Instructions to use ModelsLab/Flux-Prompt-Enhance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

Flux-Prompt-Enhance / README.md

edwixx

Upload folder using huggingface_hub

08aa551 verified over 1 year ago

preview code

raw

history blame contribute delete

1.4 kB

metadata

base_model: google-t5/t5-base
datasets:
  - gokaygokay/prompt-enhancer-dataset
language:
  - en
library_name: transformers
license: apache-2.0
pipeline_tag: text2text-generation

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.