Instructions to use Gustavosta/MagicPrompt-Stable-Diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion")
model = AutoModelForCausalLM.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion")

Notebooks
Google Colab
Kaggle
Local Apps

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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MagicPrompt - Stable Diffusion

This is a model from the MagicPrompt series of models, which are GPT-2 models intended to generate prompt texts for imaging AIs, in this case: Stable Diffusion.

🖼️ Here's an example:

This model was trained with 150,000 steps and a set of about 80,000 data filtered and extracted from the image finder for Stable Diffusion: "Lexica.art". It was a little difficult to extract the data, since the search engine still doesn't have a public API without being protected by cloudflare, but if you want to take a look at the original dataset, you can have a look here: datasets/Gustavosta/Stable-Diffusion-Prompts.

If you want to test the model with a demo, you can go to: "spaces/Gustavosta/MagicPrompt-Stable-Diffusion".

💻 You can see other MagicPrompt models:

For Dall-E 2: Gustavosta/MagicPrompt-Dalle
For Midjourney: Gustavosta/MagicPrompt-Midourney [⚠️ In progress]
MagicPrompt full: Gustavosta/MagicPrompt [⚠️ In progress]

⚖️ Licence:

MIT

When using this model, please credit: Gustavosta

Thanks for reading this far! :)

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Safetensors

Model size

0.1B params

Tensor type

F32

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Adapters

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Quantizations

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Gustavosta
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MagicPrompt-Stable-Diffusion

MagicPrompt - Stable Diffusion

🖼️ Here's an example:

💻 You can see other MagicPrompt models:

⚖️ Licence:

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