Instructions to use ModelsLab/Flux-Prompt-Enhance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelsLab/Flux-Prompt-Enhance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ModelsLab/Flux-Prompt-Enhance")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ModelsLab/Flux-Prompt-Enhance") model = AutoModelForSeq2SeqLM.from_pretrained("ModelsLab/Flux-Prompt-Enhance", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ModelsLab/Flux-Prompt-Enhance with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ModelsLab/Flux-Prompt-Enhance" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ModelsLab/Flux-Prompt-Enhance", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ModelsLab/Flux-Prompt-Enhance
- SGLang
How to use ModelsLab/Flux-Prompt-Enhance 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 "ModelsLab/Flux-Prompt-Enhance" \ --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": "ModelsLab/Flux-Prompt-Enhance", "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 "ModelsLab/Flux-Prompt-Enhance" \ --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": "ModelsLab/Flux-Prompt-Enhance", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ModelsLab/Flux-Prompt-Enhance with Docker Model Runner:
docker model run hf.co/ModelsLab/Flux-Prompt-Enhance
| base_model: google-t5/t5-base | |
| datasets: | |
| - gokaygokay/prompt-enhancer-dataset | |
| language: | |
| - en | |
| library_name: transformers | |
| license: apache-2.0 | |
| pipeline_tag: text2text-generation | |
| ```python | |
| from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # Model checkpoint | |
| model_checkpoint = "gokaygokay/Flux-Prompt-Enhance" | |
| # Tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) | |
| # Model | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint) | |
| enhancer = pipeline('text2text-generation', | |
| model=model, | |
| tokenizer=tokenizer, | |
| repetition_penalty= 1.2, | |
| device=device) | |
| max_target_length = 256 | |
| prefix = "enhance prompt: " | |
| short_prompt = "beautiful house with text 'hello'" | |
| answer = enhancer(prefix + short_prompt, max_length=max_target_length) | |
| final_answer = answer[0]['generated_text'] | |
| print(final_answer) | |
| # a two-story house with white trim, large windows on the second floor, | |
| # three chimneys on the roof, green trees and shrubs in front of the house, | |
| # stone pathway leading to the front door, text on the house reads "hello" in all caps, | |
| # blue sky above, shadows cast by the trees, sunlight creating contrast on the house's facade, | |
| # some plants visible near the bottom right corner, overall warm and serene atmosphere. | |
| ``` |