_length, (self.sample_n_frames - 1) * self.sample_stride + 1)
+            start_idx   = random.randint(0, video_length - clip_length)
+            batch_index = np.linspace(start_idx, start_idx + clip_length - 1, self.sample_n_frames, dtype=int)
+        else:
+            batch_index = [random.randint(0, video_length - 1)]
+
+        if not self.enable_bucket:
+            pixel_values = torch.from_numpy(video_reader.get_batch(batch_index).asnumpy()).permute(0, 3, 1, 2).contiguous()
+            pixel_values = pixel_values / 255.
+            del video_reader
+        else:
+            pixel_values = video_reader.get_batch(batch_index).asnumpy()
+
+        if self.is_image:
+            pixel_values = pixel_values[0]
+        return pixel_values, name
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, idx):
+        while True:
+            try:
+                pixel_values, name = self.get_batch(idx)
+                break
+
+            except Exception as e:
+                print("Error info:", e)
+                idx = random.randint(0, self.length-1)
+
+        if not self.enable_bucket:
+            pixel_values = self.pixel_transforms(pixel_values)
+        if self.enable_inpaint:
+            mask = get_random_mask(pixel_values.size())
+            mask_pixel_values = pixel_values * (1 - mask) + torch.ones_like(pixel_values) * -1 * mask
+            sample = dict(pixel_values=pixel_values, mask_pixel_values=mask_pixel_values, mask=mask, text=name)
+        else:
+            sample = dict(pixel_values=pixel_values, text=name)
+        return sample
+
+
+class VideoDataset(Dataset):
+    def __init__(
+        self,
+        json_path, video_folder=None,
+        sample_size=256, sample_stride=4, sample_n_frames=16,
+        enable_bucket=False, enable_inpaint=False
+    ):
+        print(f"loading annotations from {json_path} ...")
+        self.dataset = json.load(open(json_path, 'r'))
+        self.length = len(self.dataset)
+        print(f"data scale: {self.length}")
+
+        self.video_folder    = video_folder
+        self.sample_stride   = sample_stride
+        self.sample_n_frames = sample_n_frames
+        self.enable_bucket   = enable_bucket
+        self.enable_inpaint  = enable_inpaint
+        
+        sample_size = tuple(sample_size) if not isinstance(sample_size, int) else (sample_size, sample_size)
+        self.pixel_transforms = transforms.Compose(
+            [
+                transforms.Resize(sample_size[0]),
+                transforms.CenterCrop(sample_size),
+                transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
+            ]
+        )
+    
+    def get_batch(self, idx):
+        video_dict = self.dataset[idx]
+        video_id, name = video_dict['file_path'], video_dict['text']
+
+        if self.video_folder is None:
+            video_dir = video_id
+        else:
+            video_dir = os.path.join(self.video_folder, video_id)
+
+        with VideoReader_contextmanager(video_dir, num_threads=2) as video_reader:
+            video_length = len(video_reader)
+        
+            clip_length = min(video_length, (self.sample_n_frames - 1) * self.sample_stride + 1)
+            start_idx   = random.randint(0, video_length - clip_length)
+            batch_index = np.linspace(start_idx, start_idx + clip_length - 1, self.sample_n_frames, dtype=int)
+
+            try:
+                sample_args = (video_reader, batch_index)
+                pixel_values = func_timeout(
+                    VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
+                )
+            except FunctionTimedOut:
+                raise ValueError(f"Read {idx} timeout.")
+            except Exception as e:
+                raise ValueError(f"Failed to extract frames from video. Error is {e}.")
+
+            if not self.enable_bucket:
+                pixel_values = torch.from_numpy(pixel_values).permute(0, 3, 1, 2).contiguous()
+                pixel_values = pixel_values / 255.
+                del video_reader
+            else:
+                pixel_values = pixel_values
+
+            return pixel_values, name
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, idx):
+        while True:
+            try:
+                pixel_values, name = self.get_batch(idx)
+                break
+
+            except Exception as e:
+                print("Error info:", e)
+                idx = random.randint(0, self.length-1)
+
+        if not self.enable_bucket:
+            pixel_values = self.pixel_transforms(pixel_values)
+        if self.enable_inpaint:
+            mask = get_random_mask(pixel_values.size())
+            mask_pixel_values = pixel_values * (1 - mask) + torch.ones_like(pixel_values) * -1 * mask
+            sample = dict(pixel_values=pixel_values, mask_pixel_values=mask_pixel_values, mask=mask, text=name)
+        else:
+            sample = dict(pixel_values=pixel_values, text=name)
+        return sample
+
+
+if __name__ == "__main__":
+    if 1:
+        dataset = VideoDataset(
+            json_path="/home/zhoumo.xjq/disk3/datasets/webvidval/results_2M_val.json",
+            sample_size=256,
+            sample_stride=4, sample_n_frames=16,
+        )
+
+    if 0:
+        dataset = WebVid10M(
+            csv_path="/mnt/petrelfs/guoyuwei/projects/datasets/webvid/results_2M_val.csv",
+            video_folder="/mnt/petrelfs/guoyuwei/projects/datasets/webvid/2M_val",
+            sample_size=256,
+            sample_stride=4, sample_n_frames=16,
+            is_image=False,
+        )
+
+    dataloader = torch.utils.data.DataLoader(dataset, batch_size=4, num_workers=0,)
+    for idx, batch in enumerate(dataloader):
+        print(batch["pixel_values"].shape, len(batch["text"]))

robomaster/models/autoencoder_magvit.py

@@ -0,0 +1,1646 @@
+# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
+# All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Dict, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders.single_file_model import FromOriginalModelMixin
+from diffusers.utils import logging
+from diffusers.utils.accelerate_utils import apply_forward_hook
+from diffusers.models.activations import get_activation
+from diffusers.models.downsampling import CogVideoXDownsample3D
+from diffusers.models.modeling_outputs import AutoencoderKLOutput
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.upsampling import CogVideoXUpsample3D
+from diffusers.models.autoencoders.vae import DecoderOutput, DiagonalGaussianDistribution
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class CogVideoXSafeConv3d(nn.Conv3d):
+    r"""
+    A 3D convolution layer that splits the input tensor into smaller parts to avoid OOM in CogVideoX Model.
+    """
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        memory_count = (
+            (input.shape[0] * input.shape[1] * input.shape[2] * input.shape[3] * input.shape[4]) * 2 / 1024**3
+        )
+
+        # Set to 2GB, suitable for CuDNN
+        if memory_count > 2:
+            kernel_size = self.kernel_size[0]
+            part_num = int(memory_count / 2) + 1
+            input_chunks = torch.chunk(input, part_num, dim=2)
+
+            if kernel_size > 1:
+                input_chunks = [input_chunks[0]] + [
+                    torch.cat((input_chunks[i - 1][:, :, -kernel_size + 1 :], input_chunks[i]), dim=2)
+                    for i in range(1, len(input_chunks))
+                ]
+
+            output_chunks = []
+            for input_chunk in input_chunks:
+                output_chunks.append(super().forward(input_chunk))
+            output = torch.cat(output_chunks, dim=2)
+            return output
+        else:
+            return super().forward(input)
+
+
+class CogVideoXCausalConv3d(nn.Module):
+    r"""A 3D causal convolution layer that pads the input tensor to ensure causality in CogVideoX Model.
+
+    Args:
+        in_channels (`int`): Number of channels in the input tensor.
+        out_channels (`int`): Number of output channels produced by the convolution.
+        kernel_size (`int` or `Tuple[int, int, int]`): Kernel size of the convolutional kernel.
+        stride (`int`, defaults to `1`): Stride of the convolution.
+        dilation (`int`, defaults to `1`): Dilation rate of the convolution.
+        pad_mode (`str`, defaults to `"constant"`): Padding mode.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: Union[int, Tuple[int, int, int]],
+        stride: int = 1,
+        dilation: int = 1,
+        pad_mode: str = "constant",
+    ):
+        super().__init__()
+
+        if isinstance(kernel_size, int):
+            kernel_size = (kernel_size,) * 3
+
+        time_kernel_size, height_kernel_size, width_kernel_size = kernel_size
+
+        # TODO(aryan): configure calculation based on stride and dilation in the future.
+        # Since CogVideoX does not use it, it is currently tailored to "just work" with Mochi
+        time_pad = time_kernel_size - 1
+        height_pad = (height_kernel_size - 1) // 2
+        width_pad = (width_kernel_size - 1) // 2
+
+        self.pad_mode = pad_mode
+        self.height_pad = height_pad
+        self.width_pad = width_pad
+        self.time_pad = time_pad
+        self.time_causal_padding = (width_pad, width_pad, height_pad, height_pad, time_pad, 0)
+
+        self.temporal_dim = 2
+        self.time_kernel_size = time_kernel_size
+
+        stride = stride if isinstance(stride, tuple) else (stride, 1, 1)
+        dilation = (dilation, 1, 1)
+        self.conv = CogVideoXSafeConv3d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            dilation=dilation,
+        )
+
+    def fake_context_parallel_forward(
+        self, inputs: torch.Tensor, conv_cache: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        if self.pad_mode == "replicate":
+            inputs = F.pad(inputs, self.time_causal_padding, mode="replicate")
+        else:
+            kernel_size = self.time_kernel_size
+            if kernel_size > 1:
+                cached_inputs = [conv_cache] if conv_cache is not None else [inputs[:, :, :1]] * (kernel_size - 1)
+                inputs = torch.cat(cached_inputs + [inputs], dim=2)
+        return inputs
+
+    def forward(self, inputs: torch.Tensor, conv_cache: Optional[torch.Tensor] = None) -> torch.Tensor:
+        inputs = self.fake_context_parallel_forward(inputs, conv_cache)
+
+        if self.pad_mode == "replicate":
+            conv_cache = None
+        else:
+            padding_2d = (self.width_pad, self.width_pad, self.height_pad, self.height_pad)
+            conv_cache = inputs[:, :, -self.time_kernel_size + 1 :].clone()
+            inputs = F.pad(inputs, padding_2d, mode="constant", value=0)
+
+        output = self.conv(inputs)
+        return output, conv_cache
+
+
+class CogVideoXSpatialNorm3D(nn.Module):
+    r"""
+    Spatially conditioned normalization as defined in https://arxiv.org/abs/2209.09002. This implementation is specific
+    to 3D-video like data.
+
+    CogVideoXSafeConv3d is used instead of nn.Conv3d to avoid OOM in CogVideoX Model.
+
+    Args:
+        f_channels (`int`):
+            The number of channels for input to group normalization layer, and output of the spatial norm layer.
+        zq_channels (`int`):
+            The number of channels for the quantized vector as described in the paper.
+        groups (`int`):
+            Number of groups to separate the channels into for group normalization.
+    """
+
+    def __init__(
+        self,
+        f_channels: int,
+        zq_channels: int,
+        groups: int = 32,
+    ):
+        super().__init__()
+        self.norm_layer = nn.GroupNorm(num_channels=f_channels, num_groups=groups, eps=1e-6, affine=True)
+        self.conv_y = CogVideoXCausalConv3d(zq_channels, f_channels, kernel_size=1, stride=1)
+        self.conv_b = CogVideoXCausalConv3d(zq_channels, f_channels, kernel_size=1, stride=1)
+
+    def forward(
+        self, f: torch.Tensor, zq: torch.Tensor, conv_cache: Optional[Dict[str, torch.Tensor]] = None
+    ) -> torch.Tensor:
+        new_conv_cache = {}
+        conv_cache = conv_cache or {}
+
+        if f.shape[2] > 1 and f.shape[2] % 2 == 1:
+            f_first, f_rest = f[:, :, :1], f[:, :, 1:]
+            f_first_size, f_rest_size = f_first.shape[-3:], f_rest.shape[-3:]
+            z_first, z_rest = zq[:, :, :1], zq[:, :, 1:]
+            z_first = F.interpolate(z_first, size=f_first_size)
+            z_rest = F.interpolate(z_rest, size=f_rest_size)
+            zq = torch.cat([z_first, z_rest], dim=2)
+        else:
+            zq = F.interpolate(zq, size=f.shape[-3:])
+
+        conv_y, new_conv_cache["conv_y"] = self.conv_y(zq, conv_cache=conv_cache.get("conv_y"))
+        conv_b, new_conv_cache["conv_b"] = self.conv_b(zq, conv_cache=conv_cache.get("conv_b"))
+
+        norm_f = self.norm_layer(f)
+        new_f = norm_f * conv_y + conv_b
+        return new_f, new_conv_cache
+
+
+class CogVideoXUpsample3D(nn.Module):
+    r"""
+    A 3D Upsample layer using in CogVideoX by Tsinghua University & ZhipuAI # Todo: Wait for paper relase.
+
+    Args:
+        in_channels (`int`):
+            Number of channels in the input image.
+        out_channels (`int`):
+            Number of channels produced by the convolution.
+        kernel_size (`int`, defaults to `3`):
+            Size of the convolving kernel.
+        stride (`int`, defaults to `1`):
+            Stride of the convolution.
+        padding (`int`, defaults to `1`):
+            Padding added to all four sides of the input.
+        compress_time (`bool`, defaults to `False`):
+            Whether or not to compress the time dimension.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int = 3,
+        stride: int = 1,
+        padding: int = 1,
+        compress_time: bool = False,
+    ) -> None:
+        super().__init__()
+
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
+        self.compress_time = compress_time
+        
+        self.auto_split_process = True
+        self.first_frame_flag = False
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        if self.compress_time:
+            if self.auto_split_process:
+                if inputs.shape[2] > 1 and inputs.shape[2] % 2 == 1:
+                    # split first frame
+                    x_first, x_rest = inputs[:, :, 0], inputs[:, :, 1:]
+
+                    x_first = F.interpolate(x_first, scale_factor=2.0)
+                    x_rest = F.interpolate(x_rest, scale_factor=2.0)
+                    x_first = x_first[:, :, None, :, :]
+                    inputs = torch.cat([x_first, x_rest], dim=2)
+                elif inputs.shape[2] > 1:
+                    inputs = F.interpolate(inputs, scale_factor=2.0)
+                else:
+                    inputs = inputs.squeeze(2)
+                    inputs = F.interpolate(inputs, scale_factor=2.0)
+                    inputs = inputs[:, :, None, :, :]
+            else:
+                if self.first_frame_flag:
+                    inputs = inputs.squeeze(2)
+                    inputs = F.interpolate(inputs, scale_factor=2.0)
+                    inputs = inputs[:, :, None, :, :]
+                else:
+                    inputs = F.interpolate(inputs, scale_factor=2.0)
+        else:
+            # only interpolate 2D
+            b, c, t, h, w = inputs.shape
+            inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
+            inputs = F.interpolate(inputs, scale_factor=2.0)
+            inputs = inputs.reshape(b, t, c, *inputs.shape[2:]).permute(0, 2, 1, 3, 4)
+
+        b, c, t, h, w = inputs.shape
+        inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
+        inputs = self.conv(inputs)
+        inputs = inputs.reshape(b, t, *inputs.shape[1:]).permute(0, 2, 1, 3, 4)
+
+        return inputs
+
+
+class CogVideoXResnetBlock3D(nn.Module):
+    r"""
+    A 3D ResNet block used in the CogVideoX model.
+
+    Args:
+        in_channels (`int`):
+            Number of input channels.
+        out_channels (`int`, *optional*):
+            Number of output channels. If None, defaults to `in_channels`.
+        dropout (`float`, defaults to `0.0`):
+            Dropout rate.
+        temb_channels (`int`, defaults to `512`):
+            Number of time embedding channels.
+        groups (`int`, defaults to `32`):
+            Number of groups to separate the channels into for group normalization.
+        eps (`float`, defaults to `1e-6`):
+            Epsilon value for normalization layers.
+        non_linearity (`str`, defaults to `"swish"`):
+            Activation function to use.
+        conv_shortcut (bool, defaults to `False`):
+            Whether or not to use a convolution shortcut.
+        spatial_norm_dim (`int`, *optional*):
+            The dimension to use for spatial norm if it is to be used instead of group norm.
+        pad_mode (str, defaults to `"first"`):
+            Padding mode.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: Optional[int] = None,
+        dropout: float = 0.0,
+        temb_channels: int = 512,
+        groups: int = 32,
+        eps: float = 1e-6,
+        non_linearity: str = "swish",
+        conv_shortcut: bool = False,
+        spatial_norm_dim: Optional[int] = None,
+        pad_mode: str = "first",
+    ):
+        super().__init__()
+
+        out_channels = out_channels or in_channels
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.nonlinearity = get_activation(non_linearity)
+        self.use_conv_shortcut = conv_shortcut
+        self.spatial_norm_dim = spatial_norm_dim
+
+        if spatial_norm_dim is None:
+            self.norm1 = nn.GroupNorm(num_channels=in_channels, num_groups=groups, eps=eps)
+            self.norm2 = nn.GroupNorm(num_channels=out_channels, num_groups=groups, eps=eps)
+        else:
+            self.norm1 = CogVideoXSpatialNorm3D(
+                f_channels=in_channels,
+                zq_channels=spatial_norm_dim,
+                groups=groups,
+            )
+            self.norm2 = CogVideoXSpatialNorm3D(
+                f_channels=out_channels,
+                zq_channels=spatial_norm_dim,
+                groups=groups,
+            )
+
+        self.conv1 = CogVideoXCausalConv3d(
+            in_channels=in_channels, out_channels=out_channels, kernel_size=3, pad_mode=pad_mode
+        )
+
+        if temb_channels > 0:
+            self.temb_proj = nn.Linear(in_features=temb_channels, out_features=out_channels)
+
+        self.dropout = nn.Dropout(dropout)
+        self.conv2 = CogVideoXCausalConv3d(
+            in_channels=out_channels, out_channels=out_channels, kernel_size=3, pad_mode=pad_mode
+        )
+
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                self.conv_shortcut = CogVideoXCausalConv3d(
+                    in_channels=in_channels, out_channels=out_channels, kernel_size=3, pad_mode=pad_mode
+                )
+            else:
+                self.conv_shortcut = CogVideoXSafeConv3d(
+                    in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0
+                )
+
+    def forward(
+        self,
+        inputs: torch.Tensor,
+        temb: Optional[torch.Tensor] = None,
+        zq: Optional[torch.Tensor] = None,
+        conv_cache: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        new_conv_cache = {}
+        conv_cache = conv_cache or {}
+
+        hidden_states = inputs
+
+        if zq is not None:
+            hidden_states, new_conv_cache["norm1"] = self.norm1(hidden_states, zq, conv_cache=conv_cache.get("norm1"))
+        else:
+            hidden_states = self.norm1(hidden_states)
+
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states, new_conv_cache["conv1"] = self.conv1(hidden_states, conv_cache=conv_cache.get("conv1"))
+
+        if temb is not None:
+            hidden_states = hidden_states + self.temb_proj(self.nonlinearity(temb))[:, :, None, None, None]
+
+        if zq is not None:
+            hidden_states, new_conv_cache["norm2"] = self.norm2(hidden_states, zq, conv_cache=conv_cache.get("norm2"))
+        else:
+            hidden_states = self.norm2(hidden_states)
+
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states, new_conv_cache["conv2"] = self.conv2(hidden_states, conv_cache=conv_cache.get("conv2"))
+
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                inputs, new_conv_cache["conv_shortcut"] = self.conv_shortcut(
+                    inputs, conv_cache=conv_cache.get("conv_shortcut")
+                )
+            else:
+                inputs = self.conv_shortcut(inputs)
+
+        hidden_states = hidden_states + inputs
+        return hidden_states, new_conv_cache
+
+
+class CogVideoXDownBlock3D(nn.Module):
+    r"""
+    A downsampling block used in the CogVideoX model.
+
+    Args:
+        in_channels (`int`):
+            Number of input channels.
+        out_channels (`int`, *optional*):
+            Number of output channels. If None, defaults to `in_channels`.
+        temb_channels (`int`, defaults to `512`):
+            Number of time embedding channels.
+        num_layers (`int`, defaults to `1`):
+            Number of resnet layers.
+        dropout (`float`, defaults to `0.0`):
+            Dropout rate.
+        resnet_eps (`float`, defaults to `1e-6`):
+            Epsilon value for normalization layers.
+        resnet_act_fn (`str`, defaults to `"swish"`):
+            Activation function to use.
+        resnet_groups (`int`, defaults to `32`):
+            Number of groups to separate the channels into for group normalization.
+        add_downsample (`bool`, defaults to `True`):
+            Whether or not to use a downsampling layer. If not used, output dimension would be same as input dimension.
+        compress_time (`bool`, defaults to `False`):
+            Whether or not to downsample across temporal dimension.
+        pad_mode (str, defaults to `"first"`):
+            Padding mode.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        add_downsample: bool = True,
+        downsample_padding: int = 0,
+        compress_time: bool = False,
+        pad_mode: str = "first",
+    ):
+        super().__init__()
+
+        resnets = []
+        for i in range(num_layers):
+            in_channel = in_channels if i == 0 else out_channels
+            resnets.append(
+                CogVideoXResnetBlock3D(
+                    in_channels=in_channel,
+                    out_channels=out_channels,
+                    dropout=dropout,
+                    temb_channels=temb_channels,
+                    groups=resnet_groups,
+                    eps=resnet_eps,
+                    non_linearity=resnet_act_fn,
+                    pad_mode=pad_mode,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+        self.downsamplers = None
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [
+                    CogVideoXDownsample3D(
+                        out_channels, out_channels, padding=downsample_padding, compress_time=compress_time
+                    )
+                ]
+            )
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: Optional[torch.Tensor] = None,
+        zq: Optional[torch.Tensor] = None,
+        conv_cache: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        r"""Forward method of the `CogVideoXDownBlock3D` class."""
+
+        new_conv_cache = {}
+        conv_cache = conv_cache or {}
+
+        for i, resnet in enumerate(self.resnets):
+            conv_cache_key = f"resnet_{i}"
+
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def create_forward(*inputs):
+                        return module(*inputs)
+
+                    return create_forward
+
+                hidden_states, new_conv_cache[conv_cache_key] = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    zq,
+                    conv_cache.get(conv_cache_key),
+                )
+            else:
+                hidden_states, new_conv_cache[conv_cache_key] = resnet(
+                    hidden_states, temb, zq, conv_cache=conv_cache.get(conv_cache_key)
+                )
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+        return hidden_states, new_conv_cache
+
+
+class CogVideoXMidBlock3D(nn.Module):
+    r"""
+    A middle block used in the CogVideoX model.
+
+    Args:
+        in_channels (`int`):
+            Number of input channels.
+        temb_channels (`int`, defaults to `512`):
+            Number of time embedding channels.
+        dropout (`float`, defaults to `0.0`):
+            Dropout rate.
+        num_layers (`int`, defaults to `1`):
+            Number of resnet layers.
+        resnet_eps (`float`, defaults to `1e-6`):
+            Epsilon value for normalization layers.
+        resnet_act_fn (`str`, defaults to `"swish"`):
+            Activation function to use.
+        resnet_groups (`int`, defaults to `32`):
+            Number of groups to separate the channels into for group normalization.
+        spatial_norm_dim (`int`, *optional*):
+            The dimension to use for spatial norm if it is to be used instead of group norm.
+        pad_mode (str, defaults to `"first"`):
+            Padding mode.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    def __init__(
+        self,
+        in_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        spatial_norm_dim: Optional[int] = None,
+        pad_mode: str = "first",
+    ):
+        super().__init__()
+
+        resnets = []
+        for _ in range(num_layers):
+            resnets.append(
+                CogVideoXResnetBlock3D(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    dropout=dropout,
+                    temb_channels=temb_channels,
+                    groups=resnet_groups,
+                    eps=resnet_eps,
+                    spatial_norm_dim=spatial_norm_dim,
+                    non_linearity=resnet_act_fn,
+                    pad_mode=pad_mode,
+                )
+            )
+        self.resnets = nn.ModuleList(resnets)
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: Optional[torch.Tensor] = None,
+        zq: Optional[torch.Tensor] = None,
+        conv_cache: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        r"""Forward method of the `CogVideoXMidBlock3D` class."""
+
+        new_conv_cache = {}
+        conv_cache = conv_cache or {}
+
+        for i, resnet in enumerate(self.resnets):
+            conv_cache_key = f"resnet_{i}"
+
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def create_forward(*inputs):
+                        return module(*inputs)
+
+                    return create_forward
+
+                hidden_states, new_conv_cache[conv_cache_key] = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet), hidden_states, temb, zq, conv_cache.get(conv_cache_key)
+                )
+            else:
+                hidden_states, new_conv_cache[conv_cache_key] = resnet(
+                    hidden_states, temb, zq, conv_cache=conv_cache.get(conv_cache_key)
+                )
+
+        return hidden_states, new_conv_cache
+
+
+class CogVideoXUpBlock3D(nn.Module):
+    r"""
+    An upsampling block used in the CogVideoX model.
+
+    Args:
+        in_channels (`int`):
+            Number of input channels.
+        out_channels (`int`, *optional*):
+            Number of output channels. If None, defaults to `in_channels`.
+        temb_channels (`int`, defaults to `512`):
+            Number of time embedding channels.
+        dropout (`float`, defaults to `0.0`):
+            Dropout rate.
+        num_layers (`int`, defaults to `1`):
+            Number of resnet layers.
+        resnet_eps (`float`, defaults to `1e-6`):
+            Epsilon value for normalization layers.
+        resnet_act_fn (`str`, defaults to `"swish"`):
+            Activation function to use.
+        resnet_groups (`int`, defaults to `32`):
+            Number of groups to separate the channels into for group normalization.
+        spatial_norm_dim (`int`, defaults to `16`):
+            The dimension to use for spatial norm if it is to be used instead of group norm.
+        add_upsample (`bool`, defaults to `True`):
+            Whether or not to use a upsampling layer. If not used, output dimension would be same as input dimension.
+        compress_time (`bool`, defaults to `False`):
+            Whether or not to downsample across temporal dimension.
+        pad_mode (str, defaults to `"first"`):
+            Padding mode.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        spatial_norm_dim: int = 16,
+        add_upsample: bool = True,
+        upsample_padding: int = 1,
+        compress_time: bool = False,
+        pad_mode: str = "first",
+    ):
+        super().__init__()
+
+        resnets = []
+        for i in range(num_layers):
+            in_channel = in_channels if i == 0 else out_channels
+            resnets.append(
+                CogVideoXResnetBlock3D(
+                    in_channels=in_channel,
+                    out_channels=out_channels,
+                    dropout=dropout,
+                    temb_channels=temb_channels,
+                    groups=resnet_groups,
+                    eps=resnet_eps,
+                    non_linearity=resnet_act_fn,
+                    spatial_norm_dim=spatial_norm_dim,
+                    pad_mode=pad_mode,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+        self.upsamplers = None
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList(
+                [
+                    CogVideoXUpsample3D(
+                        out_channels, out_channels, padding=upsample_padding, compress_time=compress_time
+                    )
+                ]
+            )
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: Optional[torch.Tensor] = None,
+        zq: Optional[torch.Tensor] = None,
+        conv_cache: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        r"""Forward method of the `CogVideoXUpBlock3D` class."""
+
+        new_conv_cache = {}
+        conv_cache = conv_cache or {}
+
+        for i, resnet in enumerate(self.resnets):
+            conv_cache_key = f"resnet_{i}"
+
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def create_forward(*inputs):
+                        return module(*inputs)
+
+                    return create_forward
+
+                hidden_states, new_conv_cache[conv_cache_key] = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    zq,
+                    conv_cache.get(conv_cache_key),
+                )
+            else:
+                hidden_states, new_conv_cache[conv_cache_key] = resnet(
+                    hidden_states, temb, zq, conv_cache=conv_cache.get(conv_cache_key)
+                )
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states)
+
+        return hidden_states, new_conv_cache
+
+
+class CogVideoXEncoder3D(nn.Module):
+    r"""
+    The `CogVideoXEncoder3D` layer of a variational autoencoder that encodes its input into a latent representation.
+
+    Args:
+        in_channels (`int`, *optional*, defaults to 3):
+            The number of input channels.
+        out_channels (`int`, *optional*, defaults to 3):
+            The number of output channels.
+        down_block_types (`Tuple[str, ...]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
+            The types of down blocks to use. See `~diffusers.models.unet_2d_blocks.get_down_block` for available
+            options.
+        block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):
+            The number of output channels for each block.
+        act_fn (`str`, *optional*, defaults to `"silu"`):
+            The activation function to use. See `~diffusers.models.activations.get_activation` for available options.
+        layers_per_block (`int`, *optional*, defaults to 2):
+            The number of layers per block.
+        norm_num_groups (`int`, *optional*, defaults to 32):
+            The number of groups for normalization.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 16,
+        down_block_types: Tuple[str, ...] = (
+            "CogVideoXDownBlock3D",
+            "CogVideoXDownBlock3D",
+            "CogVideoXDownBlock3D",
+            "CogVideoXDownBlock3D",
+        ),
+        block_out_channels: Tuple[int, ...] = (128, 256, 256, 512),
+        layers_per_block: int = 3,
+        act_fn: str = "silu",
+        norm_eps: float = 1e-6,
+        norm_num_groups: int = 32,
+        dropout: float = 0.0,
+        pad_mode: str = "first",
+        temporal_compression_ratio: float = 4,
+    ):
+        super().__init__()
+
+        # log2 of temporal_compress_times
+        temporal_compress_level = int(np.log2(temporal_compression_ratio))
+
+        self.conv_in = CogVideoXCausalConv3d(in_channels, block_out_channels[0], kernel_size=3, pad_mode=pad_mode)
+        self.down_blocks = nn.ModuleList([])
+
+        # down blocks
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+            compress_time = i < temporal_compress_level
+
+            if down_block_type == "CogVideoXDownBlock3D":
+                down_block = CogVideoXDownBlock3D(
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    temb_channels=0,
+                    dropout=dropout,
+                    num_layers=layers_per_block,
+                    resnet_eps=norm_eps,
+                    resnet_act_fn=act_fn,
+                    resnet_groups=norm_num_groups,
+                    add_downsample=not is_final_block,
+                    compress_time=compress_time,
+                )
+            else:
+                raise ValueError("Invalid `down_block_type` encountered. Must be `CogVideoXDownBlock3D`")
+
+            self.down_blocks.append(down_block)
+
+        # mid block
+        self.mid_block = CogVideoXMidBlock3D(
+            in_channels=block_out_channels[-1],
+            temb_channels=0,
+            dropout=dropout,
+            num_layers=2,
+            resnet_eps=norm_eps,
+            resnet_act_fn=act_fn,
+            resnet_groups=norm_num_groups,
+            pad_mode=pad_mode,
+        )
+
+        self.norm_out = nn.GroupNorm(norm_num_groups, block_out_channels[-1], eps=1e-6)
+        self.conv_act = nn.SiLU()
+        self.conv_out = CogVideoXCausalConv3d(
+            block_out_channels[-1], 2 * out_channels, kernel_size=3, pad_mode=pad_mode
+        )
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        sample: torch.Tensor,
+        temb: Optional[torch.Tensor] = None,
+        conv_cache: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        r"""The forward method of the `CogVideoXEncoder3D` class."""
+
+        new_conv_cache = {}
+        conv_cache = conv_cache or {}
+
+        hidden_states, new_conv_cache["conv_in"] = self.conv_in(sample, conv_cache=conv_cache.get("conv_in"))
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+            def create_custom_forward(module):
+                def custom_forward(*inputs):
+                    return module(*inputs)
+
+                return custom_forward
+
+            # 1. Down
+            for i, down_block in enumerate(self.down_blocks):
+                conv_cache_key = f"down_block_{i}"
+                hidden_states, new_conv_cache[conv_cache_key] = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(down_block),
+                    hidden_states,
+                    temb,
+                    None,
+                    conv_cache.get(conv_cache_key),
+                )
+
+            # 2. Mid
+            hidden_states, new_conv_cache["mid_block"] = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(self.mid_block),
+                hidden_states,
+                temb,
+                None,
+                conv_cache.get("mid_block"),
+            )
+        else:
+            # 1. Down
+            for i, down_block in enumerate(self.down_blocks):
+                conv_cache_key = f"down_block_{i}"
+                hidden_states, new_conv_cache[conv_cache_key] = down_block(
+                    hidden_states, temb, None, conv_cache=conv_cache.get(conv_cache_key)
+                )
+
+            # 2. Mid
+            hidden_states, new_conv_cache["mid_block"] = self.mid_block(
+                hidden_states, temb, None, conv_cache=conv_cache.get("mid_block")
+            )
+
+        # 3. Post-process
+        hidden_states = self.norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+
+        hidden_states, new_conv_cache["conv_out"] = self.conv_out(hidden_states, conv_cache=conv_cache.get("conv_out"))
+
+        return hidden_states, new_conv_cache
+
+
+class CogVideoXDecoder3D(nn.Module):
+    r"""
+    The `CogVideoXDecoder3D` layer of a variational autoencoder that decodes its latent representation into an output
+    sample.
+
+    Args:
+        in_channels (`int`, *optional*, defaults to 3):
+            The number of input channels.
+        out_channels (`int`, *optional*, defaults to 3):
+            The number of output channels.
+        up_block_types (`Tuple[str, ...]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
+            The types of up blocks to use. See `~diffusers.models.unet_2d_blocks.get_up_block` for available options.
+        block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):
+            The number of output channels for each block.
+        act_fn (`str`, *optional*, defaults to `"silu"`):
+            The activation function to use. See `~diffusers.models.activations.get_activation` for available options.
+        layers_per_block (`int`, *optional*, defaults to 2):
+            The number of layers per block.
+        norm_num_groups (`int`, *optional*, defaults to 32):
+            The number of groups for normalization.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    def __init__(
+        self,
+        in_channels: int = 16,
+        out_channels: int = 3,
+        up_block_types: Tuple[str, ...] = (
+            "CogVideoXUpBlock3D",
+            "CogVideoXUpBlock3D",
+            "CogVideoXUpBlock3D",
+            "CogVideoXUpBlock3D",
+        ),
+        block_out_channels: Tuple[int, ...] = (128, 256, 256, 512),
+        layers_per_block: int = 3,
+        act_fn: str = "silu",
+        norm_eps: float = 1e-6,
+        norm_num_groups: int = 32,
+        dropout: float = 0.0,
+        pad_mode: str = "first",
+        temporal_compression_ratio: float = 4,
+    ):
+        super().__init__()
+
+        reversed_block_out_channels = list(reversed(block_out_channels))
+
+        self.conv_in = CogVideoXCausalConv3d(
+            in_channels, reversed_block_out_channels[0], kernel_size=3, pad_mode=pad_mode
+        )
+
+        # mid block
+        self.mid_block = CogVideoXMidBlock3D(
+            in_channels=reversed_block_out_channels[0],
+            temb_channels=0,
+            num_layers=2,
+            resnet_eps=norm_eps,
+            resnet_act_fn=act_fn,
+            resnet_groups=norm_num_groups,
+            spatial_norm_dim=in_channels,
+            pad_mode=pad_mode,
+        )
+
+        # up blocks
+        self.up_blocks = nn.ModuleList([])
+
+        output_channel = reversed_block_out_channels[0]
+        temporal_compress_level = int(np.log2(temporal_compression_ratio))
+
+        for i, up_block_type in enumerate(up_block_types):
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+            compress_time = i < temporal_compress_level
+
+            if up_block_type == "CogVideoXUpBlock3D":
+                up_block = CogVideoXUpBlock3D(
+                    in_channels=prev_output_channel,
+                    out_channels=output_channel,
+                    temb_channels=0,
+                    dropout=dropout,
+                    num_layers=layers_per_block + 1,
+                    resnet_eps=norm_eps,
+                    resnet_act_fn=act_fn,
+                    resnet_groups=norm_num_groups,
+                    spatial_norm_dim=in_channels,
+                    add_upsample=not is_final_block,
+                    compress_time=compress_time,
+                    pad_mode=pad_mode,
+                )
+                prev_output_channel = output_channel
+            else:
+                raise ValueError("Invalid `up_block_type` encountered. Must be `CogVideoXUpBlock3D`")
+
+            self.up_blocks.append(up_block)
+
+        self.norm_out = CogVideoXSpatialNorm3D(reversed_block_out_channels[-1], in_channels, groups=norm_num_groups)
+        self.conv_act = nn.SiLU()
+        self.conv_out = CogVideoXCausalConv3d(
+            reversed_block_out_channels[-1], out_channels, kernel_size=3, pad_mode=pad_mode
+        )
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        sample: torch.Tensor,
+        temb: Optional[torch.Tensor] = None,
+        conv_cache: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        r"""The forward method of the `CogVideoXDecoder3D` class."""
+
+        new_conv_cache = {}
+        conv_cache = conv_cache or {}
+
+        hidden_states, new_conv_cache["conv_in"] = self.conv_in(sample, conv_cache=conv_cache.get("conv_in"))
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+            def create_custom_forward(module):
+                def custom_forward(*inputs):
+                    return module(*inputs)
+
+                return custom_forward
+
+            # 1. Mid
+            hidden_states, new_conv_cache["mid_block"] = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(self.mid_block),
+                hidden_states,
+                temb,
+                sample,
+                conv_cache.get("mid_block"),
+            )
+
+            # 2. Up
+            for i, up_block in enumerate(self.up_blocks):
+                conv_cache_key = f"up_block_{i}"
+                hidden_states, new_conv_cache[conv_cache_key] = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(up_block),
+                    hidden_states,
+                    temb,
+                    sample,
+                    conv_cache.get(conv_cache_key),
+                )
+        else:
+            # 1. Mid
+            hidden_states, new_conv_cache["mid_block"] = self.mid_block(
+                hidden_states, temb, sample, conv_cache=conv_cache.get("mid_block")
+            )
+
+            # 2. Up
+            for i, up_block in enumerate(self.up_blocks):
+                conv_cache_key = f"up_block_{i}"
+                hidden_states, new_conv_cache[conv_cache_key] = up_block(
+                    hidden_states, temb, sample, conv_cache=conv_cache.get(conv_cache_key)
+                )
+
+        # 3. Post-process
+        hidden_states, new_conv_cache["norm_out"] = self.norm_out(
+            hidden_states, sample, conv_cache=conv_cache.get("norm_out")
+        )
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states, new_conv_cache["conv_out"] = self.conv_out(hidden_states, conv_cache=conv_cache.get("conv_out"))
+
+        return hidden_states, new_conv_cache
+
+
+class AutoencoderKLCogVideoX(ModelMixin, ConfigMixin, FromOriginalModelMixin):
+    r"""
+    A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
+    [CogVideoX](https://github.com/THUDM/CogVideo).
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+    for all models (such as downloading or saving).
+
+    Parameters:
+        in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
+        out_channels (int,  *optional*, defaults to 3): Number of channels in the output.
+        down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
+            Tuple of downsample block types.
+        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
+            Tuple of upsample block types.
+        block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
+            Tuple of block output channels.
+        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
+        sample_size (`int`, *optional*, defaults to `32`): Sample input size.
+        scaling_factor (`float`, *optional*, defaults to `1.15258426`):
+            The component-wise standard deviation of the trained latent space computed using the first batch of the
+            training set. This is used to scale the latent space to have unit variance when training the diffusion
+            model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
+            diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
+            / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
+            Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
+        force_upcast (`bool`, *optional*, default to `True`):
+            If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
+            can be fine-tuned / trained to a lower range without loosing too much precision in which case
+            `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
+    """
+
+    _supports_gradient_checkpointing = True
+    _no_split_modules = ["CogVideoXResnetBlock3D"]
+
+    @register_to_config
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        down_block_types: Tuple[str] = (
+            "CogVideoXDownBlock3D",
+            "CogVideoXDownBlock3D",
+            "CogVideoXDownBlock3D",
+            "CogVideoXDownBlock3D",
+        ),
+        up_block_types: Tuple[str] = (
+            "CogVideoXUpBlock3D",
+            "CogVideoXUpBlock3D",
+            "CogVideoXUpBlock3D",
+            "CogVideoXUpBlock3D",
+        ),
+        block_out_channels: Tuple[int] = (128, 256, 256, 512),
+        latent_channels: int = 16,
+        layers_per_block: int = 3,
+        act_fn: str = "silu",
+        norm_eps: float = 1e-6,
+        norm_num_groups: int = 32,
+        temporal_compression_ratio: float = 4,
+        sample_height: int = 480,
+        sample_width: int = 720,
+        scaling_factor: float = 1.15258426,
+        shift_factor: Optional[float] = None,
+        latents_mean: Optional[Tuple[float]] = None,
+        latents_std: Optional[Tuple[float]] = None,
+        force_upcast: float = True,
+        use_quant_conv: bool = False,
+        use_post_quant_conv: bool = False,
+        invert_scale_latents: bool = False,
+    ):
+        super().__init__()
+
+        self.encoder = CogVideoXEncoder3D(
+            in_channels=in_channels,
+            out_channels=latent_channels,
+            down_block_types=down_block_types,
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+            act_fn=act_fn,
+            norm_eps=norm_eps,
+            norm_num_groups=norm_num_groups,
+            temporal_compression_ratio=temporal_compression_ratio,
+        )
+        self.decoder = CogVideoXDecoder3D(
+            in_channels=latent_channels,
+            out_channels=out_channels,
+            up_block_types=up_block_types,
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+            act_fn=act_fn,
+            norm_eps=norm_eps,
+            norm_num_groups=norm_num_groups,
+            temporal_compression_ratio=temporal_compression_ratio,
+        )
+        self.quant_conv = CogVideoXSafeConv3d(2 * out_channels, 2 * out_channels, 1) if use_quant_conv else None
+        self.post_quant_conv = CogVideoXSafeConv3d(out_channels, out_channels, 1) if use_post_quant_conv else None
+
+        self.use_slicing = False
+        self.use_tiling = False
+        self.auto_split_process = False
+
+        # Can be increased to decode more latent frames at once, but comes at a reasonable memory cost and it is not
+        # recommended because the temporal parts of the VAE, here, are tricky to understand.
+        # If you decode X latent frames together, the number of output frames is:
+        #     (X + (2 conv cache) + (2 time upscale_1) + (4 time upscale_2) - (2 causal conv downscale)) => X + 6 frames
+        #
+        # Example with num_latent_frames_batch_size = 2:
+        #     - 12 latent frames: (0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (10, 11) are processed together
+        #         => (12 // 2 frame slices) * ((2 num_latent_frames_batch_size) + (2 conv cache) + (2 time upscale_1) + (4 time upscale_2) - (2 causal conv downscale))
+        #         => 6 * 8 = 48 frames
+        #     - 13 latent frames: (0, 1, 2) (special case), (3, 4), (5, 6), (7, 8), (9, 10), (11, 12) are processed together
+        #         => (1 frame slice) * ((3 num_latent_frames_batch_size) + (2 conv cache) + (2 time upscale_1) + (4 time upscale_2) - (2 causal conv downscale)) +
+        #            ((13 - 3) // 2) * ((2 num_latent_frames_batch_size) + (2 conv cache) + (2 time upscale_1) + (4 time upscale_2) - (2 causal conv downscale))
+        #         => 1 * 9 + 5 * 8 = 49 frames
+        # It has been implemented this way so as to not have "magic values" in the code base that would be hard to explain. Note that
+        # setting it to anything other than 2 would give poor results because the VAE hasn't been trained to be adaptive with different
+        # number of temporal frames.
+        self.num_latent_frames_batch_size = 2
+        self.num_sample_frames_batch_size = 8
+
+        # We make the minimum height and width of sample for tiling half that of the generally supported
+        self.tile_sample_min_height = sample_height // 2
+        self.tile_sample_min_width = sample_width // 2
+        self.tile_latent_min_height = int(
+            self.tile_sample_min_height / (2 ** (len(self.config.block_out_channels) - 1))
+        )
+        self.tile_latent_min_width = int(self.tile_sample_min_width / (2 ** (len(self.config.block_out_channels) - 1)))
+
+        # These are experimental overlap factors that were chosen based on experimentation and seem to work best for
+        # 720x480 (WxH) resolution. The above resolution is the strongly recommended generation resolution in CogVideoX
+        # and so the tiling implementation has only been tested on those specific resolutions.
+        self.tile_overlap_factor_height = 1 / 6
+        self.tile_overlap_factor_width = 1 / 5
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, (CogVideoXEncoder3D, CogVideoXDecoder3D)):
+            module.gradient_checkpointing = value
+
+    def enable_tiling(
+        self,
+        tile_sample_min_height: Optional[int] = None,
+        tile_sample_min_width: Optional[int] = None,
+        tile_overlap_factor_height: Optional[float] = None,
+        tile_overlap_factor_width: Optional[float] = None,
+    ) -> None:
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+
+        Args:
+            tile_sample_min_height (`int`, *optional*):
+                The minimum height required for a sample to be separated into tiles across the height dimension.
+            tile_sample_min_width (`int`, *optional*):
+                The minimum width required for a sample to be separated into tiles across the width dimension.
+            tile_overlap_factor_height (`int`, *optional*):
+                The minimum amount of overlap between two consecutive vertical tiles. This is to ensure that there are
+                no tiling artifacts produced across the height dimension. Must be between 0 and 1. Setting a higher
+                value might cause more tiles to be processed leading to slow down of the decoding process.
+            tile_overlap_factor_width (`int`, *optional*):
+                The minimum amount of overlap between two consecutive horizontal tiles. This is to ensure that there
+                are no tiling artifacts produced across the width dimension. Must be between 0 and 1. Setting a higher
+                value might cause more tiles to be processed leading to slow down of the decoding process.
+        """
+        self.use_tiling = True
+        self.tile_sample_min_height = tile_sample_min_height or self.tile_sample_min_height
+        self.tile_sample_min_width = tile_sample_min_width or self.tile_sample_min_width
+        self.tile_latent_min_height = int(
+            self.tile_sample_min_height / (2 ** (len(self.config.block_out_channels) - 1))
+        )
+        self.tile_latent_min_width = int(self.tile_sample_min_width / (2 ** (len(self.config.block_out_channels) - 1)))
+        self.tile_overlap_factor_height = tile_overlap_factor_height or self.tile_overlap_factor_height
+        self.tile_overlap_factor_width = tile_overlap_factor_width or self.tile_overlap_factor_width
+
+    def disable_tiling(self) -> None:
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self) -> None:
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+            
+    def _set_first_frame(self):
+        for name, module in self.named_modules():
+            if isinstance(module, CogVideoXUpsample3D):
+                module.auto_split_process = False
+                module.first_frame_flag = True
+
+    def _set_rest_frame(self):
+        for name, module in self.named_modules():
+            if isinstance(module, CogVideoXUpsample3D):
+                module.auto_split_process = False
+                module.first_frame_flag = False
+
+    def enable_auto_split_process(self) -> None:
+        self.auto_split_process = True
+        for name, module in self.named_modules():
+            if isinstance(module, CogVideoXUpsample3D):
+                module.auto_split_process = True
+
+    def disable_auto_split_process(self) -> None:
+        self.auto_split_process = False
+
+    def _encode(self, x: torch.Tensor) -> torch.Tensor:
+        batch_size, num_channels, num_frames, height, width = x.shape
+
+        if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
+            return self.tiled_encode(x)
+
+        frame_batch_size = self.num_sample_frames_batch_size
+        # Note: We expect the number of frames to be either `1` or `frame_batch_size * k` or `frame_batch_size * k + 1` for some k.
+        # As the extra single frame is handled inside the loop, it is not required to round up here.
+        num_batches = max(num_frames // frame_batch_size, 1)
+        conv_cache = None
+        enc = []
+
+        for i in range(num_batches):
+            remaining_frames = num_frames % frame_batch_size
+            start_frame = frame_batch_size * i + (0 if i == 0 else remaining_frames)
+            end_frame = frame_batch_size * (i + 1) + remaining_frames
+            x_intermediate = x[:, :, start_frame:end_frame]
+            x_intermediate, conv_cache = self.encoder(x_intermediate, conv_cache=conv_cache)
+            if self.quant_conv is not None:
+                x_intermediate = self.quant_conv(x_intermediate)
+            enc.append(x_intermediate)
+
+        enc = torch.cat(enc, dim=2)
+        return enc
+
+    @apply_forward_hook
+    def encode(
+        self, x: torch.Tensor, return_dict: bool = True
+    ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
+        """
+        Encode a batch of images into latents.
+
+        Args:
+            x (`torch.Tensor`): Input batch of images.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
+
+        Returns:
+                The latent representations of the encoded videos. If `return_dict` is True, a
+                [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
+        """
+        if self.use_slicing and x.shape[0] > 1:
+            encoded_slices = [self._encode(x_slice) for x_slice in x.split(1)]
+            h = torch.cat(encoded_slices)
+        else:
+            h = self._encode(x)
+
+        posterior = DiagonalGaussianDistribution(h)
+
+        if not return_dict:
+            return (posterior,)
+        return AutoencoderKLOutput(latent_dist=posterior)
+
+    def _decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
+        batch_size, num_channels, num_frames, height, width = z.shape
+
+        if self.use_tiling and (width > self.tile_latent_min_width or height > self.tile_latent_min_height):
+            return self.tiled_decode(z, return_dict=return_dict)
+
+        if self.auto_split_process:
+            frame_batch_size = self.num_latent_frames_batch_size
+            num_batches = max(num_frames // frame_batch_size, 1)
+            conv_cache = None
+            dec = []
+
+            for i in range(num_batches):
+                remaining_frames = num_frames % frame_batch_size
+                start_frame = frame_batch_size * i + (0 if i == 0 else remaining_frames)
+                end_frame = frame_batch_size * (i + 1) + remaining_frames
+                z_intermediate = z[:, :, start_frame:end_frame]
+                if self.post_quant_conv is not None:
+                    z_intermediate = self.post_quant_conv(z_intermediate)
+                z_intermediate, conv_cache = self.decoder(z_intermediate, conv_cache=conv_cache)
+                dec.append(z_intermediate)
+        else:
+            conv_cache = None
+            start_frame = 0
+            end_frame = 1
+            dec = []
+
+            self._set_first_frame()
+            z_intermediate = z[:, :, start_frame:end_frame]
+            if self.post_quant_conv is not None:
+                z_intermediate = self.post_quant_conv(z_intermediate)
+            z_intermediate, conv_cache = self.decoder(z_intermediate, conv_cache=conv_cache)
+            dec.append(z_intermediate)
+
+            self._set_rest_frame()
+            start_frame = end_frame
+            end_frame += self.num_latent_frames_batch_size
+
+            while start_frame < num_frames:
+                z_intermediate = z[:, :, start_frame:end_frame]
+                if self.post_quant_conv is not None:
+                    z_intermediate = self.post_quant_conv(z_intermediate)
+                z_intermediate, conv_cache = self.decoder(z_intermediate, conv_cache=conv_cache)
+                dec.append(z_intermediate)
+                start_frame = end_frame
+                end_frame += self.num_latent_frames_batch_size
+
+        dec = torch.cat(dec, dim=2)
+
+        if not return_dict:
+            return (dec,)
+
+        return DecoderOutput(sample=dec)
+
+    @apply_forward_hook
+    def decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
+        """
+        Decode a batch of images.
+
+        Args:
+            z (`torch.Tensor`): Input batch of latent vectors.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.vae.DecoderOutput`] or `tuple`:
+                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
+                returned.
+        """
+        if self.use_slicing and z.shape[0] > 1:
+            decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)]
+            decoded = torch.cat(decoded_slices)
+        else:
+            decoded = self._decode(z).sample
+
+        if not return_dict:
+            return (decoded,)
+        return DecoderOutput(sample=decoded)
+
+    def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        blend_extent = min(a.shape[3], b.shape[3], blend_extent)
+        for y in range(blend_extent):
+            b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, :, y, :] * (
+                y / blend_extent
+            )
+        return b
+
+    def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        blend_extent = min(a.shape[4], b.shape[4], blend_extent)
+        for x in range(blend_extent):
+            b[:, :, :, :, x] = a[:, :, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, :, x] * (
+                x / blend_extent
+            )
+        return b
+
+    def tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
+        r"""Encode a batch of images using a tiled encoder.
+
+        When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several
+        steps. This is useful to keep memory use constant regardless of image size. The end result of tiled encoding is
+        different from non-tiled encoding because each tile uses a different encoder. To avoid tiling artifacts, the
+        tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the
+        output, but they should be much less noticeable.
+
+        Args:
+            x (`torch.Tensor`): Input batch of videos.
+
+        Returns:
+            `torch.Tensor`:
+                The latent representation of the encoded videos.
+        """
+        # For a rough memory estimate, take a look at the `tiled_decode` method.
+        batch_size, num_channels, num_frames, height, width = x.shape
+
+        overlap_height = int(self.tile_sample_min_height * (1 - self.tile_overlap_factor_height))
+        overlap_width = int(self.tile_sample_min_width * (1 - self.tile_overlap_factor_width))
+        blend_extent_height = int(self.tile_latent_min_height * self.tile_overlap_factor_height)
+        blend_extent_width = int(self.tile_latent_min_width * self.tile_overlap_factor_width)
+        row_limit_height = self.tile_latent_min_height - blend_extent_height
+        row_limit_width = self.tile_latent_min_width - blend_extent_width
+        frame_batch_size = self.num_sample_frames_batch_size
+
+        # Split x into overlapping tiles and encode them separately.
+        # The tiles have an overlap to avoid seams between tiles.
+        rows = []
+        for i in range(0, height, overlap_height):
+            row = []
+            for j in range(0, width, overlap_width):
+                # Note: We expect the number of frames to be either `1` or `frame_batch_size * k` or `frame_batch_size * k + 1` for some k.
+                # As the extra single frame is handled inside the loop, it is not required to round up here.
+                num_batches = max(num_frames // frame_batch_size, 1)
+                conv_cache = None
+                time = []
+
+                for k in range(num_batches):
+                    remaining_frames = num_frames % frame_batch_size
+                    start_frame = frame_batch_size * k + (0 if k == 0 else remaining_frames)
+                    end_frame = frame_batch_size * (k + 1) + remaining_frames
+                    tile = x[
+                        :,
+                        :,
+                        start_frame:end_frame,
+                        i : i + self.tile_sample_min_height,
+                        j : j + self.tile_sample_min_width,
+                    ]
+                    tile, conv_cache = self.encoder(tile, conv_cache=conv_cache)
+                    if self.quant_conv is not None:
+                        tile = self.quant_conv(tile)
+                    time.append(tile)
+
+                row.append(torch.cat(time, dim=2))
+            rows.append(row)
+
+        result_rows = []
+        for i, row in enumerate(rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                # blend the above tile and the left tile
+                # to the current tile and add the current tile to the result row
+                if i > 0:
+                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent_height)
+                if j > 0:
+                    tile = self.blend_h(row[j - 1], tile, blend_extent_width)
+                result_row.append(tile[:, :, :, :row_limit_height, :row_limit_width])
+            result_rows.append(torch.cat(result_row, dim=4))
+
+        enc = torch.cat(result_rows, dim=3)
+        return enc
+
+    def tiled_decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
+        r"""
+        Decode a batch of images using a tiled decoder.
+
+        Args:
+            z (`torch.Tensor`): Input batch of latent vectors.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.vae.DecoderOutput`] or `tuple`:
+                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
+                returned.
+        """
+        # Rough memory assessment:
+        #   - In CogVideoX-2B, there are a total of 24 CausalConv3d layers.
+        #   - The biggest intermediate dimensions are: [1, 128, 9, 480, 720].
+        #   - Assume fp16 (2 bytes per value).
+        # Memory required: 1 * 128 * 9 * 480 * 720 * 24 * 2 / 1024**3 = 17.8 GB
+        #
+        # Memory assessment when using tiling:
+        #   - Assume everything as above but now HxW is 240x360 by tiling in half
+        # Memory required: 1 * 128 * 9 * 240 * 360 * 24 * 2 / 1024**3 = 4.5 GB
+
+        batch_size, num_channels, num_frames, height, width = z.shape
+
+        overlap_height = int(self.tile_latent_min_height * (1 - self.tile_overlap_factor_height))
+        overlap_width = int(self.tile_latent_min_width * (1 - self.tile_overlap_factor_width))
+        blend_extent_height = int(self.tile_sample_min_height * self.tile_overlap_factor_height)
+        blend_extent_width = int(self.tile_sample_min_width * self.tile_overlap_factor_width)
+        row_limit_height = self.tile_sample_min_height - blend_extent_height
+        row_limit_width = self.tile_sample_min_width - blend_extent_width
+        frame_batch_size = self.num_latent_frames_batch_size
+
+        # Split z into overlapping tiles and decode them separately.
+        # The tiles have an overlap to avoid seams between tiles.
+        rows = []
+        for i in range(0, height, overlap_height):
+            row = []
+            for j in range(0, width, overlap_width):
+                if self.auto_split_process:
+                    num_batches = max(num_frames // frame_batch_size, 1)
+                    conv_cache = None
+                    time = []
+
+                    for k in range(num_batches):
+                        remaining_frames = num_frames % frame_batch_size
+                        start_frame = frame_batch_size * k + (0 if k == 0 else remaining_frames)
+                        end_frame = frame_batch_size * (k + 1) + remaining_frames
+                        tile = z[
+                            :,
+                            :,
+                            start_frame:end_frame,
+                            i : i + self.tile_latent_min_height,
+                            j : j + self.tile_latent_min_width,
+                        ]
+                        if self.post_quant_conv is not None:
+                            tile = self.post_quant_conv(tile)
+                        tile, conv_cache = self.decoder(tile, conv_cache=conv_cache)
+                        time.append(tile)
+
+                    row.append(torch.cat(time, dim=2))
+                else:
+                    conv_cache = None
+                    start_frame = 0
+                    end_frame = 1
+                    dec = []
+
+                    tile = z[
+                        :,
+                        :,
+                        start_frame:end_frame,
+                        i : i + self.tile_latent_min_height,
+                        j : j + self.tile_latent_min_width,
+                    ]
+
+                    self._set_first_frame()
+                    if self.post_quant_conv is not None:
+                        tile = self.post_quant_conv(tile)
+                    tile, conv_cache = self.decoder(tile, conv_cache=conv_cache)
+                    dec.append(tile)
+                    
+                    self._set_rest_frame()
+                    start_frame = end_frame
+                    end_frame += self.num_latent_frames_batch_size
+
+                    while start_frame < num_frames:
+                        tile = z[
+                            :,
+                            :,
+                            start_frame:end_frame,
+                            i : i + self.tile_latent_min_height,
+                            j : j + self.tile_latent_min_width,
+                        ]
+                        if self.post_quant_conv is not None:
+                            tile = self.post_quant_conv(tile)
+                        tile, conv_cache = self.decoder(tile, conv_cache=conv_cache)
+                        dec.append(tile)
+                        start_frame = end_frame
+                        end_frame += self.num_latent_frames_batch_size
+
+                    row.append(torch.cat(dec, dim=2))
+            rows.append(row)
+
+        result_rows = []
+        for i, row in enumerate(rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                # blend the above tile and the left tile
+                # to the current tile and add the current tile to the result row
+                if i > 0:
+                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent_height)
+                if j > 0:
+                    tile = self.blend_h(row[j - 1], tile, blend_extent_width)
+                result_row.append(tile[:, :, :, :row_limit_height, :row_limit_width])
+            result_rows.append(torch.cat(result_row, dim=4))
+
+        dec = torch.cat(result_rows, dim=3)
+
+        if not return_dict:
+            return (dec,)
+
+        return DecoderOutput(sample=dec)
+
+    def forward(
+        self,
+        sample: torch.Tensor,
+        sample_posterior: bool = False,
+        return_dict: bool = True,
+        generator: Optional[torch.Generator] = None,
+    ) -> Union[torch.Tensor, torch.Tensor]:
+        x = sample
+        posterior = self.encode(x).latent_dist
+        if sample_posterior:
+            z = posterior.sample(generator=generator)
+        else:
+            z = posterior.mode()
+        dec = self.decode(z)
+        if not return_dict:
+            return (dec,)
+        return dec

robomaster/models/transformer3d.py

@@ -0,0 +1,863 @@
+# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
+# All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, Dict, Optional, Tuple, Union
+
+import os
+import json
+import torch
+import glob
+import torch.nn.functional as F
+from torch import nn
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.embeddings import CogVideoXPatchEmbed
+from diffusers.utils import is_torch_version, logging
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+from diffusers.models.attention import Attention, FeedForward
+from diffusers.models.attention_processor import AttentionProcessor, CogVideoXAttnProcessor2_0, FusedCogVideoXAttnProcessor2_0
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps, get_3d_sincos_pos_embed, get_2d_sincos_pos_embed
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import AdaLayerNorm, CogVideoXLayerNormZero
+
+from einops import rearrange
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+class FloatGroupNorm(nn.GroupNorm):
+    def forward(self, x):
+        return super().forward(x.to(self.bias.dtype)).type(x.dtype)
+    
+class CogVideoXPatchEmbed(nn.Module):
+    def __init__(
+        self,
+        patch_size: int = 2,
+        patch_size_t: Optional[int] = None,
+        in_channels: int = 16,
+        embed_dim: int = 1920,
+        text_embed_dim: int = 4096,
+        bias: bool = True,
+        sample_width: int = 90,
+        sample_height: int = 60,
+        sample_frames: int = 49,
+        temporal_compression_ratio: int = 4,
+        max_text_seq_length: int = 226,
+        spatial_interpolation_scale: float = 1.875,
+        temporal_interpolation_scale: float = 1.0,
+        use_positional_embeddings: bool = True,
+        use_learned_positional_embeddings: bool = True,
+    ) -> None:
+        super().__init__()
+
+        post_patch_height = sample_height // patch_size
+        post_patch_width = sample_width // patch_size
+        post_time_compression_frames = (sample_frames - 1) // temporal_compression_ratio + 1
+        self.num_patches = post_patch_height * post_patch_width * post_time_compression_frames
+        self.post_patch_height = post_patch_height
+        self.post_patch_width = post_patch_width
+        self.post_time_compression_frames = post_time_compression_frames
+        self.patch_size = patch_size
+        self.patch_size_t = patch_size_t
+        self.embed_dim = embed_dim
+        self.sample_height = sample_height
+        self.sample_width = sample_width
+        self.sample_frames = sample_frames
+        self.temporal_compression_ratio = temporal_compression_ratio
+        self.max_text_seq_length = max_text_seq_length
+        self.spatial_interpolation_scale = spatial_interpolation_scale
+        self.temporal_interpolation_scale = temporal_interpolation_scale
+        self.use_positional_embeddings = use_positional_embeddings
+        self.use_learned_positional_embeddings = use_learned_positional_embeddings
+        
+        if patch_size_t is None:
+            # CogVideoX 1.0 checkpoints
+            self.proj = nn.Conv2d(
+                in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
+            )
+        else:
+            # CogVideoX 1.5 checkpoints
+            self.proj = nn.Linear(in_channels * patch_size * patch_size * patch_size_t, embed_dim)
+
+        self.text_proj = nn.Linear(text_embed_dim, embed_dim)
+
+        if use_positional_embeddings or use_learned_positional_embeddings:
+            persistent = use_learned_positional_embeddings
+            pos_embedding = self._get_positional_embeddings(sample_height, sample_width, sample_frames)
+            self.register_buffer("pos_embedding", pos_embedding, persistent=persistent)
+
+    def _get_positional_embeddings(self, sample_height: int, sample_width: int, sample_frames: int) -> torch.Tensor:
+        post_patch_height = sample_height // self.patch_size
+        post_patch_width = sample_width // self.patch_size
+        post_time_compression_frames = (sample_frames - 1) // self.temporal_compression_ratio + 1
+        num_patches = post_patch_height * post_patch_width * post_time_compression_frames
+
+        pos_embedding = get_3d_sincos_pos_embed(
+            self.embed_dim,
+            (post_patch_width, post_patch_height),
+            post_time_compression_frames,
+            self.spatial_interpolation_scale,
+            self.temporal_interpolation_scale,
+        )
+        pos_embedding = torch.from_numpy(pos_embedding).flatten(0, 1)
+        joint_pos_embedding = torch.zeros(
+            1, self.max_text_seq_length + num_patches, self.embed_dim, requires_grad=False
+        )
+        joint_pos_embedding.data[:, self.max_text_seq_length :].copy_(pos_embedding)
+
+        return joint_pos_embedding
+
+    def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor, flow_embeds: Optional[torch.Tensor] = None):
+        r"""
+        Args:
+            text_embeds (`torch.Tensor`):
+                Input text embeddings. Expected shape: (batch_size, seq_length, embedding_dim).
+            image_embeds (`torch.Tensor`):
+                Input image embeddings. Expected shape: (batch_size, num_frames, channels, height, width).
+        """
+        text_embeds = self.text_proj(text_embeds)
+
+        text_batch_size, text_seq_length, text_channels = text_embeds.shape
+        batch_size, num_frames, channels, height, width = image_embeds.shape
+
+        if self.patch_size_t is None:
+            image_embeds = image_embeds.reshape(-1, channels, height, width)
+            image_embeds = self.proj(image_embeds)
+            image_embeds = image_embeds.view(batch_size, num_frames, *image_embeds.shape[1:])
+            image_embeds = image_embeds.flatten(3).transpose(2, 3)  # [batch, num_frames, height x width, channels]
+            image_embeds = image_embeds.flatten(1, 2)  # [batch, num_frames x height x width, channels]
+        else:
+            p = self.patch_size
+            p_t = self.patch_size_t
+
+            image_embeds = image_embeds.permute(0, 1, 3, 4, 2)
+            # b, f, h, w, c => b, f // 2, 2, h // 2, 2, w // 2, 2, c
+            image_embeds = image_embeds.reshape(
+                batch_size, num_frames // p_t, p_t, height // p, p, width // p, p, channels
+            )
+            # b, f // 2, 2, h // 2, 2, w // 2, 2, c => b, f // 2, h // 2, w // 2, c, 2, 2, 2
+            image_embeds = image_embeds.permute(0, 1, 3, 5, 7, 2, 4, 6).flatten(4, 7).flatten(1, 3)
+
+            image_embeds = self.proj(image_embeds)
+
+        embeds = torch.cat(
+            [text_embeds, image_embeds], dim=1
+        ).contiguous()  # [batch, seq_length + num_frames x height x width, channels]
+
+        if self.use_positional_embeddings or self.use_learned_positional_embeddings:
+            seq_length = height * width * num_frames // (self.patch_size**2)
+            # pos_embeds = self.pos_embedding[:, : text_seq_length + seq_length]
+            pos_embeds = self.pos_embedding
+            emb_size = embeds.size()[-1]
+            pos_embeds_without_text = pos_embeds[:, text_seq_length: ].view(1, self.post_time_compression_frames, self.post_patch_height, self.post_patch_width, emb_size)
+            pos_embeds_without_text = pos_embeds_without_text.permute([0, 4, 1, 2, 3])
+            pos_embeds_without_text = F.interpolate(pos_embeds_without_text,size=[self.post_time_compression_frames, height // self.patch_size, width // self.patch_size], mode='trilinear', align_corners=False)
+            pos_embeds_without_text = pos_embeds_without_text.permute([0, 2, 3, 4, 1]).view(1, -1, emb_size)
+            pos_embeds = torch.cat([pos_embeds[:, :text_seq_length], pos_embeds_without_text], dim = 1)
+            pos_embeds = pos_embeds[:, : text_seq_length + seq_length]
+            embeds = embeds + pos_embeds
+
+        if flow_embeds is not None:
+            
+            if self.patch_size_t is not None:
+                _, _, flow_channels, _, _ = flow_embeds.shape
+
+                flow_embeds = flow_embeds.permute(0, 1, 3, 4, 2)
+                # b, f, h, w, c => b, f // 2, 2, h // 2, 2, w // 2, 2, c
+                flow_embeds = flow_embeds.reshape(
+                    batch_size, num_frames // p_t, p_t, height // p, p, width // p, p, flow_channels
+                )
+                # b, f // 2, 2, h // 2, 2, w // 2, 2, c => b, f // 2, h // 2, w // 2, c, 2, 2, 2
+                flow_embeds = flow_embeds.permute(0, 1, 3, 5, 7, 2, 4, 6).flatten(4, 7)
+
+        return embeds, flow_embeds
+
+
+@maybe_allow_in_graph
+class CogVideoXBlock(nn.Module):
+    r"""
+    Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) model.
+
+    Parameters:
+        dim (`int`):
+            The number of channels in the input and output.
+        num_attention_heads (`int`):
+            The number of heads to use for multi-head attention.
+        attention_head_dim (`int`):
+            The number of channels in each head.
+        time_embed_dim (`int`):
+            The number of channels in timestep embedding.
+        dropout (`float`, defaults to `0.0`):
+            The dropout probability to use.
+        activation_fn (`str`, defaults to `"gelu-approximate"`):
+            Activation function to be used in feed-forward.
+        attention_bias (`bool`, defaults to `False`):
+            Whether or not to use bias in attention projection layers.
+        qk_norm (`bool`, defaults to `True`):
+            Whether or not to use normalization after query and key projections in Attention.
+        norm_elementwise_affine (`bool`, defaults to `True`):
+            Whether to use learnable elementwise affine parameters for normalization.
+        norm_eps (`float`, defaults to `1e-5`):
+            Epsilon value for normalization layers.
+        final_dropout (`bool` defaults to `False`):
+            Whether to apply a final dropout after the last feed-forward layer.
+        ff_inner_dim (`int`, *optional*, defaults to `None`):
+            Custom hidden dimension of Feed-forward layer. If not provided, `4 * dim` is used.
+        ff_bias (`bool`, defaults to `True`):
+            Whether or not to use bias in Feed-forward layer.
+        attention_out_bias (`bool`, defaults to `True`):
+            Whether or not to use bias in Attention output projection layer.
+    """
+
+    def __init__(
+        self,
+        block_idx: int,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        time_embed_dim: int,
+        block_interval: int = 1,
+        flow_in_dim: int = 128,
+        out_dim: int = 3072,
+        dropout: float = 0.0,
+        activation_fn: str = "gelu-approximate",
+        attention_bias: bool = False,
+        qk_norm: bool = True,
+        norm_elementwise_affine: bool = True,
+        norm_eps: float = 1e-5,
+        final_dropout: bool = True,
+        ff_inner_dim: Optional[int] = None,
+        ff_bias: bool = True,
+        attention_out_bias: bool = True,
+        finetune_init: bool = False,
+    ):
+        super().__init__()
+
+
+        # 1. Self Attention
+        self.norm1 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)
+
+        self.attn1 = Attention(
+            query_dim=dim,
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            qk_norm="layer_norm" if qk_norm else None,
+            eps=1e-6,
+            bias=attention_bias,
+            out_bias=attention_out_bias,
+            processor=CogVideoXAttnProcessor2_0(),
+        )
+        
+        if not finetune_init and (block_idx%block_interval==0):
+            self.flow_spatial = nn.Conv2d(flow_in_dim, out_dim // 4, 3, padding=1)
+            self.flow_temporal = zero_module(
+                    nn.Conv1d(
+                    out_dim // 4,
+                    out_dim,
+                    kernel_size=3,
+                    stride=1,
+                    padding=1,
+                    padding_mode="replicate",
+                )
+            )
+            self.flow_cond_norm = FloatGroupNorm(32, out_dim)
+
+        # 2. Feed Forward
+        self.norm2 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)
+
+        self.ff = FeedForward(
+            dim,
+            dropout=dropout,
+            activation_fn=activation_fn,
+            final_dropout=final_dropout,
+            inner_dim=ff_inner_dim,
+            bias=ff_bias,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        flow_states: torch.Tensor,
+        temb: torch.Tensor,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        text_seq_length = encoder_hidden_states.size(1)
+
+        # norm & modulate
+        norm_hidden_states, norm_encoder_hidden_states, gate_msa, enc_gate_msa = self.norm1(
+            hidden_states, encoder_hidden_states, temb
+        ) 
+
+        # attention
+        attn_hidden_states, attn_encoder_hidden_states = self.attn1(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_encoder_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+        )
+        
+        hidden_states = hidden_states + gate_msa * attn_hidden_states
+        encoder_hidden_states = encoder_hidden_states + enc_gate_msa * attn_encoder_hidden_states
+
+        if hasattr(self, "flow_spatial") and flow_states is not None:
+            
+            bz, f, h, w, c = flow_states.shape
+
+            flow_states = rearrange(flow_states, "bz f h w c -> (bz f) c h w")
+            flow_states = self.flow_spatial(flow_states)  # [5, 128, 30, 40] -> [5, 960, 30, 40]
+
+            flow_states = rearrange(flow_states, "(bz f) c h w -> (bz h w) c f", f=f)
+            flow_states = self.flow_temporal(flow_states)  # [1200, 960, 5] -> [1200, 1920, 5]
+            flow_states = rearrange(flow_states, "(bz h w) c f -> bz (f h w) c", f=f, h=h, w=w)
+            
+            norm_flow_states = self.flow_cond_norm(rearrange(flow_states, "bz (f h w) c -> (bz f) c h w", h=h, w=w))
+            norm_flow_states = rearrange(norm_flow_states, "(bz f) c h w -> bz (f h w) c", f=f, h=h, w=w)
+
+            hidden_states = hidden_states + norm_flow_states * flow_states
+
+        # norm & modulate
+        norm_hidden_states, norm_encoder_hidden_states, gate_ff, enc_gate_ff = self.norm2(
+            hidden_states, encoder_hidden_states, temb
+        )
+
+        # feed-forward
+        norm_hidden_states = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
+        ff_output = self.ff(norm_hidden_states)
+
+        hidden_states = hidden_states + gate_ff * ff_output[:, text_seq_length:]
+        encoder_hidden_states = encoder_hidden_states + enc_gate_ff * ff_output[:, :text_seq_length]
+
+        return hidden_states, encoder_hidden_states
+
+
+class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin):
+    """
+    A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo).
+
+    Parameters:
+        num_attention_heads (`int`, defaults to `30`):
+            The number of heads to use for multi-head attention.
+        attention_head_dim (`int`, defaults to `64`):
+            The number of channels in each head.
+        in_channels (`int`, defaults to `16`):
+            The number of channels in the input.
+        out_channels (`int`, *optional*, defaults to `16`):
+            The number of channels in the output.
+        flip_sin_to_cos (`bool`, defaults to `True`):
+            Whether to flip the sin to cos in the time embedding.
+        time_embed_dim (`int`, defaults to `512`):
+            Output dimension of timestep embeddings.
+        text_embed_dim (`int`, defaults to `4096`):
+            Input dimension of text embeddings from the text encoder.
+        num_layers (`int`, defaults to `30`):
+            The number of layers of Transformer blocks to use.
+        dropout (`float`, defaults to `0.0`):
+            The dropout probability to use.
+        attention_bias (`bool`, defaults to `True`):
+            Whether or not to use bias in the attention projection layers.
+        sample_width (`int`, defaults to `90`):
+            The width of the input latents.
+        sample_height (`int`, defaults to `60`):
+            The height of the input latents.
+        sample_frames (`int`, defaults to `49`):
+            The number of frames in the input latents. Note that this parameter was incorrectly initialized to 49
+            instead of 13 because CogVideoX processed 13 latent frames at once in its default and recommended settings,
+            but cannot be changed to the correct value to ensure backwards compatibility. To create a transformer with
+            K latent frames, the correct value to pass here would be: ((K - 1) * temporal_compression_ratio + 1).
+        patch_size (`int`, defaults to `2`):
+            The size of the patches to use in the patch embedding layer.
+        temporal_compression_ratio (`int`, defaults to `4`):
+            The compression ratio across the temporal dimension. See documentation for `sample_frames`.
+        max_text_seq_length (`int`, defaults to `226`):
+            The maximum sequence length of the input text embeddings.
+        activation_fn (`str`, defaults to `"gelu-approximate"`):
+            Activation function to use in feed-forward.
+        timestep_activation_fn (`str`, defaults to `"silu"`):
+            Activation function to use when generating the timestep embeddings.
+        norm_elementwise_affine (`bool`, defaults to `True`):
+            Whether or not to use elementwise affine in normalization layers.
+        norm_eps (`float`, defaults to `1e-5`):
+            The epsilon value to use in normalization layers.
+        spatial_interpolation_scale (`float`, defaults to `1.875`):
+            Scaling factor to apply in 3D positional embeddings across spatial dimensions.
+        temporal_interpolation_scale (`float`, defaults to `1.0`):
+            Scaling factor to apply in 3D positional embeddings across temporal dimensions.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int = 30,
+        attention_head_dim: int = 64,
+        in_channels: int = 16,
+        out_channels: Optional[int] = 16,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,
+        time_embed_dim: int = 512,
+        text_embed_dim: int = 4096,
+        num_layers: int = 30,
+        dropout: float = 0.0,
+        attention_bias: bool = True,
+        sample_width: int = 90,
+        sample_height: int = 60,
+        sample_frames: int = 49,
+        patch_size: int = 2,
+        patch_size_t: Optional[int] = None,
+        temporal_compression_ratio: int = 4,
+        max_text_seq_length: int = 226,
+        activation_fn: str = "gelu-approximate",
+        timestep_activation_fn: str = "silu",
+        norm_elementwise_affine: bool = True,
+        norm_eps: float = 1e-5,
+        spatial_interpolation_scale: float = 1.875,
+        temporal_interpolation_scale: float = 1.0,
+        use_rotary_positional_embeddings: bool = False,
+        use_learned_positional_embeddings: bool = False,
+        patch_bias: bool = True,
+        add_noise_in_inpaint_model: bool = False,
+        finetune_init: bool = False,
+    ):
+        super().__init__()
+        inner_dim = num_attention_heads * attention_head_dim
+        self.patch_size_t = patch_size_t
+        if not use_rotary_positional_embeddings and use_learned_positional_embeddings:
+            raise ValueError(
+                "There are no CogVideoX checkpoints available with disable rotary embeddings and learned positional "
+                "embeddings. If you're using a custom model and/or believe this should be supported, please open an "
+                "issue at https://github.com/huggingface/diffusers/issues."
+            )
+
+        # 1. Patch embedding
+        self.patch_embed = CogVideoXPatchEmbed(
+            patch_size=patch_size,
+            patch_size_t=patch_size_t,
+            in_channels=in_channels,
+            embed_dim=inner_dim,
+            text_embed_dim=text_embed_dim,
+            bias=patch_bias,
+            sample_width=sample_width,
+            sample_height=sample_height,
+            sample_frames=sample_frames,
+            temporal_compression_ratio=temporal_compression_ratio,
+            max_text_seq_length=max_text_seq_length,
+            spatial_interpolation_scale=spatial_interpolation_scale,
+            temporal_interpolation_scale=temporal_interpolation_scale,
+            use_positional_embeddings=not use_rotary_positional_embeddings,
+            use_learned_positional_embeddings=use_learned_positional_embeddings,
+        )
+        self.embedding_dropout = nn.Dropout(dropout)
+
+        # 2. Time embeddings
+        self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
+        self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn)
+
+        # 3. Define spatio-temporal transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                CogVideoXBlock(
+                    block_idx=idx,
+                    dim=inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                    time_embed_dim=time_embed_dim,
+                    dropout=dropout,
+                    activation_fn=activation_fn,
+                    attention_bias=attention_bias,
+                    norm_elementwise_affine=norm_elementwise_affine,
+                    norm_eps=norm_eps,
+                    finetune_init=finetune_init,
+                )
+                for idx in range(num_layers)
+            ]
+        )
+        self.norm_final = nn.LayerNorm(inner_dim, norm_eps, norm_elementwise_affine)
+
+        # 4. Output blocks
+        self.norm_out = AdaLayerNorm(
+            embedding_dim=time_embed_dim,
+            output_dim=2 * inner_dim,
+            norm_elementwise_affine=norm_elementwise_affine,
+            norm_eps=norm_eps,
+            chunk_dim=1,
+        )
+
+        if patch_size_t is None:
+            # For CogVideox 1.0
+            output_dim = patch_size * patch_size * out_channels
+        else:
+            # For CogVideoX 1.5
+            output_dim = patch_size * patch_size * patch_size_t * out_channels
+
+        self.proj_out = nn.Linear(inner_dim, output_dim)
+
+        self.gradient_checkpointing = False
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        self.gradient_checkpointing = value
+
+    @property
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor()
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedCogVideoXAttnProcessor2_0
+    def fuse_qkv_projections(self):
+        """
+        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
+        are fused. For cross-attention modules, key and value projection matrices are fused.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+        """
+        self.original_attn_processors = None
+
+        for _, attn_processor in self.attn_processors.items():
+            if "Added" in str(attn_processor.__class__.__name__):
+                raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
+
+        self.original_attn_processors = self.attn_processors
+
+        for module in self.modules():
+            if isinstance(module, Attention):
+                module.fuse_projections(fuse=True)
+
+        self.set_attn_processor(FusedCogVideoXAttnProcessor2_0())
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
+    def unfuse_qkv_projections(self):
+        """Disables the fused QKV projection if enabled.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+
+        """
+        if self.original_attn_processors is not None:
+            self.set_attn_processor(self.original_attn_processors)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        timestep: Union[int, float, torch.LongTensor],
+        timestep_cond: Optional[torch.Tensor] = None,
+        inpaint_latents: Optional[torch.Tensor] = None,
+        flow_latents: Optional[torch.Tensor] = None,
+        control_latents: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        return_dict: bool = True,
+    ):
+
+        batch_size, num_frames, channels, height, width = hidden_states.shape
+        if num_frames == 1 and self.patch_size_t is not None:
+            hidden_states = torch.cat([hidden_states, torch.zeros_like(hidden_states)], dim=1)
+            if inpaint_latents is not None:
+                inpaint_latents = torch.concat([inpaint_latents, torch.zeros_like(inpaint_latents)], dim=1)
+            if control_latents is not None:
+                control_latents = torch.concat([control_latents, torch.zeros_like(control_latents)], dim=1)
+            local_num_frames = num_frames + 1
+        else:
+            local_num_frames = num_frames
+
+        # 1. Time embedding
+        timesteps = timestep
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=hidden_states.dtype)
+        emb = self.time_embedding(t_emb, timestep_cond)
+
+        # 2. Patch embedding
+        if inpaint_latents is not None:
+            hidden_states = torch.concat([hidden_states, inpaint_latents], 2)
+        if control_latents is not None:
+            hidden_states = torch.concat([hidden_states, control_latents], 2)
+
+        hidden_states, flow_states = self.patch_embed(encoder_hidden_states, hidden_states, flow_latents)
+        hidden_states = self.embedding_dropout(hidden_states)
+
+        text_seq_length = encoder_hidden_states.shape[1]
+        encoder_hidden_states = hidden_states[:, :text_seq_length]
+        hidden_states = hidden_states[:, text_seq_length:]
+        
+        # 3. Transformer blocks
+        for i, block in enumerate(self.transformer_blocks):
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    flow_states,
+                    emb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+            else:
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    flow_states=flow_states,
+                    temb=emb,
+                    image_rotary_emb=image_rotary_emb,
+                )
+
+        if not self.config.use_rotary_positional_embeddings:
+            # CogVideoX-2B
+            hidden_states = self.norm_final(hidden_states)
+        else:
+            # CogVideoX-5B
+            hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+            hidden_states = self.norm_final(hidden_states)
+            hidden_states = hidden_states[:, text_seq_length:]
+
+        # 4. Final block
+        hidden_states = self.norm_out(hidden_states, temb=emb)
+        hidden_states = self.proj_out(hidden_states)
+
+        # 5. Unpatchify
+        p = self.config.patch_size
+        p_t = self.config.patch_size_t
+
+        if p_t is None:
+            output = hidden_states.reshape(batch_size, local_num_frames, height // p, width // p, -1, p, p)
+            output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
+        else:
+            output = hidden_states.reshape(
+                batch_size, (local_num_frames + p_t - 1) // p_t, height // p, width // p, -1, p_t, p, p
+            )
+            output = output.permute(0, 1, 5, 4, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(1, 2)
+        
+        if num_frames == 1:
+            output = output[:, :num_frames, :]
+
+        if not return_dict:
+            return (output,)
+        return Transformer2DModelOutput(sample=output)
+
+    @classmethod
+    def from_pretrained_2d(
+        cls, pretrained_model_path, subfolder=None, transformer_additional_kwargs={},
+        finetune_init=False, low_cpu_mem_usage=False, torch_dtype=torch.bfloat16
+    ):
+        if subfolder is not None:
+            pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
+        print(f"loaded 3D transformer's pretrained weights from {pretrained_model_path} ...")
+
+        config_file = os.path.join(pretrained_model_path, 'config.json')
+        if not os.path.isfile(config_file):
+            raise RuntimeError(f"{config_file} does not exist")
+        with open(config_file, "r") as f:
+            config = json.load(f)
+        config['finetune_init'] = finetune_init
+
+        from diffusers.utils import WEIGHTS_NAME
+        model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
+        model_file_safetensors = model_file.replace(".bin", ".safetensors")
+
+        if low_cpu_mem_usage:
+            try:
+                import re
+                from diffusers.utils import is_accelerate_available
+                from diffusers.models.modeling_utils import load_model_dict_into_meta
+                if is_accelerate_available():
+                    import accelerate
+                
+                # Instantiate model with empty weights
+                with accelerate.init_empty_weights():
+                    model = cls.from_config(config, **transformer_additional_kwargs)
+
+                param_device = "cpu"
+                if os.path.exists(model_file):
+                    state_dict = torch.load(model_file, map_location="cpu")
+                elif os.path.exists(model_file_safetensors):
+                    from safetensors.torch import load_file, safe_open
+                    state_dict = load_file(model_file_safetensors)
+                else:
+                    from safetensors.torch import load_file, safe_open
+                    model_files_safetensors = glob.glob(os.path.join(pretrained_model_path, "*.safetensors"))
+                    state_dict = {}
+                    for _model_file_safetensors in model_files_safetensors:
+                        _state_dict = load_file(_model_file_safetensors)
+                        for key in _state_dict:
+                            state_dict[key] = _state_dict[key]
+                model._convert_deprecated_attention_blocks(state_dict)
+                # move the params from meta device to cpu
+                missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
+                if len(missing_keys) > 0:
+                    raise ValueError(
+                        f"Cannot load {cls} from {pretrained_model_path} because the following keys are"
+                        f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass"
+                        " `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize"
+                        " those weights or else make sure your checkpoint file is correct."
+                    )
+
+                unexpected_keys = load_model_dict_into_meta(
+                    model,
+                    state_dict,
+                    device=param_device,
+                    dtype=torch_dtype,
+                    model_name_or_path=pretrained_model_path,
+                )
+
+                if cls._keys_to_ignore_on_load_unexpected is not None:
+                    for pat in cls._keys_to_ignore_on_load_unexpected:
+                        unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
+
+                if len(unexpected_keys) > 0:
+                    print(
+                        f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
+                    )
+                return model
+            except Exception as e:
+                print(
+                    f"The low_cpu_mem_usage mode is not work because {e}. Use low_cpu_mem_usage=False instead."
+                )
+
+        model = cls.from_config(config, **transformer_additional_kwargs)
+        if os.path.exists(model_file):
+            state_dict = torch.load(model_file, map_location="cpu")
+        elif os.path.exists(model_file_safetensors):
+            from safetensors.torch import load_file, safe_open
+            state_dict = load_file(model_file_safetensors)
+        else:
+            from safetensors.torch import load_file, safe_open
+            model_files_safetensors = glob.glob(os.path.join(pretrained_model_path, "*.safetensors"))
+            state_dict = {}
+            for _model_file_safetensors in model_files_safetensors:
+                _state_dict = load_file(_model_file_safetensors)
+                for key in _state_dict:
+                    state_dict[key] = _state_dict[key]
+        
+        if model.state_dict()['patch_embed.proj.weight'].size() != state_dict['patch_embed.proj.weight'].size():
+            new_shape   = model.state_dict()['patch_embed.proj.weight'].size()
+            if len(new_shape) == 5:
+                state_dict['patch_embed.proj.weight'] = state_dict['patch_embed.proj.weight'].unsqueeze(2).expand(new_shape).clone()
+                state_dict['patch_embed.proj.weight'][:, :, :-1] = 0
+            elif len(new_shape) == 2:
+                if model.state_dict()['patch_embed.proj.weight'].size()[1] > state_dict['patch_embed.proj.weight'].size()[1]:
+                    model.state_dict()['patch_embed.proj.weight'][:, :state_dict['patch_embed.proj.weight'].size()[1]] = state_dict['patch_embed.proj.weight']
+                    model.state_dict()['patch_embed.proj.weight'][:, state_dict['patch_embed.proj.weight'].size()[1]:] = 0
+                    state_dict['patch_embed.proj.weight'] = model.state_dict()['patch_embed.proj.weight']
+                else:
+                    model.state_dict()['patch_embed.proj.weight'][:, :] = state_dict['patch_embed.proj.weight'][:, :model.state_dict()['patch_embed.proj.weight'].size()[1]]
+                    state_dict['patch_embed.proj.weight'] = model.state_dict()['patch_embed.proj.weight']
+            else:
+                if model.state_dict()['patch_embed.proj.weight'].size()[1] > state_dict['patch_embed.proj.weight'].size()[1]:
+                    model.state_dict()['patch_embed.proj.weight'][:, :state_dict['patch_embed.proj.weight'].size()[1], :, :] = state_dict['patch_embed.proj.weight']
+                    model.state_dict()['patch_embed.proj.weight'][:, state_dict['patch_embed.proj.weight'].size()[1]:, :, :] = 0
+                    state_dict['patch_embed.proj.weight'] = model.state_dict()['patch_embed.proj.weight']
+                else:
+                    model.state_dict()['patch_embed.proj.weight'][:, :, :, :] = state_dict['patch_embed.proj.weight'][:, :model.state_dict()['patch_embed.proj.weight'].size()[1], :, :]
+                    state_dict['patch_embed.proj.weight'] = model.state_dict()['patch_embed.proj.weight']
+
+        tmp_state_dict = {} 
+        for key in state_dict:
+            if key in model.state_dict().keys() and model.state_dict()[key].size() == state_dict[key].size():
+                tmp_state_dict[key] = state_dict[key]
+            else:
+                import ipdb; ipdb.set_trace()
+                print(key, "Size don't match, skip")
+                
+        state_dict = tmp_state_dict
+
+        m, u = model.load_state_dict(state_dict, strict=False)
+        print(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
+        print(m)
+        
+        params = [p.numel() if "." in n else 0 for n, p in model.named_parameters()]
+        print(f"### All Parameters: {sum(params) / 1e6} M")
+        
+        model = model.to(torch_dtype)
+        return model

robomaster/pipeline/pipeline_cogvideox.py

@@ -0,0 +1,877 @@
+# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
+# All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+import math
+import numpy as np
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+from transformers import T5EncoderModel, T5Tokenizer
+
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel
+from diffusers.models.embeddings import get_1d_rotary_pos_embed
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler
+from diffusers.utils import BaseOutput, logging, replace_example_docstring
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.video_processor import VideoProcessor
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```python
+        >>> import torch
+        >>> from diffusers import CogVideoX_Fun_Pipeline
+        >>> from diffusers.utils import export_to_video
+
+        >>> # Models: "THUDM/CogVideoX-2b" or "THUDM/CogVideoX-5b"
+        >>> pipe = CogVideoX_Fun_Pipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.float16).to("cuda")
+        >>> prompt = (
+        ...     "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. "
+        ...     "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other "
+        ...     "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, "
+        ...     "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. "
+        ...     "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical "
+        ...     "atmosphere of this unique musical performance."
+        ... )
+        >>> video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
+        >>> export_to_video(video, "output.mp4", fps=8)
+        ```
+"""
+
+
+# Copied from diffusers.models.embeddings.get_3d_rotary_pos_embed
+def get_3d_rotary_pos_embed(
+    embed_dim,
+    crops_coords,
+    grid_size,
+    temporal_size,
+    theta: int = 10000,
+    use_real: bool = True,
+    grid_type: str = "linspace",
+    max_size: Optional[Tuple[int, int]] = None,
+) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+    """
+    RoPE for video tokens with 3D structure.
+
+    Args:
+    embed_dim: (`int`):
+        The embedding dimension size, corresponding to hidden_size_head.
+    crops_coords (`Tuple[int]`):
+        The top-left and bottom-right coordinates of the crop.
+    grid_size (`Tuple[int]`):
+        The grid size of the spatial positional embedding (height, width).
+    temporal_size (`int`):
+        The size of the temporal dimension.
+    theta (`float`):
+        Scaling factor for frequency computation.
+    grid_type (`str`):
+        Whether to use "linspace" or "slice" to compute grids.
+
+    Returns:
+        `torch.Tensor`: positional embedding with shape `(temporal_size * grid_size[0] * grid_size[1], embed_dim/2)`.
+    """
+    if use_real is not True:
+        raise ValueError(" `use_real = False` is not currently supported for get_3d_rotary_pos_embed")
+
+    if grid_type == "linspace":
+        start, stop = crops_coords
+        grid_size_h, grid_size_w = grid_size
+        grid_h = np.linspace(start[0], stop[0], grid_size_h, endpoint=False, dtype=np.float32)
+        grid_w = np.linspace(start[1], stop[1], grid_size_w, endpoint=False, dtype=np.float32)
+        grid_t = np.arange(temporal_size, dtype=np.float32)
+        grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)
+    elif grid_type == "slice":
+        max_h, max_w = max_size
+        grid_size_h, grid_size_w = grid_size
+        grid_h = np.arange(max_h, dtype=np.float32)
+        grid_w = np.arange(max_w, dtype=np.float32)
+        grid_t = np.arange(temporal_size, dtype=np.float32)
+    else:
+        raise ValueError("Invalid value passed for `grid_type`.")
+
+    # Compute dimensions for each axis
+    dim_t = embed_dim // 4
+    dim_h = embed_dim // 8 * 3
+    dim_w = embed_dim // 8 * 3
+
+    # Temporal frequencies
+    freqs_t = get_1d_rotary_pos_embed(dim_t, grid_t, use_real=True)
+    # Spatial frequencies for height and width
+    freqs_h = get_1d_rotary_pos_embed(dim_h, grid_h, use_real=True)
+    freqs_w = get_1d_rotary_pos_embed(dim_w, grid_w, use_real=True)
+
+    # BroadCast and concatenate temporal and spaial frequencie (height and width) into a 3d tensor
+    def combine_time_height_width(freqs_t, freqs_h, freqs_w):
+        freqs_t = freqs_t[:, None, None, :].expand(
+            -1, grid_size_h, grid_size_w, -1
+        )  # temporal_size, grid_size_h, grid_size_w, dim_t
+        freqs_h = freqs_h[None, :, None, :].expand(
+            temporal_size, -1, grid_size_w, -1
+        )  # temporal_size, grid_size_h, grid_size_2, dim_h
+        freqs_w = freqs_w[None, None, :, :].expand(
+            temporal_size, grid_size_h, -1, -1
+        )  # temporal_size, grid_size_h, grid_size_2, dim_w
+
+        freqs = torch.cat(
+            [freqs_t, freqs_h, freqs_w], dim=-1
+        )  # temporal_size, grid_size_h, grid_size_w, (dim_t + dim_h + dim_w)
+        freqs = freqs.view(
+            temporal_size * grid_size_h * grid_size_w, -1
+        )  # (temporal_size * grid_size_h * grid_size_w), (dim_t + dim_h + dim_w)
+        return freqs
+
+    t_cos, t_sin = freqs_t  # both t_cos and t_sin has shape: temporal_size, dim_t
+    h_cos, h_sin = freqs_h  # both h_cos and h_sin has shape: grid_size_h, dim_h
+    w_cos, w_sin = freqs_w  # both w_cos and w_sin has shape: grid_size_w, dim_w
+
+    if grid_type == "slice":
+        t_cos, t_sin = t_cos[:temporal_size], t_sin[:temporal_size]
+        h_cos, h_sin = h_cos[:grid_size_h], h_sin[:grid_size_h]
+        w_cos, w_sin = w_cos[:grid_size_w], w_sin[:grid_size_w]
+
+    cos = combine_time_height_width(t_cos, h_cos, w_cos)
+    sin = combine_time_height_width(t_sin, h_sin, w_sin)
+    return cos, sin
+
+
+# Similar to diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region_for_grid
+def get_resize_crop_region_for_grid(src, tgt_width, tgt_height):
+    tw = tgt_width
+    th = tgt_height
+    h, w = src
+    r = h / w
+    if r > (th / tw):
+        resize_height = th
+        resize_width = int(round(th / h * w))
+    else:
+        resize_width = tw
+        resize_height = int(round(tw / w * h))
+
+    crop_top = int(round((th - resize_height) / 2.0))
+    crop_left = int(round((tw - resize_width) / 2.0))
+
+    return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+@dataclass
+class CogVideoX_Fun_PipelineOutput(BaseOutput):
+    r"""
+    Output class for CogVideo pipelines.
+
+    Args:
+        video (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
+            List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
+            denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape
+            `(batch_size, num_frames, channels, height, width)`.
+    """
+
+    videos: torch.Tensor
+
+
+class CogVideoX_Fun_Pipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-video generation using CogVideoX_Fun.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
+        text_encoder ([`T5EncoderModel`]):
+            Frozen text-encoder. CogVideoX uses
+            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the
+            [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
+        tokenizer (`T5Tokenizer`):
+            Tokenizer of class
+            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
+        transformer ([`CogVideoXTransformer3DModel`]):
+            A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
+    """
+
+    _optional_components = []
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+
+    _callback_tensor_inputs = [
+        "latents",
+        "prompt_embeds",
+        "negative_prompt_embeds",
+    ]
+
+    def __init__(
+        self,
+        tokenizer: T5Tokenizer,
+        text_encoder: T5EncoderModel,
+        vae: AutoencoderKLCogVideoX,
+        transformer: CogVideoXTransformer3DModel,
+        scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler],
+    ):
+        super().__init__()
+
+        self.register_modules(
+            tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
+        )
+        self.vae_scale_factor_spatial = (
+            2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
+        )
+        self.vae_scale_factor_temporal = (
+            self.vae.config.temporal_compression_ratio if hasattr(self, "vae") and self.vae is not None else 4
+        )
+
+        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
+
+    def _get_t5_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_videos_per_prompt: int = 1,
+        max_sequence_length: int = 226,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            add_special_tokens=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because `max_sequence_length` is set to "
+                f" {max_sequence_length} tokens: {removed_text}"
+            )
+
+        prompt_embeds = self.text_encoder(text_input_ids.to(device))[0]
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
+
+        return prompt_embeds
+
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        do_classifier_free_guidance: bool = True,
+        num_videos_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 226,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
+                Whether to use classifier free guidance or not.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            device: (`torch.device`, *optional*):
+                torch device
+            dtype: (`torch.dtype`, *optional*):
+                torch dtype
+        """
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds = self._get_t5_prompt_embeds(
+                prompt=prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+
+            negative_prompt_embeds = self._get_t5_prompt_embeds(
+                prompt=negative_prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        return prompt_embeds, negative_prompt_embeds
+
+    def prepare_latents(
+        self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
+    ):
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        shape = (
+            batch_size,
+            (num_frames - 1) // self.vae_scale_factor_temporal + 1,
+            num_channels_latents,
+            height // self.vae_scale_factor_spatial,
+            width // self.vae_scale_factor_spatial,
+        )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def decode_latents(self, latents: torch.Tensor) -> torch.Tensor:
+        latents = latents.permute(0, 2, 1, 3, 4)  # [batch_size, num_channels, num_frames, height, width]
+        latents = 1 / self.vae.config.scaling_factor * latents
+
+        frames = self.vae.decode(latents).sample
+        frames = (frames / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        frames = frames.cpu().float().numpy()
+        return frames
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    # Copied from diffusers.pipelines.latte.pipeline_latte.LattePipeline.check_inputs
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        negative_prompt,
+        callback_on_step_end_tensor_inputs,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+    def fuse_qkv_projections(self) -> None:
+        r"""Enables fused QKV projections."""
+        self.fusing_transformer = True
+        self.transformer.fuse_qkv_projections()
+
+    def unfuse_qkv_projections(self) -> None:
+        r"""Disable QKV projection fusion if enabled."""
+        if not self.fusing_transformer:
+            logger.warning("The Transformer was not initially fused for QKV projections. Doing nothing.")
+        else:
+            self.transformer.unfuse_qkv_projections()
+            self.fusing_transformer = False
+
+    def _prepare_rotary_positional_embeddings(
+        self,
+        height: int,
+        width: int,
+        num_frames: int,
+        device: torch.device,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+        grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+
+        p = self.transformer.config.patch_size
+        p_t = self.transformer.config.patch_size_t
+
+        base_size_width = self.transformer.config.sample_width // p
+        base_size_height = self.transformer.config.sample_height // p
+
+        if p_t is None:
+            # CogVideoX 1.0
+            grid_crops_coords = get_resize_crop_region_for_grid(
+                (grid_height, grid_width), base_size_width, base_size_height
+            )
+            freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
+                embed_dim=self.transformer.config.attention_head_dim,
+                crops_coords=grid_crops_coords,
+                grid_size=(grid_height, grid_width),
+                temporal_size=num_frames,
+            )
+        else:
+            # CogVideoX 1.5
+            base_num_frames = (num_frames + p_t - 1) // p_t
+
+            freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
+                embed_dim=self.transformer.config.attention_head_dim,
+                crops_coords=None,
+                grid_size=(grid_height, grid_width),
+                temporal_size=base_num_frames,
+                grid_type="slice",
+                max_size=(base_size_height, base_size_width),
+            )
+
+        freqs_cos = freqs_cos.to(device=device)
+        freqs_sin = freqs_sin.to(device=device)
+        return freqs_cos, freqs_sin
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        height: int = 480,
+        width: int = 720,
+        num_frames: int = 49,
+        num_inference_steps: int = 50,
+        timesteps: Optional[List[int]] = None,
+        guidance_scale: float = 6,
+        use_dynamic_cfg: bool = False,
+        num_videos_per_prompt: int = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: str = "numpy",
+        return_dict: bool = False,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 226,
+    ) -> Union[CogVideoX_Fun_PipelineOutput, Tuple]:
+        """
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            num_frames (`int`, defaults to `48`):
+                Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will
+                contain 1 extra frame because CogVideoX is conditioned with (num_seconds * fps + 1) frames where
+                num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that
+                needs to be satisfied is that of divisibility mentioned above.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 7.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                The number of videos to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int`, defaults to `226`):
+                Maximum sequence length in encoded prompt. Must be consistent with
+                `self.transformer.config.max_text_seq_length` otherwise may lead to poor results.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoX_Fun_PipelineOutput`] or `tuple`:
+            [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoX_Fun_PipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        height = height or self.transformer.config.sample_height * self.vae_scale_factor_spatial
+        width = width or self.transformer.config.sample_width * self.vae_scale_factor_spatial
+        num_frames = num_frames or self.transformer.config.sample_frames
+
+        num_videos_per_prompt = 1
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            negative_prompt,
+            callback_on_step_end_tensor_inputs,
+            prompt_embeds,
+            negative_prompt_embeds,
+        )
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+        self._interrupt = False
+
+        # 2. Default call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+            prompt,
+            negative_prompt,
+            do_classifier_free_guidance,
+            num_videos_per_prompt=num_videos_per_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            max_sequence_length=max_sequence_length,
+            device=device,
+        )
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
+        self._num_timesteps = len(timesteps)
+
+        # 5. Prepare latents
+        latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
+
+        # For CogVideoX 1.5, the latent frames should be padded to make it divisible by patch_size_t
+        patch_size_t = self.transformer.config.patch_size_t
+        additional_frames = 0
+        if num_frames != 1 and patch_size_t is not None and latent_frames % patch_size_t != 0:
+            additional_frames = patch_size_t - latent_frames % patch_size_t
+            num_frames += additional_frames * self.vae_scale_factor_temporal
+
+        latent_channels = self.transformer.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_videos_per_prompt,
+            latent_channels,
+            num_frames,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. Create rotary embeds if required
+        image_rotary_emb = (
+            self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
+            if self.transformer.config.use_rotary_positional_embeddings
+            else None
+        )
+
+        # 8. Denoising loop
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            # for DPM-solver++
+            old_pred_original_sample = None
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0])
+
+                # predict noise model_output
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep=timestep,
+                    image_rotary_emb=image_rotary_emb,
+                    return_dict=False,
+                )[0]
+                noise_pred = noise_pred.float()
+
+                # perform guidance
+                if use_dynamic_cfg:
+                    self._guidance_scale = 1 + guidance_scale * (
+                        (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2
+                    )
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                if not isinstance(self.scheduler, CogVideoXDPMScheduler):
+                    latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+                else:
+                    latents, old_pred_original_sample = self.scheduler.step(
+                        noise_pred,
+                        old_pred_original_sample,
+                        t,
+                        timesteps[i - 1] if i > 0 else None,
+                        latents,
+                        **extra_step_kwargs,
+                        return_dict=False,
+                    )
+                latents = latents.to(prompt_embeds.dtype)
+
+                # call the callback, if provided
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        if output_type == "numpy":
+            video = self.decode_latents(latents)
+        elif not output_type == "latent":
+            video = self.decode_latents(latents)
+            video = self.video_processor.postprocess_video(video=video, output_type=output_type)
+        else:
+            video = latents
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            video = torch.from_numpy(video)
+
+        return CogVideoX_Fun_PipelineOutput(videos=video)

robomaster/pipeline/pipeline_cogvideox_control.py

@@ -0,0 +1,970 @@
+# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
+# All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+import math
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.models import (AutoencoderKLCogVideoX,
+                              CogVideoXTransformer3DModel)
+from diffusers.models.embeddings import (get_1d_rotary_pos_embed,
+                                         get_3d_rotary_pos_embed)
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler
+from diffusers.utils import BaseOutput, logging, replace_example_docstring
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.video_processor import VideoProcessor
+from einops import rearrange
+from transformers import T5EncoderModel, T5Tokenizer
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```python
+        >>> import torch
+        >>> from diffusers import CogVideoX_Fun_Pipeline
+        >>> from diffusers.utils import export_to_video
+
+        >>> # Models: "THUDM/CogVideoX-2b" or "THUDM/CogVideoX-5b"
+        >>> pipe = CogVideoX_Fun_Pipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.float16).to("cuda")
+        >>> prompt = (
+        ...     "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. "
+        ...     "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other "
+        ...     "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, "
+        ...     "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. "
+        ...     "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical "
+        ...     "atmosphere of this unique musical performance."
+        ... )
+        >>> video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
+        >>> export_to_video(video, "output.mp4", fps=8)
+        ```
+"""
+
+
+# Copied from diffusers.models.embeddings.get_3d_rotary_pos_embed
+def get_3d_rotary_pos_embed(
+    embed_dim,
+    crops_coords,
+    grid_size,
+    temporal_size,
+    theta: int = 10000,
+    use_real: bool = True,
+    grid_type: str = "linspace",
+    max_size: Optional[Tuple[int, int]] = None,
+) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+    """
+    RoPE for video tokens with 3D structure.
+
+    Args:
+    embed_dim: (`int`):
+        The embedding dimension size, corresponding to hidden_size_head.
+    crops_coords (`Tuple[int]`):
+        The top-left and bottom-right coordinates of the crop.
+    grid_size (`Tuple[int]`):
+        The grid size of the spatial positional embedding (height, width).
+    temporal_size (`int`):
+        The size of the temporal dimension.
+    theta (`float`):
+        Scaling factor for frequency computation.
+    grid_type (`str`):
+        Whether to use "linspace" or "slice" to compute grids.
+
+    Returns:
+        `torch.Tensor`: positional embedding with shape `(temporal_size * grid_size[0] * grid_size[1], embed_dim/2)`.
+    """
+    if use_real is not True:
+        raise ValueError(" `use_real = False` is not currently supported for get_3d_rotary_pos_embed")
+
+    if grid_type == "linspace":
+        start, stop = crops_coords
+        grid_size_h, grid_size_w = grid_size
+        grid_h = np.linspace(start[0], stop[0], grid_size_h, endpoint=False, dtype=np.float32)
+        grid_w = np.linspace(start[1], stop[1], grid_size_w, endpoint=False, dtype=np.float32)
+        grid_t = np.arange(temporal_size, dtype=np.float32)
+        grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)
+    elif grid_type == "slice":
+        max_h, max_w = max_size
+        grid_size_h, grid_size_w = grid_size
+        grid_h = np.arange(max_h, dtype=np.float32)
+        grid_w = np.arange(max_w, dtype=np.float32)
+        grid_t = np.arange(temporal_size, dtype=np.float32)
+    else:
+        raise ValueError("Invalid value passed for `grid_type`.")
+
+    # Compute dimensions for each axis
+    dim_t = embed_dim // 4
+    dim_h = embed_dim // 8 * 3
+    dim_w = embed_dim // 8 * 3
+
+    # Temporal frequencies
+    freqs_t = get_1d_rotary_pos_embed(dim_t, grid_t, use_real=True)
+    # Spatial frequencies for height and width
+    freqs_h = get_1d_rotary_pos_embed(dim_h, grid_h, use_real=True)
+    freqs_w = get_1d_rotary_pos_embed(dim_w, grid_w, use_real=True)
+
+    # BroadCast and concatenate temporal and spaial frequencie (height and width) into a 3d tensor
+    def combine_time_height_width(freqs_t, freqs_h, freqs_w):
+        freqs_t = freqs_t[:, None, None, :].expand(
+            -1, grid_size_h, grid_size_w, -1
+        )  # temporal_size, grid_size_h, grid_size_w, dim_t
+        freqs_h = freqs_h[None, :, None, :].expand(
+            temporal_size, -1, grid_size_w, -1
+        )  # temporal_size, grid_size_h, grid_size_2, dim_h
+        freqs_w = freqs_w[None, None, :, :].expand(
+            temporal_size, grid_size_h, -1, -1
+        )  # temporal_size, grid_size_h, grid_size_2, dim_w
+
+        freqs = torch.cat(
+            [freqs_t, freqs_h, freqs_w], dim=-1
+        )  # temporal_size, grid_size_h, grid_size_w, (dim_t + dim_h + dim_w)
+        freqs = freqs.view(
+            temporal_size * grid_size_h * grid_size_w, -1
+        )  # (temporal_size * grid_size_h * grid_size_w), (dim_t + dim_h + dim_w)
+        return freqs
+
+    t_cos, t_sin = freqs_t  # both t_cos and t_sin has shape: temporal_size, dim_t
+    h_cos, h_sin = freqs_h  # both h_cos and h_sin has shape: grid_size_h, dim_h
+    w_cos, w_sin = freqs_w  # both w_cos and w_sin has shape: grid_size_w, dim_w
+
+    if grid_type == "slice":
+        t_cos, t_sin = t_cos[:temporal_size], t_sin[:temporal_size]
+        h_cos, h_sin = h_cos[:grid_size_h], h_sin[:grid_size_h]
+        w_cos, w_sin = w_cos[:grid_size_w], w_sin[:grid_size_w]
+
+    cos = combine_time_height_width(t_cos, h_cos, w_cos)
+    sin = combine_time_height_width(t_sin, h_sin, w_sin)
+    return cos, sin
+
+
+# Similar to diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region_for_grid
+def get_resize_crop_region_for_grid(src, tgt_width, tgt_height):
+    tw = tgt_width
+    th = tgt_height
+    h, w = src
+    r = h / w
+    if r > (th / tw):
+        resize_height = th
+        resize_width = int(round(th / h * w))
+    else:
+        resize_width = tw
+        resize_height = int(round(tw / w * h))
+
+    crop_top = int(round((th - resize_height) / 2.0))
+    crop_left = int(round((tw - resize_width) / 2.0))
+
+    return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+@dataclass
+class CogVideoX_Fun_PipelineOutput(BaseOutput):
+    r"""
+    Output class for CogVideo pipelines.
+
+    Args:
+        video (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
+            List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
+            denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape
+            `(batch_size, num_frames, channels, height, width)`.
+    """
+
+    videos: torch.Tensor
+
+
+class CogVideoX_Fun_Pipeline_Control(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-video generation using CogVideoX.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
+        text_encoder ([`T5EncoderModel`]):
+            Frozen text-encoder. CogVideoX_Fun uses
+            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the
+            [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
+        tokenizer (`T5Tokenizer`):
+            Tokenizer of class
+            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
+        transformer ([`CogVideoXTransformer3DModel`]):
+            A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
+    """
+
+    _optional_components = []
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+
+    _callback_tensor_inputs = [
+        "latents",
+        "prompt_embeds",
+        "negative_prompt_embeds",
+    ]
+
+    def __init__(
+        self,
+        tokenizer: T5Tokenizer,
+        text_encoder: T5EncoderModel,
+        vae: AutoencoderKLCogVideoX,
+        transformer: CogVideoXTransformer3DModel,
+        scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler],
+    ):
+        super().__init__()
+
+        self.register_modules(
+            tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
+        )
+        self.vae_scale_factor_spatial = (
+            2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
+        )
+        self.vae_scale_factor_temporal = (
+            self.vae.config.temporal_compression_ratio if hasattr(self, "vae") and self.vae is not None else 4
+        )
+
+        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
+
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.mask_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
+        )
+
+    def _get_t5_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_videos_per_prompt: int = 1,
+        max_sequence_length: int = 226,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            add_special_tokens=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because `max_sequence_length` is set to "
+                f" {max_sequence_length} tokens: {removed_text}"
+            )
+
+        prompt_embeds = self.text_encoder(text_input_ids.to(device))[0]
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
+
+        return prompt_embeds
+
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        do_classifier_free_guidance: bool = True,
+        num_videos_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 226,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
+                Whether to use classifier free guidance or not.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            device: (`torch.device`, *optional*):
+                torch device
+            dtype: (`torch.dtype`, *optional*):
+                torch dtype
+        """
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds = self._get_t5_prompt_embeds(
+                prompt=prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+
+            negative_prompt_embeds = self._get_t5_prompt_embeds(
+                prompt=negative_prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        return prompt_embeds, negative_prompt_embeds
+
+    def prepare_latents(
+        self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
+    ):
+        shape = (
+            batch_size,
+            (num_frames - 1) // self.vae_scale_factor_temporal + 1,
+            num_channels_latents,
+            height // self.vae_scale_factor_spatial,
+            width // self.vae_scale_factor_spatial,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def prepare_control_latents(
+        self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
+    ):
+        # resize the mask to latents shape as we concatenate the mask to the latents
+        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
+        # and half precision
+
+        if mask is not None:
+            mask = mask.to(device=device, dtype=self.vae.dtype)
+            bs = 1
+            new_mask = []
+            for i in range(0, mask.shape[0], bs):
+                mask_bs = mask[i : i + bs]
+                mask_bs = self.vae.encode(mask_bs)[0]
+                mask_bs = mask_bs.mode()
+                new_mask.append(mask_bs)
+            mask = torch.cat(new_mask, dim = 0)
+            mask = mask * self.vae.config.scaling_factor
+
+        if masked_image is not None:
+            masked_image = masked_image.to(device=device, dtype=self.vae.dtype)
+            bs = 1
+            new_mask_pixel_values = []
+            for i in range(0, masked_image.shape[0], bs):
+                mask_pixel_values_bs = masked_image[i : i + bs]
+                mask_pixel_values_bs = self.vae.encode(mask_pixel_values_bs)[0]
+                mask_pixel_values_bs = mask_pixel_values_bs.mode()
+                new_mask_pixel_values.append(mask_pixel_values_bs)
+            masked_image_latents = torch.cat(new_mask_pixel_values, dim = 0)
+            masked_image_latents = masked_image_latents * self.vae.config.scaling_factor
+        else:
+            masked_image_latents = None
+
+        return mask, masked_image_latents
+
+    def decode_latents(self, latents: torch.Tensor) -> torch.Tensor:
+        latents = latents.permute(0, 2, 1, 3, 4)  # [batch_size, num_channels, num_frames, height, width]
+        latents = 1 / self.vae.config.scaling_factor * latents
+
+        frames = self.vae.decode(latents).sample
+        frames = (frames / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        frames = frames.cpu().float().numpy()
+        return frames
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    # Copied from diffusers.pipelines.latte.pipeline_latte.LattePipeline.check_inputs
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        negative_prompt,
+        callback_on_step_end_tensor_inputs,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+    def fuse_qkv_projections(self) -> None:
+        r"""Enables fused QKV projections."""
+        self.fusing_transformer = True
+        self.transformer.fuse_qkv_projections()
+
+    def unfuse_qkv_projections(self) -> None:
+        r"""Disable QKV projection fusion if enabled."""
+        if not self.fusing_transformer:
+            logger.warning("The Transformer was not initially fused for QKV projections. Doing nothing.")
+        else:
+            self.transformer.unfuse_qkv_projections()
+            self.fusing_transformer = False
+
+    def _prepare_rotary_positional_embeddings(
+        self,
+        height: int,
+        width: int,
+        num_frames: int,
+        device: torch.device,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+        grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+
+        p = self.transformer.config.patch_size
+        p_t = self.transformer.config.patch_size_t
+
+        base_size_width = self.transformer.config.sample_width // p
+        base_size_height = self.transformer.config.sample_height // p
+
+        if p_t is None:
+            # CogVideoX 1.0
+            grid_crops_coords = get_resize_crop_region_for_grid(
+                (grid_height, grid_width), base_size_width, base_size_height
+            )
+            freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
+                embed_dim=self.transformer.config.attention_head_dim,
+                crops_coords=grid_crops_coords,
+                grid_size=(grid_height, grid_width),
+                temporal_size=num_frames,
+            )
+        else:
+            # CogVideoX 1.5
+            base_num_frames = (num_frames + p_t - 1) // p_t
+
+            freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
+                embed_dim=self.transformer.config.attention_head_dim,
+                crops_coords=None,
+                grid_size=(grid_height, grid_width),
+                temporal_size=base_num_frames,
+                grid_type="slice",
+                max_size=(base_size_height, base_size_width),
+            )
+
+        freqs_cos = freqs_cos.to(device=device)
+        freqs_sin = freqs_sin.to(device=device)
+        return freqs_cos, freqs_sin
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
+    def get_timesteps(self, num_inference_steps, strength, device):
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+
+        return timesteps, num_inference_steps - t_start
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        height: int = 480,
+        width: int = 720,
+        video: Union[torch.FloatTensor] = None,
+        control_video: Union[torch.FloatTensor] = None,
+        num_frames: int = 49,
+        num_inference_steps: int = 50,
+        timesteps: Optional[List[int]] = None,
+        guidance_scale: float = 6,
+        use_dynamic_cfg: bool = False,
+        num_videos_per_prompt: int = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: str = "numpy",
+        return_dict: bool = False,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 226,
+        comfyui_progressbar: bool = False,
+    ) -> Union[CogVideoX_Fun_PipelineOutput, Tuple]:
+        """
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            num_frames (`int`, defaults to `48`):
+                Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will
+                contain 1 extra frame because CogVideoX_Fun is conditioned with (num_seconds * fps + 1) frames where
+                num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that
+                needs to be satisfied is that of divisibility mentioned above.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 7.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                The number of videos to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int`, defaults to `226`):
+                Maximum sequence length in encoded prompt. Must be consistent with
+                `self.transformer.config.max_text_seq_length` otherwise may lead to poor results.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoX_Fun_PipelineOutput`] or `tuple`:
+            [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoX_Fun_PipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        height = height or self.transformer.config.sample_height * self.vae_scale_factor_spatial
+        width = width or self.transformer.config.sample_width * self.vae_scale_factor_spatial
+        num_frames = num_frames or self.transformer.config.sample_frames
+
+        num_videos_per_prompt = 1
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            negative_prompt,
+            callback_on_step_end_tensor_inputs,
+            prompt_embeds,
+            negative_prompt_embeds,
+        )
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+        self._interrupt = False
+
+        # 2. Default call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+            prompt,
+            negative_prompt,
+            do_classifier_free_guidance,
+            num_videos_per_prompt=num_videos_per_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            max_sequence_length=max_sequence_length,
+            device=device,
+        )
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
+        self._num_timesteps = len(timesteps)
+        if comfyui_progressbar:
+            from comfy.utils import ProgressBar
+            pbar = ProgressBar(num_inference_steps + 2)
+
+        if control_video is not None:
+            video_length = control_video.shape[2]
+            control_video = self.image_processor.preprocess(rearrange(control_video, "b c f h w -> (b f) c h w"), height=height, width=width) 
+            control_video = control_video.to(dtype=torch.float32)
+            control_video = rearrange(control_video, "(b f) c h w -> b c f h w", f=video_length)
+        else:
+            control_video = None
+
+        # Magvae needs the number of frames to be 4n + 1.
+        local_latent_length = (num_frames - 1) // self.vae_scale_factor_temporal + 1
+        # For CogVideoX 1.5, the latent frames should be clipped to make it divisible by patch_size_t
+        patch_size_t = self.transformer.config.patch_size_t
+        additional_frames = 0
+        if patch_size_t is not None and local_latent_length % patch_size_t != 0:
+            additional_frames = local_latent_length % patch_size_t
+            num_frames -= additional_frames * self.vae_scale_factor_temporal
+        if num_frames <= 0:
+            num_frames = 1
+        if video_length > num_frames:
+            logger.warning("The length of condition video is not right, the latent frames should be clipped to make it divisible by patch_size_t. ")
+            video_length = num_frames
+            control_video = control_video[:, :, :video_length]
+
+        # 5. Prepare latents.
+        latent_channels = self.vae.config.latent_channels
+        latents = self.prepare_latents(
+            batch_size * num_videos_per_prompt,
+            latent_channels,
+            num_frames,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+        if comfyui_progressbar:
+            pbar.update(1)
+
+        control_video_latents = self.prepare_control_latents(
+            None,
+            control_video,
+            batch_size,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            do_classifier_free_guidance
+        )[1]
+        control_video_latents_input = (
+            torch.cat([control_video_latents] * 2) if do_classifier_free_guidance else control_video_latents
+        )
+        control_latents = rearrange(control_video_latents_input, "b c f h w -> b f c h w")
+
+        if comfyui_progressbar:
+            pbar.update(1)
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. Create rotary embeds if required
+        image_rotary_emb = (
+            self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
+            if self.transformer.config.use_rotary_positional_embeddings
+            else None
+        )
+
+        # 8. Denoising loop
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            # for DPM-solver++
+            old_pred_original_sample = None
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0])
+
+                # predict noise model_output
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep=timestep,
+                    image_rotary_emb=image_rotary_emb,
+                    return_dict=False,
+                    control_latents=control_latents,
+                )[0]
+                noise_pred = noise_pred.float()
+
+                # perform guidance
+                if use_dynamic_cfg:
+                    self._guidance_scale = 1 + guidance_scale * (
+                        (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2
+                    )
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                if not isinstance(self.scheduler, CogVideoXDPMScheduler):
+                    latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+                else:
+                    latents, old_pred_original_sample = self.scheduler.step(
+                        noise_pred,
+                        old_pred_original_sample,
+                        t,
+                        timesteps[i - 1] if i > 0 else None,
+                        latents,
+                        **extra_step_kwargs,
+                        return_dict=False,
+                    )
+                latents = latents.to(prompt_embeds.dtype)
+
+                # call the callback, if provided
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                if comfyui_progressbar:
+                    pbar.update(1)
+
+        if output_type == "numpy":
+            video = self.decode_latents(latents)
+        elif not output_type == "latent":
+            video = self.decode_latents(latents)
+            video = self.video_processor.postprocess_video(video=video, output_type=output_type)
+        else:
+            video = latents
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            video = torch.from_numpy(video)
+
+        return CogVideoX_Fun_PipelineOutput(videos=video)

robomaster/pipeline/pipeline_cogvideox_inpaint.py

@@ -0,0 +1,1156 @@
+# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
+# All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+import math
+import numpy as np
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+from transformers import T5EncoderModel, T5Tokenizer
+
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel
+from diffusers.models.embeddings import get_1d_rotary_pos_embed
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler
+from diffusers.utils import BaseOutput, logging, replace_example_docstring
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.video_processor import VideoProcessor
+from diffusers.image_processor import VaeImageProcessor
+from einops import rearrange
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```python
+        >>> import torch
+        >>> from diffusers import CogVideoX_Fun_Pipeline
+        >>> from diffusers.utils import export_to_video
+
+        >>> # Models: "THUDM/CogVideoX-2b" or "THUDM/CogVideoX-5b"
+        >>> pipe = CogVideoX_Fun_Pipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.float16).to("cuda")
+        >>> prompt = (
+        ...     "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. "
+        ...     "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other "
+        ...     "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, "
+        ...     "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. "
+        ...     "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical "
+        ...     "atmosphere of this unique musical performance."
+        ... )
+        >>> video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
+        >>> export_to_video(video, "output.mp4", fps=8)
+        ```
+"""
+
+# Copied from diffusers.models.embeddings.get_3d_rotary_pos_embed
+def get_3d_rotary_pos_embed(
+    embed_dim,
+    crops_coords,
+    grid_size,
+    temporal_size,
+    theta: int = 10000,
+    use_real: bool = True,
+    grid_type: str = "linspace",
+    max_size: Optional[Tuple[int, int]] = None,
+) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+    """
+    RoPE for video tokens with 3D structure.
+
+    Args:
+    embed_dim: (`int`):
+        The embedding dimension size, corresponding to hidden_size_head.
+    crops_coords (`Tuple[int]`):
+        The top-left and bottom-right coordinates of the crop.
+    grid_size (`Tuple[int]`):
+        The grid size of the spatial positional embedding (height, width).
+    temporal_size (`int`):
+        The size of the temporal dimension.
+    theta (`float`):
+        Scaling factor for frequency computation.
+    grid_type (`str`):
+        Whether to use "linspace" or "slice" to compute grids.
+
+    Returns:
+        `torch.Tensor`: positional embedding with shape `(temporal_size * grid_size[0] * grid_size[1], embed_dim/2)`.
+    """
+    if use_real is not True:
+        raise ValueError(" `use_real = False` is not currently supported for get_3d_rotary_pos_embed")
+
+    if grid_type == "linspace":
+        start, stop = crops_coords
+        grid_size_h, grid_size_w = grid_size
+        grid_h = np.linspace(start[0], stop[0], grid_size_h, endpoint=False, dtype=np.float32)
+        grid_w = np.linspace(start[1], stop[1], grid_size_w, endpoint=False, dtype=np.float32)
+        grid_t = np.arange(temporal_size, dtype=np.float32)
+        grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)
+    elif grid_type == "slice":
+        max_h, max_w = max_size
+        grid_size_h, grid_size_w = grid_size
+        grid_h = np.arange(max_h, dtype=np.float32)
+        grid_w = np.arange(max_w, dtype=np.float32)
+        grid_t = np.arange(temporal_size, dtype=np.float32)
+    else:
+        raise ValueError("Invalid value passed for `grid_type`.")
+
+    # Compute dimensions for each axis
+    dim_t = embed_dim // 4
+    dim_h = embed_dim // 8 * 3
+    dim_w = embed_dim // 8 * 3
+
+    # Temporal frequencies
+    freqs_t = get_1d_rotary_pos_embed(dim_t, grid_t, use_real=True)
+    # Spatial frequencies for height and width
+    freqs_h = get_1d_rotary_pos_embed(dim_h, grid_h, use_real=True)
+    freqs_w = get_1d_rotary_pos_embed(dim_w, grid_w, use_real=True)
+
+    # BroadCast and concatenate temporal and spaial frequencie (height and width) into a 3d tensor
+    def combine_time_height_width(freqs_t, freqs_h, freqs_w):
+        freqs_t = freqs_t[:, None, None, :].expand(
+            -1, grid_size_h, grid_size_w, -1
+        )  # temporal_size, grid_size_h, grid_size_w, dim_t
+        freqs_h = freqs_h[None, :, None, :].expand(
+            temporal_size, -1, grid_size_w, -1
+        )  # temporal_size, grid_size_h, grid_size_2, dim_h
+        freqs_w = freqs_w[None, None, :, :].expand(
+            temporal_size, grid_size_h, -1, -1
+        )  # temporal_size, grid_size_h, grid_size_2, dim_w
+
+        freqs = torch.cat(
+            [freqs_t, freqs_h, freqs_w], dim=-1
+        )  # temporal_size, grid_size_h, grid_size_w, (dim_t + dim_h + dim_w)
+        freqs = freqs.view(
+            temporal_size * grid_size_h * grid_size_w, -1
+        )  # (temporal_size * grid_size_h * grid_size_w), (dim_t + dim_h + dim_w)
+        return freqs
+
+    t_cos, t_sin = freqs_t  # both t_cos and t_sin has shape: temporal_size, dim_t
+    h_cos, h_sin = freqs_h  # both h_cos and h_sin has shape: grid_size_h, dim_h
+    w_cos, w_sin = freqs_w  # both w_cos and w_sin has shape: grid_size_w, dim_w
+
+    if grid_type == "slice":
+        t_cos, t_sin = t_cos[:temporal_size], t_sin[:temporal_size]
+        h_cos, h_sin = h_cos[:grid_size_h], h_sin[:grid_size_h]
+        w_cos, w_sin = w_cos[:grid_size_w], w_sin[:grid_size_w]
+
+    cos = combine_time_height_width(t_cos, h_cos, w_cos)
+    sin = combine_time_height_width(t_sin, h_sin, w_sin)
+    return cos, sin
+
+
+# Similar to diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region_for_grid
+def get_resize_crop_region_for_grid(src, tgt_width, tgt_height):
+    tw = tgt_width
+    th = tgt_height
+    h, w = src
+    r = h / w
+    if r > (th / tw):
+        resize_height = th
+        resize_width = int(round(th / h * w))
+    else:
+        resize_width = tw
+        resize_height = int(round(tw / w * h))
+
+    crop_top = int(round((th - resize_height) / 2.0))
+    crop_left = int(round((tw - resize_width) / 2.0))
+
+    return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+def resize_mask(mask, latent, process_first_frame_only=True):
+    latent_size = latent.size()
+    batch_size, channels, num_frames, height, width = mask.shape
+
+    if process_first_frame_only:
+        target_size = list(latent_size[2:])
+        target_size[0] = 1
+        first_frame_resized = F.interpolate(
+            mask[:, :, 0:1, :, :],
+            size=target_size,
+            mode='trilinear',
+            align_corners=False
+        )
+        
+        target_size = list(latent_size[2:])
+        target_size[0] = target_size[0] - 1
+        if target_size[0] != 0:
+            remaining_frames_resized = F.interpolate(
+                mask[:, :, 1:, :, :],
+                size=target_size,
+                mode='trilinear',
+                align_corners=False
+            )
+            resized_mask = torch.cat([first_frame_resized, remaining_frames_resized], dim=2)
+        else:
+            resized_mask = first_frame_resized
+    else:
+        target_size = list(latent_size[2:])
+        resized_mask = F.interpolate(
+            mask,
+            size=target_size,
+            mode='trilinear',
+            align_corners=False
+        )
+    return resized_mask
+
+
+def add_noise_to_reference_video(image, ratio=None):
+    if ratio is None:
+        sigma = torch.normal(mean=-3.0, std=0.5, size=(image.shape[0],)).to(image.device)
+        sigma = torch.exp(sigma).to(image.dtype)
+    else:
+        sigma = torch.ones((image.shape[0],)).to(image.device, image.dtype) * ratio
+    
+    image_noise = torch.randn_like(image) * sigma[:, None, None, None, None]
+    image_noise = torch.where(image==-1, torch.zeros_like(image), image_noise)
+    image = image + image_noise
+    return image
+
+
+@dataclass
+class CogVideoX_Fun_PipelineOutput(BaseOutput):
+    r"""
+    Output class for CogVideo pipelines.
+
+    Args:
+        video (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
+            List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
+            denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape
+            `(batch_size, num_frames, channels, height, width)`.
+    """
+
+    videos: torch.Tensor
+
+
+class CogVideoX_Fun_Pipeline_Inpaint(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-video generation using CogVideoX.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
+        text_encoder ([`T5EncoderModel`]):
+            Frozen text-encoder. CogVideoX_Fun uses
+            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the
+            [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
+        tokenizer (`T5Tokenizer`):
+            Tokenizer of class
+            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
+        transformer ([`CogVideoXTransformer3DModel`]):
+            A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
+    """
+
+    _optional_components = []
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+
+    _callback_tensor_inputs = [
+        "latents",
+        "prompt_embeds",
+        "negative_prompt_embeds",
+    ]
+
+    def __init__(
+        self,
+        tokenizer: T5Tokenizer,
+        text_encoder: T5EncoderModel,
+        vae: AutoencoderKLCogVideoX,
+        transformer: CogVideoXTransformer3DModel,
+        scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler],
+    ):
+        super().__init__()
+
+        self.register_modules(
+            tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
+        )
+        self.vae_scale_factor_spatial = (
+            2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
+        )
+        self.vae_scale_factor_temporal = (
+            self.vae.config.temporal_compression_ratio if hasattr(self, "vae") and self.vae is not None else 4
+        )
+
+        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
+
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.mask_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
+        )
+
+    def _get_t5_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_videos_per_prompt: int = 1,
+        max_sequence_length: int = 226,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            add_special_tokens=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+        
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because `max_sequence_length` is set to "
+                f" {max_sequence_length} tokens: {removed_text}"
+            )
+
+        prompt_embeds = self.text_encoder(text_input_ids.to(device))[0]
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
+
+        return prompt_embeds
+
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        do_classifier_free_guidance: bool = True,
+        num_videos_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 226,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
+                Whether to use classifier free guidance or not.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            device: (`torch.device`, *optional*):
+                torch device
+            dtype: (`torch.dtype`, *optional*):
+                torch dtype
+        """
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds = self._get_t5_prompt_embeds(
+                prompt=prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+
+            negative_prompt_embeds = self._get_t5_prompt_embeds(
+                prompt=negative_prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        return prompt_embeds, negative_prompt_embeds
+
+    def prepare_latents(
+        self, 
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        video_length,
+        dtype,
+        device,
+        generator,
+        latents=None,
+        video=None,
+        timestep=None,
+        is_strength_max=True,
+        return_noise=False,
+        return_video_latents=False,
+    ):
+        shape = (
+            batch_size,
+            (video_length - 1) // self.vae_scale_factor_temporal + 1,
+            num_channels_latents,
+            height // self.vae_scale_factor_spatial,
+            width // self.vae_scale_factor_spatial,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if return_video_latents or (latents is None and not is_strength_max):
+            video = video.to(device=device, dtype=self.vae.dtype)
+            
+            bs = 1
+            new_video = []
+            for i in range(0, video.shape[0], bs):
+                video_bs = video[i : i + bs]
+                video_bs = self.vae.encode(video_bs)[0]
+                video_bs = video_bs.sample()
+                new_video.append(video_bs)
+            video = torch.cat(new_video, dim = 0)
+            video = video * self.vae.config.scaling_factor
+
+            video_latents = video.repeat(batch_size // video.shape[0], 1, 1, 1, 1)
+            video_latents = video_latents.to(device=device, dtype=dtype)
+            video_latents = rearrange(video_latents, "b c f h w -> b f c h w")
+
+        if latents is None:
+            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            # if strength is 1. then initialise the latents to noise, else initial to image + noise
+            latents = noise if is_strength_max else self.scheduler.add_noise(video_latents, noise, timestep)
+            # if pure noise then scale the initial latents by the  Scheduler's init sigma
+            latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
+        else:
+            noise = latents.to(device)
+            latents = noise * self.scheduler.init_noise_sigma
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        outputs = (latents,)
+
+        if return_noise:
+            outputs += (noise,)
+
+        if return_video_latents:
+            outputs += (video_latents,)
+
+        return outputs
+
+    def prepare_mask_latents(
+        self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance, noise_aug_strength
+    ):
+        # resize the mask to latents shape as we concatenate the mask to the latents
+        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
+        # and half precision
+
+        if mask is not None:
+            mask = mask.to(device=device, dtype=self.vae.dtype)
+            bs = 1
+            new_mask = []
+            for i in range(0, mask.shape[0], bs):
+                mask_bs = mask[i : i + bs]
+                mask_bs = self.vae.encode(mask_bs)[0]
+                mask_bs = mask_bs.mode()
+                new_mask.append(mask_bs)
+            mask = torch.cat(new_mask, dim = 0)
+            mask = mask * self.vae.config.scaling_factor
+
+        if masked_image is not None:
+            if self.transformer.config.add_noise_in_inpaint_model:
+                masked_image = add_noise_to_reference_video(masked_image, ratio=noise_aug_strength)
+            masked_image = masked_image.to(device=device, dtype=self.vae.dtype)
+            bs = 1
+            new_mask_pixel_values = []
+            for i in range(0, masked_image.shape[0], bs):
+                mask_pixel_values_bs = masked_image[i : i + bs]
+                mask_pixel_values_bs = self.vae.encode(mask_pixel_values_bs)[0]
+                mask_pixel_values_bs = mask_pixel_values_bs.mode()
+                new_mask_pixel_values.append(mask_pixel_values_bs)
+            masked_image_latents = torch.cat(new_mask_pixel_values, dim = 0)
+            masked_image_latents = masked_image_latents * self.vae.config.scaling_factor
+        else:
+            masked_image_latents = None
+
+        return mask, masked_image_latents
+
+    def decode_latents(self, latents: torch.Tensor) -> torch.Tensor:
+        latents = latents.permute(0, 2, 1, 3, 4)  # [batch_size, num_channels, num_frames, height, width]
+        latents = 1 / self.vae.config.scaling_factor * latents
+
+        frames = self.vae.decode(latents).sample
+        frames = (frames / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        frames = frames.cpu().float().numpy()
+        return frames
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    # Copied from diffusers.pipelines.latte.pipeline_latte.LattePipeline.check_inputs
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        negative_prompt,
+        callback_on_step_end_tensor_inputs,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+    def fuse_qkv_projections(self) -> None:
+        r"""Enables fused QKV projections."""
+        self.fusing_transformer = True
+        self.transformer.fuse_qkv_projections()
+
+    def unfuse_qkv_projections(self) -> None:
+        r"""Disable QKV projection fusion if enabled."""
+        if not self.fusing_transformer:
+            logger.warning("The Transformer was not initially fused for QKV projections. Doing nothing.")
+        else:
+            self.transformer.unfuse_qkv_projections()
+            self.fusing_transformer = False
+
+    def _prepare_rotary_positional_embeddings(
+        self,
+        height: int,
+        width: int,
+        num_frames: int,
+        device: torch.device,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+        grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+
+        p = self.transformer.config.patch_size
+        p_t = self.transformer.config.patch_size_t
+
+        base_size_width = self.transformer.config.sample_width // p
+        base_size_height = self.transformer.config.sample_height // p
+
+        if p_t is None:
+            # CogVideoX 1.0
+            grid_crops_coords = get_resize_crop_region_for_grid(
+                (grid_height, grid_width), base_size_width, base_size_height
+            )
+            freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
+                embed_dim=self.transformer.config.attention_head_dim,
+                crops_coords=grid_crops_coords,
+                grid_size=(grid_height, grid_width),
+                temporal_size=num_frames,
+            )
+        else:
+            # CogVideoX 1.5
+            base_num_frames = (num_frames + p_t - 1) // p_t
+
+            freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
+                embed_dim=self.transformer.config.attention_head_dim,
+                crops_coords=None,
+                grid_size=(grid_height, grid_width),
+                temporal_size=base_num_frames,
+                grid_type="slice",
+                max_size=(base_size_height, base_size_width),
+            )
+
+        freqs_cos = freqs_cos.to(device=device)
+        freqs_sin = freqs_sin.to(device=device)
+        return freqs_cos, freqs_sin
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
+    def get_timesteps(self, num_inference_steps, strength, device):
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+
+        return timesteps, num_inference_steps - t_start
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        height: int = 480,
+        width: int = 720,
+        video: Union[torch.FloatTensor] = None,
+        mask_video: Union[torch.FloatTensor] = None,
+        masked_video_latents: Union[torch.FloatTensor] = None,
+        flow_latents: Optional[torch.FloatTensor] = None,
+        num_frames: int = 49,
+        num_inference_steps: int = 50,
+        timesteps: Optional[List[int]] = None,
+        guidance_scale: float = 6,
+        use_dynamic_cfg: bool = False,
+        num_videos_per_prompt: int = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: str = "numpy",
+        return_dict: bool = False,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 226,
+        strength: float = 1,
+        noise_aug_strength: float = 0.0563,
+        comfyui_progressbar: bool = False,
+    ) -> Union[CogVideoX_Fun_PipelineOutput, Tuple]:
+        """
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            num_frames (`int`, defaults to `48`):
+                Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will
+                contain 1 extra frame because CogVideoX_Fun is conditioned with (num_seconds * fps + 1) frames where
+                num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that
+                needs to be satisfied is that of divisibility mentioned above.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 7.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                The number of videos to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int`, defaults to `226`):
+                Maximum sequence length in encoded prompt. Must be consistent with
+                `self.transformer.config.max_text_seq_length` otherwise may lead to poor results.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoX_Fun_PipelineOutput`] or `tuple`:
+            [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoX_Fun_PipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        height = height or self.transformer.config.sample_height * self.vae_scale_factor_spatial
+        width = width or self.transformer.config.sample_width * self.vae_scale_factor_spatial
+        num_frames = num_frames or self.transformer.config.sample_frames
+
+        num_videos_per_prompt = 1
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            negative_prompt,
+            callback_on_step_end_tensor_inputs,
+            prompt_embeds,
+            negative_prompt_embeds,
+        )
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+        self._interrupt = False
+
+        # 2. Default call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+            prompt,
+            negative_prompt,
+            do_classifier_free_guidance,
+            num_videos_per_prompt=num_videos_per_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            max_sequence_length=max_sequence_length,
+            device=device,
+        )
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+
+        # 4. set timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps, num_inference_steps = self.get_timesteps(
+            num_inference_steps=num_inference_steps, strength=strength, device=device
+        )
+        self._num_timesteps = len(timesteps)
+        if comfyui_progressbar:
+            from comfy.utils import ProgressBar
+            pbar = ProgressBar(num_inference_steps + 2)
+        # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)
+        # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
+        is_strength_max = strength == 1.0
+
+        # 5. Prepare latents.
+        if video is not None:
+            video_length = video.shape[2]
+            init_video = self.image_processor.preprocess(rearrange(video, "b c f h w -> (b f) c h w"), height=height, width=width) 
+            init_video = init_video.to(dtype=torch.float32)
+            init_video = rearrange(init_video, "(b f) c h w -> b c f h w", f=video_length)
+        else:
+            init_video = None
+
+        # Magvae needs the number of frames to be 4n + 1.
+        local_latent_length = (num_frames - 1) // self.vae_scale_factor_temporal + 1
+        # For CogVideoX 1.5, the latent frames should be clipped to make it divisible by patch_size_t
+        patch_size_t = self.transformer.config.patch_size_t
+        additional_frames = 0
+        if patch_size_t is not None and local_latent_length % patch_size_t != 0:
+            additional_frames = local_latent_length % patch_size_t
+            num_frames -= additional_frames * self.vae_scale_factor_temporal
+        if num_frames <= 0:
+            num_frames = 1
+        if video_length > num_frames:
+            logger.warning("The length of condition video is not right, the latent frames should be clipped to make it divisible by patch_size_t. ")
+            video_length = num_frames
+            video = video[:, :, :video_length]
+            init_video = init_video[:, :, :video_length]
+            mask_video = mask_video[:, :, :video_length]
+
+        num_channels_latents = self.vae.config.latent_channels
+        num_channels_transformer = self.transformer.config.in_channels
+        return_image_latents = num_channels_transformer == num_channels_latents
+
+        latents_outputs = self.prepare_latents(
+            batch_size * num_videos_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            video_length,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+            video=init_video,
+            timestep=latent_timestep,
+            is_strength_max=is_strength_max,
+            return_noise=True,
+            return_video_latents=return_image_latents,
+        )
+        if return_image_latents:
+            latents, noise, image_latents = latents_outputs
+        else:
+            latents, noise = latents_outputs
+        if comfyui_progressbar:
+            pbar.update(1)
+        
+        if mask_video is not None:
+            if (mask_video == 255).all():
+                mask_latents = torch.zeros_like(latents)[:, :, :1].to(latents.device, latents.dtype)
+                masked_video_latents = torch.zeros_like(latents).to(latents.device, latents.dtype)
+
+                mask_input = torch.cat([mask_latents] * 2) if do_classifier_free_guidance else mask_latents
+                masked_video_latents_input = (
+                    torch.cat([masked_video_latents] * 2) if do_classifier_free_guidance else masked_video_latents
+                )
+                inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=2).to(latents.dtype)
+            else:
+                # Prepare mask latent variables
+                video_length = video.shape[2]
+                mask_condition = self.mask_processor.preprocess(rearrange(mask_video, "b c f h w -> (b f) c h w"), height=height, width=width) 
+                mask_condition = mask_condition.to(dtype=torch.float32)
+                mask_condition = rearrange(mask_condition, "(b f) c h w -> b c f h w", f=video_length)
+
+                if num_channels_transformer != num_channels_latents:
+                    mask_condition_tile = torch.tile(mask_condition, [1, 3, 1, 1, 1])
+                    if masked_video_latents is None:
+                        masked_video = init_video * (mask_condition_tile < 0.5) + torch.ones_like(init_video) * (mask_condition_tile > 0.5) * -1
+                    else:
+                        masked_video = masked_video_latents
+
+                    _, masked_video_latents = self.prepare_mask_latents(
+                        None,
+                        masked_video,
+                        batch_size,
+                        height,
+                        width,
+                        prompt_embeds.dtype,
+                        device,
+                        generator,
+                        do_classifier_free_guidance,
+                        noise_aug_strength=noise_aug_strength,
+                    )
+                    mask_latents = resize_mask(1 - mask_condition, masked_video_latents)
+                    mask_latents = mask_latents.to(masked_video_latents.device) * self.vae.config.scaling_factor
+
+                    mask = torch.tile(mask_condition, [1, num_channels_latents, 1, 1, 1])
+                    mask = F.interpolate(mask, size=latents.size()[-3:], mode='trilinear', align_corners=True).to(latents.device, latents.dtype)
+                    
+                    mask_input = torch.cat([mask_latents] * 2) if do_classifier_free_guidance else mask_latents
+                    masked_video_latents_input = (
+                        torch.cat([masked_video_latents] * 2) if do_classifier_free_guidance else masked_video_latents
+                    )
+
+                    mask = rearrange(mask, "b c f h w -> b f c h w")
+                    mask_input = rearrange(mask_input, "b c f h w -> b f c h w")
+                    masked_video_latents_input = rearrange(masked_video_latents_input, "b c f h w -> b f c h w")
+
+                    inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=2).to(latents.dtype)
+                else:
+                    mask = torch.tile(mask_condition, [1, num_channels_latents, 1, 1, 1])
+                    mask = F.interpolate(mask, size=latents.size()[-3:], mode='trilinear', align_corners=True).to(latents.device, latents.dtype)
+                    mask = rearrange(mask, "b c f h w -> b f c h w")
+                    
+                    inpaint_latents = None
+        else:
+            if num_channels_transformer != num_channels_latents:
+                mask = torch.zeros_like(latents).to(latents.device, latents.dtype)
+                masked_video_latents = torch.zeros_like(latents).to(latents.device, latents.dtype)
+
+                mask_input = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
+                masked_video_latents_input = (
+                    torch.cat([masked_video_latents] * 2) if do_classifier_free_guidance else masked_video_latents
+                )
+                inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=1).to(latents.dtype)
+            else:
+                mask = torch.zeros_like(init_video[:, :1])
+                mask = torch.tile(mask, [1, num_channels_latents, 1, 1, 1])
+                mask = F.interpolate(mask, size=latents.size()[-3:], mode='trilinear', align_corners=True).to(latents.device, latents.dtype)
+                mask = rearrange(mask, "b c f h w -> b f c h w")
+
+                inpaint_latents = None
+        if comfyui_progressbar:
+            pbar.update(1)
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. Create rotary embeds if required
+        image_rotary_emb = (
+            self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
+            if self.transformer.config.use_rotary_positional_embeddings
+            else None
+        )
+
+        # 8. Denoising loop
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        if flow_latents is not None:
+            flow_latents = torch.cat([flow_latents] * 2) if do_classifier_free_guidance else latents
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            # for DPM-solver++
+            old_pred_original_sample = None
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+                
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0])
+
+                # predict noise model_output
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep=timestep,
+                    image_rotary_emb=image_rotary_emb,
+                    return_dict=False,
+                    inpaint_latents=inpaint_latents,
+                    flow_latents=flow_latents,
+                )[0]
+                noise_pred = noise_pred.float()
+
+                # perform guidance
+                if use_dynamic_cfg:
+                    self._guidance_scale = 1 + guidance_scale * (
+                        (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2
+                    )
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                if not isinstance(self.scheduler, CogVideoXDPMScheduler):
+                    latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+                else:
+                    latents, old_pred_original_sample = self.scheduler.step(
+                        noise_pred,
+                        old_pred_original_sample,
+                        t,
+                        timesteps[i - 1] if i > 0 else None,
+                        latents,
+                        **extra_step_kwargs,
+                        return_dict=False,
+                    )
+                latents = latents.to(prompt_embeds.dtype)
+
+                # call the callback, if provided
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                if comfyui_progressbar:
+                    pbar.update(1)
+
+        if output_type == "numpy":
+            video = self.decode_latents(latents)
+        elif not output_type == "latent":
+            video = self.decode_latents(latents)
+            video = self.video_processor.postprocess_video(video=video, output_type=output_type)
+        else:
+            video = latents
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            video = torch.from_numpy(video)
+
+        return CogVideoX_Fun_PipelineOutput(videos=video)

robomaster/reward/MPS/README.md

@@ -0,0 +1 @@
+This folder is modified from the official [MPS](https://github.com/Kwai-Kolors/MPS/tree/main) repository.

robomaster/reward/MPS/trainer/models/base_model.py

@@ -0,0 +1,7 @@
+from dataclasses import dataclass
+
+
+
+@dataclass
+class BaseModelConfig:
+    pass

robomaster/reward/MPS/trainer/models/clip_model.py

@@ -0,0 +1,154 @@
+from dataclasses import dataclass
+from transformers import CLIPModel as HFCLIPModel
+from transformers import AutoTokenizer
+
+from torch import nn, einsum
+
+# Modified: import
+# from trainer.models.base_model import BaseModelConfig
+from .base_model import BaseModelConfig
+
+from transformers import CLIPConfig
+from typing import Any, Optional, Tuple, Union
+import torch
+
+# Modified: import
+# from trainer.models.cross_modeling import Cross_model
+from .cross_modeling import Cross_model
+
+import gc
+
+class XCLIPModel(HFCLIPModel):
+    def __init__(self, config: CLIPConfig):
+        super().__init__(config)
+    
+    def get_text_features(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> torch.FloatTensor:
+
+        # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        text_outputs = self.text_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        # pooled_output = text_outputs[1]
+        # text_features = self.text_projection(pooled_output)
+        last_hidden_state = text_outputs[0]
+        text_features = self.text_projection(last_hidden_state)
+
+        pooled_output = text_outputs[1]
+        text_features_EOS = self.text_projection(pooled_output)
+
+
+        # del last_hidden_state, text_outputs
+        # gc.collect()
+
+        return text_features, text_features_EOS
+
+    def get_image_features(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> torch.FloatTensor:
+        
+        # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        vision_outputs = self.vision_model(
+            pixel_values=pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        # pooled_output = vision_outputs[1]  # pooled_output
+        # image_features = self.visual_projection(pooled_output)
+        last_hidden_state = vision_outputs[0]
+        image_features = self.visual_projection(last_hidden_state)
+
+        return image_features
+
+
+
+@dataclass
+class ClipModelConfig(BaseModelConfig):
+    _target_: str = "trainer.models.clip_model.CLIPModel"
+    pretrained_model_name_or_path: str ="openai/clip-vit-base-patch32"
+
+
+class CLIPModel(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        # Modified: We convert the original ckpt (contains the entire model) to a `state_dict`.
+        # self.model = XCLIPModel.from_pretrained(ckpt)
+        self.model = XCLIPModel(config)
+        self.cross_model = Cross_model(dim=1024, layer_num=4, heads=16)
+    
+    def get_text_features(self, *args, **kwargs):
+        return self.model.get_text_features(*args, **kwargs)
+
+    def get_image_features(self, *args, **kwargs):
+        return self.model.get_image_features(*args, **kwargs)
+
+    def forward(self, text_inputs=None, image_inputs=None, condition_inputs=None):
+        outputs = ()
+
+        text_f, text_EOS = self.model.get_text_features(text_inputs) # B*77*1024
+        outputs += text_EOS,
+
+        image_f = self.model.get_image_features(image_inputs.half()) # 2B*257*1024
+        # [B, 77, 1024]
+        condition_f, _ = self.model.get_text_features(condition_inputs) # B*5*1024
+
+        sim_text_condition = einsum('b i d, b j d -> b j i', text_f, condition_f)
+        sim_text_condition = torch.max(sim_text_condition, dim=1, keepdim=True)[0]
+        sim_text_condition = sim_text_condition / sim_text_condition.max()
+        mask = torch.where(sim_text_condition > 0.01, 0, float('-inf')) # B*1*77
+
+        # Modified: Support both torch.float16 and torch.bfloat16
+        # mask = mask.repeat(1,image_f.shape[1],1) # B*257*77
+        model_dtype = next(self.cross_model.parameters()).dtype
+        mask = mask.repeat(1,image_f.shape[1],1).to(model_dtype) # B*257*77
+        # bc = int(image_f.shape[0]/2)
+
+        # Modified: The original input consists of a (batch of) text and two (batches of) images, 
+        # primarily used to compute which (batch of) image is more consistent with the text.
+        # The modified input consists of a (batch of) text and a (batch of) images.
+        # sim0 = self.cross_model(image_f[:bc,:,:], text_f,mask.half())
+        # sim1 = self.cross_model(image_f[bc:,:,:], text_f,mask.half())
+        # outputs += sim0[:,0,:],
+        # outputs += sim1[:,0,:],
+        sim = self.cross_model(image_f, text_f,mask)
+        outputs += sim[:,0,:],
+
+        return outputs
+
+    @property
+    def logit_scale(self):
+        return self.model.logit_scale
+
+    def save(self, path):
+        self.model.save_pretrained(path)

robomaster/reward/MPS/trainer/models/cross_modeling.py

@@ -0,0 +1,291 @@
+import torch
+from torch import einsum, nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+
+# helper functions
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
+# normalization
+# they use layernorm without bias, something that pytorch does not offer
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(dim))
+        self.register_buffer("bias", torch.zeros(dim))
+
+    def forward(self, x):
+        return F.layer_norm(x, x.shape[-1:], self.weight, self.bias)
+
+# residual
+
+
+class Residual(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+
+    def forward(self, x, *args, **kwargs):
+        return self.fn(x, *args, **kwargs) + x
+
+
+# rotary positional embedding
+# https://arxiv.org/abs/2104.09864
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq)
+
+    def forward(self, max_seq_len, *, device):
+        seq = torch.arange(max_seq_len, device=device, dtype=self.inv_freq.dtype)
+        freqs = einsum("i , j -> i j", seq, self.inv_freq)
+        return torch.cat((freqs, freqs), dim=-1)
+
+
+def rotate_half(x):
+    x = rearrange(x, "... (j d) -> ... j d", j=2)
+    x1, x2 = x.unbind(dim=-2)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(pos, t):
+    return (t * pos.cos()) + (rotate_half(t) * pos.sin())
+
+
+# classic Noam Shazeer paper, except here they use SwiGLU instead of the more popular GEGLU for gating the feedforward
+# https://arxiv.org/abs/2002.05202
+
+
+class SwiGLU(nn.Module):
+    def forward(self, x):
+        x, gate = x.chunk(2, dim=-1)
+        return F.silu(gate) * x
+
+
+# parallel attention and feedforward with residual
+# discovered by Wang et al + EleutherAI from GPT-J fame
+
+class ParallelTransformerBlock(nn.Module):
+    def __init__(self, dim, dim_head=64, heads=8, ff_mult=4):
+        super().__init__()
+        self.norm = LayerNorm(dim)
+
+        attn_inner_dim = dim_head * heads
+        ff_inner_dim = dim * ff_mult
+        self.fused_dims = (attn_inner_dim, dim_head, dim_head, (ff_inner_dim * 2))
+
+        self.heads = heads
+        self.scale = dim_head**-0.5
+        self.rotary_emb = RotaryEmbedding(dim_head)
+
+        self.fused_attn_ff_proj = nn.Linear(dim, sum(self.fused_dims), bias=False)
+        self.attn_out = nn.Linear(attn_inner_dim, dim, bias=False)
+
+        self.ff_out = nn.Sequential(
+            SwiGLU(),
+            nn.Linear(ff_inner_dim, dim, bias=False)
+        )
+
+        self.register_buffer("pos_emb", None, persistent=False)
+
+
+    def get_rotary_embedding(self, n, device):
+        if self.pos_emb is not None and self.pos_emb.shape[-2] >= n:
+            return self.pos_emb[:n]
+
+        pos_emb = self.rotary_emb(n, device=device)
+        self.register_buffer("pos_emb", pos_emb, persistent=False)
+        return pos_emb
+
+    def forward(self, x, attn_mask=None):
+        """
+        einstein notation
+        b - batch
+        h - heads
+        n, i, j - sequence length (base sequence length, source, target)
+        d - feature dimension
+        """
+
+        n, device, h = x.shape[1], x.device, self.heads
+
+        # pre layernorm
+
+        x = self.norm(x)
+
+        # attention queries, keys, values, and feedforward inner
+
+        q, k, v, ff = self.fused_attn_ff_proj(x).split(self.fused_dims, dim=-1)
+
+        # split heads
+        # they use multi-query single-key-value attention, yet another Noam Shazeer paper
+        # they found no performance loss past a certain scale, and more efficient decoding obviously
+        # https://arxiv.org/abs/1911.02150
+
+        q = rearrange(q, "b n (h d) -> b h n d", h=h)
+
+        # rotary embeddings
+
+        positions = self.get_rotary_embedding(n, device)
+        q, k = map(lambda t: apply_rotary_pos_emb(positions, t), (q, k))
+
+        # scale
+
+        q = q * self.scale
+
+        # similarity
+
+        sim = einsum("b h i d, b j d -> b h i j", q, k)
+
+
+        # extra attention mask - for masking out attention from text CLS token to padding
+
+        if exists(attn_mask):
+            attn_mask = rearrange(attn_mask, 'b i j -> b 1 i j')
+            sim = sim.masked_fill(~attn_mask, -torch.finfo(sim.dtype).max)
+
+        # attention
+
+        sim = sim - sim.amax(dim=-1, keepdim=True).detach()
+        attn = sim.softmax(dim=-1)
+
+        # aggregate values
+
+        out = einsum("b h i j, b j d -> b h i d", attn, v)
+
+        # merge heads
+
+        out = rearrange(out, "b h n d -> b n (h d)")
+        return self.attn_out(out) + self.ff_out(ff)
+
+# cross attention - using multi-query + one-headed key / values as in PaLM w/ optional parallel feedforward
+
+class CrossAttention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        *,
+        context_dim=None,
+        dim_head=64,
+        heads=12,
+        parallel_ff=False,
+        ff_mult=4,
+        norm_context=False
+    ):
+        super().__init__()
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+        inner_dim = heads * dim_head
+        context_dim = default(context_dim, dim)
+
+        self.norm = LayerNorm(dim)
+        self.context_norm = LayerNorm(context_dim) if norm_context else nn.Identity()
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(context_dim, dim_head * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+        # whether to have parallel feedforward
+
+        ff_inner_dim = ff_mult * dim
+
+        self.ff = nn.Sequential(
+            nn.Linear(dim, ff_inner_dim * 2, bias=False),
+            SwiGLU(),
+            nn.Linear(ff_inner_dim, dim, bias=False)
+        ) if parallel_ff else None
+
+    def forward(self, x, context, mask):
+        """
+        einstein notation
+        b - batch
+        h - heads
+        n, i, j - sequence length (base sequence length, source, target)
+        d - feature dimension
+        """
+
+        # pre-layernorm, for queries and context
+
+        x = self.norm(x)
+        context = self.context_norm(context)
+
+        # get queries
+
+        q = self.to_q(x)
+        q = rearrange(q, 'b n (h d) -> b h n d', h = self.heads)
+
+        # scale
+
+        q = q * self.scale
+
+        # get key / values
+
+        k, v = self.to_kv(context).chunk(2, dim=-1)
+
+        # query / key similarity
+
+        sim = einsum('b h i d, b j d -> b h i j', q, k)
+
+        # attention
+        mask = mask.unsqueeze(1).repeat(1,self.heads,1,1)
+        sim = sim + mask  # context mask
+        sim = sim - sim.amax(dim=-1, keepdim=True)
+        attn = sim.softmax(dim=-1)
+
+        # aggregate
+
+        out = einsum('b h i j, b j d -> b h i d', attn, v)
+
+        # merge and combine heads
+
+        out = rearrange(out, 'b h n d -> b n (h d)')
+        out = self.to_out(out)
+
+        # add parallel feedforward (for multimodal layers)
+
+        if exists(self.ff):
+            out = out + self.ff(x)
+
+        return out
+
+
+class Cross_model(nn.Module):
+    def __init__(
+        self,
+        dim=512,
+        layer_num=4,
+        dim_head=64,
+        heads=8,
+        ff_mult=4
+    ):
+        super().__init__()
+
+        self.layers = nn.ModuleList([])
+
+
+        for ind in range(layer_num):
+            self.layers.append(nn.ModuleList([
+                Residual(CrossAttention(dim=dim, dim_head=dim_head, heads=heads, parallel_ff=True, ff_mult=ff_mult)),
+                Residual(ParallelTransformerBlock(dim=dim, dim_head=dim_head, heads=heads, ff_mult=ff_mult))
+            ]))
+
+    def forward(
+        self,
+        query_tokens,
+        context_tokens,
+        mask
+    ):
+        for cross_attn, self_attn_ff in self.layers:
+            query_tokens = cross_attn(query_tokens, context_tokens,mask)
+            query_tokens = self_attn_ff(query_tokens)
+
+        return query_tokens

robomaster/reward/aesthetic_predictor_v2_5/__init__.py

@@ -0,0 +1,13 @@
+from .siglip_v2_5 import (
+    AestheticPredictorV2_5Head,
+    AestheticPredictorV2_5Model,
+    AestheticPredictorV2_5Processor,
+    convert_v2_5_from_siglip,
+)
+
+__all__ = [
+    "AestheticPredictorV2_5Head",
+    "AestheticPredictorV2_5Model",
+    "AestheticPredictorV2_5Processor",
+    "convert_v2_5_from_siglip",
+]

robomaster/reward/aesthetic_predictor_v2_5/siglip_v2_5.py

@@ -0,0 +1,133 @@
+# Borrowed from https://github.com/discus0434/aesthetic-predictor-v2-5/blob/3125a9e/src/aesthetic_predictor_v2_5/siglip_v2_5.py
+import os
+from collections import OrderedDict
+from os import PathLike
+from typing import Final
+
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+from transformers import (
+    SiglipImageProcessor,
+    SiglipVisionConfig,
+    SiglipVisionModel,
+    logging,
+)
+from transformers.image_processing_utils import BatchFeature
+from transformers.modeling_outputs import ImageClassifierOutputWithNoAttention
+
+logging.set_verbosity_error()
+
+URL: Final[str] = (
+    "https://github.com/discus0434/aesthetic-predictor-v2-5/raw/main/models/aesthetic_predictor_v2_5.pth"
+)
+
+
+class AestheticPredictorV2_5Head(nn.Module):
+    def __init__(self, config: SiglipVisionConfig) -> None:
+        super().__init__()
+        self.scoring_head = nn.Sequential(
+            nn.Linear(config.hidden_size, 1024),
+            nn.Dropout(0.5),
+            nn.Linear(1024, 128),
+            nn.Dropout(0.5),
+            nn.Linear(128, 64),
+            nn.Dropout(0.5),
+            nn.Linear(64, 16),
+            nn.Dropout(0.2),
+            nn.Linear(16, 1),
+        )
+
+    def forward(self, image_embeds: torch.Tensor) -> torch.Tensor:
+        return self.scoring_head(image_embeds)
+
+
+class AestheticPredictorV2_5Model(SiglipVisionModel):
+    PATCH_SIZE = 14
+
+    def __init__(self, config: SiglipVisionConfig, *args, **kwargs) -> None:
+        super().__init__(config, *args, **kwargs)
+        self.layers = AestheticPredictorV2_5Head(config)
+        self.post_init()
+        self.transforms = transforms.Compose([
+            transforms.Resize((384, 384)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+        ])
+
+    def forward(
+        self,
+        pixel_values: torch.FloatTensor | None = None,
+        labels: torch.Tensor | None = None,
+        return_dict: bool | None = None,
+    ) -> tuple | ImageClassifierOutputWithNoAttention:
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = super().forward(
+            pixel_values=pixel_values,
+            return_dict=return_dict,
+        )
+        image_embeds = outputs.pooler_output
+        image_embeds_norm = image_embeds / image_embeds.norm(dim=-1, keepdim=True)
+        prediction = self.layers(image_embeds_norm)
+
+        loss = None
+        if labels is not None:
+            loss_fct = nn.MSELoss()
+            loss = loss_fct()
+
+        if not return_dict:
+            return (loss, prediction, image_embeds)
+
+        return ImageClassifierOutputWithNoAttention(
+            loss=loss,
+            logits=prediction,
+            hidden_states=image_embeds,
+        )
+
+
+class AestheticPredictorV2_5Processor(SiglipImageProcessor):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+    def __call__(self, *args, **kwargs) -> BatchFeature:
+        return super().__call__(*args, **kwargs)
+
+    @classmethod
+    def from_pretrained(
+        self,
+        pretrained_model_name_or_path: str
+        | PathLike = "google/siglip-so400m-patch14-384",
+        *args,
+        **kwargs,
+    ) -> "AestheticPredictorV2_5Processor":
+        return super().from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
+
+
+def convert_v2_5_from_siglip(
+    predictor_name_or_path: str | PathLike | None = None,
+    encoder_model_name: str = "google/siglip-so400m-patch14-384",
+    *args,
+    **kwargs,
+) -> tuple[AestheticPredictorV2_5Model, AestheticPredictorV2_5Processor]:
+    model = AestheticPredictorV2_5Model.from_pretrained(
+        encoder_model_name, *args, **kwargs
+    )
+
+    processor = AestheticPredictorV2_5Processor.from_pretrained(
+        encoder_model_name, *args, **kwargs
+    )
+
+    if predictor_name_or_path is None or not os.path.exists(predictor_name_or_path):
+        state_dict = torch.hub.load_state_dict_from_url(URL, map_location="cpu")
+    else:
+        state_dict = torch.load(predictor_name_or_path, map_location="cpu")
+
+    assert isinstance(state_dict, OrderedDict)
+
+    model.layers.load_state_dict(state_dict)
+    model.eval()
+
+    return model, processor

robomaster/reward/improved_aesthetic_predictor.py

@@ -0,0 +1,49 @@
+import os
+
+import torch
+import torch.nn as nn
+from transformers import CLIPModel
+from torchvision.datasets.utils import download_url
+
+URL = "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Third_Party/sac%2Blogos%2Bava1-l14-linearMSE.pth"
+FILENAME = "sac+logos+ava1-l14-linearMSE.pth"
+MD5 = "b1047fd767a00134b8fd6529bf19521a"
+
+
+class MLP(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.layers = nn.Sequential(
+            nn.Linear(768, 1024),
+            nn.Dropout(0.2),
+            nn.Linear(1024, 128),
+            nn.Dropout(0.2),
+            nn.Linear(128, 64),
+            nn.Dropout(0.1),
+            nn.Linear(64, 16),
+            nn.Linear(16, 1),
+        )
+
+
+    def forward(self, embed):
+        return self.layers(embed)
+
+
+class ImprovedAestheticPredictor(nn.Module):
+    def __init__(self, encoder_path="openai/clip-vit-large-patch14", predictor_path=None):
+        super().__init__()
+        self.encoder = CLIPModel.from_pretrained(encoder_path)
+        self.predictor = MLP()
+        if predictor_path is None or not os.path.exists(predictor_path):
+            download_url(URL, torch.hub.get_dir(), FILENAME, md5=MD5)
+            predictor_path = os.path.join(torch.hub.get_dir(), FILENAME)
+        state_dict = torch.load(predictor_path, map_location="cpu")
+        self.predictor.load_state_dict(state_dict)
+        self.eval()
+    
+
+    def forward(self, pixel_values):
+        embed = self.encoder.get_image_features(pixel_values=pixel_values)
+        embed = embed / torch.linalg.vector_norm(embed, dim=-1, keepdim=True)
+
+        return self.predictor(embed).squeeze(1)

robomaster/reward/reward_fn.py

@@ -0,0 +1,385 @@
+import os
+from abc import ABC, abstractmethod
+
+import torch
+import torchvision.transforms as transforms
+from einops import rearrange
+from torchvision.datasets.utils import download_url
+from typing import Optional, Tuple
+
+
+# All reward models.
+__all__ = ["AestheticReward", "HPSReward", "PickScoreReward", "MPSReward"]
+
+
+class BaseReward(ABC):
+    """An base class for reward models. A custom Reward class must implement two functions below.
+    """
+    def __init__(self):
+        """Define your reward model and image transformations (optional) here.
+        """
+        pass
+
+    @abstractmethod
+    def __call__(self, batch_frames: torch.Tensor, batch_prompt: Optional[list[str]]=None) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Given batch frames with shape `[B, C, T, H, W]` extracted from a list of videos and a list of prompts 
+        (optional) correspondingly, return the loss and reward computed by your reward model (reduction by mean).
+        """
+        pass
+
+class AestheticReward(BaseReward):
+    """Aesthetic Predictor [V2](https://github.com/christophschuhmann/improved-aesthetic-predictor) 
+    and [V2.5](https://github.com/discus0434/aesthetic-predictor-v2-5) reward model.
+    """
+    def __init__(
+        self,
+        encoder_path="openai/clip-vit-large-patch14",
+        predictor_path=None,
+        version="v2",
+        device="cpu",
+        dtype=torch.float16,
+        max_reward=10,
+        loss_scale=0.1,
+    ):
+        from .improved_aesthetic_predictor import ImprovedAestheticPredictor
+        from ..video_caption.utils.siglip_v2_5 import convert_v2_5_from_siglip
+
+        self.encoder_path = encoder_path
+        self.predictor_path = predictor_path
+        self.version = version
+        self.device = device
+        self.dtype = dtype
+        self.max_reward = max_reward
+        self.loss_scale = loss_scale
+
+        if self.version != "v2" and self.version != "v2.5":
+            raise ValueError("Only v2 and v2.5 are supported.")
+        if self.version == "v2":
+            assert "clip-vit-large-patch14" in encoder_path.lower()
+            self.model = ImprovedAestheticPredictor(encoder_path=self.encoder_path, predictor_path=self.predictor_path)
+            # https://huggingface.co/openai/clip-vit-large-patch14/blob/main/preprocessor_config.json
+            # TODO: [transforms.Resize(224), transforms.CenterCrop(224)] for any aspect ratio.
+            self.transform = transforms.Compose([
+                transforms.Resize((224, 224), interpolation=transforms.InterpolationMode.BICUBIC),
+                transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711]),
+            ])
+        elif self.version == "v2.5":
+            assert "siglip-so400m-patch14-384" in encoder_path.lower()
+            self.model, _ = convert_v2_5_from_siglip(encoder_model_name=self.encoder_path)
+            # https://huggingface.co/google/siglip-so400m-patch14-384/blob/main/preprocessor_config.json
+            self.transform = transforms.Compose([
+                transforms.Resize((384, 384), interpolation=transforms.InterpolationMode.BICUBIC),
+                transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+            ])
+        
+        self.model.to(device=self.device, dtype=self.dtype)
+        self.model.requires_grad_(False)
+    
+
+    def __call__(self, batch_frames: torch.Tensor, batch_prompt: Optional[list[str]]=None) -> Tuple[torch.Tensor, torch.Tensor]:
+        batch_frames = rearrange(batch_frames, "b c t h w -> t b c h w")
+        batch_loss, batch_reward = 0, 0
+        for frames in batch_frames:
+            pixel_values = torch.stack([self.transform(frame) for frame in frames])
+            pixel_values = pixel_values.to(self.device, dtype=self.dtype)
+            if self.version == "v2":
+                reward = self.model(pixel_values)
+            elif self.version == "v2.5":
+                reward = self.model(pixel_values).logits.squeeze()
+            # Convert reward to loss in [0, 1].
+            if self.max_reward is None:
+                loss = (-1 * reward) * self.loss_scale
+            else:
+                loss = abs(reward - self.max_reward) * self.loss_scale
+            batch_loss, batch_reward = batch_loss + loss.mean(), batch_reward + reward.mean()
+        
+        return batch_loss / batch_frames.shape[0], batch_reward / batch_frames.shape[0]
+
+
+class HPSReward(BaseReward):
+    """[HPS](https://github.com/tgxs002/HPSv2) v2 and v2.1 reward model.
+    """
+    def __init__(
+        self,
+        model_path=None,
+        version="v2.0",
+        device="cpu",
+        dtype=torch.float16,
+        max_reward=1,
+        loss_scale=1,
+    ):
+        from hpsv2.src.open_clip import create_model_and_transforms, get_tokenizer
+
+        self.model_path = model_path
+        self.version = version
+        self.device = device
+        self.dtype = dtype
+        self.max_reward = max_reward
+        self.loss_scale = loss_scale
+
+        self.model, _, _ = create_model_and_transforms(
+            "ViT-H-14",
+            "laion2B-s32B-b79K",
+            precision=self.dtype,
+            device=self.device,
+            jit=False,
+            force_quick_gelu=False,
+            force_custom_text=False,
+            force_patch_dropout=False,
+            force_image_size=None,
+            pretrained_image=False,
+            image_mean=None,
+            image_std=None,
+            light_augmentation=True,
+            aug_cfg={},
+            output_dict=True,
+            with_score_predictor=False,
+            with_region_predictor=False,
+        )
+        self.tokenizer = get_tokenizer("ViT-H-14")
+
+        # https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K/blob/main/preprocessor_config.json
+        # TODO: [transforms.Resize(224), transforms.CenterCrop(224)] for any aspect ratio.
+        self.transform = transforms.Compose([
+            transforms.Resize((224, 224), interpolation=transforms.InterpolationMode.BICUBIC),
+            transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711]),
+        ])
+
+        if version == "v2.0":
+            url = "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Third_Party/HPS_v2_compressed.pt"
+            filename = "HPS_v2_compressed.pt"
+            md5 = "fd9180de357abf01fdb4eaad64631db4"
+        elif version == "v2.1":
+            url = "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Third_Party/HPS_v2.1_compressed.pt"
+            filename = "HPS_v2.1_compressed.pt"
+            md5 = "4067542e34ba2553a738c5ac6c1d75c0"
+        else:
+            raise ValueError("Only v2.0 and v2.1 are supported.")
+        if self.model_path is None or not os.path.exists(self.model_path):
+            download_url(url, torch.hub.get_dir(), md5=md5)
+            model_path = os.path.join(torch.hub.get_dir(), filename)
+
+        state_dict = torch.load(model_path, map_location="cpu")["state_dict"]
+        self.model.load_state_dict(state_dict)
+        self.model.to(device=self.device, dtype=self.dtype)
+        self.model.requires_grad_(False)
+        self.model.eval()
+    
+    def __call__(self, batch_frames: torch.Tensor, batch_prompt: list[str]) -> Tuple[torch.Tensor, torch.Tensor]:
+        assert batch_frames.shape[0] == len(batch_prompt)
+        # Compute batch reward and loss in frame-wise.
+        batch_frames = rearrange(batch_frames, "b c t h w -> t b c h w")
+        batch_loss, batch_reward = 0, 0
+        for frames in batch_frames:
+            image_inputs = torch.stack([self.transform(frame) for frame in frames])
+            image_inputs = image_inputs.to(device=self.device, dtype=self.dtype)
+            text_inputs = self.tokenizer(batch_prompt).to(device=self.device)
+            outputs = self.model(image_inputs, text_inputs)
+
+            image_features, text_features = outputs["image_features"], outputs["text_features"]
+            logits = image_features @ text_features.T
+            reward = torch.diagonal(logits)
+            # Convert reward to loss in [0, 1].
+            if self.max_reward is None:
+                loss = (-1 * reward) * self.loss_scale
+            else:
+                loss = abs(reward - self.max_reward) * self.loss_scale
+            
+            batch_loss, batch_reward = batch_loss + loss.mean(), batch_reward + reward.mean()
+        
+        return batch_loss / batch_frames.shape[0], batch_reward / batch_frames.shape[0]
+
+
+class PickScoreReward(BaseReward):
+    """[PickScore](https://github.com/yuvalkirstain/PickScore) reward model.
+    """
+    def __init__(
+        self,
+        model_path="yuvalkirstain/PickScore_v1",
+        device="cpu",
+        dtype=torch.float16,
+        max_reward=1,
+        loss_scale=1,
+    ):
+        from transformers import AutoProcessor, AutoModel
+
+        self.model_path = model_path
+        self.device = device
+        self.dtype = dtype
+        self.max_reward = max_reward
+        self.loss_scale = loss_scale
+
+        # https://huggingface.co/yuvalkirstain/PickScore_v1/blob/main/preprocessor_config.json
+        self.transform = transforms.Compose([
+            transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC),
+            transforms.CenterCrop(224),
+            transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711]),
+        ])
+        self.processor = AutoProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K", torch_dtype=self.dtype)
+        self.model = AutoModel.from_pretrained(model_path, torch_dtype=self.dtype).eval().to(device)
+        self.model.requires_grad_(False)
+        self.model.eval()
+     
+    def __call__(self, batch_frames: torch.Tensor, batch_prompt: list[str]) -> Tuple[torch.Tensor, torch.Tensor]:
+        assert batch_frames.shape[0] == len(batch_prompt)
+        # Compute batch reward and loss in frame-wise.
+        batch_frames = rearrange(batch_frames, "b c t h w -> t b c h w")
+        batch_loss, batch_reward = 0, 0
+        for frames in batch_frames:
+            image_inputs = torch.stack([self.transform(frame) for frame in frames])
+            image_inputs = image_inputs.to(device=self.device, dtype=self.dtype)
+            text_inputs = self.processor(
+                text=batch_prompt,
+                padding=True,
+                truncation=True,
+                max_length=77,
+                return_tensors="pt",
+            ).to(self.device)
+            image_features = self.model.get_image_features(pixel_values=image_inputs)
+            text_features = self.model.get_text_features(**text_inputs)
+            image_features = image_features / torch.norm(image_features, dim=-1, keepdim=True)
+            text_features = text_features / torch.norm(text_features, dim=-1, keepdim=True)
+
+            logits = image_features @ text_features.T
+            reward = torch.diagonal(logits)
+            # Convert reward to loss in [0, 1].
+            if self.max_reward is None:
+                loss = (-1 * reward) * self.loss_scale
+            else:
+                loss = abs(reward - self.max_reward) * self.loss_scale
+
+            batch_loss, batch_reward = batch_loss + loss.mean(), batch_reward + reward.mean()
+
+        return batch_loss / batch_frames.shape[0], batch_reward / batch_frames.shape[0]
+
+
+class MPSReward(BaseReward):
+    """[MPS](https://github.com/Kwai-Kolors/MPS) reward model.
+    """
+    def __init__(
+        self,
+        model_path=None,
+        device="cpu",
+        dtype=torch.float16,
+        max_reward=1,
+        loss_scale=1,
+    ):
+        from transformers import AutoTokenizer, AutoConfig
+        from .MPS.trainer.models.clip_model import CLIPModel
+
+        self.model_path = model_path
+        self.device = device
+        self.dtype = dtype
+        self.condition = "light, color, clarity, tone, style, ambiance, artistry, shape, face, hair, hands, limbs, structure, instance, texture, quantity, attributes, position, number, location, word, things."
+        self.max_reward = max_reward
+        self.loss_scale = loss_scale
+
+        processor_name_or_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
+        # https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K/blob/main/preprocessor_config.json
+        # TODO: [transforms.Resize(224), transforms.CenterCrop(224)] for any aspect ratio.
+        self.transform = transforms.Compose([
+            transforms.Resize((224, 224), interpolation=transforms.InterpolationMode.BICUBIC),
+            transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711]),
+        ])
+
+        # We convert the original [ckpt](http://drive.google.com/file/d/17qrK_aJkVNM75ZEvMEePpLj6L867MLkN/view?usp=sharing)
+        # (contains the entire model) to a `state_dict`.
+        url = "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Third_Party/MPS_overall.pth"
+        filename = "MPS_overall.pth"
+        md5 = "1491cbbbd20565747fe07e7572e2ac56"
+        if self.model_path is None or not os.path.exists(self.model_path):
+            download_url(url, torch.hub.get_dir(), md5=md5)
+            model_path = os.path.join(torch.hub.get_dir(), filename)
+
+        self.tokenizer = AutoTokenizer.from_pretrained(processor_name_or_path, trust_remote_code=True)
+        config = AutoConfig.from_pretrained(processor_name_or_path)
+        self.model = CLIPModel(config)
+        state_dict = torch.load(model_path, map_location="cpu")
+        self.model.load_state_dict(state_dict, strict=False)
+        self.model.to(device=self.device, dtype=self.dtype)
+        self.model.requires_grad_(False)
+        self.model.eval()
+    
+    def _tokenize(self, caption):
+        input_ids = self.tokenizer(
+            caption,
+            max_length=self.tokenizer.model_max_length,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt"
+        ).input_ids
+
+        return input_ids
+    
+    def __call__(
+        self,
+        batch_frames: torch.Tensor,
+        batch_prompt: list[str],
+        batch_condition: Optional[list[str]] = None
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        if batch_condition is None:
+            batch_condition = [self.condition] * len(batch_prompt)
+        batch_frames = rearrange(batch_frames, "b c t h w -> t b c h w")
+        batch_loss, batch_reward = 0, 0
+        for frames in batch_frames:
+            image_inputs = torch.stack([self.transform(frame) for frame in frames])
+            image_inputs = image_inputs.to(device=self.device, dtype=self.dtype)
+            text_inputs = self._tokenize(batch_prompt).to(self.device)
+            condition_inputs = self._tokenize(batch_condition).to(device=self.device)
+            text_features, image_features = self.model(text_inputs, image_inputs, condition_inputs)
+
+            text_features = text_features / text_features.norm(dim=-1, keepdim=True)
+            image_features = image_features / image_features.norm(dim=-1, keepdim=True)
+            # reward = self.model.logit_scale.exp() * torch.diag(torch.einsum('bd,cd->bc', text_features, image_features))
+            logits = image_features @ text_features.T
+            reward = torch.diagonal(logits)
+            # Convert reward to loss in [0, 1].
+            if self.max_reward is None:
+                loss = (-1 * reward) * self.loss_scale
+            else:
+                loss = abs(reward - self.max_reward) * self.loss_scale
+            
+            batch_loss, batch_reward = batch_loss + loss.mean(), batch_reward + reward.mean()
+        
+        return batch_loss / batch_frames.shape[0], batch_reward / batch_frames.shape[0]
+
+
+if __name__ == "__main__":
+    import numpy as np
+    from decord import VideoReader
+
+    video_path_list = ["your_video_path_1.mp4", "your_video_path_2.mp4"]
+    prompt_list = ["your_prompt_1", "your_prompt_2"]
+    num_sampled_frames = 8
+
+    to_tensor = transforms.ToTensor()
+
+    sampled_frames_list = []
+    for video_path in video_path_list:
+        vr = VideoReader(video_path)
+        sampled_frame_indices = np.linspace(0, len(vr), num_sampled_frames, endpoint=False, dtype=int)
+        sampled_frames = vr.get_batch(sampled_frame_indices).asnumpy()
+        sampled_frames = torch.stack([to_tensor(frame) for frame in sampled_frames])
+        sampled_frames_list.append(sampled_frames)
+    sampled_frames = torch.stack(sampled_frames_list)
+    sampled_frames = rearrange(sampled_frames, "b t c h w -> b c t h w")
+
+    aesthetic_reward_v2 = AestheticReward(device="cuda", dtype=torch.bfloat16)
+    print(f"aesthetic_reward_v2: {aesthetic_reward_v2(sampled_frames)}")
+
+    aesthetic_reward_v2_5 = AestheticReward(
+        encoder_path="google/siglip-so400m-patch14-384", version="v2.5", device="cuda", dtype=torch.bfloat16
+    )
+    print(f"aesthetic_reward_v2_5: {aesthetic_reward_v2_5(sampled_frames)}")
+
+    hps_reward_v2 = HPSReward(device="cuda", dtype=torch.bfloat16)
+    print(f"hps_reward_v2: {hps_reward_v2(sampled_frames, prompt_list)}")
+
+    hps_reward_v2_1 = HPSReward(version="v2.1", device="cuda", dtype=torch.bfloat16)
+    print(f"hps_reward_v2_1: {hps_reward_v2_1(sampled_frames, prompt_list)}")
+
+    pick_score = PickScoreReward(device="cuda", dtype=torch.bfloat16)
+    print(f"pick_score_reward: {pick_score(sampled_frames, prompt_list)}")
+
+    mps_score = MPSReward(device="cuda", dtype=torch.bfloat16)
+    print(f"mps_reward: {mps_score(sampled_frames, prompt_list)}")

robomaster/ui/ui.py

@@ -0,0 +1,1634 @@
+"""Modified from https://github.com/guoyww/AnimateDiff/blob/main/app.py
+"""
+import base64
+import gc
+import json
+import os
+import random
+from datetime import datetime
+from glob import glob
+
+import cv2
+import gradio as gr
+import numpy as np
+import pkg_resources
+import requests
+import torch
+from diffusers import (AutoencoderKL, AutoencoderKLCogVideoX,
+                       CogVideoXDDIMScheduler, DDIMScheduler,
+                       DPMSolverMultistepScheduler,
+                       EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
+                       PNDMScheduler)
+from diffusers.utils.import_utils import is_xformers_available
+from omegaconf import OmegaConf
+from PIL import Image
+from safetensors import safe_open
+from transformers import (CLIPImageProcessor, CLIPVisionModelWithProjection,
+                          T5EncoderModel, T5Tokenizer)
+
+from cogvideox.data.bucket_sampler import ASPECT_RATIO_512, get_closest_ratio
+from cogvideox.models.autoencoder_magvit import AutoencoderKLCogVideoX
+from cogvideox.models.transformer3d import CogVideoXTransformer3DModel
+from cogvideox.pipeline.pipeline_cogvideox import CogVideoX_Fun_Pipeline
+from cogvideox.pipeline.pipeline_cogvideox_control import \
+    CogVideoX_Fun_Pipeline_Control
+from cogvideox.pipeline.pipeline_cogvideox_inpaint import \
+    CogVideoX_Fun_Pipeline_Inpaint
+from cogvideox.utils.lora_utils import merge_lora, unmerge_lora
+from cogvideox.utils.utils import (
+    get_image_to_video_latent, get_video_to_video_latent,
+    get_width_and_height_from_image_and_base_resolution, save_videos_grid)
+
+scheduler_dict = {
+    "Euler": EulerDiscreteScheduler,
+    "Euler A": EulerAncestralDiscreteScheduler,
+    "DPM++": DPMSolverMultistepScheduler, 
+    "PNDM": PNDMScheduler,
+    "DDIM_Cog": CogVideoXDDIMScheduler,
+    "DDIM_Origin": DDIMScheduler,
+}
+
+gradio_version = pkg_resources.get_distribution("gradio").version
+gradio_version_is_above_4 = True if int(gradio_version.split('.')[0]) >= 4 else False
+
+css = """
+.toolbutton {
+    margin-buttom: 0em 0em 0em 0em;
+    max-width: 2.5em;
+    min-width: 2.5em !important;
+    height: 2.5em;
+}
+"""
+
+class CogVideoX_Fun_Controller:
+    def __init__(self, low_gpu_memory_mode, weight_dtype):
+        # config dirs
+        self.basedir                    = os.getcwd()
+        self.config_dir                 = os.path.join(self.basedir, "config")
+        self.diffusion_transformer_dir  = os.path.join(self.basedir, "models", "Diffusion_Transformer")
+        self.motion_module_dir          = os.path.join(self.basedir, "models", "Motion_Module")
+        self.personalized_model_dir     = os.path.join(self.basedir, "models", "Personalized_Model")
+        self.savedir                    = os.path.join(self.basedir, "samples", datetime.now().strftime("Gradio-%Y-%m-%dT%H-%M-%S"))
+        self.savedir_sample             = os.path.join(self.savedir, "sample")
+        self.model_type                 = "Inpaint"
+        os.makedirs(self.savedir, exist_ok=True)
+
+        self.diffusion_transformer_list = []
+        self.motion_module_list      = []
+        self.personalized_model_list = []
+        
+        self.refresh_diffusion_transformer()
+        self.refresh_motion_module()
+        self.refresh_personalized_model()
+
+        # config models
+        self.tokenizer             = None
+        self.text_encoder          = None
+        self.vae                   = None
+        self.transformer           = None
+        self.pipeline              = None
+        self.motion_module_path    = "none"
+        self.base_model_path       = "none"
+        self.lora_model_path       = "none"
+        self.low_gpu_memory_mode   = low_gpu_memory_mode
+        
+        self.weight_dtype = weight_dtype
+
+    def refresh_diffusion_transformer(self):
+        self.diffusion_transformer_list = sorted(glob(os.path.join(self.diffusion_transformer_dir, "*/")))
+
+    def refresh_motion_module(self):
+        motion_module_list = sorted(glob(os.path.join(self.motion_module_dir, "*.safetensors")))
+        self.motion_module_list = [os.path.basename(p) for p in motion_module_list]
+
+    def refresh_personalized_model(self):
+        personalized_model_list = sorted(glob(os.path.join(self.personalized_model_dir, "*.safetensors")))
+        self.personalized_model_list = [os.path.basename(p) for p in personalized_model_list]
+
+    def update_model_type(self, model_type):
+        self.model_type = model_type
+
+    def update_diffusion_transformer(self, diffusion_transformer_dropdown):
+        print("Update diffusion transformer")
+        if diffusion_transformer_dropdown == "none":
+            return gr.update()
+        self.vae = AutoencoderKLCogVideoX.from_pretrained(
+            diffusion_transformer_dropdown, 
+            subfolder="vae", 
+        ).to(self.weight_dtype)
+
+        # Get Transformer
+        self.transformer = CogVideoXTransformer3DModel.from_pretrained_2d(
+            diffusion_transformer_dropdown, 
+            subfolder="transformer",
+            low_cpu_mem_usage=True, 
+        ).to(self.weight_dtype)
+        
+        # Get pipeline
+        if self.model_type == "Inpaint":
+            if self.transformer.config.in_channels != self.vae.config.latent_channels:
+                self.pipeline = CogVideoX_Fun_Pipeline_Inpaint.from_pretrained(
+                    diffusion_transformer_dropdown,
+                    vae=self.vae, 
+                    transformer=self.transformer,
+                    scheduler=scheduler_dict["Euler"].from_pretrained(diffusion_transformer_dropdown, subfolder="scheduler"),
+                    torch_dtype=self.weight_dtype
+                )
+            else:
+                self.pipeline = CogVideoX_Fun_Pipeline.from_pretrained(
+                    diffusion_transformer_dropdown,
+                    vae=self.vae, 
+                    transformer=self.transformer,
+                    scheduler=scheduler_dict["Euler"].from_pretrained(diffusion_transformer_dropdown, subfolder="scheduler"),
+                    torch_dtype=self.weight_dtype
+                )
+        else:
+            self.pipeline = CogVideoX_Fun_Pipeline_Control.from_pretrained(
+                diffusion_transformer_dropdown,
+                vae=self.vae, 
+                transformer=self.transformer,
+                scheduler=scheduler_dict["Euler"].from_pretrained(diffusion_transformer_dropdown, subfolder="scheduler"),
+                torch_dtype=self.weight_dtype
+            )
+
+        if self.low_gpu_memory_mode:
+            self.pipeline.enable_sequential_cpu_offload()
+        else:
+            self.pipeline.enable_model_cpu_offload()
+        print("Update diffusion transformer done")
+        return gr.update()
+
+    def update_base_model(self, base_model_dropdown):
+        self.base_model_path = base_model_dropdown
+        print("Update base model")
+        if base_model_dropdown == "none":
+            return gr.update()
+        if self.transformer is None:
+            gr.Info(f"Please select a pretrained model path.")
+            return gr.update(value=None)
+        else:
+            base_model_dropdown = os.path.join(self.personalized_model_dir, base_model_dropdown)
+            base_model_state_dict = {}
+            with safe_open(base_model_dropdown, framework="pt", device="cpu") as f:
+                for key in f.keys():
+                    base_model_state_dict[key] = f.get_tensor(key)
+            self.transformer.load_state_dict(base_model_state_dict, strict=False)
+            print("Update base done")
+            return gr.update()
+
+    def update_lora_model(self, lora_model_dropdown):
+        print("Update lora model")
+        if lora_model_dropdown == "none":
+            self.lora_model_path = "none"
+            return gr.update()
+        lora_model_dropdown = os.path.join(self.personalized_model_dir, lora_model_dropdown)
+        self.lora_model_path = lora_model_dropdown
+        return gr.update()
+
+    def generate(
+        self,
+        diffusion_transformer_dropdown,
+        base_model_dropdown,
+        lora_model_dropdown, 
+        lora_alpha_slider,
+        prompt_textbox, 
+        negative_prompt_textbox, 
+        sampler_dropdown, 
+        sample_step_slider, 
+        resize_method,
+        width_slider, 
+        height_slider, 
+        base_resolution, 
+        generation_method, 
+        length_slider, 
+        overlap_video_length, 
+        partial_video_length, 
+        cfg_scale_slider, 
+        start_image, 
+        end_image, 
+        validation_video,
+        validation_video_mask,
+        control_video,
+        denoise_strength,
+        seed_textbox,
+        is_api = False,
+    ):
+        gc.collect()
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+
+        if self.transformer is None:
+            raise gr.Error(f"Please select a pretrained model path.")
+
+        if self.base_model_path != base_model_dropdown:
+            self.update_base_model(base_model_dropdown)
+
+        if self.lora_model_path != lora_model_dropdown:
+            print("Update lora model")
+            self.update_lora_model(lora_model_dropdown)
+
+        if control_video is not None and self.model_type == "Inpaint":
+            if is_api:
+                return "", f"If specifying the control video, please set the model_type == \"Control\". "
+            else:
+                raise gr.Error(f"If specifying the control video, please set the model_type == \"Control\". ")
+
+        if control_video is None and self.model_type == "Control":
+            if is_api:
+                return "", f"If set the model_type == \"Control\", please specifying the control video. "
+            else:
+                raise gr.Error(f"If set the model_type == \"Control\", please specifying the control video. ")
+
+        if resize_method == "Resize according to Reference":
+            if start_image is None and validation_video is None and control_video is None:
+                if is_api:
+                    return "", f"Please upload an image when using \"Resize according to Reference\"."
+                else:
+                    raise gr.Error(f"Please upload an image when using \"Resize according to Reference\".")
+
+            aspect_ratio_sample_size    = {key : [x / 512 * base_resolution for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
+            if self.model_type == "Inpaint":
+                if validation_video is not None:
+                    original_width, original_height = Image.fromarray(cv2.VideoCapture(validation_video).read()[1]).size
+                else:
+                    original_width, original_height = start_image[0].size if type(start_image) is list else Image.open(start_image).size
+            else:
+                original_width, original_height = Image.fromarray(cv2.VideoCapture(control_video).read()[1]).size
+            closest_size, closest_ratio = get_closest_ratio(original_height, original_width, ratios=aspect_ratio_sample_size)
+            height_slider, width_slider = [int(x / 16) * 16 for x in closest_size]
+
+        if self.transformer.config.in_channels == self.vae.config.latent_channels and start_image is not None:
+            if is_api:
+                return "", f"Please select an image to video pretrained model while using image to video."
+            else:
+                raise gr.Error(f"Please select an image to video pretrained model while using image to video.")
+
+        if self.transformer.config.in_channels == self.vae.config.latent_channels and generation_method == "Long Video Generation":
+            if is_api:
+                return "", f"Please select an image to video pretrained model while using long video generation."
+            else:
+                raise gr.Error(f"Please select an image to video pretrained model while using long video generation.")
+        
+        if start_image is None and end_image is not None:
+            if is_api:
+                return "", f"If specifying the ending image of the video, please specify a starting image of the video."
+            else:
+                raise gr.Error(f"If specifying the ending image of the video, please specify a starting image of the video.")
+
+        is_image = True if generation_method == "Image Generation" else False
+
+        self.pipeline.scheduler = scheduler_dict[sampler_dropdown].from_config(self.pipeline.scheduler.config)
+        if self.lora_model_path != "none":
+            # lora part
+            self.pipeline = merge_lora(self.pipeline, self.lora_model_path, multiplier=lora_alpha_slider)
+
+        if int(seed_textbox) != -1 and seed_textbox != "": torch.manual_seed(int(seed_textbox))
+        else: seed_textbox = np.random.randint(0, 1e10)
+        generator = torch.Generator(device="cuda").manual_seed(int(seed_textbox))
+        
+        try:
+            if self.model_type == "Inpaint":
+                if self.transformer.config.in_channels != self.vae.config.latent_channels:
+                    if generation_method == "Long Video Generation":
+                        if validation_video is not None:
+                            raise gr.Error(f"Video to Video is not Support Long Video Generation now.")
+                        init_frames = 0
+                        last_frames = init_frames + partial_video_length
+                        while init_frames < length_slider:
+                            if last_frames >= length_slider:
+                                _partial_video_length = length_slider - init_frames
+                                _partial_video_length = int((_partial_video_length - 1) // self.vae.config.temporal_compression_ratio * self.vae.config.temporal_compression_ratio) + 1
+                                
+                                if _partial_video_length <= 0:
+                                    break
+                            else:
+                                _partial_video_length = partial_video_length
+
+                            if last_frames >= length_slider:
+                                input_video, input_video_mask, clip_image = get_image_to_video_latent(start_image, end_image, video_length=_partial_video_length, sample_size=(height_slider, width_slider))
+                            else:
+                                input_video, input_video_mask, clip_image = get_image_to_video_latent(start_image, None, video_length=_partial_video_length, sample_size=(height_slider, width_slider))
+
+                            with torch.no_grad():
+                                sample = self.pipeline(
+                                    prompt_textbox, 
+                                    negative_prompt     = negative_prompt_textbox,
+                                    num_inference_steps = sample_step_slider,
+                                    guidance_scale      = cfg_scale_slider,
+                                    width               = width_slider,
+                                    height              = height_slider,
+                                    num_frames          = _partial_video_length,
+                                    generator           = generator,
+
+                                    video        = input_video,
+                                    mask_video   = input_video_mask,
+                                    strength     = 1,
+                                ).videos
+                            
+                            if init_frames != 0:
+                                mix_ratio = torch.from_numpy(
+                                    np.array([float(_index) / float(overlap_video_length) for _index in range(overlap_video_length)], np.float32)
+                                ).unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+                                
+                                new_sample[:, :, -overlap_video_length:] = new_sample[:, :, -overlap_video_length:] * (1 - mix_ratio) + \
+                                    sample[:, :, :overlap_video_length] * mix_ratio
+                                new_sample = torch.cat([new_sample, sample[:, :, overlap_video_length:]], dim = 2)
+
+                                sample = new_sample
+                            else:
+                                new_sample = sample
+
+                            if last_frames >= length_slider:
+                                break
+
+                            start_image = [
+                                Image.fromarray(
+                                    (sample[0, :, _index].transpose(0, 1).transpose(1, 2) * 255).numpy().astype(np.uint8)
+                                ) for _index in range(-overlap_video_length, 0)
+                            ]
+
+                            init_frames = init_frames + _partial_video_length - overlap_video_length
+                            last_frames = init_frames + _partial_video_length
+                    else:
+                        if validation_video is not None:
+                            input_video, input_video_mask, clip_image = get_video_to_video_latent(validation_video, length_slider if not is_image else 1, sample_size=(height_slider, width_slider), validation_video_mask=validation_video_mask, fps=8)
+                            strength = denoise_strength
+                        else:
+                            input_video, input_video_mask, clip_image = get_image_to_video_latent(start_image, end_image, length_slider if not is_image else 1, sample_size=(height_slider, width_slider))
+                            strength = 1
+
+                        sample = self.pipeline(
+                            prompt_textbox,
+                            negative_prompt     = negative_prompt_textbox,
+                            num_inference_steps = sample_step_slider,
+                            guidance_scale      = cfg_scale_slider,
+                            width               = width_slider,
+                            height              = height_slider,
+                            num_frames          = length_slider if not is_image else 1,
+                            generator           = generator,
+
+                            video        = input_video,
+                            mask_video   = input_video_mask,
+                            strength     = strength,
+                        ).videos
+                else:
+                    sample = self.pipeline(
+                        prompt_textbox,
+                        negative_prompt     = negative_prompt_textbox,
+                        num_inference_steps = sample_step_slider,
+                        guidance_scale      = cfg_scale_slider,
+                        width               = width_slider,
+                        height              = height_slider,
+                        num_frames          = length_slider if not is_image else 1,
+                        generator           = generator
+                    ).videos
+            else:
+                input_video, input_video_mask, clip_image = get_video_to_video_latent(control_video, length_slider if not is_image else 1, sample_size=(height_slider, width_slider), fps=8)
+
+                sample = self.pipeline(
+                    prompt_textbox,
+                    negative_prompt     = negative_prompt_textbox,
+                    num_inference_steps = sample_step_slider,
+                    guidance_scale      = cfg_scale_slider,
+                    width               = width_slider,
+                    height              = height_slider,
+                    num_frames          = length_slider if not is_image else 1,
+                    generator           = generator,
+
+                    control_video = input_video,
+                ).videos
+        except Exception as e:
+            gc.collect()
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
+            if self.lora_model_path != "none":
+                self.pipeline = unmerge_lora(self.pipeline, self.lora_model_path, multiplier=lora_alpha_slider)
+            if is_api:
+                return "", f"Error. error information is {str(e)}"
+            else:
+                return gr.update(), gr.update(), f"Error. error information is {str(e)}"
+
+        gc.collect()
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+
+        # lora part
+        if self.lora_model_path != "none":
+            self.pipeline = unmerge_lora(self.pipeline, self.lora_model_path, multiplier=lora_alpha_slider)
+
+        sample_config = {
+            "prompt": prompt_textbox,
+            "n_prompt": negative_prompt_textbox,
+            "sampler": sampler_dropdown,
+            "num_inference_steps": sample_step_slider,
+            "guidance_scale": cfg_scale_slider,
+            "width": width_slider,
+            "height": height_slider,
+            "video_length": length_slider,
+            "seed_textbox": seed_textbox
+        }
+        json_str = json.dumps(sample_config, indent=4)
+        with open(os.path.join(self.savedir, "logs.json"), "a") as f:
+            f.write(json_str)
+            f.write("\n\n")
+            
+        if not os.path.exists(self.savedir_sample):
+            os.makedirs(self.savedir_sample, exist_ok=True)
+        index = len([path for path in os.listdir(self.savedir_sample)]) + 1
+        prefix = str(index).zfill(3)
+
+        gc.collect()
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+        if is_image or length_slider == 1:
+            save_sample_path = os.path.join(self.savedir_sample, prefix + f".png")
+
+            image = sample[0, :, 0]
+            image = image.transpose(0, 1).transpose(1, 2)
+            image = (image * 255).numpy().astype(np.uint8)
+            image = Image.fromarray(image)
+            image.save(save_sample_path)
+
+            if is_api:
+                return save_sample_path, "Success"
+            else:
+                if gradio_version_is_above_4:
+                    return gr.Image(value=save_sample_path, visible=True), gr.Video(value=None, visible=False), "Success"
+                else:
+                    return gr.Image.update(value=save_sample_path, visible=True), gr.Video.update(value=None, visible=False), "Success"
+        else:
+            save_sample_path = os.path.join(self.savedir_sample, prefix + f".mp4")
+            save_videos_grid(sample, save_sample_path, fps=8)
+
+            if is_api:
+                return save_sample_path, "Success"
+            else:
+                if gradio_version_is_above_4:
+                    return gr.Image(visible=False, value=None), gr.Video(value=save_sample_path, visible=True), "Success"
+                else:
+                    return gr.Image.update(visible=False, value=None), gr.Video.update(value=save_sample_path, visible=True), "Success"
+
+
+def ui(low_gpu_memory_mode, weight_dtype):
+    controller = CogVideoX_Fun_Controller(low_gpu_memory_mode, weight_dtype)
+
+    with gr.Blocks(css=css) as demo:
+        gr.Markdown(
+            """
+            # CogVideoX-Fun:
+
+            A CogVideoX with more flexible generation conditions, capable of producing videos of different resolutions, around 6 seconds, and fps 8 (frames 1 to 49), as well as image generated videos. 
+
+            [Github](https://github.com/aigc-apps/CogVideoX-Fun/)
+            """
+        )
+        with gr.Column(variant="panel"):
+            gr.Markdown(
+                """
+                ### 1. CogVideoX-Fun Model Type (CogVideoX-Fun模型的种类，正常模型还是控制模型).
+                """
+            )
+            with gr.Row():
+                model_type = gr.Dropdown(
+                    label="The model type of CogVideoX-Fun (CogVideoX-Fun模型的种类，正常模型还是控制模型)",
+                    choices=["Inpaint", "Control"],
+                    value="Inpaint",
+                    interactive=True,
+                )
+
+            gr.Markdown(
+                """
+                ### 2. Model checkpoints (模型路径).
+                """
+            )
+            with gr.Row():
+                diffusion_transformer_dropdown = gr.Dropdown(
+                    label="Pretrained Model Path (预训练模型路径)",
+                    choices=controller.diffusion_transformer_list,
+                    value="none",
+                    interactive=True,
+                )
+                diffusion_transformer_dropdown.change(
+                    fn=controller.update_diffusion_transformer, 
+                    inputs=[diffusion_transformer_dropdown], 
+                    outputs=[diffusion_transformer_dropdown]
+                )
+                
+                diffusion_transformer_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton")
+                def refresh_diffusion_transformer():
+                    controller.refresh_diffusion_transformer()
+                    return gr.update(choices=controller.diffusion_transformer_list)
+                diffusion_transformer_refresh_button.click(fn=refresh_diffusion_transformer, inputs=[], outputs=[diffusion_transformer_dropdown])
+
+            with gr.Row():
+                base_model_dropdown = gr.Dropdown(
+                    label="Select base Dreambooth model (选择基模型[非必需])",
+                    choices=controller.personalized_model_list,
+                    value="none",
+                    interactive=True,
+                )
+                
+                lora_model_dropdown = gr.Dropdown(
+                    label="Select LoRA model (选择LoRA模型[非必需])",
+                    choices=["none"] + controller.personalized_model_list,
+                    value="none",
+                    interactive=True,
+                )
+
+                lora_alpha_slider = gr.Slider(label="LoRA alpha (LoRA权重)", value=0.55, minimum=0, maximum=2, interactive=True)
+                
+                personalized_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton")
+                def update_personalized_model():
+                    controller.refresh_personalized_model()
+                    return [
+                        gr.update(choices=controller.personalized_model_list),
+                        gr.update(choices=["none"] + controller.personalized_model_list)
+                    ]
+                personalized_refresh_button.click(fn=update_personalized_model, inputs=[], outputs=[base_model_dropdown, lora_model_dropdown])
+
+        with gr.Column(variant="panel"):
+            gr.Markdown(
+                """
+                ### 3. Configs for Generation (生成参数配置).
+                """
+            )
+            
+            prompt_textbox = gr.Textbox(label="Prompt (正向提示词)", lines=2, value="A young woman with beautiful and clear eyes and blonde hair standing and white dress in a forest wearing a crown. She seems to be lost in thought, and the camera focuses on her face. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.")
+            negative_prompt_textbox = gr.Textbox(label="Negative prompt (负向提示词)", lines=2, value="The video is not of a high quality, it has a low resolution. Watermark present in each frame. The background is solid. Strange body and strange trajectory. Distortion. " )
+                
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        sampler_dropdown   = gr.Dropdown(label="Sampling method (采样器种类)", choices=list(scheduler_dict.keys()), value=list(scheduler_dict.keys())[0])
+                        sample_step_slider = gr.Slider(label="Sampling steps (生成步数)", value=50, minimum=10, maximum=100, step=1)
+                        
+                    resize_method = gr.Radio(
+                        ["Generate by", "Resize according to Reference"],
+                        value="Generate by",
+                        show_label=False,
+                    )
+                    width_slider     = gr.Slider(label="Width (视频宽度)",            value=672, minimum=128, maximum=1344, step=16)
+                    height_slider    = gr.Slider(label="Height (视频高度)",           value=384, minimum=128, maximum=1344, step=16)
+                    base_resolution  = gr.Radio(label="Base Resolution of Pretrained Models", value=512, choices=[512, 768, 960], visible=False)
+
+                    gr.Markdown(
+                        """
+                        V1.0 and V1.1 support up to 49 frames of video generation, while V1.5 supports up to 85 frames.  
+                        (V1.0和V1.1支持最大49帧视频生成，V1.5支持最大85帧视频生成。)
+                        """
+                    )
+                    with gr.Group():
+                        generation_method = gr.Radio(
+                            ["Video Generation", "Image Generation", "Long Video Generation"],
+                            value="Video Generation",
+                            show_label=False,
+                        )
+                        with gr.Row():
+                            length_slider = gr.Slider(label="Animation length (视频帧数)", value=49, minimum=1,   maximum=85,  step=4)
+                            overlap_video_length = gr.Slider(label="Overlap length (视频续写的重叠帧数)", value=4, minimum=1,   maximum=4,  step=1, visible=False)
+                            partial_video_length = gr.Slider(label="Partial video generation length (每个部分的视频生成帧数)", value=25, minimum=5,   maximum=85,  step=4, visible=False)
+                    
+                    source_method = gr.Radio(
+                        ["Text to Video (文本到视频)", "Image to Video (图片到视频)", "Video to Video (视频到视频)", "Video Control (视频控制)"],
+                        value="Text to Video (文本到视频)",
+                        show_label=False,
+                    )
+                    with gr.Column(visible = False) as image_to_video_col:
+                        start_image = gr.Image(
+                            label="The image at the beginning of the video (图片到视频的开始图片)",  show_label=True, 
+                            elem_id="i2v_start", sources="upload", type="filepath", 
+                        )
+                        
+                        template_gallery_path = ["asset/1.png", "asset/2.png", "asset/3.png", "asset/4.png", "asset/5.png"]
+                        def select_template(evt: gr.SelectData):
+                            text = {
+                                "asset/1.png": "The dog is shaking head. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.", 
+                                "asset/2.png": "a sailboat sailing in rough seas with a dramatic sunset. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.", 
+                                "asset/3.png": "a beautiful woman with long hair and a dress blowing in the wind. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.", 
+                                "asset/4.png": "a man in an astronaut suit playing a guitar. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.", 
+                                "asset/5.png": "fireworks display over night city. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.", 
+                            }[template_gallery_path[evt.index]]
+                            return template_gallery_path[evt.index], text
+
+                        template_gallery = gr.Gallery(
+                            template_gallery_path,
+                            columns=5, rows=1,
+                            height=140,
+                            allow_preview=False,
+                            container=False,
+                            label="Template Examples",
+                        )
+                        template_gallery.select(select_template, None, [start_image, prompt_textbox])
+                        
+                        with gr.Accordion("The image at the ending of the video (图片到视频的结束图片[非必需, Optional])", open=False):
+                            end_image   = gr.Image(label="The image at the ending of the video (图片到视频的结束图片[非必需, Optional])", show_label=False, elem_id="i2v_end", sources="upload", type="filepath")
+
+                    with gr.Column(visible = False) as video_to_video_col:
+                        with gr.Row():
+                            validation_video = gr.Video(
+                                label="The video to convert (视频转视频的参考视频)",  show_label=True, 
+                                elem_id="v2v", sources="upload", 
+                            )
+                        with gr.Accordion("The mask of the video to inpaint (视频重新绘制的mask[非必需, Optional])", open=False):
+                            gr.Markdown(
+                                """
+                                - Please set a larger denoise_strength when using validation_video_mask, such as 1.00 instead of 0.70  
+                                (请设置更大的denoise_strength，当使用validation_video_mask的时候，比如1而不是0.70)
+                                """
+                            )
+                            validation_video_mask = gr.Image(
+                                label="The mask of the video to inpaint (视频重新绘制的mask[非必需, Optional])",
+                                show_label=False, elem_id="v2v_mask", sources="upload", type="filepath"
+                            )
+                        denoise_strength = gr.Slider(label="Denoise strength (重绘系数)", value=0.70, minimum=0.10, maximum=1.00, step=0.01)
+
+                    with gr.Column(visible = False) as control_video_col:
+                        gr.Markdown(
+                            """
+                            Demo pose control video can be downloaded here [URL](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/asset/v1.1/pose.mp4).
+                            """
+                        )
+                        control_video = gr.Video(
+                            label="The control video (用于提供控制信号的video)",  show_label=True, 
+                            elem_id="v2v_control", sources="upload", 
+                        )
+
+                    cfg_scale_slider  = gr.Slider(label="CFG Scale (引导系数)",        value=6.0, minimum=0,   maximum=20)
+                    
+                    with gr.Row():
+                        seed_textbox = gr.Textbox(label="Seed (随机种子)", value=43)
+                        seed_button  = gr.Button(value="\U0001F3B2", elem_classes="toolbutton")
+                        seed_button.click(
+                            fn=lambda: gr.Textbox(value=random.randint(1, 1e8)) if gradio_version_is_above_4 else gr.Textbox.update(value=random.randint(1, 1e8)), 
+                            inputs=[], 
+                            outputs=[seed_textbox]
+                        )
+
+                    generate_button = gr.Button(value="Generate (生成)", variant='primary')
+                    
+                with gr.Column():
+                    result_image = gr.Image(label="Generated Image (生成图片)", interactive=False, visible=False)
+                    result_video = gr.Video(label="Generated Animation (生成视频)", interactive=False)
+                    infer_progress = gr.Textbox(
+                        label="Generation Info (生成信息)",
+                        value="No task currently",
+                        interactive=False
+                    )
+
+            model_type.change(
+                fn=controller.update_model_type, 
+                inputs=[model_type], 
+                outputs=[]
+            )
+
+            def upload_generation_method(generation_method):
+                if generation_method == "Video Generation":
+                    return [gr.update(visible=True, maximum=85, value=49, interactive=True), gr.update(visible=False), gr.update(visible=False)]
+                elif generation_method == "Image Generation":
+                    return [gr.update(minimum=1, maximum=1, value=1, interactive=False), gr.update(visible=False), gr.update(visible=False)]
+                else:
+                    return [gr.update(visible=True, maximum=1344), gr.update(visible=True), gr.update(visible=True)]
+            generation_method.change(
+                upload_generation_method, generation_method, [length_slider, overlap_video_length, partial_video_length]
+            )
+
+            def upload_source_method(source_method):
+                if source_method == "Text to Video (文本到视频)":
+                    return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None)]
+                elif source_method == "Image to Video (图片到视频)":
+                    return [gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(), gr.update(), gr.update(value=None), gr.update(value=None), gr.update(value=None)]
+                elif source_method == "Video to Video (视频到视频)":
+                    return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(value=None), gr.update(value=None), gr.update(), gr.update(), gr.update(value=None)]
+                else:
+                    return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update()]
+            source_method.change(
+                upload_source_method, source_method, [
+                    image_to_video_col, video_to_video_col, control_video_col, start_image, end_image, 
+                    validation_video, validation_video_mask, control_video
+                ]
+            )
+
+            def upload_resize_method(resize_method):
+                if resize_method == "Generate by":
+                    return [gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)]
+                else:
+                    return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)]
+            resize_method.change(
+                upload_resize_method, resize_method, [width_slider, height_slider, base_resolution]
+            )
+
+            generate_button.click(
+                fn=controller.generate,
+                inputs=[
+                    diffusion_transformer_dropdown,
+                    base_model_dropdown,
+                    lora_model_dropdown,
+                    lora_alpha_slider,
+                    prompt_textbox, 
+                    negative_prompt_textbox, 
+                    sampler_dropdown, 
+                    sample_step_slider, 
+                    resize_method,
+                    width_slider, 
+                    height_slider, 
+                    base_resolution, 
+                    generation_method, 
+                    length_slider, 
+                    overlap_video_length, 
+                    partial_video_length, 
+                    cfg_scale_slider, 
+                    start_image, 
+                    end_image, 
+                    validation_video,
+                    validation_video_mask,
+                    control_video,
+                    denoise_strength, 
+                    seed_textbox,
+                ],
+                outputs=[result_image, result_video, infer_progress]
+            )
+    return demo, controller
+
+
+class CogVideoX_Fun_Controller_Modelscope:
+    def __init__(self, model_name, model_type, savedir_sample, low_gpu_memory_mode, weight_dtype):
+        # Basic dir
+        self.basedir                    = os.getcwd()
+        self.personalized_model_dir     = os.path.join(self.basedir, "models", "Personalized_Model")
+        self.lora_model_path            = "none"
+        self.savedir_sample             = savedir_sample
+        self.refresh_personalized_model()
+        os.makedirs(self.savedir_sample, exist_ok=True)
+
+        # model path
+        self.model_type = model_type
+        self.weight_dtype = weight_dtype
+        
+        self.vae = AutoencoderKLCogVideoX.from_pretrained(
+            model_name, 
+            subfolder="vae", 
+        ).to(self.weight_dtype)
+
+        # Get Transformer
+        self.transformer = CogVideoXTransformer3DModel.from_pretrained_2d(
+            model_name, 
+            subfolder="transformer",
+            low_cpu_mem_usage=True, 
+        ).to(self.weight_dtype)
+        
+        # Get pipeline
+        if model_type == "Inpaint":
+            if self.transformer.config.in_channels != self.vae.config.latent_channels:
+                self.pipeline = CogVideoX_Fun_Pipeline_Inpaint.from_pretrained(
+                    model_name,
+                    vae=self.vae, 
+                    transformer=self.transformer,
+                    scheduler=scheduler_dict["Euler"].from_pretrained(model_name, subfolder="scheduler"),
+                    torch_dtype=self.weight_dtype
+                )
+            else:
+                self.pipeline = CogVideoX_Fun_Pipeline.from_pretrained(
+                    model_name,
+                    vae=self.vae, 
+                    transformer=self.transformer,
+                    scheduler=scheduler_dict["Euler"].from_pretrained(model_name, subfolder="scheduler"),
+                    torch_dtype=self.weight_dtype
+                )
+        else:
+            self.pipeline = CogVideoX_Fun_Pipeline_Control.from_pretrained(
+                model_name,
+                vae=self.vae, 
+                transformer=self.transformer,
+                scheduler=scheduler_dict["Euler"].from_pretrained(model_name, subfolder="scheduler"),
+                torch_dtype=self.weight_dtype
+            )
+
+        if low_gpu_memory_mode:
+            self.pipeline.enable_sequential_cpu_offload()
+        else:
+            self.pipeline.enable_model_cpu_offload()
+        print("Update diffusion transformer done")
+
+
+    def refresh_personalized_model(self):
+        personalized_model_list = sorted(glob(os.path.join(self.personalized_model_dir, "*.safetensors")))
+        self.personalized_model_list = [os.path.basename(p) for p in personalized_model_list]
+
+
+    def update_lora_model(self, lora_model_dropdown):
+        print("Update lora model")
+        if lora_model_dropdown == "none":
+            self.lora_model_path = "none"
+            return gr.update()
+        lora_model_dropdown = os.path.join(self.personalized_model_dir, lora_model_dropdown)
+        self.lora_model_path = lora_model_dropdown
+        return gr.update()
+
+    
+    def generate(
+        self,
+        diffusion_transformer_dropdown,
+        base_model_dropdown,
+        lora_model_dropdown, 
+        lora_alpha_slider,
+        prompt_textbox, 
+        negative_prompt_textbox, 
+        sampler_dropdown, 
+        sample_step_slider, 
+        resize_method,
+        width_slider, 
+        height_slider, 
+        base_resolution, 
+        generation_method, 
+        length_slider, 
+        overlap_video_length, 
+        partial_video_length, 
+        cfg_scale_slider, 
+        start_image, 
+        end_image, 
+        validation_video,
+        validation_video_mask,
+        control_video,
+        denoise_strength,
+        seed_textbox,
+        is_api = False,
+    ):    
+        gc.collect()
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+
+        if self.transformer is None:
+            raise gr.Error(f"Please select a pretrained model path.")
+
+        if self.lora_model_path != lora_model_dropdown:
+            print("Update lora model")
+            self.update_lora_model(lora_model_dropdown)
+        
+        if control_video is not None and self.model_type == "Inpaint":
+            if is_api:
+                return "", f"If specifying the control video, please set the model_type == \"Control\". "
+            else:
+                raise gr.Error(f"If specifying the control video, please set the model_type == \"Control\". ")
+
+        if control_video is None and self.model_type == "Control":
+            if is_api:
+                return "", f"If set the model_type == \"Control\", please specifying the control video. "
+            else:
+                raise gr.Error(f"If set the model_type == \"Control\", please specifying the control video. ")
+
+        if resize_method == "Resize according to Reference":
+            if start_image is None and validation_video is None and control_video is None:
+                if is_api:
+                    return "", f"Please upload an image when using \"Resize according to Reference\"."
+                else:
+                    raise gr.Error(f"Please upload an image when using \"Resize according to Reference\".")
+        
+            aspect_ratio_sample_size    = {key : [x / 512 * base_resolution for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
+            if self.model_type == "Inpaint":
+                if validation_video is not None:
+                    original_width, original_height = Image.fromarray(cv2.VideoCapture(validation_video).read()[1]).size
+                else:
+                    original_width, original_height = start_image[0].size if type(start_image) is list else Image.open(start_image).size
+            else:
+                original_width, original_height = Image.fromarray(cv2.VideoCapture(control_video).read()[1]).size
+            closest_size, closest_ratio = get_closest_ratio(original_height, original_width, ratios=aspect_ratio_sample_size)
+            height_slider, width_slider = [int(x / 16) * 16 for x in closest_size]
+
+        if self.transformer.config.in_channels == self.vae.config.latent_channels and start_image is not None:
+            if is_api:
+                return "", f"Please select an image to video pretrained model while using image to video."
+            else:
+                raise gr.Error(f"Please select an image to video pretrained model while using image to video.")
+
+        if start_image is None and end_image is not None:
+            if is_api:
+                return "", f"If specifying the ending image of the video, please specify a starting image of the video."
+            else:
+                raise gr.Error(f"If specifying the ending image of the video, please specify a starting image of the video.")
+
+        is_image = True if generation_method == "Image Generation" else False
+
+        self.pipeline.scheduler = scheduler_dict[sampler_dropdown].from_config(self.pipeline.scheduler.config)
+        if self.lora_model_path != "none":
+            # lora part
+            self.pipeline = merge_lora(self.pipeline, self.lora_model_path, multiplier=lora_alpha_slider)
+
+        if int(seed_textbox) != -1 and seed_textbox != "": torch.manual_seed(int(seed_textbox))
+        else: seed_textbox = np.random.randint(0, 1e10)
+        generator = torch.Generator(device="cuda").manual_seed(int(seed_textbox))
+        
+        try:
+            if self.model_type == "Inpaint":
+                if self.transformer.config.in_channels != self.vae.config.latent_channels:
+                    if validation_video is not None:
+                        input_video, input_video_mask, clip_image = get_video_to_video_latent(validation_video, length_slider if not is_image else 1, sample_size=(height_slider, width_slider), validation_video_mask=validation_video_mask, fps=8)
+                        strength = denoise_strength
+                    else:
+                        input_video, input_video_mask, clip_image = get_image_to_video_latent(start_image, end_image, length_slider if not is_image else 1, sample_size=(height_slider, width_slider))
+                        strength = 1
+
+                    sample = self.pipeline(
+                        prompt_textbox,
+                        negative_prompt     = negative_prompt_textbox,
+                        num_inference_steps = sample_step_slider,
+                        guidance_scale      = cfg_scale_slider,
+                        width               = width_slider,
+                        height              = height_slider,
+                        num_frames          = length_slider if not is_image else 1,
+                        generator           = generator,
+
+                        video        = input_video,
+                        mask_video   = input_video_mask,
+                        strength     = strength,
+                    ).videos
+                else:
+                    sample = self.pipeline(
+                        prompt_textbox,
+                        negative_prompt     = negative_prompt_textbox,
+                        num_inference_steps = sample_step_slider,
+                        guidance_scale      = cfg_scale_slider,
+                        width               = width_slider,
+                        height              = height_slider,
+                        num_frames          = length_slider if not is_image else 1,
+                        generator           = generator
+                    ).videos
+            else:
+                input_video, input_video_mask, clip_image = get_video_to_video_latent(control_video, length_slider if not is_image else 1, sample_size=(height_slider, width_slider), fps=8)
+
+                sample = self.pipeline(
+                    prompt_textbox,
+                    negative_prompt     = negative_prompt_textbox,
+                    num_inference_steps = sample_step_slider,
+                    guidance_scale      = cfg_scale_slider,
+                    width               = width_slider,
+                    height              = height_slider,
+                    num_frames          = length_slider if not is_image else 1,
+                    generator           = generator,
+
+                    control_video = input_video,
+                ).videos
+        except Exception as e:
+            gc.collect()
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
+            if self.lora_model_path != "none":
+                self.pipeline = unmerge_lora(self.pipeline, self.lora_model_path, multiplier=lora_alpha_slider)
+            if is_api:
+                return "", f"Error. error information is {str(e)}"
+            else:
+                return gr.update(), gr.update(), f"Error. error information is {str(e)}"
+
+        gc.collect()
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+        
+        # lora part
+        if self.lora_model_path != "none":
+            self.pipeline = unmerge_lora(self.pipeline, self.lora_model_path, multiplier=lora_alpha_slider)
+
+        if not os.path.exists(self.savedir_sample):
+            os.makedirs(self.savedir_sample, exist_ok=True)
+        index = len([path for path in os.listdir(self.savedir_sample)]) + 1
+        prefix = str(index).zfill(3)
+        
+        gc.collect()
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+        if is_image or length_slider == 1:
+            save_sample_path = os.path.join(self.savedir_sample, prefix + f".png")
+
+            image = sample[0, :, 0]
+            image = image.transpose(0, 1).transpose(1, 2)
+            image = (image * 255).numpy().astype(np.uint8)
+            image = Image.fromarray(image)
+            image.save(save_sample_path)
+            if is_api:
+                return save_sample_path, "Success"
+            else:
+                if gradio_version_is_above_4:
+                    return gr.Image(value=save_sample_path, visible=True), gr.Video(value=None, visible=False), "Success"
+                else:
+                    return gr.Image.update(value=save_sample_path, visible=True), gr.Video.update(value=None, visible=False), "Success"
+        else:
+            save_sample_path = os.path.join(self.savedir_sample, prefix + f".mp4")
+            save_videos_grid(sample, save_sample_path, fps=8)
+            if is_api:
+                return save_sample_path, "Success"
+            else:
+                if gradio_version_is_above_4:
+                    return gr.Image(visible=False, value=None), gr.Video(value=save_sample_path, visible=True), "Success"
+                else:
+                    return gr.Image.update(visible=False, value=None), gr.Video.update(value=save_sample_path, visible=True), "Success"
+
+
+def ui_modelscope(model_name, model_type, savedir_sample, low_gpu_memory_mode, weight_dtype):
+    controller = CogVideoX_Fun_Controller_Modelscope(model_name, model_type, savedir_sample, low_gpu_memory_mode, weight_dtype)
+
+    with gr.Blocks(css=css) as demo:
+        gr.Markdown(
+            """
+            # CogVideoX-Fun
+
+            A CogVideoX with more flexible generation conditions, capable of producing videos of different resolutions, around 6 seconds, and fps 8 (frames 1 to 49), as well as image generated videos. 
+
+            [Github](https://github.com/aigc-apps/CogVideoX-Fun/)
+            """
+        )
+        with gr.Column(variant="panel"):
+            gr.Markdown(
+                """
+                ### 1. CogVideoX-Fun Model Type (CogVideoX-Fun模型的种类，正常模型还是控制模型).
+                """
+            )
+            with gr.Row():
+                model_type = gr.Dropdown(
+                    label="The model type of CogVideoX-Fun (CogVideoX-Fun模型的种类，正常模型还是控制模型)",
+                    choices=[model_type],
+                    value=model_type,
+                    interactive=False,
+                )
+
+            gr.Markdown(
+                """
+                ### 2. Model checkpoints (模型路径).
+                """
+            )
+            with gr.Row():
+                diffusion_transformer_dropdown = gr.Dropdown(
+                    label="Pretrained Model Path (预训练模型路径)",
+                    choices=[model_name],
+                    value=model_name,
+                    interactive=False,
+                )
+            with gr.Row():
+                base_model_dropdown = gr.Dropdown(
+                    label="Select base Dreambooth model (选择基模型[非必需])",
+                    choices=["none"],
+                    value="none",
+                    interactive=False,
+                    visible=False
+                )
+                with gr.Column(visible=False):
+                    gr.Markdown(
+                        """
+                        ### Minimalism is an example portrait of Lora, triggered by specific prompt words. More details can be found on [Wiki](https://github.com/aigc-apps/CogVideoX-Fun/wiki/Training-Lora).
+                        """
+                    )
+                    with gr.Row():
+                        lora_model_dropdown = gr.Dropdown(
+                            label="Select LoRA model",
+                            choices=["none"],
+                            value="none",
+                            interactive=True,
+                        )
+
+                        lora_alpha_slider = gr.Slider(label="LoRA alpha (LoRA权重)", value=0.55, minimum=0, maximum=2, interactive=True)
+                
+        with gr.Column(variant="panel"):
+            gr.Markdown(
+                """
+                ### 3. Configs for Generation (生成参数配置).
+                """
+            )
+
+            prompt_textbox = gr.Textbox(label="Prompt (正向提示词)", lines=2, value="A young woman with beautiful and clear eyes and blonde hair standing and white dress in a forest wearing a crown. She seems to be lost in thought, and the camera focuses on her face. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.")
+            negative_prompt_textbox = gr.Textbox(label="Negative prompt (负向提示词)", lines=2, value="The video is not of a high quality, it has a low resolution. Watermark present in each frame. The background is solid. Strange body and strange trajectory. Distortion. " )
+                
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        sampler_dropdown   = gr.Dropdown(label="Sampling method (采样器种类)", choices=list(scheduler_dict.keys()), value=list(scheduler_dict.keys())[0])
+                        sample_step_slider = gr.Slider(label="Sampling steps (生成步数)", value=50, minimum=10, maximum=50, step=1, interactive=False)
+                    
+                    resize_method = gr.Radio(
+                        ["Generate by", "Resize according to Reference"],
+                        value="Generate by",
+                        show_label=False,
+                    )                        
+                    width_slider     = gr.Slider(label="Width (视频宽度)",            value=672, minimum=128, maximum=1280, step=16, interactive=False)
+                    height_slider    = gr.Slider(label="Height (视频高度)",           value=384, minimum=128, maximum=1280, step=16, interactive=False)
+                    base_resolution  = gr.Radio(label="Base Resolution of Pretrained Models", value=512, choices=[512, 768, 960], interactive=False, visible=False)
+
+                    gr.Markdown(
+                        """
+                        V1.0 and V1.1 support up to 49 frames of video generation, while V1.5 supports up to 85 frames.  
+                        (V1.0和V1.1支持最大49帧视频生成，V1.5支持最大85帧视频生成。)
+                        """
+                    )
+                    with gr.Group():
+                        generation_method = gr.Radio(
+                            ["Video Generation", "Image Generation"],
+                            value="Video Generation",
+                            show_label=False,
+                            visible=True,
+                        )
+                        length_slider = gr.Slider(label="Animation length (视频帧数)", value=49, minimum=5,   maximum=85,  step=4)
+                        overlap_video_length = gr.Slider(label="Overlap length (视频续写的重叠帧数)", value=4, minimum=1,   maximum=4,  step=1, visible=False)
+                        partial_video_length = gr.Slider(label="Partial video generation length (每个部分的视频生成帧数)", value=25, minimum=5,   maximum=85,  step=4, visible=False)
+                        
+                    source_method = gr.Radio(
+                        ["Text to Video (文本到视频)", "Image to Video (图片到视频)", "Video to Video (视频到视频)", "Video Control (视频控制)"],
+                        value="Text to Video (文本到视频)",
+                        show_label=False,
+                    )
+                    with gr.Column(visible = False) as image_to_video_col:
+                        with gr.Row():
+                            start_image = gr.Image(label="The image at the beginning of the video (图片到视频的开始图片)", show_label=True, elem_id="i2v_start", sources="upload", type="filepath")
+                        
+                        template_gallery_path = ["asset/1.png", "asset/2.png", "asset/3.png", "asset/4.png", "asset/5.png"]
+                        def select_template(evt: gr.SelectData):
+                            text = {
+                                "asset/1.png": "The dog is shaking head. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.", 
+                                "asset/2.png": "a sailboat sailing in rough seas with a dramatic sunset. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.", 
+                                "asset/3.png": "a beautiful woman with long hair and a dress blowing in the wind. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.", 
+                                "asset/4.png": "a man in an astronaut suit playing a guitar. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.", 
+                                "asset/5.png": "fireworks display over night city. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.", 
+                            }[template_gallery_path[evt.index]]
+                            return template_gallery_path[evt.index], text
+
+                        template_gallery = gr.Gallery(
+                            template_gallery_path,
+                            columns=5, rows=1,
+                            height=140,
+                            allow_preview=False,
+                            container=False,
+                            label="Template Examples",
+                        )
+                        template_gallery.select(select_template, None, [start_image, prompt_textbox])
+
+                        with gr.Accordion("The image at the ending of the video (图片到视频的结束图片[非必需, Optional])", open=False):
+                            end_image   = gr.Image(label="The image at the ending of the video (图片到视频的结束图片[非必需, Optional])", show_label=False, elem_id="i2v_end", sources="upload", type="filepath")
+
+                    with gr.Column(visible = False) as video_to_video_col:
+                        with gr.Row():
+                            validation_video = gr.Video(
+                                label="The video to convert (视频转视频的参考视频)",  show_label=True, 
+                                elem_id="v2v", sources="upload", 
+                            ) 
+                        with gr.Accordion("The mask of the video to inpaint (视频重新绘制的mask[非必需, Optional])", open=False):
+                            gr.Markdown(
+                                """
+                                - Please set a larger denoise_strength when using validation_video_mask, such as 1.00 instead of 0.70  
+                                (请设置更大的denoise_strength，当使用validation_video_mask的时候，比如1而不是0.70)
+                                """
+                            )
+                            validation_video_mask = gr.Image(
+                                label="The mask of the video to inpaint (视频重新绘制的mask[非必需, Optional])",
+                                show_label=False, elem_id="v2v_mask", sources="upload", type="filepath"
+                            )
+                        denoise_strength = gr.Slider(label="Denoise strength (重绘系数)", value=0.70, minimum=0.10, maximum=1.00, step=0.01)
+
+                    with gr.Column(visible = False) as control_video_col:
+                        gr.Markdown(
+                            """
+                            Demo pose control video can be downloaded here [URL](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/asset/v1.1/pose.mp4).
+                            """
+                        )
+                        control_video = gr.Video(
+                            label="The control video (用于提供控制信号的video)",  show_label=True, 
+                            elem_id="v2v_control", sources="upload", 
+                        )
+
+                    cfg_scale_slider  = gr.Slider(label="CFG Scale (引导系数)",        value=6.0, minimum=0,   maximum=20)
+                    
+                    with gr.Row():
+                        seed_textbox = gr.Textbox(label="Seed (随机种子)", value=43)
+                        seed_button  = gr.Button(value="\U0001F3B2", elem_classes="toolbutton")
+                        seed_button.click(
+                            fn=lambda: gr.Textbox(value=random.randint(1, 1e8)) if gradio_version_is_above_4 else gr.Textbox.update(value=random.randint(1, 1e8)), 
+                            inputs=[], 
+                            outputs=[seed_textbox]
+                        )
+
+                    generate_button = gr.Button(value="Generate (生成)", variant='primary')
+                    
+                with gr.Column():
+                    result_image = gr.Image(label="Generated Image (生成图片)", interactive=False, visible=False)
+                    result_video = gr.Video(label="Generated Animation (生成视频)", interactive=False)
+                    infer_progress = gr.Textbox(
+                        label="Generation Info (生成信息)",
+                        value="No task currently",
+                        interactive=False
+                    )
+
+            def upload_generation_method(generation_method):
+                if generation_method == "Video Generation":
+                    return gr.update(visible=True, minimum=8, maximum=85, value=49, interactive=True)
+                elif generation_method == "Image Generation":
+                    return gr.update(minimum=1, maximum=1, value=1, interactive=False)
+            generation_method.change(
+                upload_generation_method, generation_method, [length_slider]
+            )
+
+            def upload_source_method(source_method):
+                if source_method == "Text to Video (文本到视频)":
+                    return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None)]
+                elif source_method == "Image to Video (图片到视频)":
+                    return [gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(), gr.update(), gr.update(value=None), gr.update(value=None), gr.update(value=None)]
+                elif source_method == "Video to Video (视频到视频)":
+                    return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(value=None), gr.update(value=None), gr.update(), gr.update(), gr.update(value=None)]
+                else:
+                    return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update()]
+            source_method.change(
+                upload_source_method, source_method, [
+                    image_to_video_col, video_to_video_col, control_video_col, start_image, end_image, 
+                    validation_video, validation_video_mask, control_video
+                ]
+            )
+
+            def upload_resize_method(resize_method):
+                if resize_method == "Generate by":
+                    return [gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)]
+                else:
+                    return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)]
+            resize_method.change(
+                upload_resize_method, resize_method, [width_slider, height_slider, base_resolution]
+            )
+
+            generate_button.click(
+                fn=controller.generate,
+                inputs=[
+                    diffusion_transformer_dropdown,
+                    base_model_dropdown,
+                    lora_model_dropdown, 
+                    lora_alpha_slider,
+                    prompt_textbox, 
+                    negative_prompt_textbox, 
+                    sampler_dropdown, 
+                    sample_step_slider, 
+                    resize_method,
+                    width_slider, 
+                    height_slider, 
+                    base_resolution, 
+                    generation_method, 
+                    length_slider, 
+                    overlap_video_length, 
+                    partial_video_length, 
+                    cfg_scale_slider, 
+                    start_image, 
+                    end_image, 
+                    validation_video,
+                    validation_video_mask,
+                    control_video,
+                    denoise_strength, 
+                    seed_textbox,
+                ],
+                outputs=[result_image, result_video, infer_progress]
+            )
+    return demo, controller
+
+
+def post_eas(
+    diffusion_transformer_dropdown,
+    base_model_dropdown, lora_model_dropdown, lora_alpha_slider,
+    prompt_textbox, negative_prompt_textbox, 
+    sampler_dropdown, sample_step_slider, resize_method, width_slider, height_slider,
+    base_resolution, generation_method, length_slider, cfg_scale_slider, 
+    start_image, end_image, validation_video, validation_video_mask, denoise_strength, seed_textbox,
+):
+    if start_image is not None:
+        with open(start_image, 'rb') as file:
+            file_content = file.read()
+            start_image_encoded_content = base64.b64encode(file_content)
+            start_image = start_image_encoded_content.decode('utf-8')
+
+    if end_image is not None:
+        with open(end_image, 'rb') as file:
+            file_content = file.read()
+            end_image_encoded_content = base64.b64encode(file_content)
+            end_image = end_image_encoded_content.decode('utf-8')
+
+    if validation_video is not None:
+        with open(validation_video, 'rb') as file:
+            file_content = file.read()
+            validation_video_encoded_content = base64.b64encode(file_content)
+            validation_video = validation_video_encoded_content.decode('utf-8')
+
+    if validation_video_mask is not None:
+        with open(validation_video_mask, 'rb') as file:
+            file_content = file.read()
+            validation_video_mask_encoded_content = base64.b64encode(file_content)
+            validation_video_mask = validation_video_mask_encoded_content.decode('utf-8')
+
+    datas = {
+        "base_model_path": base_model_dropdown,
+        "lora_model_path": lora_model_dropdown, 
+        "lora_alpha_slider": lora_alpha_slider, 
+        "prompt_textbox": prompt_textbox, 
+        "negative_prompt_textbox": negative_prompt_textbox, 
+        "sampler_dropdown": sampler_dropdown, 
+        "sample_step_slider": sample_step_slider, 
+        "resize_method": resize_method,
+        "width_slider": width_slider, 
+        "height_slider": height_slider, 
+        "base_resolution": base_resolution,
+        "generation_method": generation_method,
+        "length_slider": length_slider,
+        "cfg_scale_slider": cfg_scale_slider,
+        "start_image": start_image,
+        "end_image": end_image,
+        "validation_video": validation_video,
+        "validation_video_mask": validation_video_mask,
+        "denoise_strength": denoise_strength,
+        "seed_textbox": seed_textbox,
+    }
+
+    session = requests.session()
+    session.headers.update({"Authorization": os.environ.get("EAS_TOKEN")})
+
+    response = session.post(url=f'{os.environ.get("EAS_URL")}/cogvideox_fun/infer_forward', json=datas, timeout=300)
+
+    outputs = response.json()
+    return outputs
+
+
+class CogVideoX_Fun_Controller_EAS:
+    def __init__(self, model_name, savedir_sample):
+        self.savedir_sample = savedir_sample
+        os.makedirs(self.savedir_sample, exist_ok=True)
+
+    def generate(
+        self,
+        diffusion_transformer_dropdown,
+        base_model_dropdown,
+        lora_model_dropdown, 
+        lora_alpha_slider,
+        prompt_textbox, 
+        negative_prompt_textbox, 
+        sampler_dropdown, 
+        sample_step_slider, 
+        resize_method,
+        width_slider, 
+        height_slider, 
+        base_resolution, 
+        generation_method, 
+        length_slider, 
+        cfg_scale_slider, 
+        start_image, 
+        end_image, 
+        validation_video, 
+        validation_video_mask, 
+        denoise_strength,
+        seed_textbox
+    ):
+        is_image = True if generation_method == "Image Generation" else False
+
+        outputs = post_eas(
+            diffusion_transformer_dropdown,
+            base_model_dropdown, lora_model_dropdown, lora_alpha_slider,
+            prompt_textbox, negative_prompt_textbox, 
+            sampler_dropdown, sample_step_slider, resize_method, width_slider, height_slider,
+            base_resolution, generation_method, length_slider, cfg_scale_slider, 
+            start_image, end_image, validation_video, validation_video_mask, denoise_strength, 
+            seed_textbox
+        )
+        try:
+            base64_encoding = outputs["base64_encoding"]
+        except:
+            return gr.Image(visible=False, value=None), gr.Video(None, visible=True), outputs["message"]
+            
+        decoded_data = base64.b64decode(base64_encoding)
+
+        if not os.path.exists(self.savedir_sample):
+            os.makedirs(self.savedir_sample, exist_ok=True)
+        index = len([path for path in os.listdir(self.savedir_sample)]) + 1
+        prefix = str(index).zfill(3)
+        
+        if is_image or length_slider == 1:
+            save_sample_path = os.path.join(self.savedir_sample, prefix + f".png")
+            with open(save_sample_path, "wb") as file:
+                file.write(decoded_data)
+            if gradio_version_is_above_4:
+                return gr.Image(value=save_sample_path, visible=True), gr.Video(value=None, visible=False), "Success"
+            else:
+                return gr.Image.update(value=save_sample_path, visible=True), gr.Video.update(value=None, visible=False), "Success"
+        else:
+            save_sample_path = os.path.join(self.savedir_sample, prefix + f".mp4")
+            with open(save_sample_path, "wb") as file:
+                file.write(decoded_data)
+            if gradio_version_is_above_4:
+                return gr.Image(visible=False, value=None), gr.Video(value=save_sample_path, visible=True), "Success"
+            else:
+                return gr.Image.update(visible=False, value=None), gr.Video.update(value=save_sample_path, visible=True), "Success"
+
+
+def ui_eas(model_name, savedir_sample):
+    controller = CogVideoX_Fun_Controller_EAS(model_name, savedir_sample)
+
+    with gr.Blocks(css=css) as demo:
+        gr.Markdown(
+            """
+            # CogVideoX-Fun
+
+            A CogVideoX with more flexible generation conditions, capable of producing videos of different resolutions, around 6 seconds, and fps 8 (frames 1 to 49), as well as image generated videos. 
+
+            [Github](https://github.com/aigc-apps/CogVideoX-Fun/)
+            """
+        )
+        with gr.Column(variant="panel"):
+            gr.Markdown(
+                """
+                ### 1. Model checkpoints (模型路径).
+                """
+            )
+            with gr.Row():
+                diffusion_transformer_dropdown = gr.Dropdown(
+                    label="Pretrained Model Path",
+                    choices=[model_name],
+                    value=model_name,
+                    interactive=False,
+                )
+            with gr.Row():
+                base_model_dropdown = gr.Dropdown(
+                    label="Select base Dreambooth model",
+                    choices=["none"],
+                    value="none",
+                    interactive=False,
+                    visible=False
+                )
+                with gr.Column(visible=False):
+                    gr.Markdown(
+                        """
+                        ### Minimalism is an example portrait of Lora, triggered by specific prompt words. More details can be found on [Wiki](https://github.com/aigc-apps/CogVideoX-Fun/wiki/Training-Lora).
+                        """
+                    )
+                    with gr.Row():
+                        lora_model_dropdown = gr.Dropdown(
+                            label="Select LoRA model",
+                            choices=["none"],
+                            value="none",
+                            interactive=True,
+                        )
+
+                        lora_alpha_slider = gr.Slider(label="LoRA alpha (LoRA权重)", value=0.55, minimum=0, maximum=2, interactive=True)
+                
+        with gr.Column(variant="panel"):
+            gr.Markdown(
+                """
+                ### 2. Configs for Generation.
+                """
+            )
+            
+            prompt_textbox = gr.Textbox(label="Prompt", lines=2, value="A young woman with beautiful and clear eyes and blonde hair standing and white dress in a forest wearing a crown. She seems to be lost in thought, and the camera focuses on her face. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.")
+            negative_prompt_textbox = gr.Textbox(label="Negative prompt", lines=2, value="The video is not of a high quality, it has a low resolution. Watermark present in each frame. The background is solid. Strange body and strange trajectory. Distortion. " )
+                
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        sampler_dropdown   = gr.Dropdown(label="Sampling method", choices=list(scheduler_dict.keys()), value=list(scheduler_dict.keys())[0])
+                        sample_step_slider = gr.Slider(label="Sampling steps", value=50, minimum=10, maximum=50, step=1, interactive=False)
+                    
+                    resize_method = gr.Radio(
+                        ["Generate by", "Resize according to Reference"],
+                        value="Generate by",
+                        show_label=False,
+                    )
+                    width_slider     = gr.Slider(label="Width (视频宽度)",            value=672, minimum=128, maximum=1280, step=16, interactive=False)
+                    height_slider    = gr.Slider(label="Height (视频高度)",           value=384, minimum=128, maximum=1280, step=16, interactive=False)
+                    base_resolution  = gr.Radio(label="Base Resolution of Pretrained Models", value=512, choices=[512, 768, 960], interactive=False, visible=False)
+
+                    gr.Markdown(
+                        """
+                        V1.0 and V1.1 support up to 49 frames of video generation, while V1.5 supports up to 85 frames.  
+                        (V1.0和V1.1支持最大49帧视频生成，V1.5支持最大85帧视频生成。)
+                        """
+                    )
+                    with gr.Group():
+                        generation_method = gr.Radio(
+                            ["Video Generation", "Image Generation"],
+                            value="Video Generation",
+                            show_label=False,
+                            visible=True,
+                        )
+                        length_slider = gr.Slider(label="Animation length (视频帧数)", value=49, minimum=5,   maximum=85,  step=4)
+                    
+                    source_method = gr.Radio(
+                        ["Text to Video (文本到视频)", "Image to Video (图片到视频)", "Video to Video (视频到视频)"],
+                        value="Text to Video (文本到视频)",
+                        show_label=False,
+                    )
+                    with gr.Column(visible = False) as image_to_video_col:
+                        start_image = gr.Image(label="The image at the beginning of the video", show_label=True, elem_id="i2v_start", sources="upload", type="filepath")
+                        
+                        template_gallery_path = ["asset/1.png", "asset/2.png", "asset/3.png", "asset/4.png", "asset/5.png"]
+                        def select_template(evt: gr.SelectData):
+                            text = {
+                                "asset/1.png": "The dog is shaking head. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.", 
+                                "asset/2.png": "a sailboat sailing in rough seas with a dramatic sunset. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.", 
+                                "asset/3.png": "a beautiful woman with long hair and a dress blowing in the wind. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.", 
+                                "asset/4.png": "a man in an astronaut suit playing a guitar. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.", 
+                                "asset/5.png": "fireworks display over night city. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.", 
+                            }[template_gallery_path[evt.index]]
+                            return template_gallery_path[evt.index], text
+
+                        template_gallery = gr.Gallery(
+                            template_gallery_path,
+                            columns=5, rows=1,
+                            height=140,
+                            allow_preview=False,
+                            container=False,
+                            label="Template Examples",
+                        )
+                        template_gallery.select(select_template, None, [start_image, prompt_textbox])
+
+                        with gr.Accordion("The image at the ending of the video (Optional)", open=False):
+                            end_image   = gr.Image(label="The image at the ending of the video (Optional)", show_label=True, elem_id="i2v_end", sources="upload", type="filepath")
+                    
+                    with gr.Column(visible = False) as video_to_video_col:
+                        with gr.Row():
+                            validation_video = gr.Video(
+                                label="The video to convert (视频转视频的参考视频)",  show_label=True, 
+                                elem_id="v2v", sources="upload", 
+                            )
+                        with gr.Accordion("The mask of the video to inpaint (视频重新绘制的mask[非必需, Optional])", open=False):
+                            gr.Markdown(
+                                """
+                                - Please set a larger denoise_strength when using validation_video_mask, such as 1.00 instead of 0.70  
+                                (请设置更大的denoise_strength，当使用validation_video_mask的时候，比如1而不是0.70)
+                                """
+                            )
+                            validation_video_mask = gr.Image(
+                                label="The mask of the video to inpaint (视频重新绘制的mask[非必需, Optional])",
+                                show_label=False, elem_id="v2v_mask", sources="upload", type="filepath"
+                            )
+                        denoise_strength = gr.Slider(label="Denoise strength (重绘系数)", value=0.70, minimum=0.10, maximum=1.00, step=0.01)
+
+                    cfg_scale_slider  = gr.Slider(label="CFG Scale (引导系数)",        value=6.0, minimum=0,   maximum=20)
+                    
+                    with gr.Row():
+                        seed_textbox = gr.Textbox(label="Seed", value=43)
+                        seed_button  = gr.Button(value="\U0001F3B2", elem_classes="toolbutton")
+                        seed_button.click(
+                            fn=lambda: gr.Textbox(value=random.randint(1, 1e8)) if gradio_version_is_above_4 else gr.Textbox.update(value=random.randint(1, 1e8)), 
+                            inputs=[], 
+                            outputs=[seed_textbox]
+                        )
+
+                    generate_button = gr.Button(value="Generate", variant='primary')
+                    
+                with gr.Column():
+                    result_image = gr.Image(label="Generated Image", interactive=False, visible=False)
+                    result_video = gr.Video(label="Generated Animation", interactive=False)
+                    infer_progress = gr.Textbox(
+                        label="Generation Info",
+                        value="No task currently",
+                        interactive=False
+                    )
+
+            def upload_generation_method(generation_method):
+                if generation_method == "Video Generation":
+                    return gr.update(visible=True, minimum=5, maximum=85, value=49, interactive=True)
+                elif generation_method == "Image Generation":
+                    return gr.update(minimum=1, maximum=1, value=1, interactive=False)
+            generation_method.change(
+                upload_generation_method, generation_method, [length_slider]
+            )
+
+            def upload_source_method(source_method):
+                if source_method == "Text to Video (文本到视频)":
+                    return [gr.update(visible=False), gr.update(visible=False), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None)]
+                elif source_method == "Image to Video (图片到视频)":
+                    return [gr.update(visible=True), gr.update(visible=False), gr.update(), gr.update(), gr.update(value=None), gr.update(value=None)]
+                else:
+                    return [gr.update(visible=False), gr.update(visible=True), gr.update(value=None), gr.update(value=None), gr.update(), gr.update()]
+            source_method.change(
+                upload_source_method, source_method, [image_to_video_col, video_to_video_col, start_image, end_image, validation_video, validation_video_mask]
+            )
+
+            def upload_resize_method(resize_method):
+                if resize_method == "Generate by":
+                    return [gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)]
+                else:
+                    return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)]
+            resize_method.change(
+                upload_resize_method, resize_method, [width_slider, height_slider, base_resolution]
+            )
+
+            generate_button.click(
+                fn=controller.generate,
+                inputs=[
+                    diffusion_transformer_dropdown,
+                    base_model_dropdown,
+                    lora_model_dropdown, 
+                    lora_alpha_slider,
+                    prompt_textbox, 
+                    negative_prompt_textbox, 
+                    sampler_dropdown, 
+                    sample_step_slider, 
+                    resize_method,
+                    width_slider, 
+                    height_slider, 
+                    base_resolution, 
+                    generation_method, 
+                    length_slider, 
+                    cfg_scale_slider, 
+                    start_image, 
+                    end_image, 
+                    validation_video,
+                    validation_video_mask,
+                    denoise_strength, 
+                    seed_textbox,
+                ],
+                outputs=[result_image, result_video, infer_progress]
+            )
+    return demo, controller

robomaster/utils/discrete_sampler.py

@@ -0,0 +1,46 @@
+"""Modified from https://github.com/THUDM/CogVideo/blob/3710a612d8760f5cdb1741befeebb65b9e0f2fe0/sat/sgm/modules/diffusionmodules/sigma_sampling.py
+"""
+import torch
+
+class DiscreteSampling:
+    def __init__(self, num_idx, uniform_sampling=False):
+        self.num_idx = num_idx
+        self.uniform_sampling = uniform_sampling
+        self.is_distributed = torch.distributed.is_available() and torch.distributed.is_initialized()
+
+        if self.is_distributed and self.uniform_sampling:
+            world_size = torch.distributed.get_world_size()
+            self.rank = torch.distributed.get_rank()
+
+            i = 1
+            while True:
+                if world_size % i != 0 or num_idx % (world_size // i) != 0:
+                    i += 1
+                else: 
+                    self.group_num = world_size // i
+                    break
+            assert self.group_num > 0 
+            assert world_size % self.group_num == 0 
+            # the number of rank in one group 
+            self.group_width = world_size // self.group_num  
+            self.sigma_interval = self.num_idx // self.group_num
+            print('rank=%d world_size=%d group_num=%d group_width=%d sigma_interval=%s' % (
+                  self.rank, world_size, self.group_num,
+                  self.group_width, self.sigma_interval))
+        
+    def __call__(self, n_samples, generator=None, device=None):
+        if self.is_distributed and self.uniform_sampling: 
+            group_index = self.rank // self.group_width
+            idx = torch.randint(
+                    group_index * self.sigma_interval,
+                    (group_index + 1) * self.sigma_interval,
+                    (n_samples,), 
+                    generator=generator, device=device,
+                )
+            print('proc[%d] idx=%s' % (self.rank, idx))
+        else:   
+            idx = torch.randint(
+                    0, self.num_idx, (n_samples,), 
+                    generator=generator, device=device,
+                )
+        return idx

robomaster/utils/lora_utils.py

@@ -0,0 +1,477 @@
+# LoRA network module
+# reference:
+# https://github.com/microsoft/LoRA/blob/main/loralib/layers.py
+# https://github.com/cloneofsimo/lora/blob/master/lora_diffusion/lora.py
+# https://github.com/bmaltais/kohya_ss
+
+import hashlib
+import math
+import os
+from collections import defaultdict
+from io import BytesIO
+from typing import List, Optional, Type, Union
+
+import safetensors.torch
+import torch
+import torch.utils.checkpoint
+from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear
+from safetensors.torch import load_file
+from transformers import T5EncoderModel
+
+
+class LoRAModule(torch.nn.Module):
+    """
+    replaces forward method of the original Linear, instead of replacing the original Linear module.
+    """
+
+    def __init__(
+        self,
+        lora_name,
+        org_module: torch.nn.Module,
+        multiplier=1.0,
+        lora_dim=4,
+        alpha=1,
+        dropout=None,
+        rank_dropout=None,
+        module_dropout=None,
+    ):
+        """if alpha == 0 or None, alpha is rank (no scaling)."""
+        super().__init__()
+        self.lora_name = lora_name
+
+        if org_module.__class__.__name__ == "Conv2d":
+            in_dim = org_module.in_channels
+            out_dim = org_module.out_channels
+        else:
+            in_dim = org_module.in_features
+            out_dim = org_module.out_features
+
+        self.lora_dim = lora_dim
+        if org_module.__class__.__name__ == "Conv2d":
+            kernel_size = org_module.kernel_size
+            stride = org_module.stride
+            padding = org_module.padding
+            self.lora_down = torch.nn.Conv2d(in_dim, self.lora_dim, kernel_size, stride, padding, bias=False)
+            self.lora_up = torch.nn.Conv2d(self.lora_dim, out_dim, (1, 1), (1, 1), bias=False)
+        else:
+            self.lora_down = torch.nn.Linear(in_dim, self.lora_dim, bias=False)
+            self.lora_up = torch.nn.Linear(self.lora_dim, out_dim, bias=False)
+
+        if type(alpha) == torch.Tensor:
+            alpha = alpha.detach().float().numpy()  # without casting, bf16 causes error
+        alpha = self.lora_dim if alpha is None or alpha == 0 else alpha
+        self.scale = alpha / self.lora_dim
+        self.register_buffer("alpha", torch.tensor(alpha))
+
+        # same as microsoft's
+        torch.nn.init.kaiming_uniform_(self.lora_down.weight, a=math.sqrt(5))
+        torch.nn.init.zeros_(self.lora_up.weight)
+
+        self.multiplier = multiplier
+        self.org_module = org_module  # remove in applying
+        self.dropout = dropout
+        self.rank_dropout = rank_dropout
+        self.module_dropout = module_dropout
+
+    def apply_to(self):
+        self.org_forward = self.org_module.forward
+        self.org_module.forward = self.forward
+        del self.org_module
+
+    def forward(self, x, *args, **kwargs):
+        weight_dtype = x.dtype
+        org_forwarded = self.org_forward(x)
+
+        # module dropout
+        if self.module_dropout is not None and self.training:
+            if torch.rand(1) < self.module_dropout:
+                return org_forwarded
+
+        lx = self.lora_down(x.to(self.lora_down.weight.dtype))
+
+        # normal dropout
+        if self.dropout is not None and self.training:
+            lx = torch.nn.functional.dropout(lx, p=self.dropout)
+
+        # rank dropout
+        if self.rank_dropout is not None and self.training:
+            mask = torch.rand((lx.size(0), self.lora_dim), device=lx.device) > self.rank_dropout
+            if len(lx.size()) == 3:
+                mask = mask.unsqueeze(1)  # for Text Encoder
+            elif len(lx.size()) == 4:
+                mask = mask.unsqueeze(-1).unsqueeze(-1)  # for Conv2d
+            lx = lx * mask
+
+            # scaling for rank dropout: treat as if the rank is changed
+            scale = self.scale * (1.0 / (1.0 - self.rank_dropout))  # redundant for readability
+        else:
+            scale = self.scale
+
+        lx = self.lora_up(lx)
+
+        return org_forwarded.to(weight_dtype) + lx.to(weight_dtype) * self.multiplier * scale
+
+
+def addnet_hash_legacy(b):
+    """Old model hash used by sd-webui-additional-networks for .safetensors format files"""
+    m = hashlib.sha256()
+
+    b.seek(0x100000)
+    m.update(b.read(0x10000))
+    return m.hexdigest()[0:8]
+
+
+def addnet_hash_safetensors(b):
+    """New model hash used by sd-webui-additional-networks for .safetensors format files"""
+    hash_sha256 = hashlib.sha256()
+    blksize = 1024 * 1024
+
+    b.seek(0)
+    header = b.read(8)
+    n = int.from_bytes(header, "little")
+
+    offset = n + 8
+    b.seek(offset)
+    for chunk in iter(lambda: b.read(blksize), b""):
+        hash_sha256.update(chunk)
+
+    return hash_sha256.hexdigest()
+
+
+def precalculate_safetensors_hashes(tensors, metadata):
+    """Precalculate the model hashes needed by sd-webui-additional-networks to
+    save time on indexing the model later."""
+
+    # Because writing user metadata to the file can change the result of
+    # sd_models.model_hash(), only retain the training metadata for purposes of
+    # calculating the hash, as they are meant to be immutable
+    metadata = {k: v for k, v in metadata.items() if k.startswith("ss_")}
+
+    bytes = safetensors.torch.save(tensors, metadata)
+    b = BytesIO(bytes)
+
+    model_hash = addnet_hash_safetensors(b)
+    legacy_hash = addnet_hash_legacy(b)
+    return model_hash, legacy_hash
+
+
+class LoRANetwork(torch.nn.Module):
+    TRANSFORMER_TARGET_REPLACE_MODULE = ["CogVideoXTransformer3DModel"]
+    TEXT_ENCODER_TARGET_REPLACE_MODULE = ["T5LayerSelfAttention", "T5LayerFF", "BertEncoder"]
+    LORA_PREFIX_TRANSFORMER = "lora_unet"
+    LORA_PREFIX_TEXT_ENCODER = "lora_te"
+    def __init__(
+        self,
+        text_encoder: Union[List[T5EncoderModel], T5EncoderModel],
+        unet,
+        multiplier: float = 1.0,
+        lora_dim: int = 4,
+        alpha: float = 1,
+        dropout: Optional[float] = None,
+        module_class: Type[object] = LoRAModule,
+        add_lora_in_attn_temporal: bool = False,
+        varbose: Optional[bool] = False,
+    ) -> None:
+        super().__init__()
+        self.multiplier = multiplier
+
+        self.lora_dim = lora_dim
+        self.alpha = alpha
+        self.dropout = dropout
+
+        print(f"create LoRA network. base dim (rank): {lora_dim}, alpha: {alpha}")
+        print(f"neuron dropout: p={self.dropout}")
+
+        # create module instances
+        def create_modules(
+            is_unet: bool,
+            root_module: torch.nn.Module,
+            target_replace_modules: List[torch.nn.Module],
+        ) -> List[LoRAModule]:
+            prefix = (
+                self.LORA_PREFIX_TRANSFORMER
+                if is_unet
+                else self.LORA_PREFIX_TEXT_ENCODER
+            )
+            loras = []
+            skipped = []
+            for name, module in root_module.named_modules():
+                if module.__class__.__name__ in target_replace_modules:
+                    for child_name, child_module in module.named_modules():
+                        is_linear = child_module.__class__.__name__ == "Linear" or child_module.__class__.__name__ == "LoRACompatibleLinear"
+                        is_conv2d = child_module.__class__.__name__ == "Conv2d" or child_module.__class__.__name__ == "LoRACompatibleConv"
+                        is_conv2d_1x1 = is_conv2d and child_module.kernel_size == (1, 1)
+                        
+                        if not add_lora_in_attn_temporal:
+                            if "attn_temporal" in child_name:
+                                continue
+
+                        if is_linear or is_conv2d:
+                            lora_name = prefix + "." + name + "." + child_name
+                            lora_name = lora_name.replace(".", "_")
+
+                            dim = None
+                            alpha = None
+
+                            if is_linear or is_conv2d_1x1:
+                                dim = self.lora_dim
+                                alpha = self.alpha
+
+                            if dim is None or dim == 0:
+                                if is_linear or is_conv2d_1x1:
+                                    skipped.append(lora_name)
+                                continue
+
+                            lora = module_class(
+                                lora_name,
+                                child_module,
+                                self.multiplier,
+                                dim,
+                                alpha,
+                                dropout=dropout,
+                            )
+                            loras.append(lora)
+            return loras, skipped
+
+        text_encoders = text_encoder if type(text_encoder) == list else [text_encoder]
+
+        self.text_encoder_loras = []
+        skipped_te = []
+        for i, text_encoder in enumerate(text_encoders):
+            if text_encoder is not None:
+                text_encoder_loras, skipped = create_modules(False, text_encoder, LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
+                self.text_encoder_loras.extend(text_encoder_loras)
+                skipped_te += skipped
+        print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.")
+        
+        self.unet_loras, skipped_un = create_modules(True, unet, LoRANetwork.TRANSFORMER_TARGET_REPLACE_MODULE)
+        print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
+
+        # assertion
+        names = set()
+        for lora in self.text_encoder_loras + self.unet_loras:
+            assert lora.lora_name not in names, f"duplicated lora name: {lora.lora_name}"
+            names.add(lora.lora_name)
+
+    def apply_to(self, text_encoder, unet, apply_text_encoder=True, apply_unet=True):
+        if apply_text_encoder:
+            print("enable LoRA for text encoder")
+        else:
+            self.text_encoder_loras = []
+
+        if apply_unet:
+            print("enable LoRA for U-Net")
+        else:
+            self.unet_loras = []
+
+        for lora in self.text_encoder_loras + self.unet_loras:
+            lora.apply_to()
+            self.add_module(lora.lora_name, lora)
+
+    def set_multiplier(self, multiplier):
+        self.multiplier = multiplier
+        for lora in self.text_encoder_loras + self.unet_loras:
+            lora.multiplier = self.multiplier
+
+    def load_weights(self, file):
+        if os.path.splitext(file)[1] == ".safetensors":
+            from safetensors.torch import load_file
+
+            weights_sd = load_file(file)
+        else:
+            weights_sd = torch.load(file, map_location="cpu")
+        info = self.load_state_dict(weights_sd, False)
+        return info
+
+    def prepare_optimizer_params(self, text_encoder_lr, unet_lr, default_lr):
+        self.requires_grad_(True)
+        all_params = []
+
+        def enumerate_params(loras):
+            params = []
+            for lora in loras:
+                params.extend(lora.parameters())
+            return params
+
+        if self.text_encoder_loras:
+            param_data = {"params": enumerate_params(self.text_encoder_loras)}
+            if text_encoder_lr is not None:
+                param_data["lr"] = text_encoder_lr
+            all_params.append(param_data)
+
+        if self.unet_loras:
+            param_data = {"params": enumerate_params(self.unet_loras)}
+            if unet_lr is not None:
+                param_data["lr"] = unet_lr
+            all_params.append(param_data)
+
+        return all_params
+
+    def enable_gradient_checkpointing(self):
+        pass
+
+    def get_trainable_params(self):
+        return self.parameters()
+
+    def save_weights(self, file, dtype, metadata):
+        if metadata is not None and len(metadata) == 0:
+            metadata = None
+
+        state_dict = self.state_dict()
+
+        if dtype is not None:
+            for key in list(state_dict.keys()):
+                v = state_dict[key]
+                v = v.detach().clone().to("cpu").to(dtype)
+                state_dict[key] = v
+
+        if os.path.splitext(file)[1] == ".safetensors":
+            from safetensors.torch import save_file
+
+            # Precalculate model hashes to save time on indexing
+            if metadata is None:
+                metadata = {}
+            model_hash, legacy_hash = precalculate_safetensors_hashes(state_dict, metadata)
+            metadata["sshs_model_hash"] = model_hash
+            metadata["sshs_legacy_hash"] = legacy_hash
+
+            save_file(state_dict, file, metadata)
+        else:
+            torch.save(state_dict, file)
+
+def create_network(
+    multiplier: float,
+    network_dim: Optional[int],
+    network_alpha: Optional[float],
+    text_encoder: Union[T5EncoderModel, List[T5EncoderModel]],
+    transformer,
+    neuron_dropout: Optional[float] = None,
+    add_lora_in_attn_temporal: bool = False,
+    **kwargs,
+):
+    if network_dim is None:
+        network_dim = 4  # default
+    if network_alpha is None:
+        network_alpha = 1.0
+
+    network = LoRANetwork(
+        text_encoder,
+        transformer,
+        multiplier=multiplier,
+        lora_dim=network_dim,
+        alpha=network_alpha,
+        dropout=neuron_dropout,
+        add_lora_in_attn_temporal=add_lora_in_attn_temporal,
+        varbose=True,
+    )
+    return network
+
+def merge_lora(pipeline, lora_path, multiplier, device='cpu', dtype=torch.float32, state_dict=None, transformer_only=False):
+    LORA_PREFIX_TRANSFORMER = "lora_unet"
+    LORA_PREFIX_TEXT_ENCODER = "lora_te"
+    if state_dict is None:
+        state_dict = load_file(lora_path, device=device)
+    else:
+        state_dict = state_dict
+    updates = defaultdict(dict)
+    for key, value in state_dict.items():
+        layer, elem = key.split('.', 1)
+        updates[layer][elem] = value
+
+    for layer, elems in updates.items():
+
+        if "lora_te" in layer:
+            if transformer_only:
+                continue
+            else:
+                layer_infos = layer.split(LORA_PREFIX_TEXT_ENCODER + "_")[-1].split("_")
+                curr_layer = pipeline.text_encoder
+        else:
+            layer_infos = layer.split(LORA_PREFIX_TRANSFORMER + "_")[-1].split("_")
+            curr_layer = pipeline.transformer
+
+        temp_name = layer_infos.pop(0)
+        while len(layer_infos) > -1:
+            try:
+                curr_layer = curr_layer.__getattr__(temp_name)
+                if len(layer_infos) > 0:
+                    temp_name = layer_infos.pop(0)
+                elif len(layer_infos) == 0:
+                    break
+            except Exception:
+                if len(layer_infos) == 0:
+                    print('Error loading layer')
+                if len(temp_name) > 0:
+                    temp_name += "_" + layer_infos.pop(0)
+                else:
+                    temp_name = layer_infos.pop(0)
+
+        weight_up = elems['lora_up.weight'].to(dtype)
+        weight_down = elems['lora_down.weight'].to(dtype)
+        if 'alpha' in elems.keys():
+            alpha = elems['alpha'].item() / weight_up.shape[1]
+        else:
+            alpha = 1.0
+
+        curr_layer.weight.data = curr_layer.weight.data.to(device)
+        if len(weight_up.shape) == 4:
+            curr_layer.weight.data += multiplier * alpha * torch.mm(weight_up.squeeze(3).squeeze(2),
+                                                                    weight_down.squeeze(3).squeeze(2)).unsqueeze(
+                2).unsqueeze(3)
+        else:
+            curr_layer.weight.data += multiplier * alpha * torch.mm(weight_up, weight_down)
+
+    return pipeline
+
+# TODO: Refactor with merge_lora.
+def unmerge_lora(pipeline, lora_path, multiplier=1, device="cpu", dtype=torch.float32):
+    """Unmerge state_dict in LoRANetwork from the pipeline in diffusers."""
+    LORA_PREFIX_UNET = "lora_unet"
+    LORA_PREFIX_TEXT_ENCODER = "lora_te"
+    state_dict = load_file(lora_path, device=device)
+
+    updates = defaultdict(dict)
+    for key, value in state_dict.items():
+        layer, elem = key.split('.', 1)
+        updates[layer][elem] = value
+
+    for layer, elems in updates.items():
+
+        if "lora_te" in layer:
+            layer_infos = layer.split(LORA_PREFIX_TEXT_ENCODER + "_")[-1].split("_")
+            curr_layer = pipeline.text_encoder
+        else:
+            layer_infos = layer.split(LORA_PREFIX_UNET + "_")[-1].split("_")
+            curr_layer = pipeline.transformer
+
+        temp_name = layer_infos.pop(0)
+        while len(layer_infos) > -1:
+            try:
+                curr_layer = curr_layer.__getattr__(temp_name)
+                if len(layer_infos) > 0:
+                    temp_name = layer_infos.pop(0)
+                elif len(layer_infos) == 0:
+                    break
+            except Exception:
+                if len(layer_infos) == 0:
+                    print('Error loading layer')
+                if len(temp_name) > 0:
+                    temp_name += "_" + layer_infos.pop(0)
+                else:
+                    temp_name = layer_infos.pop(0)
+
+        weight_up = elems['lora_up.weight'].to(dtype)
+        weight_down = elems['lora_down.weight'].to(dtype)
+        if 'alpha' in elems.keys():
+            alpha = elems['alpha'].item() / weight_up.shape[1]
+        else:
+            alpha = 1.0
+
+        curr_layer.weight.data = curr_layer.weight.data.to(device)
+        if len(weight_up.shape) == 4:
+            curr_layer.weight.data -= multiplier * alpha * torch.mm(weight_up.squeeze(3).squeeze(2),
+                                                                    weight_down.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
+        else:
+            curr_layer.weight.data -= multiplier * alpha * torch.mm(weight_up, weight_down)
+
+    return pipeline

robomaster/utils/utils.py

@@ -0,0 +1,208 @@
+import os
+import gc
+import imageio
+import numpy as np
+import torch
+import torchvision
+import cv2
+from einops import rearrange
+from PIL import Image
+
+def get_width_and_height_from_image_and_base_resolution(image, base_resolution):
+    target_pixels = int(base_resolution) * int(base_resolution)
+    original_width, original_height = Image.open(image).size
+    ratio = (target_pixels / (original_width * original_height)) ** 0.5
+    width_slider = round(original_width * ratio)
+    height_slider = round(original_height * ratio)
+    return height_slider, width_slider
+
+def color_transfer(sc, dc):
+    """
+    Transfer color distribution from of sc, referred to dc.
+
+    Args:
+        sc (numpy.ndarray): input image to be transfered.
+        dc (numpy.ndarray): reference image
+
+    Returns:
+        numpy.ndarray: Transferred color distribution on the sc.
+    """
+
+    def get_mean_and_std(img):
+        x_mean, x_std = cv2.meanStdDev(img)
+        x_mean = np.hstack(np.around(x_mean, 2))
+        x_std = np.hstack(np.around(x_std, 2))
+        return x_mean, x_std
+
+    sc = cv2.cvtColor(sc, cv2.COLOR_RGB2LAB)
+    s_mean, s_std = get_mean_and_std(sc)
+    dc = cv2.cvtColor(dc, cv2.COLOR_RGB2LAB)
+    t_mean, t_std = get_mean_and_std(dc)
+    img_n = ((sc - s_mean) * (t_std / s_std)) + t_mean
+    np.putmask(img_n, img_n > 255, 255)
+    np.putmask(img_n, img_n < 0, 0)
+    dst = cv2.cvtColor(cv2.convertScaleAbs(img_n), cv2.COLOR_LAB2RGB)
+    return dst
+
+def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=12, imageio_backend=True, color_transfer_post_process=False):
+    videos = rearrange(videos, "b c t h w -> t b c h w")
+    outputs = []
+    for x in videos:
+        x = torchvision.utils.make_grid(x, nrow=n_rows)
+        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
+        if rescale:
+            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
+        x = (x * 255).numpy().astype(np.uint8)
+        outputs.append(Image.fromarray(x))
+
+    if color_transfer_post_process:
+        for i in range(1, len(outputs)):
+            outputs[i] = Image.fromarray(color_transfer(np.uint8(outputs[i]), np.uint8(outputs[0])))
+
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    if imageio_backend:
+        if path.endswith("mp4"):
+            imageio.mimsave(path, outputs, fps=fps)
+        else:
+            imageio.mimsave(path, outputs, duration=(1000 * 1/fps))
+    else:
+        if path.endswith("mp4"):
+            path = path.replace('.mp4', '.gif')
+        outputs[0].save(path, format='GIF', append_images=outputs, save_all=True, duration=100, loop=0)
+
+def get_image_to_video_latent(validation_image_start, validation_image_end, video_length, sample_size):
+    if validation_image_start is not None and validation_image_end is not None:
+        if type(validation_image_start) is str and os.path.isfile(validation_image_start):
+            image_start = clip_image = Image.open(validation_image_start).convert("RGB")
+            image_start = image_start.resize([sample_size[1], sample_size[0]])
+            clip_image = clip_image.resize([sample_size[1], sample_size[0]])
+        else:
+            image_start = clip_image = validation_image_start
+            image_start = [_image_start.resize([sample_size[1], sample_size[0]]) for _image_start in image_start]
+            clip_image = [_clip_image.resize([sample_size[1], sample_size[0]]) for _clip_image in clip_image]
+
+        if type(validation_image_end) is str and os.path.isfile(validation_image_end):
+            image_end = Image.open(validation_image_end).convert("RGB")
+            image_end = image_end.resize([sample_size[1], sample_size[0]])
+        else:
+            image_end = validation_image_end
+            image_end = [_image_end.resize([sample_size[1], sample_size[0]]) for _image_end in image_end]
+
+        if type(image_start) is list:
+            clip_image = clip_image[0]
+            start_video = torch.cat(
+                [torch.from_numpy(np.array(_image_start)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0) for _image_start in image_start], 
+                dim=2
+            )
+            input_video = torch.tile(start_video[:, :, :1], [1, 1, video_length, 1, 1])
+            input_video[:, :, :len(image_start)] = start_video
+            
+            input_video_mask = torch.zeros_like(input_video[:, :1])
+            input_video_mask[:, :, len(image_start):] = 255
+        else:
+            input_video = torch.tile(
+                torch.from_numpy(np.array(image_start)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0), 
+                [1, 1, video_length, 1, 1]
+            )
+            input_video_mask = torch.zeros_like(input_video[:, :1])
+            input_video_mask[:, :, 1:] = 255
+
+        if type(image_end) is list:
+            image_end = [_image_end.resize(image_start[0].size if type(image_start) is list else image_start.size) for _image_end in image_end]
+            end_video = torch.cat(
+                [torch.from_numpy(np.array(_image_end)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0) for _image_end in image_end], 
+                dim=2
+            )
+            input_video[:, :, -len(end_video):] = end_video
+            
+            input_video_mask[:, :, -len(image_end):] = 0
+        else:
+            image_end = image_end.resize(image_start[0].size if type(image_start) is list else image_start.size)
+            input_video[:, :, -1:] = torch.from_numpy(np.array(image_end)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0)
+            input_video_mask[:, :, -1:] = 0
+
+        input_video = input_video / 255
+
+    elif validation_image_start is not None:
+        if type(validation_image_start) is str and os.path.isfile(validation_image_start):
+            image_start = clip_image = Image.open(validation_image_start).convert("RGB")
+            image_start = image_start.resize([sample_size[1], sample_size[0]])
+            clip_image = clip_image.resize([sample_size[1], sample_size[0]])
+        else:
+            image_start = clip_image = validation_image_start
+            image_start = [_image_start.resize([sample_size[1], sample_size[0]]) for _image_start in image_start]
+            clip_image = [_clip_image.resize([sample_size[1], sample_size[0]]) for _clip_image in clip_image]
+        image_end = None
+
+        if type(image_start) is list:
+            clip_image = clip_image[0]
+            start_video = torch.cat(
+                [torch.from_numpy(np.array(_image_start)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0) for _image_start in image_start], 
+                dim=2
+            )
+            input_video = torch.tile(start_video[:, :, :1], [1, 1, video_length, 1, 1])
+            input_video[:, :, :len(image_start)] = start_video
+            input_video = input_video / 255
+            
+            input_video_mask = torch.zeros_like(input_video[:, :1])
+            input_video_mask[:, :, len(image_start):] = 255
+        else:
+            input_video = torch.tile(
+                torch.from_numpy(np.array(image_start)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0), 
+                [1, 1, video_length, 1, 1]
+            ) / 255
+            input_video_mask = torch.zeros_like(input_video[:, :1])
+            input_video_mask[:, :, 1:, ] = 255
+    else:
+        image_start = None
+        image_end = None
+        input_video = torch.zeros([1, 3, video_length, sample_size[0], sample_size[1]])
+        input_video_mask = torch.ones([1, 1, video_length, sample_size[0], sample_size[1]]) * 255
+        clip_image = None
+
+    del image_start
+    del image_end
+    gc.collect()
+
+    return  input_video, input_video_mask, clip_image
+
+def get_video_to_video_latent(input_video_path, video_length, sample_size, fps=None, validation_video_mask=None):
+    if isinstance(input_video_path, str):
+        cap = cv2.VideoCapture(input_video_path)
+        input_video = []
+
+        original_fps = cap.get(cv2.CAP_PROP_FPS)
+        frame_skip = 1 if fps is None else int(original_fps // fps)
+
+        frame_count = 0
+
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            if frame_count % frame_skip == 0:
+                frame = cv2.resize(frame, (sample_size[1], sample_size[0]))
+                input_video.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+
+            frame_count += 1
+
+        cap.release()
+    else:
+        input_video = input_video_path
+
+    input_video = torch.from_numpy(np.array(input_video))[:video_length]
+    input_video = input_video.permute([3, 0, 1, 2]).unsqueeze(0) / 255
+
+    if validation_video_mask is not None:
+        validation_video_mask = Image.open(validation_video_mask).convert('L').resize((sample_size[1], sample_size[0]))
+        input_video_mask = np.where(np.array(validation_video_mask) < 240, 0, 255)
+        
+        input_video_mask = torch.from_numpy(np.array(input_video_mask)).unsqueeze(0).unsqueeze(-1).permute([3, 0, 1, 2]).unsqueeze(0)
+        input_video_mask = torch.tile(input_video_mask, [1, 1, input_video.size()[2], 1, 1])
+        input_video_mask = input_video_mask.to(input_video.device, input_video.dtype)
+    else:
+        input_video_mask = torch.zeros_like(input_video[:, :1])
+        input_video_mask[:, :, :] = 255
+
+    return  input_video, input_video_mask, None

robomaster/video_caption/README.md

@@ -0,0 +1,174 @@
+# Video Caption
+English | [简体中文](./README_zh-CN.md)
+
+The folder contains codes for dataset preprocessing (i.e., video splitting, filtering, and recaptioning), and beautiful prompt used by CogVideoX-Fun.
+The entire process supports distributed parallel processing, capable of handling large-scale datasets.
+
+Meanwhile, we are collaborating with [Data-Juicer](https://github.com/modelscope/data-juicer/blob/main/docs/DJ_SORA.md),
+allowing you to easily perform video data processing on [Aliyun PAI-DLC](https://help.aliyun.com/zh/pai/user-guide/video-preprocessing/).
+
+# Table of Content
+- [Video Caption](#video-caption)
+- [Table of Content](#table-of-content)
+  - [Quick Start](#quick-start)
+    - [Setup](#setup)
+    - [Data Preprocessing](#data-preprocessing)
+      - [Data Preparation](#data-preparation)
+      - [Video Splitting](#video-splitting)
+      - [Video Filtering](#video-filtering)
+      - [Video Recaptioning](#video-recaptioning)
+    - [Beautiful Prompt (For CogVideoX-Fun Inference)](#beautiful-prompt-for-cogvideox-inference)
+      - [Batched Inference](#batched-inference)
+      - [OpenAI Server](#openai-server)
+
+## Quick Start
+
+### Setup
+AliyunDSW or Docker is recommended to setup the environment, please refer to [Quick Start](../../README.md#quick-start).
+You can also refer to the image build process in the [Dockerfile](../../Dockerfile.ds) to configure the conda environment and other dependencies locally.
+
+Since the video recaptioning depends on [llm-awq](https://github.com/mit-han-lab/llm-awq) for faster and memory efficient inference,
+the minimum GPU requirment should be RTX 3060 or A2 (CUDA Compute Capability >= 8.0).
+
+```shell
+# pull image
+docker pull mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun
+
+# enter image
+docker run -it -p 7860:7860 --network host --gpus all --security-opt seccomp:unconfined --shm-size 200g mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun
+
+# clone code
+git clone https://github.com/aigc-apps/CogVideoX-Fun.git
+
+# enter video_caption
+cd CogVideoX-Fun/cogvideox/video_caption
+```
+
+### Data Preprocessing
+#### Data Preparation
+Place the downloaded videos into a folder under [datasets](./datasets/) (preferably without nested structures, as the video names are used as unique IDs in subsequent processes).
+Taking Panda-70M as an example, the entire dataset directory structure is shown as follows:
+```
+📦 datasets/
+├── 📂 panda_70m/
+│   ├── 📂 videos/
+│   │   ├── 📂 data/
+│   │   │   └── 📄 --C66yU3LjM_2.mp4
+│   │   │   └── 📄 ...
+```
+
+#### Video Splitting
+CogVideoX-Fun utilizes [PySceneDetect](https://github.com/Breakthrough/PySceneDetect) to identify scene changes within the video
+and performs video splitting via FFmpeg based on certain threshold values to ensure consistency of the video clip.
+Video clips shorter than 3 seconds will be discarded, and those longer than 10 seconds will be splitted recursively.
+
+The entire workflow of video splitting is in the [stage_1_video_splitting.sh](./scripts/stage_1_video_splitting.sh).
+After running
+```shell
+sh scripts/stage_1_video_splitting.sh
+```
+the video clips are obtained in `cogvideox/video_caption/datasets/panda_70m/videos_clips/data/`.
+
+#### Video Filtering
+Based on the videos obtained in the previous step, CogVideoX-Fun provides a simple yet effective pipeline to filter out high-quality videos for recaptioning.
+The overall process is as follows:
+
+- Aesthetic filtering: Filter out videos with poor content (blurry, dim, etc.) by calculating the average aesthetic score of uniformly sampled 4 frames via [aesthetic-predictor-v2-5](https://github.com/discus0434/aesthetic-predictor-v2-5).
+- Text filtering: Use [EasyOCR](https://github.com/JaidedAI/EasyOCR) to calculate the text area proportion of the middle frame to filter out videos with a large area of text.
+- Motion filtering: Calculate interframe optical flow differences to filter out videos that move too slowly or too quickly.
+
+The entire workflow of video filtering is in the [stage_2_video_filtering.sh](./scripts/stage_2_video_filtering.sh).
+After running
+```shell
+sh scripts/stage_2_video_filtering.sh
+```
+the aesthetic score, text score, and motion score of videos will be saved in the corresponding meta files in the folder `cogvideox/video_caption/datasets/panda_70m/videos_clips/`.
+
+> [!NOTE]
+> The computation of the aesthetic score depends on the [google/siglip-so400m-patch14-384 model](https://huggingface.co/google/siglip-so400m-patch14-384).
+Please run `HF_ENDPOINT=https://hf-mirror.com sh scripts/stage_2_video_filtering.sh` if you cannot access to huggingface.com.
+
+
+#### Video Recaptioning
+After obtaining the aboved high-quality filtered videos, CogVideoX-Fun utilizes [VILA1.5](https://github.com/NVlabs/VILA) to perform video recaptioning. 
+Subsequently, the recaptioning results are rewritten by LLMs to better meet with the requirements of video generation tasks. 
+Finally, an advanced VideoCLIPXL model is developed to filter out video-caption pairs with poor alignment, resulting in the final training dataset.
+
+Please download the video caption model from [VILA1.5](https://huggingface.co/collections/Efficient-Large-Model/vila-on-pre-training-for-visual-language-models-65d8022a3a52cd9bcd62698e) of the appropriate size based on the GPU memory of your machine.
+For A100 with 40G VRAM, you can download [VILA1.5-40b-AWQ](https://huggingface.co/Efficient-Large-Model/VILA1.5-40b-AWQ) by running
+```shell
+# Add HF_ENDPOINT=https://hf-mirror.com before the command if you cannot access to huggingface.com
+huggingface-cli download Efficient-Large-Model/VILA1.5-40b-AWQ --local-dir-use-symlinks False --local-dir /PATH/TO/VILA_MODEL
+```
+
+Optionally, you can prepare local LLMs to rewrite the recaption results.
+For example, you can download [Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) by running
+```shell
+# Add HF_ENDPOINT=https://hf-mirror.com before the command if you cannot access to huggingface.com
+huggingface-cli download NousResearch/Meta-Llama-3-8B-Instruct --local-dir-use-symlinks False --local-dir /PATH/TO/REWRITE_MODEL
+```
+
+The entire workflow of video recaption is in the [stage_3_video_recaptioning.sh](./scripts/stage_3_video_recaptioning.sh).
+After running
+```shell
+VILA_MODEL_PATH=/PATH/TO/VILA_MODEL REWRITE_MODEL_PATH=/PATH/TO/REWRITE_MODEL sh scripts/stage_3_video_recaptioning.sh
+``` 
+the final train file is obtained in `cogvideox/video_caption/datasets/panda_70m/videos_clips/meta_train_info.json`.
+
+
+### Beautiful Prompt (For CogVideoX-Fun Inference)
+Beautiful Prompt aims to rewrite and beautify the user-uploaded prompt via LLMs, mapping it to the style of CogVideoX-Fun's training captions,
+making it more suitable as the inference prompt and thus improving the quality of the generated videos.
+We support batched inference with local LLMs or OpenAI compatible server based on [vLLM](https://github.com/vllm-project/vllm) for beautiful prompt.
+
+#### Batched Inference
+1. Prepare original prompts in a jsonl file `cogvideox/video_caption/datasets/original_prompt.jsonl` with the following format:
+    ```json
+    {"prompt": "A stylish woman in a black leather jacket, red dress, and boots walks confidently down a damp Tokyo street."}
+    {"prompt": "An underwater world with realistic fish and other creatures of the sea."}
+    {"prompt": "a monarch butterfly perched on a tree trunk in the forest."}
+    {"prompt": "a child in a room with a bottle of wine and a lamp."}
+    {"prompt": "two men in suits walking down a hallway."}
+    ```
+
+2. Then you can perform beautiful prompt by running
+    ```shell
+    # Meta-Llama-3-8B-Instruct is sufficient for this task.
+    # Download it from https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct or https://www.modelscope.cn/models/LLM-Research/Meta-Llama-3-8B-Instruct to /path/to/your_llm
+
+    python caption_rewrite.py \
+        --video_metadata_path datasets/original_prompt.jsonl \
+        --caption_column "prompt" \
+        --batch_size 1 \
+        --model_name /path/to/your_llm \
+        --prompt prompt/beautiful_prompt.txt \
+        --prefix '"detailed description": ' \
+        --saved_path datasets/beautiful_prompt.jsonl \
+        --saved_freq 1
+    ```
+
+#### OpenAI Server
++ You can request OpenAI compatible server to perform beautiful prompt by running
+    ```shell
+    OPENAI_API_KEY="your_openai_api_key" OPENAI_BASE_URL="your_openai_base_url" python beautiful_prompt.py \
+        --model "your_model_name" \
+        --prompt "your_prompt"
+    ```
+
++ You can also deploy the OpenAI Compatible Server locally using vLLM. For example:
+    ```shell
+    # Meta-Llama-3-8B-Instruct is sufficient for this task.
+    # Download it from https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct or https://www.modelscope.cn/models/LLM-Research/Meta-Llama-3-8B-Instruct to /path/to/your_llm
+
+    # deploy the OpenAI compatible server
+    python -m vllm.entrypoints.openai.api_server serve /path/to/your_llm --dtype auto --api-key "your_api_key"
+    ```
+
+    Then you can perform beautiful prompt by running
+    ```shell
+    python -m beautiful_prompt.py \
+        --model /path/to/your_llm \
+        --prompt "your_prompt" \
+        --base_url "http://localhost:8000/v1" \
+        --api_key "your_api_key"
+    ```

robomaster/video_caption/README_zh-CN.md

@@ -0,0 +1,159 @@
+# 数据预处理
+[English](./README.md) | 简体中文
+
+该文件夹包含 CogVideoX-Fun 使用的数据集预处理（即视频切分、过滤和生成描述）和提示词美化的代码。整个过程支持分布式并行处理，能够处理大规模数据集。
+
+此外，我们和 [Data-Juicer](https://github.com/modelscope/data-juicer/blob/main/docs/DJ_SORA.md) 合作，能让你在 [Aliyun PAI-DLC](https://help.aliyun.com/zh/pai/user-guide/video-preprocessing/) 轻松进行视频数据的处理。
+
+# 目录
+- [数据预处理](#数据预处理)
+- [目录](#目录)
+  - [快速开始](#快速开始)
+    - [安装](#安装)
+    - [数据集预处理](#数据集预处理)
+      - [数据准备](#数据准备)
+      - [视频切分](#视频切分)
+      - [视频过滤](#视频过滤)
+      - [视频描述](#视频描述)
+    - [提示词美化](#提示词美化)
+      - [批量推理](#批量推理)
+      - [OpenAI 服务器](#openai-服务器)
+
+
+## 快速开始
+### 安装
+推荐使用阿里云 DSW 和 Docker 来安装环境，请参考 [快速开始](../../README_zh-CN.md#1-云使用-aliyundswdocker). 你也可以参考 [Dockerfile](../../Dockerfile.ds) 中的镜像构建流程在本地安装对应的 conda 环境和其余依赖。
+
+为了提高推理速度和节省推理的显存，生成视频描述依赖于 [llm-awq](https://github.com/mit-han-lab/llm-awq)。因此，需要 RTX 3060 或者 A2 及以上的显卡 (CUDA Compute Capability >= 8.0)。
+
+```shell
+# pull image
+docker pull mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun
+
+# enter image
+docker run -it -p 7860:7860 --network host --gpus all --security-opt seccomp:unconfined --shm-size 200g mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun
+
+# clone code
+git clone https://github.com/aigc-apps/CogVideoX-Fun.git
+
+# enter video_caption
+cd CogVideoX-Fun/cogvideox/video_caption
+```
+
+### 数据集预处理
+#### 数据准备
+将下载的视频准备到文件夹 [datasets](./datasets/)（最好不使用嵌套结构，因为视频名称在后续处理中用作唯一 ID）。以 Panda-70M 为例，完整的数据集目录结构如下所示：
+```
+📦 datasets/
+├── 📂 panda_70m/
+│   ├── 📂 videos/
+│   │   ├── 📂 data/
+│   │   │   └── 📄 --C66yU3LjM_2.mp4
+│   │   │   └── 📄 ...
+```
+
+#### 视频切分
+CogVideoX-Fun 使用 [PySceneDetect](https://github.com/Breakthrough/PySceneDetect) 来识别视频中的场景变化
+并根据某些阈值通过 FFmpeg 执行视频分割，以确保视频片段的一致性。
+短于 3 秒的视频片段将被丢弃，长于 10 秒的视频片段将被递归切分。
+
+视频切分的完整流程在 [stage_1_video_splitting.sh](./scripts/stage_1_video_splitting.sh)。执行
+```shell
+sh scripts/stage_1_video_splitting.sh
+```
+后，切分后的视频位于 `cogvideox/video_caption/datasets/panda_70m/videos_clips/data/`。
+
+#### 视频过滤
+基于上一步获得的视频，CogVideoX-Fun 提供了一个简单而有效的流程来过滤出高质量的视频。总体流程如下：
+
+- 美学过滤：通过 [aesthetic-predictor-v2-5](https://github.com/discus0434/aesthetic-predictor-v2-5) 计算均匀采样的 4 帧视频的平均美学分数，从而筛选出内容不佳（模糊、昏暗等）的视频。
+- 文本过滤：使用 [EasyOCR](https://github.com/JaidedAI/EasyOCR) 计算中间帧的文本区域比例，过滤掉含有大面积文本的视频。
+- 运动过滤：计算帧间光流差，过滤掉移动太慢或太快的视频。
+
+视频过滤的完整流程在 [stage_2_video_filtering.sh](./scripts/stage_2_video_filtering.sh)。执行
+```shell
+sh scripts/stage_2_video_filtering.sh
+```
+后，视频的美学得分、文本得分和运动得分对应的元文件保存在 `cogvideox/video_caption/datasets/panda_70m/videos_clips/`。
+
+> [!NOTE]
+> 美学得分的计算依赖于 [google/siglip-so400m-patch14-384 model](https://huggingface.co/google/siglip-so400m-patch14-384).
+请执行 `HF_ENDPOINT=https://hf-mirror.com sh scripts/stage_2_video_filtering.sh` 如果你无法访问 huggingface.com.
+
+#### 视频描述
+在获得上述高质量的过滤视频后，CogVideoX-Fun 利用 [VILA1.5](https://github.com/NVlabs/VILA) 来生成视频描述。随后，使用 LLMs 对生成的视频描述进行重写，以更好地满足视频生成任务的要求。最后，使用自研的 VideoCLIPXL 模型来过滤掉描述和视频内容不一致的数据，从而得到最终的训练数据集。
+
+请根据机器的显存从 [VILA1.5](https://huggingface.co/collections/Efficient-Large-Model/vila-on-pre-training-for-visual-language-models-65d8022a3a52cd9bcd62698e) 下载合适大小的模型。对于 A100 40G，你可以执行下面的命令来下载 [VILA1.5-40b-AWQ](https://huggingface.co/Efficient-Large-Model/VILA1.5-40b-AWQ)
+```shell
+# Add HF_ENDPOINT=https://hf-mirror.com before the command if you cannot access to huggingface.com
+huggingface-cli download Efficient-Large-Model/VILA1.5-40b-AWQ --local-dir-use-symlinks False --local-dir /PATH/TO/VILA_MODEL
+```
+
+你可以选择性地准备 LLMs 来改写上述视频描述的结果。例如，你执行下面的命令来下载 [Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct)
+```shell
+# Add HF_ENDPOINT=https://hf-mirror.com before the command if you cannot access to huggingface.com
+huggingface-cli download NousResearch/Meta-Llama-3-8B-Instruct --local-dir-use-symlinks False --local-dir /PATH/TO/REWRITE_MODEL
+```
+
+视频描述的完整流程在 [stage_3_video_recaptioning.sh](./scripts/stage_3_video_recaptioning.sh).
+执行
+```shell
+VILA_MODEL_PATH=/PATH/TO/VILA_MODEL REWRITE_MODEL_PATH=/PATH/TO/REWRITE_MODEL sh scripts/stage_3_video_recaptioning.sh
+```
+后，最后的训练文件会保存在 `cogvideox/video_caption/datasets/panda_70m/videos_clips/meta_train_info.json`。
+
+### 提示词美化
+提示词美化旨在通过 LLMs 重写和美化用户上传的提示，将其映射为 CogVideoX-Fun 训练所使用的视频描述风格、
+使其更适合用作推理提示词，从而提高生成视频的质量。
+
+基于 [vLLM](https://github.com/vllm-project/vllm)，我们支持使用本地 LLM 进行批量推理或请求 OpenAI 服务器的方式，以进行提示词美化。
+
+#### 批量推理
+1. 将原始的提示词以下面的格式准备在文件 `cogvideox/video_caption/datasets/original_prompt.jsonl` 中：
+    ```json
+    {"prompt": "A stylish woman in a black leather jacket, red dress, and boots walks confidently down a damp Tokyo street."}
+    {"prompt": "An underwater world with realistic fish and other creatures of the sea."}
+    {"prompt": "a monarch butterfly perched on a tree trunk in the forest."}
+    {"prompt": "a child in a room with a bottle of wine and a lamp."}
+    {"prompt": "two men in suits walking down a hallway."}
+    ```
+
+2. 随后你可以通过执行以下的命令进行提示词美化
+    ```shell
+    # Meta-Llama-3-8B-Instruct is sufficient for this task.
+    # Download it from https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct or https://www.modelscope.cn/models/LLM-Research/Meta-Llama-3-8B-Instruct to /path/to/your_llm
+
+    python caption_rewrite.py \
+        --video_metadata_path datasets/original_prompt.jsonl \
+        --caption_column "prompt" \
+        --batch_size 1 \
+        --model_name /path/to/your_llm \
+        --prompt prompt/beautiful_prompt.txt \
+        --prefix '"detailed description": ' \
+        --saved_path datasets/beautiful_prompt.jsonl \
+        --saved_freq 1
+    ```
+
+#### OpenAI 服务器
++ 你可以通过请求 OpenAI 服务器的方式来进行提示词美化
+    ```shell
+    OPENAI_API_KEY="your_openai_api_key" OPENAI_BASE_URL="your_openai_base_url" python beautiful_prompt.py \
+        --model "your_model_name" \
+        --prompt "your_prompt"
+    ```
+
++ 你也可以执行以下命令，通过 vLLM 将本地 LLMs 部署成兼容 OpenAI 的服务器
+    ```shell
+    OPENAI_API_KEY="your_openai_api_key" OPENAI_BASE_URL="your_openai_base_url" python beautiful_prompt.py \
+        --model "your_model_name" \
+        --prompt "your_prompt"
+    ```
+
+    然后再执行下面的命令来进行提示词美化
+    ```shell
+    python -m beautiful_prompt.py \
+        --model /path/to/your_llm \
+        --prompt "your_prompt" \
+        --base_url "http://localhost:8000/v1" \
+        --api_key "your_api_key"
+    ```

robomaster/video_caption/beautiful_prompt.py

@@ -0,0 +1,103 @@
+"""
+This script (optional) can rewrite and beautify the user-uploaded prompt via LLMs, mapping it to the style of cogvideox's training captions,
+making it more suitable as the inference prompt and thus improving the quality of the generated videos.
+
+Usage:
++ You can request OpenAI compatible server to perform beautiful prompt by running
+```shell
+export OPENAI_API_KEY="your_openai_api_key" OPENAI_BASE_URL="your_openai_base_url" python beautiful_prompt.py \
+    --model "your_model_name" \
+    --prompt "your_prompt"
+```
++ You can also deploy the OpenAI Compatible Server locally using vLLM. For example:
+```shell
+# Meta-Llama-3-8B-Instruct is sufficient for this task.
+# Download it from https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct or https://www.modelscope.cn/models/LLM-Research/Meta-Llama-3-8B-Instruct to /path/to/your_llm
+
+# deploy the OpenAI compatible server
+python -m vllm.entrypoints.openai.api_server serve /path/to/your_llm --dtype auto --api-key "your_api_key"
+```
+
+Then you can perform beautiful prompt by running
+```shell
+python -m beautiful_prompt.py \
+    --model /path/to/your_llm \
+    --prompt "your_prompt" \
+    --base_url "http://localhost:8000/v1" \
+    --api_key "your_api_key"
+```
+"""
+import argparse
+import os
+
+from openai import OpenAI
+
+from cogvideox.video_caption.caption_rewrite import extract_output
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Beautiful prompt.")
+    parser.add_argument("--model", type=str, required=True, help="The OpenAI model or the path to your local LLM.")
+    parser.add_argument("--prompt", type=str, required=True, help="The user-uploaded prompt.")
+    parser.add_argument(
+        "--template",
+        type=str,
+        default="cogvideox/video_caption/prompt/beautiful_prompt.txt",
+        help="A string or a txt file contains the template for beautiful prompt."
+    )
+    parser.add_argument(
+        "--max_retry_nums",
+        type=int,
+        default=5,
+        help="Maximum number of retries to obtain an output that meets the JSON format."
+    )
+    parser.add_argument(
+        "--base_url",
+        type=str,
+        default=None,
+        help="OpenAI API server url. If it is None, the OPENAI_BASE_URL from the environment variables will be used.",
+    )
+    parser.add_argument(
+        "--api_key",
+        type=str,
+        default=None,
+        help="OpenAI API key. If it is None, the OPENAI_API_KEY from the environment variables will be used.",
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    client = OpenAI(
+        base_url=os.getenv("OPENAI_BASE_URL", args.base_url),
+        api_key=os.environ.get("OPENAI_API_KEY", args.api_key),
+    )
+    if args.template.endswith(".txt") and os.path.exists(args.template):
+        with open(args.template, "r") as f:
+            args.template = "".join(f.readlines())
+    # print(f"Beautiful prompt template: {args.template}")
+
+    for _ in range(args.max_retry_nums):
+        completion = client.chat.completions.create(
+            model=args.model,
+            messages=[
+                # {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": args.template + "\n" + str(args.prompt)}
+            ],
+            temperature=0.7,
+            top_p=1,
+            max_tokens=1024,
+        )
+
+        output = completion.choices[0].message.content
+        output = extract_output(output, prefix='"detailed description": ')
+        if output is not None:
+            break
+    print(f"Beautiful prompt: {output}")
+
+
+if __name__ == "__main__":
+    main()

robomaster/video_caption/caption_rewrite.py

@@ -0,0 +1,224 @@
+import argparse
+import re
+import os
+from tqdm import tqdm
+
+import pandas as pd
+import torch
+from natsort import index_natsorted
+from vllm import LLM, SamplingParams
+from transformers import AutoTokenizer
+
+from utils.logger import logger
+
+
+def extract_output(s, prefix='"rewritten description": '):
+    """Customize the function according to the prompt."""
+    # Since some LLMs struggles to output strictly formatted JSON strings as specified by the prompt,
+    # thus manually parse the output string `{"rewritten description": "your rewritten description here"}`.
+    match = re.search(r"{(.+?)}", s, re.DOTALL)
+    if not match:
+        logger.warning(f"{s} is not in the json format. Return None.")
+        return None
+    output = match.group(1).strip()
+    if output.startswith(prefix):
+        output = output[len(prefix) :]
+        if output[0] == '"' and output[-1] == '"':
+            return output[1:-1]
+        else:
+            logger.warning(f"{output} does not start and end with the double quote. Return None.")
+            return None
+    else:
+        logger.warning(f"{output} does not start with {prefix}. Return None.")
+        return None
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Rewrite the video caption by LLMs.")
+    parser.add_argument(
+        "--video_metadata_path", type=str, required=True, help="The path to the video dataset metadata (csv/jsonl)."
+    )
+    parser.add_argument(
+        "--video_path_column",
+        type=str,
+        default=None,
+        help="The column contains the video path (an absolute path or a relative path w.r.t the video_folder).",
+    )
+    parser.add_argument(
+        "--caption_column",
+        type=str,
+        default="caption",
+        help="The column contains the video caption.",
+    )
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=128,
+        required=False,
+        help="The batch size for vllm inference. Adjust according to the number of GPUs to maximize inference throughput.",
+    )
+    parser.add_argument(
+        "--model_name",
+        type=str,
+        default="NousResearch/Meta-Llama-3-8B-Instruct",
+    )
+    parser.add_argument(
+        "--prompt",
+        type=str,
+        required=True,
+        help="A string or a txt file contains the prompt.",
+    )
+    parser.add_argument(
+        "--prefix",
+        type=str,
+        required=True,
+        help="The prefix to extract the output from LLMs.",
+    )
+    parser.add_argument("--saved_path", type=str, required=True, help="The save path to the output results (csv/jsonl).")
+    parser.add_argument("--saved_freq", type=int, default=1, help="The frequency to save the output results.")
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    if args.video_metadata_path.endswith(".csv"):
+        video_metadata_df = pd.read_csv(args.video_metadata_path)
+    elif args.video_metadata_path.endswith(".jsonl"):
+        video_metadata_df = pd.read_json(args.video_metadata_path, lines=True)
+    elif args.video_metadata_path.endswith(".json"):
+        video_metadata_df = pd.read_json(args.video_metadata_path)
+    else:
+        raise ValueError(f"The {args.video_metadata_path} must end with .csv, .jsonl or .json.")
+
+    saved_suffix = os.path.splitext(args.saved_path)[1]
+    if saved_suffix not in set([".csv", ".jsonl", ".json"]):
+        raise ValueError(f"The saved_path must end with .csv, .jsonl or .json.")
+
+    if os.path.exists(args.saved_path) and args.video_path_column is not None:
+        if args.saved_path.endswith(".csv"):
+            saved_metadata_df = pd.read_csv(args.saved_path)
+        elif args.saved_path.endswith(".jsonl"):
+            saved_metadata_df = pd.read_json(args.saved_path, lines=True)
+
+        # Filter out the unprocessed video-caption pairs by setting the indicator=True.
+        merged_df = video_metadata_df.merge(saved_metadata_df, on=args.video_path_column, how="outer", indicator=True)
+        video_metadata_df = merged_df[merged_df["_merge"] == "left_only"]
+        # Sorting to guarantee the same result for each process.
+        video_metadata_df = video_metadata_df.iloc[index_natsorted(video_metadata_df[args.video_path_column])].reset_index(
+            drop=True
+        )
+        logger.info(
+            f"Resume from {args.saved_path}: {len(saved_metadata_df)} processed and {len(video_metadata_df)} to be processed."
+        )
+
+    if args.prompt.endswith(".txt") and os.path.exists(args.prompt):
+        with open(args.prompt, "r") as f:
+            args.prompt = "".join(f.readlines())
+    logger.info(f"Prompt: {args.prompt}")
+
+    if args.video_path_column is not None:
+        video_path_list = video_metadata_df[args.video_path_column].tolist()
+    if args.caption_column in video_metadata_df.columns:
+        sampled_frame_caption_list = video_metadata_df[args.caption_column].tolist()
+    else:
+        # When two columns with the same name, the dataframe merge operation on will distinguish them by adding 'x' and 'y'.
+        sampled_frame_caption_list = video_metadata_df[args.caption_column + "_x"].tolist()
+
+    CUDA_VISIBLE_DEVICES = os.getenv("CUDA_VISIBLE_DEVICES", None)
+    tensor_parallel_size = torch.cuda.device_count() if CUDA_VISIBLE_DEVICES is None else len(CUDA_VISIBLE_DEVICES.split(","))
+    logger.info(f"Automatically set tensor_parallel_size={tensor_parallel_size} based on the available devices.")
+
+    llm = LLM(model=args.model_name, trust_remote_code=True, tensor_parallel_size=tensor_parallel_size)
+    if "Meta-Llama-3" in args.model_name:
+        if "Meta-Llama-3-70B" in args.model_name:
+            # Llama-3-70B should use the tokenizer from Llama-3-8B
+            # https://github.com/vllm-project/vllm/issues/4180#issuecomment-2068292942
+            tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B-Instruct")
+        else:
+            tokenizer = AutoTokenizer.from_pretrained(args.model_name)
+        stop_token_ids = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")]
+        sampling_params = SamplingParams(temperature=0.7, top_p=1, max_tokens=1024, stop_token_ids=stop_token_ids)
+    else:
+        tokenizer = AutoTokenizer.from_pretrained(args.model_name)
+        sampling_params = SamplingParams(temperature=0.7, top_p=1, max_tokens=1024)
+
+    result_dict = {args.caption_column: []}
+    if args.video_path_column is not None:
+        result_dict = {args.video_path_column: [], args.caption_column: []}
+
+    for i in tqdm(range(0, len(sampled_frame_caption_list), args.batch_size)):
+        if args.video_path_column is not None:
+            batch_video_path = video_path_list[i : i + args.batch_size]
+        batch_caption = sampled_frame_caption_list[i : i + args.batch_size]
+        batch_prompt = []
+        for caption in batch_caption:
+            # batch_prompt.append("user:" + args.prompt + str(caption) + "\n assistant:")
+            messages = [
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": args.prompt + "\n" + str(caption)},
+            ]
+            text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+            batch_prompt.append(text)
+
+        batch_output = llm.generate(batch_prompt, sampling_params)
+        batch_output = [output.outputs[0].text.rstrip() for output in batch_output]
+        batch_output = [extract_output(output, prefix=args.prefix) for output in batch_output]
+
+        # Filter out data that does not meet the output format.
+        batch_result = []
+        if args.video_path_column is not None:
+            for video_path, output in zip(batch_video_path, batch_output):
+                if output is not None:
+                    batch_result.append((video_path, output))
+            batch_video_path, batch_output = zip(*batch_result)
+
+            result_dict[args.video_path_column].extend(batch_video_path)
+        else:
+            for output in batch_output:
+                if output is not None:
+                    batch_result.append(output)
+
+            result_dict[args.caption_column].extend(batch_result)
+
+        # Save the metadata every args.saved_freq.
+        if i != 0 and ((i // args.batch_size) % args.saved_freq) == 0:
+            if len(result_dict[args.caption_column]) > 0:
+                result_df = pd.DataFrame(result_dict)
+                if args.saved_path.endswith(".csv"):
+                    header = True if not os.path.exists(args.saved_path) else False
+                    result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
+                elif args.saved_path.endswith(".jsonl"):
+                    result_df.to_json(args.saved_path, orient="records", lines=True, mode="a", force_ascii=False)
+                elif args.saved_path.endswith(".json"):
+                    # Append is not supported.
+                    if os.path.exists(args.saved_path):
+                        saved_df = pd.read_json(args.saved_path, orient="records")
+                        result_df = pd.concat([saved_df, result_df], ignore_index=True)
+                    result_df.to_json(args.saved_path, orient="records", indent=4, force_ascii=False)
+                logger.info(f"Save result to {args.saved_path}.")
+
+            result_dict = {args.caption_column: []}
+            if args.video_path_column is not None:
+                result_dict = {args.video_path_column: [], args.caption_column: []}
+
+    if len(result_dict[args.caption_column]) > 0:
+        result_df = pd.DataFrame(result_dict)
+        if args.saved_path.endswith(".csv"):
+            header = True if not os.path.exists(args.saved_path) else False
+            result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
+        elif args.saved_path.endswith(".jsonl"):
+            result_df.to_json(args.saved_path, orient="records", lines=True, mode="a")
+        elif args.saved_path.endswith(".json"):
+            # Append is not supported.
+            if os.path.exists(args.saved_path):
+                saved_df = pd.read_json(args.saved_path, orient="records")
+                result_df = pd.concat([saved_df, result_df], ignore_index=True)
+            result_df.to_json(args.saved_path, orient="records", indent=4, force_ascii=False)
+        logger.info(f"Save the final result to {args.saved_path}.")
+
+
+if __name__ == "__main__":
+    main()

robomaster/video_caption/compute_motion_score.py

@@ -0,0 +1,186 @@
+import ast
+import argparse
+import gc
+import os
+from contextlib import contextmanager
+from pathlib import Path
+
+import cv2
+import numpy as np
+import pandas as pd
+from joblib import Parallel, delayed
+from natsort import natsorted
+from tqdm import tqdm
+
+from utils.logger import logger
+from utils.filter import filter
+
+
+@contextmanager
+def VideoCapture(video_path):
+    cap = cv2.VideoCapture(video_path)
+    try:
+        yield cap
+    finally:
+        cap.release()
+        del cap
+        gc.collect()
+
+
+def compute_motion_score(video_path):
+    video_motion_scores = []
+    sampling_fps = 2
+
+    try:
+        with VideoCapture(video_path) as cap:
+            fps = cap.get(cv2.CAP_PROP_FPS)
+            valid_fps = min(max(sampling_fps, 1), fps)
+            frame_interval = int(fps / valid_fps)
+            total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+            # if cannot get the second frame, use the last one
+            frame_interval = min(frame_interval, total_frames - 1)
+
+            prev_frame = None
+            frame_count = -1
+            while cap.isOpened():
+                ret, frame = cap.read()
+                frame_count += 1
+
+                if not ret:
+                    break
+
+                # skip middle frames
+                if frame_count % frame_interval != 0:
+                    continue
+
+                gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+                if prev_frame is None:
+                    prev_frame = gray_frame
+                    continue
+
+                flow = cv2.calcOpticalFlowFarneback(
+                    prev_frame,
+                    gray_frame,
+                    None,
+                    pyr_scale=0.5,
+                    levels=3,
+                    winsize=15,
+                    iterations=3,
+                    poly_n=5,
+                    poly_sigma=1.2,
+                    flags=0,
+                )
+                mag, _ = cv2.cartToPolar(flow[..., 0], flow[..., 1])
+                frame_motion_score = np.mean(mag)
+                video_motion_scores.append(frame_motion_score)
+                prev_frame = gray_frame
+
+            video_meta_info = {
+                "video_path": Path(video_path).name,
+                "motion_score": round(float(np.mean(video_motion_scores)), 5),
+            }
+            return video_meta_info
+
+    except Exception as e:
+        print(f"Compute motion score for video {video_path} with error: {e}.")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Compute the motion score of the videos.")
+    parser.add_argument("--video_folder", type=str, default="", help="The video folder.")
+    parser.add_argument(
+        "--video_metadata_path", type=str, default=None, help="The path to the video dataset metadata (csv/jsonl)."
+    )
+    parser.add_argument(
+        "--video_path_column",
+        type=str,
+        default="video_path",
+        help="The column contains the video path (an absolute path or a relative path w.r.t the video_folder).",
+    )
+    parser.add_argument("--saved_path", type=str, required=True, help="The save path to the output results (csv/jsonl).")
+    parser.add_argument("--saved_freq", type=int, default=100, help="The frequency to save the output results.")
+    parser.add_argument("--n_jobs", type=int, default=1, help="The number of concurrent processes.")
+
+    parser.add_argument(
+        "--basic_metadata_path", type=str, default=None, help="The path to the basic metadata (csv/jsonl)."
+    )
+    parser.add_argument("--min_resolution", type=float, default=0, help="The resolution threshold.")
+    parser.add_argument("--min_duration", type=float, default=-1, help="The minimum duration.")
+    parser.add_argument("--max_duration", type=float, default=-1, help="The maximum duration.")
+    parser.add_argument(
+        "--asethetic_score_metadata_path", type=str, default=None, help="The path to the video quality metadata (csv/jsonl)."
+    )
+    parser.add_argument("--min_asethetic_score", type=float, default=4.0, help="The asethetic score threshold.")
+    parser.add_argument(
+        "--asethetic_score_siglip_metadata_path", type=str, default=None, help="The path to the video quality metadata (csv/jsonl)."
+    )
+    parser.add_argument("--min_asethetic_score_siglip", type=float, default=4.0, help="The asethetic score (SigLIP) threshold.")
+    parser.add_argument(
+        "--text_score_metadata_path", type=str, default=None, help="The path to the video text score metadata (csv/jsonl)."
+    )
+    parser.add_argument("--min_text_score", type=float, default=0.02, help="The text threshold.")
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    if args.video_metadata_path.endswith(".csv"):
+        video_metadata_df = pd.read_csv(args.video_metadata_path)
+    elif args.video_metadata_path.endswith(".jsonl"):
+        video_metadata_df = pd.read_json(args.video_metadata_path, lines=True)
+    else:
+        raise ValueError("The video_metadata_path must end with .csv or .jsonl.")
+    video_path_list = video_metadata_df[args.video_path_column].tolist()
+
+    if not (args.saved_path.endswith(".csv") or args.saved_path.endswith(".jsonl")):
+        raise ValueError("The saved_path must end with .csv or .jsonl.")
+    
+    if os.path.exists(args.saved_path):
+        if args.saved_path.endswith(".csv"):
+            saved_metadata_df = pd.read_csv(args.saved_path)
+        elif args.saved_path.endswith(".jsonl"):
+            saved_metadata_df = pd.read_json(args.saved_path, lines=True)
+        saved_video_path_list = saved_metadata_df[args.video_path_column].tolist()
+        video_path_list = list(set(video_path_list).difference(set(saved_video_path_list)))
+        logger.info(f"Resume from {args.saved_path}: {len(saved_video_path_list)} processed and {len(video_path_list)} to be processed.")
+    
+    video_path_list = filter(
+        video_path_list,
+        basic_metadata_path=args.basic_metadata_path,
+        min_resolution=args.min_resolution,
+        min_duration=args.min_duration,
+        max_duration=args.max_duration,
+        asethetic_score_metadata_path=args.asethetic_score_metadata_path,
+        min_asethetic_score=args.min_asethetic_score,
+        asethetic_score_siglip_metadata_path=args.asethetic_score_siglip_metadata_path,
+        min_asethetic_score_siglip=args.min_asethetic_score_siglip,
+        text_score_metadata_path=args.text_score_metadata_path,
+        min_text_score=args.min_text_score,
+    )
+    video_path_list = [os.path.join(args.video_folder, video_path) for video_path in video_path_list]
+    # Sorting to guarantee the same result for each process.
+    video_path_list = natsorted(video_path_list)
+
+    for i in tqdm(range(0, len(video_path_list), args.saved_freq)):
+        result_list = Parallel(n_jobs=args.n_jobs)(
+            delayed(compute_motion_score)(video_path) for video_path in tqdm(video_path_list[i: i + args.saved_freq])
+        )
+        result_list = [result for result in result_list if result is not None]
+        if len(result_list) == 0:
+            continue
+
+        result_df = pd.DataFrame(result_list)
+        if args.saved_path.endswith(".csv"):
+            header = False if os.path.exists(args.saved_path) else True
+            result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
+        elif args.saved_path.endswith(".jsonl"):
+            result_df.to_json(args.saved_path, orient="records", lines=True, mode="a", force_ascii=False)
+        logger.info(f"Save result to {args.saved_path}.")
+
+
+if __name__ == "__main__":
+    main()

robomaster/video_caption/compute_text_score.py

@@ -0,0 +1,214 @@
+import argparse
+import os
+from pathlib import Path
+
+import easyocr
+import numpy as np
+import pandas as pd
+from accelerate import PartialState
+from accelerate.utils import gather_object
+from natsort import natsorted
+from tqdm import tqdm
+from torchvision.datasets.utils import download_url
+
+from utils.logger import logger
+from utils.video_utils import extract_frames
+from utils.filter import filter
+
+
+def init_ocr_reader(root: str = "~/.cache/easyocr", device: str = "gpu"):
+    root = os.path.expanduser(root)
+    if not os.path.exists(root):
+        os.makedirs(root)
+    download_url(
+        "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/video_caption/easyocr/craft_mlt_25k.pth",
+        root,
+        filename="craft_mlt_25k.pth",
+        md5="2f8227d2def4037cdb3b34389dcf9ec1",
+    )
+    ocr_reader = easyocr.Reader(
+        lang_list=["en", "ch_sim"],
+        gpu=device,
+        recognizer=False,
+        verbose=False,
+        model_storage_directory=root,
+    )
+
+    return ocr_reader
+
+
+def triangle_area(p1, p2, p3):
+    """Compute the triangle area according to its coordinates.
+    """
+    x1, y1 = p1
+    x2, y2 = p2
+    x3, y3 = p3
+    tri_area = 0.5 * np.abs(x1 * y2 + x2 * y3 + x3 * y1 - x2 * y1 - x3 * y2 - x1 * y3)
+    return tri_area
+
+
+def compute_text_score(video_path, ocr_reader):
+    _, images = extract_frames(video_path, sample_method="mid")
+    images = [np.array(image) for image in images]
+
+    frame_ocr_area_ratios = []
+    for image in images:
+        # horizontal detected results and free-form detected
+        horizontal_list, free_list = ocr_reader.detect(np.asarray(image))
+        width, height = image.shape[0], image.shape[1]
+
+        total_area = width * height
+        # rectangles
+        rect_area = 0
+        for xmin, xmax, ymin, ymax in horizontal_list[0]:
+            if xmax < xmin or ymax < ymin:
+                continue
+            rect_area += (xmax - xmin) * (ymax - ymin)
+        # free-form
+        quad_area = 0
+        try:
+            for points in free_list[0]:
+                triangle1 = points[:3]
+                quad_area += triangle_area(*triangle1)
+                triangle2 = points[3:] + [points[0]]
+                quad_area += triangle_area(*triangle2)
+        except:
+            quad_area = 0
+        text_area = rect_area + quad_area
+
+        frame_ocr_area_ratios.append(text_area / total_area)
+
+    video_meta_info = {
+        "video_path": Path(video_path).name,
+        "text_score": round(np.mean(frame_ocr_area_ratios), 5),
+    }
+
+    return video_meta_info
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Compute the text score of the middle frame in the videos.")
+    parser.add_argument("--video_folder", type=str, default="", help="The video folder.")
+    parser.add_argument(
+        "--video_metadata_path", type=str, default=None, help="The path to the video dataset metadata (csv/jsonl)."
+    )
+    parser.add_argument(
+        "--video_path_column",
+        type=str,
+        default="video_path",
+        help="The column contains the video path (an absolute path or a relative path w.r.t the video_folder).",
+    )
+    parser.add_argument("--saved_path", type=str, required=True, help="The save path to the output results (csv/jsonl).")
+    parser.add_argument("--saved_freq", type=int, default=100, help="The frequency to save the output results.")
+
+    parser.add_argument(
+        "--basic_metadata_path", type=str, default=None, help="The path to the basic metadata (csv/jsonl)."
+    )
+    parser.add_argument("--min_resolution", type=float, default=0, help="The resolution threshold.")
+    parser.add_argument("--min_duration", type=float, default=-1, help="The minimum duration.")
+    parser.add_argument("--max_duration", type=float, default=-1, help="The maximum duration.")
+    parser.add_argument(
+        "--asethetic_score_metadata_path", type=str, default=None, help="The path to the video quality metadata (csv/jsonl)."
+    )
+    parser.add_argument("--min_asethetic_score", type=float, default=4.0, help="The asethetic score threshold.")
+    parser.add_argument(
+        "--asethetic_score_siglip_metadata_path", type=str, default=None, help="The path to the video quality metadata (csv/jsonl)."
+    )
+    parser.add_argument("--min_asethetic_score_siglip", type=float, default=4.0, help="The asethetic score (SigLIP) threshold.")
+    parser.add_argument(
+        "--motion_score_metadata_path", type=str, default=None, help="The path to the video motion score metadata (csv/jsonl)."
+    )
+    parser.add_argument("--min_motion_score", type=float, default=2, help="The motion threshold.")
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    if args.video_metadata_path.endswith(".csv"):
+        video_metadata_df = pd.read_csv(args.video_metadata_path)
+    elif args.video_metadata_path.endswith(".jsonl"):
+        video_metadata_df = pd.read_json(args.video_metadata_path, lines=True)
+    else:
+        raise ValueError("The video_metadata_path must end with .csv or .jsonl.")
+    video_path_list = video_metadata_df[args.video_path_column].tolist()
+
+    if not (args.saved_path.endswith(".csv") or args.saved_path.endswith(".jsonl")):
+        raise ValueError("The saved_path must end with .csv or .jsonl.")
+
+    if os.path.exists(args.saved_path):
+        if args.saved_path.endswith(".csv"):
+            saved_metadata_df = pd.read_csv(args.saved_path)
+        elif args.saved_path.endswith(".jsonl"):
+            saved_metadata_df = pd.read_json(args.saved_path, lines=True)
+        saved_video_path_list = saved_metadata_df[args.video_path_column].tolist()
+        video_path_list = list(set(video_path_list).difference(set(saved_video_path_list)))
+        logger.info(f"Resume from {args.saved_path}: {len(saved_video_path_list)} processed and {len(video_path_list)} to be processed.")
+    
+    video_path_list = filter(
+        video_path_list,
+        basic_metadata_path=args.basic_metadata_path,
+        min_resolution=args.min_resolution,
+        min_duration=args.min_duration,
+        max_duration=args.max_duration,
+        asethetic_score_metadata_path=args.asethetic_score_metadata_path,
+        min_asethetic_score=args.min_asethetic_score,
+        asethetic_score_siglip_metadata_path=args.asethetic_score_siglip_metadata_path,
+        min_asethetic_score_siglip=args.min_asethetic_score_siglip,
+        motion_score_metadata_path=args.motion_score_metadata_path,
+        min_motion_score=args.min_motion_score,
+    )
+    video_path_list = [os.path.join(args.video_folder, video_path) for video_path in video_path_list]
+    # Sorting to guarantee the same result for each process.
+    video_path_list = natsorted(video_path_list)
+
+    state = PartialState()
+    if state.is_main_process:
+        # Check if the model is downloaded in the main process.
+        ocr_reader = init_ocr_reader(device="cpu")
+    state.wait_for_everyone()
+    ocr_reader = init_ocr_reader(device=state.device)
+
+    index = len(video_path_list) - len(video_path_list) % state.num_processes
+    # Avoid the NCCL timeout in the final gather operation.
+    logger.info(f"Drop {len(video_path_list) % state.num_processes} videos to ensure each process handles the same number of videos.")
+    video_path_list = video_path_list[:index]
+    logger.info(f"{len(video_path_list)} videos are to be processed.")
+
+    result_list = []
+    with state.split_between_processes(video_path_list) as splitted_video_path_list:
+        for i, video_path in enumerate(tqdm(splitted_video_path_list)):
+            try:
+                video_meta_info = compute_text_score(video_path, ocr_reader)
+                result_list.append(video_meta_info)
+            except Exception as e:
+                logger.warning(f"Compute text score for video {video_path} with error: {e}.")
+            if i != 0 and i % args.saved_freq == 0:
+                state.wait_for_everyone()
+                gathered_result_list = gather_object(result_list)
+                if state.is_main_process and len(gathered_result_list) != 0:
+                    result_df = pd.DataFrame(gathered_result_list)
+                    if args.saved_path.endswith(".csv"):
+                        header = False if os.path.exists(args.saved_path) else True
+                        result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
+                    elif args.saved_path.endswith(".jsonl"):
+                        result_df.to_json(args.saved_path, orient="records", lines=True, mode="a", force_ascii=False)
+                    logger.info(f"Save result to {args.saved_path}.")
+                result_list = []
+
+    state.wait_for_everyone()
+    gathered_result_list = gather_object(result_list)
+    if state.is_main_process and len(gathered_result_list) != 0:
+        result_df = pd.DataFrame(gathered_result_list)
+        if args.saved_path.endswith(".csv"):
+            header = False if os.path.exists(args.saved_path) else True
+            result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
+        elif args.saved_path.endswith(".jsonl"):
+            result_df.to_json(args.saved_path, orient="records", lines=True, mode="a", force_ascii=False)
+        logger.info(f"Save the final result to {args.saved_path}.")
+
+
+if __name__ == "__main__":
+    main()

robomaster/video_caption/compute_video_quality.py

@@ -0,0 +1,201 @@
+import argparse
+import os
+
+import pandas as pd
+from accelerate import PartialState
+from accelerate.utils import gather_object
+from natsort import index_natsorted
+from tqdm import tqdm
+from torch.utils.data import DataLoader
+
+import utils.image_evaluator as image_evaluator
+import utils.video_evaluator as video_evaluator
+from utils.logger import logger
+from utils.video_dataset import VideoDataset, collate_fn
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Compute scores of uniform sampled frames from videos.")
+    parser.add_argument(
+        "--video_metadata_path", type=str, default=None, help="The path to the video dataset metadata (csv/jsonl)."
+    )
+    parser.add_argument(
+        "--video_path_column",
+        type=str,
+        default="video_path",
+        help="The column contains the video path (an absolute path or a relative path w.r.t the video_folder).",
+    )
+    parser.add_argument("--video_folder", type=str, default="", help="The video folder.")
+    parser.add_argument(
+        "--caption_column",
+        type=str,
+        default=None,
+        help="The column contains the caption.",
+    )
+    parser.add_argument(
+        "--frame_sample_method",
+        type=str,
+        choices=["mid", "uniform", "image"],
+        default="uniform",
+    )
+    parser.add_argument(
+        "--num_sampled_frames",
+        type=int,
+        default=8,
+        help="num_sampled_frames",
+    )
+    parser.add_argument("--metrics", nargs="+", type=str, required=True, help="The evaluation metric(s) for generated images.")
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=10,
+        required=False,
+        help="The batch size for the video dataset.",
+    )
+    parser.add_argument(
+        "--num_workers",
+        type=int,
+        default=4,
+        required=False,
+        help="The number of workers for the video dataset.",
+    )
+    parser.add_argument("--saved_path", type=str, required=True, help="The save path to the output results (csv/jsonl).")
+    parser.add_argument("--saved_freq", type=int, default=1000, help="The frequency to save the output results.")
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    
+    if args.video_metadata_path.endswith(".csv"):
+        video_metadata_df = pd.read_csv(args.video_metadata_path)
+    elif args.video_metadata_path.endswith(".jsonl"):
+        video_metadata_df = pd.read_json(args.video_metadata_path, lines=True)
+    else:
+        raise ValueError("The video_metadata_path must end with .csv or .jsonl.")
+
+    if not (args.saved_path.endswith(".csv") or args.saved_path.endswith(".jsonl")):
+        raise ValueError("The saved_path must end with .csv or .jsonl.")
+    
+    if os.path.exists(args.saved_path):
+        if args.saved_path.endswith(".csv"):
+            saved_metadata_df = pd.read_csv(args.saved_path)
+        elif args.saved_path.endswith(".jsonl"):
+            saved_metadata_df = pd.read_json(args.saved_path, lines=True)
+
+        # Filter out the unprocessed video-caption pairs by setting the indicator=True.
+        merged_df = video_metadata_df.merge(saved_metadata_df, on="video_path", how="outer", indicator=True)
+        video_metadata_df = merged_df[merged_df["_merge"] == "left_only"]
+        # Sorting to guarantee the same result for each process.
+        video_metadata_df = video_metadata_df.iloc[index_natsorted(video_metadata_df["video_path"])].reset_index(drop=True)
+        if args.caption_column is None:
+            video_metadata_df = video_metadata_df[[args.video_path_column]]
+        else:
+            video_metadata_df = video_metadata_df[[args.video_path_column, args.caption_column + "_x"]]
+            video_metadata_df.rename(columns={args.caption_column + "_x": args.caption_column}, inplace=True)
+        logger.info(f"Resume from {args.saved_path}: {len(saved_metadata_df)} processed and {len(video_metadata_df)} to be processed.")
+
+    state = PartialState()
+    metric_fns = []
+    for metric in args.metrics:
+        if hasattr(image_evaluator, metric):  # frame-wise
+            if state.is_main_process:
+                logger.info("Initializing frame-wise evaluator metrics...")
+                # Check if the model is downloaded in the main process.
+                getattr(image_evaluator, metric)(device="cpu")
+            state.wait_for_everyone()
+            metric_fns.append(getattr(image_evaluator, metric)(device=state.device))
+        else:  # video-wise
+            if state.is_main_process:
+                logger.info("Initializing video-wise evaluator metrics...")
+                # Check if the model is downloaded in the main process.
+                getattr(video_evaluator, metric)(device="cpu")
+            state.wait_for_everyone()
+            metric_fns.append(getattr(video_evaluator, metric)(device=state.device))
+
+    result_dict = {args.video_path_column: [], "sample_frame_idx": []}
+    for metric in metric_fns:
+        result_dict[str(metric)] = []
+    if args.caption_column is not None:
+        result_dict[args.caption_column] = []
+
+    if args.frame_sample_method == "image":
+        logger.warning("Set args.num_sampled_frames to 1 since args.frame_sample_method is image.")
+        args.num_sampled_frames = 1
+    
+    index = len(video_metadata_df) - len(video_metadata_df) % state.num_processes
+    # Avoid the NCCL timeout in the final gather operation.
+    logger.info(f"Drop {len(video_metadata_df) % state.num_processes} videos to ensure each process handles the same number of videos.")
+    video_metadata_df = video_metadata_df.iloc[:index]
+    logger.info(f"{len(video_metadata_df)} videos are to be processed.")
+
+    video_metadata_list = video_metadata_df.to_dict(orient='list')
+    with state.split_between_processes(video_metadata_list) as splitted_video_metadata:
+        video_dataset = VideoDataset(
+            dataset_inputs=splitted_video_metadata,
+            video_folder=args.video_folder,
+            text_column=args.caption_column,
+            sample_method=args.frame_sample_method,
+            num_sampled_frames=args.num_sampled_frames
+        )
+        video_loader = DataLoader(video_dataset, batch_size=args.batch_size, num_workers=args.num_workers, collate_fn=collate_fn)
+
+        for idx, batch in enumerate(tqdm(video_loader)):
+            if len(batch) > 0:
+                batch_video_path = batch["path"]
+                result_dict["sample_frame_idx"].extend(batch["sampled_frame_idx"])
+                batch_frame = batch["sampled_frame"]  # [batch_size, num_sampled_frames, H, W, C]
+                batch_caption = None
+                if args.caption_column is not None:
+                    batch_caption = batch["text"]
+                    result_dict["caption"].extend(batch_caption)
+                # Compute the quality.
+                for i, metric in enumerate(args.metrics):
+                    quality_scores = metric_fns[i](batch_frame, batch_caption)
+                    if isinstance(quality_scores[0], list):  # frame-wise
+                        quality_scores = [
+                            [round(score, 5) for score in inner_list]
+                            for inner_list in quality_scores
+                        ]
+                    else:  # video-wise
+                        quality_scores = [round(score, 5) for score in quality_scores]
+                    result_dict[str(metric_fns[i])].extend(quality_scores)
+        
+                if args.video_folder == "":
+                    saved_video_path_list = batch_video_path
+                else:
+                    saved_video_path_list = [os.path.relpath(video_path, args.video_folder) for video_path in batch_video_path]
+                result_dict[args.video_path_column].extend(saved_video_path_list)
+
+            # Save the metadata in the main process every saved_freq.
+            if (idx != 0) and (idx % args.saved_freq == 0):
+                state.wait_for_everyone()
+                gathered_result_dict = {k: gather_object(v) for k, v in result_dict.items()}
+                if state.is_main_process and len(gathered_result_dict[args.video_path_column]) != 0:
+                    result_df = pd.DataFrame(gathered_result_dict)
+                    if args.saved_path.endswith(".csv"):
+                        header = False if os.path.exists(args.saved_path) else True
+                        result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
+                    elif args.saved_path.endswith(".jsonl"):
+                        result_df.to_json(args.saved_path, orient="records", lines=True, mode="a", force_ascii=False)
+                    logger.info(f"Save result to {args.saved_path}.")
+                for k in result_dict.keys():
+                    result_dict[k] = []
+    
+    # Wait for all processes to finish and gather the final result.
+    state.wait_for_everyone()
+    gathered_result_dict = {k: gather_object(v) for k, v in result_dict.items()}
+    # Save the metadata in the main process.
+    if state.is_main_process and len(gathered_result_dict[args.video_path_column]) != 0:
+        result_df = pd.DataFrame(gathered_result_dict)
+        if args.saved_path.endswith(".csv"):
+            header = False if os.path.exists(args.saved_path) else True
+            result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
+        elif args.saved_path.endswith(".jsonl"):
+            result_df.to_json(args.saved_path, orient="records", lines=True, mode="a", force_ascii=False)
+        logger.info(f"Save the final result to {args.saved_path}.")
+
+if __name__ == "__main__":
+    main()

robomaster/video_caption/cutscene_detect.py

@@ -0,0 +1,97 @@
+import argparse
+import os
+from copy import deepcopy
+from pathlib import Path
+from multiprocessing import Pool
+
+import pandas as pd
+from scenedetect import open_video, SceneManager
+from scenedetect.detectors import ContentDetector
+from tqdm import tqdm
+
+from utils.logger import logger
+
+
+def cutscene_detection_star(args):
+    return cutscene_detection(*args)
+
+
+def cutscene_detection(video_path, saved_path, cutscene_threshold=27, min_scene_len=15):
+    try:
+        if os.path.exists(saved_path):
+            logger.info(f"{video_path} has been processed.")
+            return
+        # Use PyAV as the backend to avoid (to some exent) containing the last frame of the previous scene.
+        # https://github.com/Breakthrough/PySceneDetect/issues/279#issuecomment-2152596761.
+        video = open_video(video_path, backend="pyav")
+        frame_rate, frame_size = video.frame_rate, video.frame_size
+        duration = deepcopy(video.duration)
+
+        frame_points, frame_timecode = [], {}
+        scene_manager = SceneManager()
+        scene_manager.add_detector(
+            # [ContentDetector, ThresholdDetector, AdaptiveDetector]
+            ContentDetector(threshold=cutscene_threshold, min_scene_len=min_scene_len)
+        )
+        scene_manager.detect_scenes(video, show_progress=False)
+        scene_list = scene_manager.get_scene_list()
+        for scene in scene_list:
+            for frame_time_code in scene:
+                frame_index = frame_time_code.get_frames()
+                if frame_index not in frame_points:
+                    frame_points.append(frame_index)
+                frame_timecode[frame_index] = frame_time_code
+        
+        del video, scene_manager
+        
+        frame_points = sorted(frame_points)
+        output_scene_list = []
+        for idx in range(len(frame_points) - 1):
+            output_scene_list.append((frame_timecode[frame_points[idx]], frame_timecode[frame_points[idx+1]]))
+        
+        timecode_list = [(frame_timecode_tuple[0].get_timecode(), frame_timecode_tuple[1].get_timecode()) for frame_timecode_tuple in output_scene_list]
+        meta_scene = [{
+            "video_path": Path(video_path).name,
+            "timecode_list": timecode_list,
+            "fram_rate": frame_rate,
+            "frame_size": frame_size,
+            "duration": str(duration)  # __repr__
+        }]
+        pd.DataFrame(meta_scene).to_json(saved_path, orient="records", lines=True)
+    except Exception as e:
+        logger.warning(f"Cutscene detection with {video_path} failed. Error is: {e}.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Cutscene Detection")
+    parser.add_argument(
+        "--video_metadata_path", type=str, required=True, help="The path to the video dataset metadata (csv/jsonl)."
+    )
+    parser.add_argument(
+        "--video_path_column",
+        type=str,
+        default="video_path",
+        help="The column contains the video path (an absolute path or a relative path w.r.t the video_folder).",
+    )
+    parser.add_argument("--video_folder", type=str, default="", help="The video folder.")
+    parser.add_argument("--saved_folder", type=str, required=True, help="The save path to the output results (csv/jsonl).")
+    parser.add_argument("--n_jobs", type=int, default=1, help="The number of processes.")
+
+    args = parser.parse_args()
+
+    metadata_df = pd.read_json(args.video_metadata_path, lines=True)
+    video_path_list = metadata_df[args.video_path_column].tolist()
+    video_path_list = [os.path.join(args.video_folder, video_path) for video_path in video_path_list]
+
+    if not os.path.exists(args.saved_folder):
+        os.makedirs(args.saved_folder, exist_ok=True)
+    # The glob can be slow when there are many small jsonl files.
+    saved_path_list = [os.path.join(args.saved_folder, Path(video_path).stem + ".jsonl") for video_path in video_path_list]
+    args_list = [
+        (video_path, saved_path)
+        for video_path, saved_path in zip(video_path_list, saved_path_list)
+    ]
+    # Since the length of the video is not uniform, the gather operation is not performed.
+    # We need to run easyanimate/video_caption/utils/gather_jsonl.py after the program finised.
+    with Pool(args.n_jobs) as pool:
+        results = list(tqdm(pool.imap(cutscene_detection_star, args_list), total=len(video_path_list)))

robomaster/video_caption/filter_meta_train.py

@@ -0,0 +1,88 @@
+import argparse
+import os
+
+import pandas as pd
+from natsort import natsorted
+
+from utils.logger import logger
+from utils.filter import filter
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--caption_metadata_path", type=str, default=None, help="The path to the video quality metadata (csv/jsonl)."
+    )
+    parser.add_argument(
+        "--video_path_column",
+        type=str,
+        default="video_path",
+        help="The column contains the video path (an absolute path or a relative path w.r.t the video_folder).",
+    )
+    parser.add_argument("--video_folder", type=str, default="", help="The video folder.")
+    parser.add_argument(
+        "--basic_metadata_path", type=str, default=None, help="The path to the basic metadata (csv/jsonl)."
+    )
+    parser.add_argument("--min_resolution", type=float, default=720*1280, help="The resolution threshold.")
+    parser.add_argument("--min_duration", type=float, default=-1, help="The minimum duration.")
+    parser.add_argument("--max_duration", type=float, default=-1, help="The maximum duration.")
+    parser.add_argument(
+        "--asethetic_score_metadata_path", type=str, default=None, help="The path to the video quality metadata (csv/jsonl)."
+    )
+    parser.add_argument("--min_asethetic_score", type=float, default=4.0, help="The asethetic score threshold.")
+    parser.add_argument(
+        "--asethetic_score_siglip_metadata_path", type=str, default=None, help="The path to the video quality (SigLIP) metadata (csv/jsonl)."
+    )
+    parser.add_argument("--min_asethetic_score_siglip", type=float, default=4.0, help="The asethetic score (SigLIP) threshold.")
+    parser.add_argument(
+        "--text_score_metadata_path", type=str, default=None, help="The path to the video text score metadata (csv/jsonl)."
+    )
+    parser.add_argument("--min_text_score", type=float, default=0.02, help="The text threshold.")
+    parser.add_argument(
+        "--motion_score_metadata_path", type=str, default=None, help="The path to the video motion score metadata (csv/jsonl)."
+    )
+    parser.add_argument("--min_motion_score", type=float, default=2, help="The motion threshold.")
+    parser.add_argument(
+        "--videoclipxl_score_metadata_path", type=str, default=None, help="The path to the video-caption VideoCLIPXL score metadata (csv/jsonl)."
+    )
+    parser.add_argument("--min_videoclipxl_score", type=float, default=0.20, help="The VideoCLIPXL score threshold.")
+    parser.add_argument("--saved_path", type=str, required=True)
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    raw_caption_df = pd.read_json(args.caption_metadata_path, lines=True)
+    video_path_list = raw_caption_df[args.video_path_column].to_list()
+    filtered_video_path_list = filter(
+        video_path_list,
+        basic_metadata_path=args.basic_metadata_path,
+        min_resolution=args.min_resolution,
+        min_duration=args.min_duration,
+        max_duration=args.max_duration,
+        asethetic_score_metadata_path=args.asethetic_score_metadata_path,
+        min_asethetic_score=args.min_asethetic_score,
+        asethetic_score_siglip_metadata_path=args.asethetic_score_siglip_metadata_path,
+        min_asethetic_score_siglip=args.min_asethetic_score_siglip,
+        text_score_metadata_path=args.text_score_metadata_path,
+        min_text_score=args.min_text_score,
+        motion_score_metadata_path=args.motion_score_metadata_path,
+        min_motion_score=args.min_motion_score,
+        videoclipxl_score_metadata_path=args.videoclipxl_score_metadata_path,
+        min_videoclipxl_score=args.min_videoclipxl_score,
+        video_path_column=args.video_path_column
+    )
+    filtered_video_path_list = natsorted(filtered_video_path_list)
+    filtered_caption_df = raw_caption_df[raw_caption_df[args.video_path_column].isin(filtered_video_path_list)]
+    train_df = filtered_caption_df.rename(columns={"video_path": "file_path", "caption": "text"})
+    train_df["file_path"] = train_df["file_path"].map(lambda x: os.path.join(args.video_folder, x))
+    train_df["type"] = "video"
+    train_df.to_json(args.saved_path, orient="records", force_ascii=False, indent=2)
+    logger.info(f"The final train file with {len(train_df)} videos are saved to {args.saved_path}.")
+
+
+if __name__ == "__main__":
+    main()

robomaster/video_caption/package_patches/easyocr_detection_patched.py

@@ -0,0 +1,114 @@
+"""Modified from https://github.com/JaidedAI/EasyOCR/blob/803b907/easyocr/detection.py.
+1. Disable DataParallel.
+"""
+import torch
+import torch.backends.cudnn as cudnn
+from torch.autograd import Variable
+from PIL import Image
+from collections import OrderedDict
+
+import cv2
+import numpy as np
+from .craft_utils import getDetBoxes, adjustResultCoordinates
+from .imgproc import resize_aspect_ratio, normalizeMeanVariance
+from .craft import CRAFT
+
+def copyStateDict(state_dict):
+    if list(state_dict.keys())[0].startswith("module"):
+        start_idx = 1
+    else:
+        start_idx = 0
+    new_state_dict = OrderedDict()
+    for k, v in state_dict.items():
+        name = ".".join(k.split(".")[start_idx:])
+        new_state_dict[name] = v
+    return new_state_dict
+
+def test_net(canvas_size, mag_ratio, net, image, text_threshold, link_threshold, low_text, poly, device, estimate_num_chars=False):
+    if isinstance(image, np.ndarray) and len(image.shape) == 4:  # image is batch of np arrays
+        image_arrs = image
+    else:                                                        # image is single numpy array
+        image_arrs = [image]
+
+    img_resized_list = []
+    # resize
+    for img in image_arrs:
+        img_resized, target_ratio, size_heatmap = resize_aspect_ratio(img, canvas_size,
+                                                                      interpolation=cv2.INTER_LINEAR,
+                                                                      mag_ratio=mag_ratio)
+        img_resized_list.append(img_resized)
+    ratio_h = ratio_w = 1 / target_ratio
+    # preprocessing
+    x = [np.transpose(normalizeMeanVariance(n_img), (2, 0, 1))
+         for n_img in img_resized_list]
+    x = torch.from_numpy(np.array(x))
+    x = x.to(device)
+
+    # forward pass
+    with torch.no_grad():
+        y, feature = net(x)
+
+    boxes_list, polys_list = [], []
+    for out in y:
+        # make score and link map
+        score_text = out[:, :, 0].cpu().data.numpy()
+        score_link = out[:, :, 1].cpu().data.numpy()
+
+        # Post-processing
+        boxes, polys, mapper = getDetBoxes(
+            score_text, score_link, text_threshold, link_threshold, low_text, poly, estimate_num_chars)
+
+        # coordinate adjustment
+        boxes = adjustResultCoordinates(boxes, ratio_w, ratio_h)
+        polys = adjustResultCoordinates(polys, ratio_w, ratio_h)
+        if estimate_num_chars:
+            boxes = list(boxes)
+            polys = list(polys)
+        for k in range(len(polys)):
+            if estimate_num_chars:
+                boxes[k] = (boxes[k], mapper[k])
+            if polys[k] is None:
+                polys[k] = boxes[k]
+        boxes_list.append(boxes)
+        polys_list.append(polys)
+
+    return boxes_list, polys_list
+
+def get_detector(trained_model, device='cpu', quantize=True, cudnn_benchmark=False):
+    net = CRAFT()
+
+    if device == 'cpu':
+        net.load_state_dict(copyStateDict(torch.load(trained_model, map_location=device)))
+        if quantize:
+            try:
+                torch.quantization.quantize_dynamic(net, dtype=torch.qint8, inplace=True)
+            except:
+                pass
+    else:
+        net.load_state_dict(copyStateDict(torch.load(trained_model, map_location=device)))
+        # net = torch.nn.DataParallel(net).to(device)
+        net = net.to(device)
+        cudnn.benchmark = cudnn_benchmark
+
+    net.eval()
+    return net
+
+def get_textbox(detector, image, canvas_size, mag_ratio, text_threshold, link_threshold, low_text, poly, device, optimal_num_chars=None, **kwargs):
+    result = []
+    estimate_num_chars = optimal_num_chars is not None
+    bboxes_list, polys_list = test_net(canvas_size, mag_ratio, detector,
+                                       image, text_threshold,
+                                       link_threshold, low_text, poly,
+                                       device, estimate_num_chars)
+    if estimate_num_chars:
+        polys_list = [[p for p, _ in sorted(polys, key=lambda x: abs(optimal_num_chars - x[1]))]
+                      for polys in polys_list]
+
+    for polys in polys_list:
+        single_img_result = []
+        for i, box in enumerate(polys):
+            poly = np.array(box).astype(np.int32).reshape((-1))
+            single_img_result.append(poly)
+        result.append(single_img_result)
+
+    return result

robomaster/video_caption/package_patches/vila_siglip_encoder_patched.py

@@ -0,0 +1,42 @@
+# Modified from https://github.com/NVlabs/VILA/blob/1c88211/llava/model/multimodal_encoder/siglip_encoder.py
+# 1. Support transformers >= 4.36.2.
+import torch
+import transformers
+from packaging import version
+from transformers import AutoConfig, AutoModel, PretrainedConfig
+
+from llava.model.multimodal_encoder.vision_encoder import VisionTower, VisionTowerS2
+
+if version.parse(transformers.__version__) > version.parse("4.36.2"):
+    from transformers import SiglipImageProcessor, SiglipVisionConfig, SiglipVisionModel
+else:
+    from .siglip import SiglipImageProcessor, SiglipVisionConfig, SiglipVisionModel
+
+
+class SiglipVisionTower(VisionTower):
+    def __init__(self, model_name_or_path: str, config: PretrainedConfig, state_dict=None):
+        super().__init__(model_name_or_path, config)
+        self.image_processor = SiglipImageProcessor.from_pretrained(model_name_or_path)
+        self.vision_tower = SiglipVisionModel.from_pretrained(
+            # TODO(ligeng): why pass config here leading to errors?
+            model_name_or_path, torch_dtype=eval(config.model_dtype), state_dict=state_dict
+        )
+        self.is_loaded = True
+
+
+class SiglipVisionTowerS2(VisionTowerS2):
+    def __init__(self, model_name_or_path: str, config: PretrainedConfig):
+        super().__init__(model_name_or_path, config)
+        self.image_processor = SiglipImageProcessor.from_pretrained(model_name_or_path)
+        self.vision_tower = SiglipVisionModel.from_pretrained(
+            model_name_or_path, torch_dtype=eval(config.model_dtype)
+        )
+
+        # Make sure it crops/resizes the image to the largest scale in self.scales to maintain high-res information
+        self.image_processor.size['height'] = self.image_processor.size['width'] = self.scales[-1]
+
+        self.is_loaded = True
+
+if version.parse(transformers.__version__) <= version.parse("4.36.2"):
+    AutoConfig.register("siglip_vision_model", SiglipVisionConfig)
+    AutoModel.register(SiglipVisionConfig, SiglipVisionModel)

robomaster/video_caption/prompt/beautiful_prompt.txt

@@ -0,0 +1,9 @@
+I will upload some brief prompt words to be used for AI-generated videos. Please expand these brief prompt words into a more detailed description to enhance the quality of the generated videos. The detailed description should include the main subject (person, object, animal, or none) actions and their attributes or status sequence, the background (the objects, location, weather, and time), the view shot and camera movement.
+The final detailed description must not exceed 200 words. Output with the following json format:
+{"detailed description": "your detailed description here"}
+
+Here is an example:
+brief prompt words: "A stylish woman in a black leather jacket, red dress, and boots walks confidently down a damp Tokyo street."
+{"detailed description": "A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about."}
+
+Here are the brief prompt words:

robomaster/video_caption/prompt/rewrite.txt

@@ -0,0 +1,9 @@
+Please rewrite the video description to be useful for AI to re-generate the video, according to the following requirements
+1. Do not start with something similar to 'The video/scene/frame shows' or "In this video/scene/frame".
+2. Remove the subjective content deviates from describing the visual content of the video. For instance, a sentence like "It gives a feeling of ease and tranquility and makes people feel comfortable" is considered subjective.
+3. Remove the non-existent description that does not in the visual content of the video, For instance, a sentence like "There is no visible detail that could be used to identify the individual beyond what is shown." is considered as the non-existent description.
+4. Here are some examples of good descriptions: 1) A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about. 2) A large orange octopus is seen resting on the bottom of the ocean floor, blending in with the sandy and rocky terrain. Its tentacles are spread out around its body, and its eyes are closed. The octopus is unaware of a king crab that is crawling towards it from behind a rock, its claws raised and ready to attack. The crab is brown and spiny, with long legs and antennae. The scene is captured from a wide angle, showing the vastness and depth of the ocean. The water is clear and blue, with rays of sunlight filtering through. The shot is sharp and crisp, with a high dynamic range. The octopus and the crab are in focus, while the background is slightly blurred, creating a depth of field effect.
+5. Output with the following json format:
+{"rewritten description": "your rewritten description here"}
+
+Here is the video description:

robomaster/video_caption/requirements.txt

@@ -0,0 +1,9 @@
+pandas>=2.0.0
+easyocr==1.7.1
+git+https://github.com/openai/CLIP.git
+natsort
+joblib
+scenedetect
+av
+# https://github.com/NVlabs/VILA/issues/78#issuecomment-2195568292
+numpy<2.0.0

robomaster/video_caption/scripts/stage_1_video_splitting.sh

@@ -0,0 +1,39 @@
+VIDEO_FOLDER="datasets/panda_70m/videos/data/"
+META_FILE_PATH="datasets/panda_70m/videos/meta_file_info.jsonl"
+SCENE_FOLDER="datasets/panda_70m/videos/meta_scene_info/"
+SCENE_SAVED_PATH="datasets/panda_70m/videos/meta_scene_info.jsonl"
+OUTPUT_FOLDER="datasets/panda_70m/videos_clips/data/"
+RESOLUTION_THRESHOLD=$((512*512))
+
+# Set the duration range of video clips.
+export MIN_SECONDS=3
+export MAX_SECONDS=10
+
+# Save all video names in a video folder as a meta file.
+python -m utils.get_meta_file \
+    --video_folder $VIDEO_FOLDER \
+    --saved_path $META_FILE_PATH
+
+# Perform scene detection on the video dataset.
+# Adjust the n_jobs parameter based on the actual number of CPU cores in the machine.
+python cutscene_detect.py \
+    --video_metadata_path $META_FILE_PATH \
+    --video_folder $VIDEO_FOLDER \
+    --saved_folder $SCENE_FOLDER \
+    --n_jobs 32
+
+# Gather all scene jsonl files to a single scene jsonl file.
+# Adjust the n_jobs parameter based on the actual I/O speed in the machine.
+python -m utils.gather_jsonl \
+    --meta_folder $SCENE_FOLDER \
+    --meta_file_path $SCENE_SAVED_PATH \
+    --n_jobs 64
+
+# Perform video splitting filtered by the RESOLUTION_THRESHOLD.
+# It consumes more CPU computing resources compared to the above operations.
+python video_splitting.py \
+    --video_metadata_path $SCENE_SAVED_PATH \
+    --video_folder $VIDEO_FOLDER \
+    --output_folder $OUTPUT_FOLDER \
+    --n_jobs 16 \
+    --resolution_threshold $RESOLUTION_THRESHOLD

robomaster/video_caption/scripts/stage_2_video_filtering.sh

@@ -0,0 +1,41 @@
+META_FILE_PATH="datasets/panda_70m/videos_clips/data/meta_file_info.jsonl"
+VIDEO_FOLDER="datasets/panda_70m/videos_clips/data/"
+VIDEO_QUALITY_SAVED_PATH="datasets/panda_70m/videos_clips/meta_quality_info_siglip.jsonl"
+MIN_ASETHETIC_SCORE_SIGLIP=4.0
+TEXT_SAVED_PATH="datasets/panda_70m/videos_clips/meta_text_info.jsonl"
+MIN_TEXT_SCORE=0.02
+MOTION_SAVED_PATH="datasets/panda_70m/videos_clips/meta_motion_info.jsonl"
+
+python -m utils.get_meta_file \
+    --video_folder $VIDEO_FOLDER \
+    --saved_path $META_FILE_PATH
+
+# Get the asethetic score (SigLIP) of all videos
+accelerate launch compute_video_quality.py \
+    --video_metadata_path $META_FILE_PATH \
+    --video_folder $VIDEO_FOLDER \
+    --metrics "AestheticScoreSigLIP" \
+    --frame_sample_method uniform \
+    --num_sampled_frames 4 \
+    --saved_freq 10 \
+    --saved_path $VIDEO_QUALITY_SAVED_PATH \
+    --batch_size 4
+
+# Get the text score of all videos filtered by the video quality score.
+accelerate launch compute_text_score.py \
+    --video_metadata_path $META_FILE_PATH \
+    --video_folder $VIDEO_FOLDER  \
+    --saved_freq 10 \
+    --saved_path $TEXT_SAVED_PATH \
+    --asethetic_score_siglip_metadata_path $VIDEO_QUALITY_SAVED_PATH \
+    --min_asethetic_score_siglip $MIN_ASETHETIC_SCORE_SIGLIP
+
+# Get the motion score of all videos filtered by the video quality score and text score.
+python compute_motion_score.py \
+    --video_metadata_path $META_FILE_PATH \
+    --video_folder $VIDEO_FOLDER \
+    --saved_freq 10 \
+    --saved_path $MOTION_SAVED_PATH \
+    --n_jobs 8 \
+    --text_score_metadata_path $TEXT_SAVED_PATH \
+    --min_text_score $MIN_TEXT_SCORE

robomaster/video_caption/scripts/stage_3_video_recaptioning.sh

@@ -0,0 +1,52 @@
+META_FILE_PATH="datasets/panda_70m/videos_clips/data/meta_file_info.jsonl"
+VIDEO_FOLDER="datasets/panda_70m/videos_clips/data/"
+MOTION_SAVED_PATH="datasets/panda_70m/videos_clips/meta_motion_info.jsonl"
+MIN_MOTION_SCORE=2
+VIDEO_CAPTION_SAVED_PATH="datasets/panda_70m/meta_caption_info_vila_8b.jsonl"
+REWRITTEN_VIDEO_CAPTION_SAVED_PATH="datasets/panda_70m/meta_caption_info_vila_8b_rewritten.jsonl"
+VIDEOCLIPXL_SCORE_SAVED_PATH="datasets/panda_70m/meta_caption_info_vila_8b_rewritten_videoclipxl.jsonl"
+MIN_VIDEOCLIPXL_SCORE=0.20
+TRAIN_SAVED_PATH="datasets/panda_70m/train_panda_70m.json"
+# Manually download Efficient-Large-Model/Llama-3-VILA1.5-8b-AWQ to VILA_MODEL_PATH.
+# Manually download meta-llama/Meta-Llama-3-8B-Instruct to REWRITE_MODEL_PATH.
+
+# Use VILA1.5-AWQ to perform recaptioning.
+accelerate launch vila_video_recaptioning.py \
+    --video_metadata_path ${META_FILE_PATH} \
+    --video_folder ${VIDEO_FOLDER} \
+    --model_path ${VILA_MODEL_PATH} \
+    --precision "W4A16" \
+    --saved_path $VIDEO_CAPTION_SAVED_PATH \
+    --saved_freq 1 \
+    --motion_score_metadata_path $MOTION_SAVED_PATH \
+    --min_motion_score $MIN_MOTION_SCORE
+
+# Rewrite video captions (optional).
+python caption_rewrite.py \
+    --video_metadata_path $VIDEO_CAPTION_SAVED_PATH \
+    --batch_size 4096 \
+    --model_name $REWRITE_MODEL_PATH \
+    --prompt prompt/rewrite.txt \
+    --prefix '"rewritten description": ' \
+    --saved_path $REWRITTEN_VIDEO_CAPTION_SAVED_PATH \
+    --saved_freq 1
+
+# Compute caption-video alignment (optional).
+accelerate launch compute_video_quality.py \
+    --video_metadata_path $REWRITTEN_VIDEO_CAPTION_SAVED_PATH \
+    --caption_column caption \
+    --video_folder $VIDEO_FOLDER \
+    --frame_sample_method uniform \
+    --num_sampled_frames 8 \
+    --metrics VideoCLIPXLScore \
+    --batch_size 4 \
+    --saved_path $VIDEOCLIPXL_SCORE_SAVED_PATH \
+    --saved_freq 10
+
+# Get the final train file.
+python filter_meta_train.py \
+    --caption_metadata_path $REWRITTEN_VIDEO_CAPTION_SAVED_PATH \
+    --video_folder=$VIDEO_FOLDER \
+    --videoclipxl_score_metadata_path $VIDEOCLIPXL_SCORE_SAVED_PATH \
+    --min_videoclipxl_score $MIN_VIDEOCLIPXL_SCORE \
+    --saved_path=$TRAIN_SAVED_PATH

robomaster/video_caption/utils/filter.py

@@ -0,0 +1,162 @@
+import ast
+import os
+
+import pandas as pd
+
+from .logger import logger
+
+
+def filter(
+    video_path_list,
+    basic_metadata_path=None,
+    min_resolution=0,
+    min_duration=-1,
+    max_duration=-1,
+    asethetic_score_metadata_path=None,
+    min_asethetic_score=4,
+    asethetic_score_siglip_metadata_path=None,
+    min_asethetic_score_siglip=4,
+    text_score_metadata_path=None,
+    min_text_score=0.02,
+    motion_score_metadata_path=None,
+    min_motion_score=2,
+    videoclipxl_score_metadata_path=None,
+    min_videoclipxl_score=0.20,
+    video_path_column="video_path",
+):
+    video_path_list = [os.path.basename(video_path) for video_path in video_path_list]
+
+    if basic_metadata_path is not None:
+        if basic_metadata_path.endswith(".csv"):
+            basic_df = pd.read_csv(basic_metadata_path)
+        elif basic_metadata_path.endswith(".jsonl"):
+            basic_df = pd.read_json(basic_metadata_path, lines=True)
+
+        basic_df["resolution"] = basic_df["frame_size"].apply(lambda x: x[0] * x[1])
+        filtered_basic_df = basic_df[basic_df["resolution"] < min_resolution]
+        filtered_video_path_list = filtered_basic_df[video_path_column].tolist()
+        filtered_video_path_list = [os.path.basename(video_path) for video_path in filtered_video_path_list]
+
+        video_path_list = list(set(video_path_list).difference(set(filtered_video_path_list)))
+        logger.info(
+            f"Load {basic_metadata_path} ({len(basic_df)}) and filter {len(filtered_video_path_list)} videos "
+            f"with resolution less than {min_resolution}."
+        )
+
+        if min_duration != -1:
+            filtered_basic_df = basic_df[basic_df["duration"] < min_duration]
+            filtered_video_path_list = filtered_basic_df[video_path_column].tolist()
+            filtered_video_path_list = [os.path.basename(video_path) for video_path in filtered_video_path_list]
+
+            video_path_list = list(set(video_path_list).difference(set(filtered_video_path_list)))
+            logger.info(
+                f"Load {basic_metadata_path} and filter {len(filtered_video_path_list)} videos "
+                f"with duration less than {min_duration}."
+            )
+
+        if max_duration != -1:
+            filtered_basic_df = basic_df[basic_df["duration"] > max_duration]
+            filtered_video_path_list = filtered_basic_df[video_path_column].tolist()
+            filtered_video_path_list = [os.path.basename(video_path) for video_path in filtered_video_path_list]
+
+            video_path_list = list(set(video_path_list).difference(set(filtered_video_path_list)))
+            logger.info(
+                f"Load {basic_metadata_path} and filter {len(filtered_video_path_list)} videos "
+                f"with duration greater than {max_duration}."
+            )
+
+    if asethetic_score_metadata_path is not None:
+        if asethetic_score_metadata_path.endswith(".csv"):
+            asethetic_score_df = pd.read_csv(asethetic_score_metadata_path)
+        elif asethetic_score_metadata_path.endswith(".jsonl"):
+            asethetic_score_df = pd.read_json(asethetic_score_metadata_path, lines=True)
+
+        # In pandas, csv will save lists as strings, whereas jsonl will not.
+        asethetic_score_df["aesthetic_score"] = asethetic_score_df["aesthetic_score"].apply(
+            lambda x: ast.literal_eval(x) if isinstance(x, str) else x
+        )
+        asethetic_score_df["aesthetic_score_mean"] = asethetic_score_df["aesthetic_score"].apply(lambda x: sum(x) / len(x))
+        filtered_asethetic_score_df = asethetic_score_df[asethetic_score_df["aesthetic_score_mean"] < min_asethetic_score]
+        filtered_video_path_list = filtered_asethetic_score_df[video_path_column].tolist()
+        filtered_video_path_list = [os.path.basename(video_path) for video_path in filtered_video_path_list]
+
+        video_path_list = list(set(video_path_list).difference(set(filtered_video_path_list)))
+        logger.info(
+            f"Load {asethetic_score_metadata_path} ({len(asethetic_score_df)}) and filter {len(filtered_video_path_list)} videos "
+            f"with aesthetic score less than {min_asethetic_score}."
+        )
+
+    if asethetic_score_siglip_metadata_path is not None:
+        if asethetic_score_siglip_metadata_path.endswith(".csv"):
+            asethetic_score_siglip_df = pd.read_csv(asethetic_score_siglip_metadata_path)
+        elif asethetic_score_siglip_metadata_path.endswith(".jsonl"):
+            asethetic_score_siglip_df = pd.read_json(asethetic_score_siglip_metadata_path, lines=True)
+
+        # In pandas, csv will save lists as strings, whereas jsonl will not.
+        asethetic_score_siglip_df["aesthetic_score_siglip"] = asethetic_score_siglip_df["aesthetic_score_siglip"].apply(
+            lambda x: ast.literal_eval(x) if isinstance(x, str) else x
+        )
+        asethetic_score_siglip_df["aesthetic_score_siglip_mean"] = asethetic_score_siglip_df["aesthetic_score_siglip"].apply(
+            lambda x: sum(x) / len(x)
+        )
+        filtered_asethetic_score_siglip_df = asethetic_score_siglip_df[
+            asethetic_score_siglip_df["aesthetic_score_siglip_mean"] < min_asethetic_score_siglip
+        ]
+        filtered_video_path_list = filtered_asethetic_score_siglip_df[video_path_column].tolist()
+        filtered_video_path_list = [os.path.basename(video_path) for video_path in filtered_video_path_list]
+
+        video_path_list = list(set(video_path_list).difference(set(filtered_video_path_list)))
+        logger.info(
+            f"Load {asethetic_score_siglip_metadata_path} ({len(asethetic_score_siglip_df)}) and filter {len(filtered_video_path_list)} videos "
+            f"with aesthetic score (SigLIP) less than {min_asethetic_score_siglip}."
+        )
+
+    if text_score_metadata_path is not None:
+        if text_score_metadata_path.endswith(".csv"):
+            text_score_df = pd.read_csv(text_score_metadata_path)
+        elif text_score_metadata_path.endswith(".jsonl"):
+            text_score_df = pd.read_json(text_score_metadata_path, lines=True)
+
+        filtered_text_score_df = text_score_df[text_score_df["text_score"] > min_text_score]
+        filtered_video_path_list = filtered_text_score_df[video_path_column].tolist()
+        filtered_video_path_list = [os.path.basename(video_path) for video_path in filtered_video_path_list]
+
+        video_path_list = list(set(video_path_list).difference(set(filtered_video_path_list)))
+        logger.info(
+            f"Load {text_score_metadata_path} ({len(text_score_df)}) and filter {len(filtered_video_path_list)} videos "
+            f"with text score greater than {min_text_score}."
+        )
+
+    if motion_score_metadata_path is not None:
+        if motion_score_metadata_path.endswith(".csv"):
+            motion_score_df = pd.read_csv(motion_score_metadata_path)
+        elif motion_score_metadata_path.endswith(".jsonl"):
+            motion_score_df = pd.read_json(motion_score_metadata_path, lines=True)
+
+        filtered_motion_score_df = motion_score_df[motion_score_df["motion_score"] < min_motion_score]
+        filtered_video_path_list = filtered_motion_score_df[video_path_column].tolist()
+        filtered_video_path_list = [os.path.basename(video_path) for video_path in filtered_video_path_list]
+
+        video_path_list = list(set(video_path_list).difference(set(filtered_video_path_list)))
+        logger.info(
+            f"Load {motion_score_metadata_path} ({len(motion_score_df)}) and filter {len(filtered_video_path_list)} videos "
+            f"with motion score smaller than {min_motion_score}."
+        )
+    
+    if videoclipxl_score_metadata_path is not None:
+        if videoclipxl_score_metadata_path.endswith(".csv"):
+            videoclipxl_score_df = pd.read_csv(videoclipxl_score_metadata_path)
+        elif videoclipxl_score_metadata_path.endswith(".jsonl"):
+            videoclipxl_score_df = pd.read_json(videoclipxl_score_metadata_path, lines=True)
+        
+        filtered_videoclipxl_score_df = videoclipxl_score_df[videoclipxl_score_df["videoclipxl_score"] < min_videoclipxl_score]
+        filtered_video_path_list = filtered_videoclipxl_score_df[video_path_column].tolist()
+        filtered_video_path_list = [os.path.basename(video_path) for video_path in filtered_video_path_list]
+
+        video_path_list = list(set(video_path_list).difference(set(filtered_video_path_list)))
+        logger.info(
+            f"Load {videoclipxl_score_metadata_path} ({len(videoclipxl_score_df)}) and "
+            f"filter {len(filtered_video_path_list)} videos with mixclip score smaller than {min_videoclipxl_score}."
+        )
+
+    return video_path_list

robomaster/video_caption/utils/gather_jsonl.py

@@ -0,0 +1,55 @@
+import argparse
+import os
+import glob
+import json
+from multiprocessing import Pool, Manager
+
+import pandas as pd
+from natsort import index_natsorted
+
+from .logger import logger
+
+
+def process_file(file_path, shared_list):
+    with open(file_path, "r") as f:
+        for line in f:
+            data = json.loads(line)
+            shared_list.append(data)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Gather all jsonl files in a folder (meta_folder) to a single jsonl file (meta_file_path).")
+    parser.add_argument("--meta_folder", type=str, required=True)
+    parser.add_argument("--meta_file_path", type=str, required=True)
+    parser.add_argument("--video_path_column", type=str, default="video_path")
+    parser.add_argument("--n_jobs", type=int, default=1)
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    jsonl_files = glob.glob(os.path.join(args.meta_folder, "*.jsonl"))
+
+    with Manager() as manager:
+        shared_list = manager.list()
+        with Pool(processes=args.n_jobs) as pool:
+            for file_path in jsonl_files:
+                pool.apply_async(process_file, args=(file_path, shared_list))
+            pool.close()
+            pool.join()
+
+        with open(args.meta_file_path, "w") as f:
+            for item in shared_list:
+                f.write(json.dumps(item) + '\n')
+    
+    df = pd.read_json(args.meta_file_path, lines=True)
+    df = df.iloc[index_natsorted(df[args.video_path_column])].reset_index(drop=True)
+    logger.info(f"Save the gathered single jsonl file to {args.meta_file_path}.")
+    df.to_json(args.meta_file_path, orient="records", lines=True, force_ascii=False)
+
+
+if __name__ == '__main__':
+    main()

robomaster/video_caption/utils/get_meta_file.py

@@ -0,0 +1,74 @@
+import argparse
+from pathlib import Path
+
+import pandas as pd
+from natsort import natsorted
+from tqdm import tqdm
+
+from .logger import logger
+
+
+ALL_VIDEO_EXT = set(["mp4", "webm", "mkv", "avi", "flv", "mov"])
+ALL_IMGAE_EXT = set(["png", "webp", "jpg", "jpeg", "bmp", "gif"])
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Compute scores of uniform sampled frames from videos.")
+    parser.add_argument(
+        "--image_path_column",
+        type=str,
+        default="image_path",
+        help="The column contains the image path (an absolute path or a relative path w.r.t the image_folder).",
+    )
+    parser.add_argument("--image_folder", type=str, default=None, help="The video folder.")
+    parser.add_argument(
+        "--video_path_column",
+        type=str,
+        default="video_path",
+        help="The column contains the video path (an absolute path or a relative path w.r.t the video_folder).",
+    )
+    parser.add_argument("--video_folder", type=str, default=None, help="The video folder.")
+    parser.add_argument("--saved_path", type=str, required=True, help="The save path to the output results (csv/jsonl).")
+    parser.add_argument("--recursive", action="store_true", help="Whether to search sub-folders recursively.")
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    if args.video_folder is None and args.image_folder is None:
+        raise ValueError("Either video_folder or image_folder should be specified in the arguments.")
+    if args.video_folder is not None and args.image_folder is not None:
+        raise ValueError("Both video_folder and image_folder can not be specified in the arguments at the same time.")
+
+    # Use the path name instead of the file name as video_path/image_path (unique ID).
+    if args.video_folder is not None:
+        video_path_list = []
+        video_folder = Path(args.video_folder)
+        for ext in tqdm(list(ALL_VIDEO_EXT)):
+            if args.recursive:
+                video_path_list += [str(file.relative_to(video_folder)) for file in video_folder.rglob(f"*.{ext}")]
+            else:
+                video_path_list += [str(file.relative_to(video_folder)) for file in video_folder.glob(f"*.{ext}")]
+        video_path_list = natsorted(video_path_list)
+        meta_file_df = pd.DataFrame({args.video_path_column: video_path_list})
+    
+    if args.image_folder is not None:
+        image_path_list = []
+        image_folder = Path(args.image_folder)
+        for ext in tqdm(list(ALL_IMGAE_EXT)):
+            if args.recursive:
+                image_path_list += [str(file.relative_to(image_folder)) for file in image_folder.rglob(f"*.{ext}")]
+            else:
+                image_path_list += [str(file.relative_to(image_folder)) for file in image_folder.glob(f"*.{ext}")]
+        image_path_list = natsorted(image_path_list)
+        meta_file_df = pd.DataFrame({args.image_path_column: image_path_list})
+
+    logger.info(f"{len(meta_file_df)} files in total. Save the result to {args.saved_path}.")
+    meta_file_df.to_json(args.saved_path, orient="records", lines=True)
+
+
+if __name__ == "__main__":
+    main()

robomaster/video_caption/utils/image_evaluator.py

@@ -0,0 +1,248 @@
+import os
+from typing import Union
+
+import clip
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from PIL import Image
+from torchvision.datasets.utils import download_url
+from transformers import AutoModel, AutoProcessor
+
+from .siglip_v2_5 import convert_v2_5_from_siglip
+
+# All metrics.
+__all__ = ["AestheticScore", "AestheticScoreSigLIP", "CLIPScore"]
+
+_MODELS = {
+    "CLIP_ViT-L/14": "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/video_caption/clip/ViT-L-14.pt",
+    "Aesthetics_V2": "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/video_caption/clip/sac%2Blogos%2Bava1-l14-linearMSE.pth",
+    "aesthetic_predictor_v2_5": "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/video_caption/clip/aesthetic_predictor_v2_5.pth",
+}
+_MD5 = {
+    "CLIP_ViT-L/14": "096db1af569b284eb76b3881534822d9",
+    "Aesthetics_V2": "b1047fd767a00134b8fd6529bf19521a",
+    "aesthetic_predictor_v2_5": "c46eb8c29f714c9231dc630b8226842a",
+}
+
+
+def get_list_depth(lst):
+    if isinstance(lst, list):
+        return 1 + max(get_list_depth(item) for item in lst)
+    else:
+        return 0
+
+
+def reshape_images(images: Union[list[list[Image.Image]], list[Image.Image]]):
+    # Check the input sanity.
+    depth = get_list_depth(images)
+    if depth == 1:  # batch image input
+        if not isinstance(images[0], Image.Image):
+            raise ValueError("The item in 1D images should be Image.Image.")
+        num_sampled_frames = None
+    elif depth == 2:  # batch video input
+        if not isinstance(images[0][0], Image.Image):
+            raise ValueError("The item in 2D images (videos) should be Image.Image.")
+        num_sampled_frames = len(images[0])
+        if not all(len(video_frames) == num_sampled_frames for video_frames in images):
+            raise ValueError("All item in 2D images should be with the same length.")
+        # [batch_size, num_sampled_frames, H, W, C] => [batch_size * num_sampled_frames, H, W, C].
+        reshaped_images = []
+        for video_frames in images:
+            reshaped_images.extend([frame for frame in video_frames])
+        images = reshaped_images
+    else:
+        raise ValueError("The input images should be in 1/2D list.")
+    
+    return images, num_sampled_frames
+
+
+def reshape_scores(scores: list[float], num_sampled_frames: int) -> list[float]:
+    if isinstance(scores, list):
+        if num_sampled_frames is not None: # Batch video input
+            batch_size = len(scores) // num_sampled_frames
+            scores = [
+                scores[i * num_sampled_frames:(i + 1) * num_sampled_frames]
+                for i in range(batch_size)
+            ]
+        return scores
+    else:
+        return [scores]
+
+
+# if you changed the MLP architecture during training, change it also here:
+class _MLP(nn.Module):
+    def __init__(self, input_size):
+        super().__init__()
+        self.input_size = input_size
+        self.layers = nn.Sequential(
+            nn.Linear(self.input_size, 1024),
+            # nn.ReLU(),
+            nn.Dropout(0.2),
+            nn.Linear(1024, 128),
+            # nn.ReLU(),
+            nn.Dropout(0.2),
+            nn.Linear(128, 64),
+            # nn.ReLU(),
+            nn.Dropout(0.1),
+            nn.Linear(64, 16),
+            # nn.ReLU(),
+            nn.Linear(16, 1),
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class AestheticScore:
+    """Compute LAION Aesthetics Score V2 based on openai/clip. Note that the default
+    inference dtype with GPUs is fp16 in openai/clip.
+
+    Ref:
+    1. https://github.com/christophschuhmann/improved-aesthetic-predictor/blob/main/simple_inference.py.
+    2. https://github.com/openai/CLIP/issues/30.
+    """
+
+    def __init__(self, root: str = "~/.cache/clip", device: str = "cpu"):
+        # The CLIP model is loaded in the evaluation mode.
+        self.root = os.path.expanduser(root)
+        if not os.path.exists(self.root):
+            os.makedirs(self.root)
+        filename = "ViT-L-14.pt"
+        download_url(_MODELS["CLIP_ViT-L/14"], self.root, filename=filename, md5=_MD5["CLIP_ViT-L/14"])
+        self.clip_model, self.preprocess = clip.load(os.path.join(self.root, filename), device=device)
+        self.device = device
+        self._load_mlp()
+
+    def _load_mlp(self):
+        filename = "sac+logos+ava1-l14-linearMSE.pth"
+        download_url(_MODELS["Aesthetics_V2"], self.root, filename=filename, md5=_MD5["Aesthetics_V2"])
+        state_dict = torch.load(os.path.join(self.root, filename))
+        self.mlp = _MLP(768)
+        self.mlp.load_state_dict(state_dict)
+        self.mlp.to(self.device)
+        self.mlp.eval()
+
+    def __call__(self, images: Union[list[list[Image.Image]], list[Image.Image]], texts=None) -> list[float]:
+        images, num_sampled_frames = reshape_images(images)
+
+        with torch.no_grad():
+            images = torch.stack([self.preprocess(image) for image in images]).to(self.device)
+            image_embs = F.normalize(self.clip_model.encode_image(images))
+            scores = self.mlp(image_embs.float())  # torch.float16 -> torch.float32, [N, 1]
+        
+        scores = scores.squeeze().tolist()  # scalar or list
+        return reshape_scores(scores, num_sampled_frames)
+    
+    def __repr__(self) -> str:
+        return "aesthetic_score"
+
+
+class AestheticScoreSigLIP:
+    """Compute Aesthetics Score V2.5 based on google/siglip-so400m-patch14-384.
+
+    Ref:
+    1. https://github.com/discus0434/aesthetic-predictor-v2-5.
+    2. https://github.com/discus0434/aesthetic-predictor-v2-5/issues/2.
+    """
+
+    def __init__(
+        self,
+        root: str = "~/.cache/clip",
+        device: str = "cpu",
+        torch_dtype=torch.float16
+    ):
+        self.root = os.path.expanduser(root)
+        if not os.path.exists(self.root):
+            os.makedirs(self.root)
+        filename = "aesthetic_predictor_v2_5.pth"
+        download_url(_MODELS["aesthetic_predictor_v2_5"], self.root, filename=filename, md5=_MD5["aesthetic_predictor_v2_5"])
+        self.model, self.preprocessor = convert_v2_5_from_siglip(
+            predictor_name_or_path=os.path.join(self.root, filename),
+            low_cpu_mem_usage=True,
+            trust_remote_code=True,
+        )
+        self.model = self.model.to(device=device, dtype=torch_dtype)
+        self.device = device
+        self.torch_dtype = torch_dtype
+
+    def __call__(self, images: Union[list[list[Image.Image]], list[Image.Image]], texts=None) -> list[float]:
+        images, num_sampled_frames = reshape_images(images)
+
+        pixel_values = self.preprocessor(images, return_tensors="pt").pixel_values
+        pixel_values = pixel_values.to(self.device, self.torch_dtype)
+        with torch.no_grad():
+            scores = self.model(pixel_values).logits.squeeze().float().cpu().numpy()
+        
+        scores = scores.squeeze().tolist()  # scalar or list
+        return reshape_scores(scores, num_sampled_frames)
+    
+    def __repr__(self) -> str:
+        return "aesthetic_score_siglip"
+
+
+class CLIPScore:
+    """Compute CLIP scores for image-text pairs based on huggingface/transformers."""
+
+    def __init__(
+        self,
+        model_name_or_path: str = "openai/clip-vit-large-patch14",
+        torch_dtype=torch.float16,
+        device: str = "cpu",
+    ):
+        self.model = AutoModel.from_pretrained(model_name_or_path, torch_dtype=torch_dtype).eval().to(device)
+        self.processor = AutoProcessor.from_pretrained(model_name_or_path)
+        self.torch_dtype = torch_dtype
+        self.device = device
+
+    def __call__(self, images: Union[list[list[Image.Image]], list[Image.Image]], texts: list[str]) -> list[float]:
+        assert len(images) == len(texts)
+        images, num_sampled_frames = reshape_images(images)
+        # Expand texts in the batch video input case.
+        if num_sampled_frames is not None:
+            texts = [[text] * num_sampled_frames for text in texts]
+            texts = [item for sublist in texts for item in sublist]
+
+        image_inputs = self.processor(images=images, return_tensors="pt")  # {"pixel_values": }
+        if self.torch_dtype == torch.float16:
+            image_inputs["pixel_values"] = image_inputs["pixel_values"].half()
+        text_inputs = self.processor(text=texts, return_tensors="pt", padding=True, truncation=True)  # {"inputs_id": }
+        image_inputs, text_inputs = image_inputs.to(self.device), text_inputs.to(self.device)
+        with torch.no_grad():
+            image_embs = F.normalize(self.model.get_image_features(**image_inputs))
+            text_embs = F.normalize(self.model.get_text_features(**text_inputs))
+            scores = text_embs @ image_embs.T  # [N, N]
+
+        scores = scores.squeeze().tolist()  # scalar or list
+        return reshape_scores(scores, num_sampled_frames)
+    
+    def __repr__(self) -> str:
+        return "clip_score"
+
+
+if __name__ == "__main__":
+    from torch.utils.data import DataLoader
+    from tqdm import tqdm
+    from .video_dataset import VideoDataset, collate_fn
+
+    aesthetic_score = AestheticScore(device="cuda")
+    aesthetic_score_siglip = AestheticScoreSigLIP(device="cuda")
+    # clip_score = CLIPScore(device="cuda")
+
+    paths = ["your_image_path"] * 3
+    # texts = ["a joker", "a woman", "a man"]
+    images = [Image.open(p).convert("RGB") for p in paths]
+
+    print(aesthetic_score(images))
+    # print(clip_score(images, texts))
+
+    test_dataset = VideoDataset(
+        dataset_inputs={"video_path": ["your_video_path"] * 3},
+        sample_method="mid",
+        num_sampled_frames=2
+    )
+    test_loader = DataLoader(test_dataset, batch_size=1, num_workers=1, collate_fn=collate_fn)
+
+    for idx, batch in enumerate(tqdm(test_loader)):
+        batch_frame = batch["sampled_frame"]
+        print(aesthetic_score_siglip(batch_frame))

robomaster/video_caption/utils/logger.py

@@ -0,0 +1,36 @@
+# Borrowed from sd-webui-controlnet/scripts/logging.py
+import copy
+import logging
+import sys
+
+
+class ColoredFormatter(logging.Formatter):
+    COLORS = {
+        "DEBUG": "\033[0;36m",  # CYAN
+        "INFO": "\033[0;32m",  # GREEN
+        "WARNING": "\033[0;33m",  # YELLOW
+        "ERROR": "\033[0;31m",  # RED
+        "CRITICAL": "\033[0;37;41m",  # WHITE ON RED
+        "RESET": "\033[0m",  # RESET COLOR
+    }
+
+    def format(self, record):
+        colored_record = copy.copy(record)
+        levelname = colored_record.levelname
+        seq = self.COLORS.get(levelname, self.COLORS["RESET"])
+        colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}"
+        return super().format(colored_record)
+
+
+# Create a new logger
+logger = logging.getLogger("VideoCaption")
+logger.propagate = False
+
+# Add handler if we don't have one.
+if not logger.handlers:
+    handler = logging.StreamHandler(sys.stdout)
+    handler.setFormatter(ColoredFormatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s"))
+    logger.addHandler(handler)
+
+# Configure logger
+logger.setLevel("INFO")

robomaster/video_caption/utils/longclip/README.md

@@ -0,0 +1,19 @@
+# Long-CLIP
+Codes in this directory are borrowed from https://github.com/beichenzbc/Long-CLIP/tree/4e6f5da/model.
+
+We only modify the following code in [model_longclip.py](model_longclip.py) from
+```python
+@property
+def dtype(self):
+    return self.visual.conv1.weight.dtype
+```
+to
+```python
+@property
+def dtype(self):
+    # Fix: the VideoCLIP-XL inference.
+    if hasattr(self, "visual"):
+        return self.visual.conv1.weight.dtype
+    else:
+        return self.token_embedding.weight.dtype
+```

robomaster/video_caption/utils/longclip/__init__.py

@@ -0,0 +1 @@
+from .longclip import *

robomaster/video_caption/utils/longclip/bpe_simple_vocab_16e6.txt.gz

@@ -0,0 +1,3 @@
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+oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a
+size 1356917