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---
license: apache-2.0
language:
- en
inference: false
---
<h1>VPO: Aligning Text-to-Video Generation Models with Prompt Optimization</h1>
- **Repository:** https://github.com/thu-coai/VPO
<!-- - **Paper:** -->
- **Data:** https://huggingface.co/datasets/CCCCCC/VPO
# VPO
VPO is a principled prompt optimization framework grounded in the principles of harmlessness, accuracy, and helpfulness.
VPO employs a two-stage process that first constructs a supervised fine-tuning dataset guided by safety and alignment, and then conducts preference learning with both text-level and video-level feedback. As a result, VPO preserves user intent while enhancing video quality and safety.
## Model Details
### Video Generation Model
This model is trained to optimize user prompt for CogVideoX-2B. [VPO-5B](https://huggingface.co/CCCCCC/VPO-5B) is for CogVideoX-5B.
### Data
Our dataset can be found [here](https://huggingface.co/datasets/CCCCCC/VPO).
### Language
English
## Intended Use
### Prompt Template
We adopt a prompt template as
```
In this task, your goal is to expand the user's short query into a detailed and well-structured English prompt for generating short videos.
Please ensure that the generated video prompt adheres to the following principles:
1. **Harmless**: The prompt must be safe, respectful, and free from any harmful, offensive, or unethical content.
2. **Aligned**: The prompt should fully preserve the user's intent, incorporating all relevant details from the original query while ensuring clarity and coherence.
3. **Helpful for High-Quality Video Generation**: The prompt should be descriptive and vivid to facilitate high-quality video creation. Keep the scene feasible and well-suited for a brief duration, avoiding unnecessary complexity or unrealistic elements not mentioned in the query.
User Query:{user prompt}
Video Prompt:
```
### Inference code
Here is an example code for inference:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = ''
prompt_template = """In this task, your goal is to expand the user's short query into a detailed and well-structured English prompt for generating short videos.
Please ensure that the generated video prompt adheres to the following principles:
1. **Harmless**: The prompt must be safe, respectful, and free from any harmful, offensive, or unethical content.
2. **Aligned**: The prompt should fully preserve the user's intent, incorporating all relevant details from the original query while ensuring clarity and coherence.
3. **Helpful for High-Quality Video Generation**: The prompt should be descriptive and vivid to facilitate high-quality video creation. Keep the scene feasible and well-suited for a brief duration, avoiding unnecessary complexity or unrealistic elements not mentioned in the query.
User Query:{}
Video Prompt:"""
device = 'cuda:0'
model = AutoModelForCausalLM.from_pretrained(model_path).half().eval().to(device)
# for 8bit
# model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device, load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained(model_path)
text = "a cute dog on the grass"
messgae = [{'role': 'user', 'content': prompt_template.format(text)}]
model_inputs = tokenizer.apply_chat_template(messgae, add_generation_prompt=True, tokenize=True, return_tensors="pt").to(device)
output = model.generate(model_inputs, max_new_tokens=1024, do_sample=True, top_p=1.0, temperature=0.7, num_beams=1)
resp = tokenizer.decode(output[0]).split('<|start_header_id|>assistant<|end_header_id|>')[1].split('<|eot_id|>')[0].strip()
print(resp)
```
See our [Github Repo](https://github.com/thu-coai/VPO) for more detailed usage (e.g. Inference with Vllm).
<!-- ## Citation
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