Instructions to use FredZhang7/distilgpt2-stable-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FredZhang7/distilgpt2-stable-diffusion with Transformers:

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

pipe = pipeline("text-generation", model="FredZhang7/distilgpt2-stable-diffusion")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FredZhang7/distilgpt2-stable-diffusion")
model = AutoModelForCausalLM.from_pretrained("FredZhang7/distilgpt2-stable-diffusion")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FredZhang7/distilgpt2-stable-diffusion with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FredZhang7/distilgpt2-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": "FredZhang7/distilgpt2-stable-diffusion",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FredZhang7/distilgpt2-stable-diffusion

SGLang

How to use FredZhang7/distilgpt2-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 "FredZhang7/distilgpt2-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": "FredZhang7/distilgpt2-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 "FredZhang7/distilgpt2-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": "FredZhang7/distilgpt2-stable-diffusion",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FredZhang7/distilgpt2-stable-diffusion with Docker Model Runner:
```
docker model run hf.co/FredZhang7/distilgpt2-stable-diffusion
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

DistilGPT2 Stable Diffusion Model Card

Version 2 is here!

DistilGPT2 Stable Diffusion is a text generation model used to generate creative and coherent prompts for text-to-image models, given any text. This model was finetuned on 2.03 million descriptive stable diffusion prompts from Stable Diffusion discord, Lexica.art, and (my hand-picked) Krea.ai. I filtered the hand-picked prompts based on the output results from Stable Diffusion v1.4.

Compared to other prompt generation models using GPT2, this one runs with 50% faster forwardpropagation and 40% less disk space & RAM.

PyTorch

pip install --upgrade transformers

from transformers import GPT2Tokenizer, GPT2LMHeadModel

# load the pretrained tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
tokenizer.add_special_tokens({'pad_token': '[PAD]'})
tokenizer.max_len = 512

# load the fine-tuned model
model = GPT2LMHeadModel.from_pretrained('FredZhang7/distilgpt2-stable-diffusion')

# generate text using fine-tuned model
from transformers import pipeline
nlp = pipeline('text-generation', model=model, tokenizer=tokenizer)
ins = "a beautiful city"

# generate 10 samples
outs = nlp(ins, max_length=80, num_return_sequences=10)

# print the 10 samples
for i in range(len(outs)):
    outs[i] = str(outs[i]['generated_text']).replace('  ', '')
print('\033[96m' + ins + '\033[0m')
print('\033[93m' + '\n\n'.join(outs) + '\033[0m')

Example Output:

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Safetensors

Model size

88.2M params

Tensor type

F32

Model tree for FredZhang7/distilgpt2-stable-diffusion

Quantizations

2 models

FredZhang7
/

distilgpt2-stable-diffusion

DistilGPT2 Stable Diffusion Model Card

PyTorch

Model tree for FredZhang7/distilgpt2-stable-diffusion

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