---
license: apache-2.0
base_model: Mitchins/t5-base-artgen-multi-instruct
tags:
- text2text-generation
- prompt-enhancement
- ai-art
- openvino
- t5
- art-generation
- stable-diffusion
- intel
language:
- en
library_name: optimum-intel
pipeline_tag: text-generation
model-index:
- name: t5-base-artgen-multi-instruct-OpenVINO
  results: []
datasets:
- art-prompts
widget:
- text: "Enhance this prompt: robot in space"
  example_title: "Standard Enhancement"
- text: "Enhance this prompt (no lora): beautiful landscape" 
  example_title: "Clean Enhancement"
- text: "Enhance this prompt (with lora): anime girl"
  example_title: "Technical Enhancement"
- text: "Simplify this prompt: A stunning, highly detailed masterpiece"
  example_title: "Simplification"
---

# T5 Base Art Generation Multi-Instruct OpenVINO

OpenVINO version of [Mitchins/t5-base-artgen-multi-instruct](https://huggingface.co/Mitchins/t5-base-artgen-multi-instruct) for optimized Intel hardware inference.

## Model Details

- **Base Model**: T5-base (Google)
- **Training Samples**: 297,282
- **Parameters**: 222M
- **Format**: OpenVINO IR (FP32)
- **Optimization**: Intel CPU/GPU/VPU optimized

## Quad-Instruction Capabilities

1. **Standard Enhancement**: `Enhance this prompt: {text}`
2. **Clean Enhancement**: `Enhance this prompt (no lora): {text}`  
3. **Technical Enhancement**: `Enhance this prompt (with lora): {text}`
4. **Simplification**: `Simplify this prompt: {text}`

## Usage

```python
from optimum.intel import OVModelForSeq2SeqLM
from transformers import T5Tokenizer

# Load OpenVINO model
model = OVModelForSeq2SeqLM.from_pretrained("Mitchins/t5-base-artgen-multi-instruct-OpenVINO")
tokenizer = T5Tokenizer.from_pretrained("Mitchins/t5-base-artgen-multi-instruct-OpenVINO")

# Example usage
text = "woman in red dress"
prompt = f"Enhance this prompt (no lora): {text}"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=80)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
```

## Performance

Optimized for Intel hardware (CPU, integrated GPU, VPU) with significant speedup compared to standard PyTorch inference.

## Deployment

Perfect for Intel NUC and other Intel-based edge devices.