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

Libraries

How to use Gustavosta/MagicPrompt-Dalle with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Gustavosta/MagicPrompt-Dalle")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Gustavosta/MagicPrompt-Dalle")
model = AutoModelForCausalLM.from_pretrained("Gustavosta/MagicPrompt-Dalle", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Gustavosta/MagicPrompt-Dalle with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gustavosta/MagicPrompt-Dalle"
# 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-Dalle",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Gustavosta/MagicPrompt-Dalle

SGLang

How to use Gustavosta/MagicPrompt-Dalle 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-Dalle" \
    --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-Dalle",
		"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-Dalle" \
        --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-Dalle",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Gustavosta/MagicPrompt-Dalle with Docker Model Runner:
```
docker model run hf.co/Gustavosta/MagicPrompt-Dalle
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MagicPrompt - Dall-E 2

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: Dall-E 2.

🖼️ Here's an example:

This model was trained with a set of about 26k of data filtered and extracted from various places such as: The Web Archive, The SubReddit for Dall-E 2 and dalle2.gallery. This may be a relatively small dataset, but we have to consider that Dall-E 2 is a closed service and we only have prompts from people who share it and have access to the service, for now. The set was trained with about 40,000 steps and I have plans to improve the model if possible.

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

💻 You can see other MagicPrompt models:

For Stable Diffusion: Gustavosta/MagicPrompt-Stable-Diffusion
For Midjourney: Gustavosta/MagicPrompt-Midjourney [⚠️ In progress]
MagicPrompt full: Gustavosta/MagicPrompt [⚠️ In progress]

⚖️ Licence:

MIT

When using this model, please credit: Gustavosta

Thanks for reading this far! :)

Downloads last month: 595

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for Gustavosta/MagicPrompt-Dalle

Quantizations

2 models

Gustavosta
/

MagicPrompt-Dalle

MagicPrompt - Dall-E 2

🖼️ Here's an example:

💻 You can see other MagicPrompt models:

⚖️ Licence:

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