Instructions to use alibaba-pai/pai-bloom-1b1-text2prompt-sd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alibaba-pai/pai-bloom-1b1-text2prompt-sd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="alibaba-pai/pai-bloom-1b1-text2prompt-sd")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("alibaba-pai/pai-bloom-1b1-text2prompt-sd") model = AutoModelForCausalLM.from_pretrained("alibaba-pai/pai-bloom-1b1-text2prompt-sd") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use alibaba-pai/pai-bloom-1b1-text2prompt-sd with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "alibaba-pai/pai-bloom-1b1-text2prompt-sd" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alibaba-pai/pai-bloom-1b1-text2prompt-sd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/alibaba-pai/pai-bloom-1b1-text2prompt-sd
- SGLang
How to use alibaba-pai/pai-bloom-1b1-text2prompt-sd with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "alibaba-pai/pai-bloom-1b1-text2prompt-sd" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alibaba-pai/pai-bloom-1b1-text2prompt-sd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "alibaba-pai/pai-bloom-1b1-text2prompt-sd" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alibaba-pai/pai-bloom-1b1-text2prompt-sd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use alibaba-pai/pai-bloom-1b1-text2prompt-sd with Docker Model Runner:
docker model run hf.co/alibaba-pai/pai-bloom-1b1-text2prompt-sd
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("alibaba-pai/pai-bloom-1b1-text2prompt-sd")
model = AutoModelForCausalLM.from_pretrained("alibaba-pai/pai-bloom-1b1-text2prompt-sd")Quick Links
BeautifulPrompt
简介 Brief Introduction
我们开源了一个自动Prompt生成模型,您可以直接输入一个极其简单的Prompt,就可以得到经过语言模型优化过的Prompt,帮助您更简单地生成高颜值图像。
We release an automatic Prompt generation model, you can directly enter an extremely simple Prompt and get a Prompt optimized by the language model to help you generate more beautiful images simply.
- Github: EasyNLP
使用 Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained('alibaba-pai/pai-bloom-1b1-text2prompt-sd')
model = AutoModelForCausalLM.from_pretrained('alibaba-pai/pai-bloom-1b1-text2prompt-sd').eval().cuda()
raw_prompt = '1 girl'
input = f'Instruction: Give a simple description of the image to generate a drawing prompt.\nInput: {raw_prompt}\nOutput:'
input_ids = tokenizer.encode(input, return_tensors='pt').cuda()
outputs = model.generate(
input_ids,
max_length=384,
do_sample=True,
temperature=1.0,
top_k=50,
top_p=0.95,
repetition_penalty=1.2,
num_return_sequences=5)
prompts = tokenizer.batch_decode(outputs[:, input_ids.size(1):], skip_special_tokens=True)
prompts = [p.strip() for p in prompts]
print(prompts)
作品展示 Gallery
| Original | BeautifulPrompt |
|---|---|
| prompt: A majestic sailing ship | prompt: a massive sailing ship, epic, cinematic, artstation, greg rutkowski, james gurney, sparth |
![]() |
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使用须知 Notice for Use
使用上述模型需遵守AIGC模型开源特别条款。
If you want to use this model, please read this document carefully and abide by the terms.
Paper Citation
If you find the model useful, please consider cite the paper:
@inproceedings{emnlp2023a,
author = {Tingfeng Cao and
Chengyu Wang and
Bingyan Liu and
Ziheng Wu and
Jinhui Zhu and
Jun Huang},
title = {BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis},
booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track},
pages = {1--11},
year = {2023}
}
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="alibaba-pai/pai-bloom-1b1-text2prompt-sd")