File size: 8,389 Bytes
e87d958
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
import gradio as gr
import spaces
import os
import time
import json
import numpy as np
import av
import torch
from PIL import Image
import functools
from transformers import AutoProcessor, AutoConfig
from models.idefics2 import Idefics2ForSequenceClassification
from models.conversation import conv_templates
from typing import List


processor = AutoProcessor.from_pretrained("TIGER-Lab/VideoScore")
model = Idefics2ForSequenceClassification.from_pretrained("TIGER-Lab/VideoScore", torch_dtype=torch.bfloat16).eval()

MAX_NUM_FRAMES = 24
conv_template = conv_templates["idefics_2"]

with open("./examples/all_subsets.json", 'r') as f:
    examples = json.load(f)

for item in examples:
    video_id = item['images'][0].split("_")[0]
    item['images'] = [os.path.join("./examples", video_id, x) for x in item['images']]
    item['video'] = os.path.join("./examples", item['video'])
    
with open("./examples/hd.json", 'r') as f:
    hd_examples = json.load(f)

for item in hd_examples:
    item['video'] = os.path.join("./examples", item['video'])

examples = hd_examples + examples

VIDEO_EVAL_PROMPT = """
Suppose you are an expert in judging and evaluating the quality of AI-generated videos, 
please watch the following frames of a given video and see the text prompt for generating the video, 
then give scores from 5 different dimensions:
(1) visual quality: the quality of the video in terms of clearness, resolution, brightness, and color
(2) temporal consistency, the consistency of objects or humans in video
(3) dynamic degree, the degree of dynamic changes
(4) text-to-video alignment, the alignment between the text prompt and the video content
(5) factual consistency, the consistency of the video content with the common-sense and factual knowledge

For each dimension, output a number from [1,2,3,4], 
in which '1' means 'Bad', '2' means 'Average', '3' means 'Good', 
'4' means 'Real' or 'Perfect' (the video is like a real video)
Here is an output example:
visual quality: 4
temporal consistency: 4
dynamic degree: 3
text-to-video alignment: 1
factual consistency: 2

For this video, the text prompt is "{text_prompt}",
all the frames of video are as follows: 

"""



space_description="""\
[📃Paper](https://arxiv.org/abs/2406.15252) | [🌐Website](https://tiger-ai-lab.github.io/VideoScore/) | [💻Github](https://github.com/TIGER-AI-Lab/VideoScore) | [🛢️Datasets](https://huggingface.co/datasets/TIGER-Lab/VideoFeedback) | [🤗Model](https://huggingface.co/TIGER-Lab/VideoScore) | [🤗Demo](https://huggingface.co/spaces/TIGER-Lab/VideoScore)

- VideoScore is a video quality evaluation model, taking [Mantis-8B-Idefics2](https://huggingface.co/TIGER-Lab/Mantis-8B-Idefics2) as base-model
and trained on [VideoFeedback](https://huggingface.co/datasets/TIGER-Lab/VideoFeedback),
a large video evaluation dataset with multi-aspect human scores. 

- VideoScore can reach 75+ Spearman correlation with humans on VideoEval-test, surpassing all the MLLM-prompting methods and feature-based metrics. 

- VideoScore also beat the best baselines on other three benchmarks EvalCrafter, GenAI-Bench and VBench, showing high alignment with human evaluations.
"""


aspect_mapping= [
    "visual quality",
    "temporal consistency",
    "dynamic degree",
    "text-to-video alignment",
    "factual consistency",
]


@spaces.GPU(duration=60)
def score(prompt:str, images:List[Image.Image]):
    if not prompt:
        raise gr.Error("Please provide a prompt")
    model.to("cuda")
    if not images:
        images = None
    
    flatten_images = []
    for x in images:
        if isinstance(x, list):
            flatten_images.extend(x)
        else:
            flatten_images.append(x)
    
    flatten_images = [Image.open(x) if isinstance(x, str) else x for x in flatten_images]
    inputs = processor(text=prompt, images=flatten_images, return_tensors="pt")
    inputs = {k: v.to(model.device) for k, v in inputs.items()}
    with torch.no_grad():
        outputs = model(**inputs)
    
    logits = outputs.logits
    num_aspects = logits.shape[-1]
    aspects = [aspect_mapping[i] for i in range(num_aspects)]
    
    aspect_scores = {}
    for i, aspect in enumerate(aspects):
        aspect_scores[aspect] = round(logits[0, i].item(), 2)
    return aspect_scores
    
    
def read_video_pyav(container, indices):
    '''
    Decode the video with PyAV decoder.

    Args:
        container (av.container.input.InputContainer): PyAV container.
        indices (List[int]): List of frame indices to decode.

    Returns:
        np.ndarray: np array of decoded frames of shape (num_frames, height, width, 3).
    '''
    frames = []
    container.seek(0)
    start_index = indices[0]
    end_index = indices[-1]
    for i, frame in enumerate(container.decode(video=0)):
        if i > end_index:
            break
        if i >= start_index and i in indices:
            frames.append(frame)
    return np.stack([x.to_ndarray(format="rgb24") for x in frames])

def eval_video(prompt, video:str):
    container = av.open(video)

    # sample uniformly 8 frames from the video
    total_frames = container.streams.video[0].frames
    if total_frames > MAX_NUM_FRAMES:
        indices = np.arange(0, total_frames, total_frames / MAX_NUM_FRAMES).astype(int)
    else:
        indices = np.arange(total_frames)
    video_frames = read_video_pyav(container, indices)

    frames = [Image.fromarray(x) for x in video_frames]
    
    eval_prompt = VIDEO_EVAL_PROMPT.format(text_prompt=prompt)
    
    num_image_token = eval_prompt.count("<image>")
    if num_image_token < len(frames):
        eval_prompt += "<image> " * (len(frames) - num_image_token)
    
    aspect_scores = score(eval_prompt, [frames])
    return aspect_scores

def build_demo():
    with gr.Blocks() as demo:

        gr.Markdown("## VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation")
        with gr.Row():
            gr.Markdown(space_description)
            gr.Image("https://tiger-ai-lab.github.io/VideoScore/static/images/teaser.png", label="Teaser")
        
        gr.Markdown("### Try VideoScore (Regression) with your own text prompt and videos.")
        with gr.Row():
            video = gr.Video(width=500, label="Video")
            with gr.Column():
                eval_prompt_template = gr.Textbox(VIDEO_EVAL_PROMPT.strip(' \n'), label="Evaluation Prompt Template", interactive=False, max_lines=26)
                video_prompt = gr.Textbox(label="Text Prompt", lines=1)
                with gr.Row():
                    eval_button = gr.Button("Evaluate Video")
                    clear_button = gr.ClearButton([video, video_prompt])
                # eval_result = gr.Textbox(label="Evaluation result", interactive=False, lines=7)
                eval_result = gr.Json(label="Evaluation result")
                
        
        eval_button.click(
            eval_video, [video_prompt, video], [eval_result]
        )
        
        dummy_id = gr.Textbox("id", label="id", visible=False, min_width=50)
        # dummy_output = gr.Textbox("reference score", label="reference scores", visible=False, lines=7)
        
        gr.Examples(
            examples=
            [
                [
                    # item['id'], 
                    item['prompt'],
                    item['video'],
                    # item['conversations'][1]['value']
                ] for item in examples if item['prompt']
            ],
            inputs=[video_prompt, video],
            # inputs=[dummy_id, video_prompt, video, dummy_output],
            
        )        
        
        gr.Markdown("""
## Citation
```
@article{he2024videoscore,
  title = {VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation},
  author = {He, Xuan and Jiang, Dongfu and Zhang, Ge and Ku, Max and Soni, Achint and Siu, Sherman and Chen, Haonan and Chandra, Abhranil and Jiang, Ziyan and Arulraj, Aaran and Wang, Kai and Do, Quy Duc and Ni, Yuansheng and Lyu, Bohan and Narsupalli, Yaswanth and Fan, Rongqi and Lyu, Zhiheng and Lin, Yuchen and Chen, Wenhu},
  journal = {ArXiv},
  year = {2024},
  volume={abs/2406.15252},
  url = {https://arxiv.org/abs/2406.15252},
}
```""")
    return demo    
    

if __name__ == "__main__":
    demo = build_demo()
    demo.launch(share=True)