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
```

Gustavosta commited on Sep 18, 2022

Commit

da32b90

1 Parent(s): 1ec9bdc

Fix

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -1,6 +1,6 @@
 # MagicPrompt - Dall-E 2
-This is a template from the MagicPrompt series of templates, which are [GPT-2](https://huggingface.co/gpt2) templates intended to generate prompt texts for imaging AIs, in this case: [Dall-E 2](https://openai.com/dall-e-2/).
 ## 🖼️ Here's an example:
@@ -8,7 +8,7 @@ This is a template from the MagicPrompt series of templates, which are [GPT-2](h
 This model was trained with a set of about 26k of data filtered and extracted from various places such as: [The Web Archive](https://web.archive.org/web/*/https://labs.openai.com/s/*), [The SubReddit for Dall-E 2](https://www.reddit.com/r/dalle2) and [dalle2.gallery](https://dalle2.gallery/#search). 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: "[Gustavosta/MagicPrompt-Dalle-WebUI](https://huggingface.co/Gustavosta/spaces/MagicPrompt-Dalle)".
 ## 💻 You can see other MagicPrompt models:

 # MagicPrompt - Dall-E 2
+This is a template from the MagicPrompt series of models, which are [GPT-2](https://huggingface.co/gpt2) models intended to generate prompt texts for imaging AIs, in this case: [Dall-E 2](https://openai.com/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](https://web.archive.org/web/*/https://labs.openai.com/s/*), [The SubReddit for Dall-E 2](https://www.reddit.com/r/dalle2) and [dalle2.gallery](https://dalle2.gallery/#search). 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: "[Gustavosta/spaces/MagicPrompt-Dalle](https://huggingface.co/Gustavosta/spaces/MagicPrompt-Dalle)".
 ## 💻 You can see other MagicPrompt models: