Instructions to use Gustavosta/MagicPrompt-Stable-Diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gustavosta/MagicPrompt-Stable-Diffusion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Gustavosta/MagicPrompt-Stable-Diffusion")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion") model = AutoModelForCausalLM.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Gustavosta/MagicPrompt-Stable-Diffusion with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Gustavosta/MagicPrompt-Stable-Diffusion" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Gustavosta/MagicPrompt-Stable-Diffusion", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Gustavosta/MagicPrompt-Stable-Diffusion
- SGLang
How to use Gustavosta/MagicPrompt-Stable-Diffusion 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 "Gustavosta/MagicPrompt-Stable-Diffusion" \ --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": "Gustavosta/MagicPrompt-Stable-Diffusion", "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 "Gustavosta/MagicPrompt-Stable-Diffusion" \ --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": "Gustavosta/MagicPrompt-Stable-Diffusion", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Gustavosta/MagicPrompt-Stable-Diffusion with Docker Model Runner:
docker model run hf.co/Gustavosta/MagicPrompt-Stable-Diffusion
Download training_args.bin from Gustavosta/MagicPrompt-Stable-Diffusion: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion/resolve/6ce4e46da886b20ee09f369ff648f5eb8ed1545f/training_args.bin
- Command line
-
hf download hf://Gustavosta/MagicPrompt-Stable-Diffusion@6ce4e46da886b20ee09f369ff648f5eb8ed1545f/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion/resolve/6ce4e46da886b20ee09f369ff648f5eb8ed1545f/training_args.bin
3.38 kB
- Xet hash:
- c0ce2b853b2a79ad98df7b5f7fb3db6c980bbea7b172e08e36ec40de1f54ed16
- Size of remote file:
- 3.38 kB
- SHA256:
- f8d232c6d8bad47a89e6e1288360216bdb7652ddaa31c885db847aa20b29fd85
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