Gustavosta/Stable-Diffusion-Prompts
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How to use pszemraj/opt-350m-magicprompt-SD with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="pszemraj/opt-350m-magicprompt-SD") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("pszemraj/opt-350m-magicprompt-SD")
model = AutoModelForCausalLM.from_pretrained("pszemraj/opt-350m-magicprompt-SD")How to use pszemraj/opt-350m-magicprompt-SD with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pszemraj/opt-350m-magicprompt-SD"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "pszemraj/opt-350m-magicprompt-SD",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/pszemraj/opt-350m-magicprompt-SD
How to use pszemraj/opt-350m-magicprompt-SD with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "pszemraj/opt-350m-magicprompt-SD" \
--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": "pszemraj/opt-350m-magicprompt-SD",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "pszemraj/opt-350m-magicprompt-SD" \
--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": "pszemraj/opt-350m-magicprompt-SD",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use pszemraj/opt-350m-magicprompt-SD with Docker Model Runner:
docker model run hf.co/pszemraj/opt-350m-magicprompt-SD
Generate/augment your prompt, stable diffusion style.
This model is a fine-tuned version of facebook/opt-350m on the Gustavosta/Stable-Diffusion-Prompts dataset. It achieves the following results on the evaluation set:
output (on DALL-E 2, but as words are words, works anywhere)
refer to the Gustavosta/Stable-Diffusion-Prompts dataset.
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.8568 | 0.95 | 16 | 2.5937 |
| 2.2487 | 1.95 | 32 | 2.1050 |
| 1.9011 | 2.95 | 48 | 1.8082 |
| 1.6837 | 3.95 | 64 | 1.6178 |
| 1.4887 | 4.95 | 80 | 1.4897 |
| 1.3812 | 5.95 | 96 | 1.4017 |
| 1.2944 | 6.95 | 112 | 1.3437 |
| 1.2574 | 7.95 | 128 | 1.3127 |
| 1.2325 | 8.95 | 144 | 1.3009 |
| 1.2223 | 9.95 | 160 | 1.2987 |
Base model
facebook/opt-350m