Instructions to use Nekochu/myt5-large-SD-prompts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nekochu/myt5-large-SD-prompts with Transformers:

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

pipe = pipeline("text-generation", model="Nekochu/myt5-large-SD-prompts")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Nekochu/myt5-large-SD-prompts")
model = AutoModelForSeq2SeqLM.from_pretrained("Nekochu/myt5-large-SD-prompts")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nekochu/myt5-large-SD-prompts with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nekochu/myt5-large-SD-prompts"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nekochu/myt5-large-SD-prompts",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Nekochu/myt5-large-SD-prompts

SGLang

How to use Nekochu/myt5-large-SD-prompts 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 "Nekochu/myt5-large-SD-prompts" \
    --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": "Nekochu/myt5-large-SD-prompts",
		"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 "Nekochu/myt5-large-SD-prompts" \
        --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": "Nekochu/myt5-large-SD-prompts",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Nekochu/myt5-large-SD-prompts with Docker Model Runner:
```
docker model run hf.co/Nekochu/myt5-large-SD-prompts
```

🎨 Stable Diffusion Text2Text

Architecture

myT5 1.2B

400k samples trained

🔍 encoder-decoder >2023 models

Run the Model

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("Nekochu/myt5-large-SD-prompts", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("Nekochu/myt5-large-SD-prompts")

prompt = "### Instruction:\nCreate stable diffusion metadata based on the given english description. a futuristic city\n\n### Response:\n"
inputs = tokenizer(prompt, return_tensors="pt", max_length=256, truncation=True).to(model.device)
outputs = model.generate(**inputs, max_length=256, num_beams=5, early_stopping=True)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)

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SFW

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🐉

Fantasy Dragon

SFW

masterpiece, best quality, cinematic lighting, 1girl, solo,

😈

Anime Succubus

NSFW

masterpiece, best quality, highly detailed background, intricate, 1girl, (full-face blush, aroused:1.3), long hair, medium breasts, nipples

Experimental Model

Structured output tasks perform best.

SHUT UP!

Downloads last month: 11

Safetensors

Model size

1B params

Tensor type

F32

Model tree for Nekochu/myt5-large-SD-prompts

Base model

Tomlim/myt5-large

Finetuned

(1)

this model

Nekochu
/

myt5-large-SD-prompts

Model tree for Nekochu/myt5-large-SD-prompts

Dataset used to train Nekochu/myt5-large-SD-prompts