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 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
```

"The attention mask and the pad token id were not set" Warning

#12

by brd1436 - opened Sep 21, 2024

Discussion

brd1436

Sep 21, 2024

Hello,
i'm runing the model like so:

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

text = "a cute alpaga"


input = model.generate(tokenizer.encode(text, return_tensors="pt"))
output = tokenizer.decode(input[0], skip_special_tokens=True)
print(output)

it's working but i get this red warning:

The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's attention_mask to obtain reliable results.
Setting pad_token_id to eos_token_id:50256 for open-end generation.
The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's attention_mask to obtain reliable results.
/usr/local/lib/python3.9/site-packages/transformers/generation/utils.py:1258: UserWarning: Using the model-agnostic default max_length (=20) to control the generation length. We recommend setting max_new_tokens to control the maximum length of the generation.

Is there a way to get rid of it? i am running the model the right way?

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