README.md

metadata

license: apache-2.0
base_model: Mitchins/t5-base-artgen-multi-instruct
tags:
  - text2text-generation
  - prompt-enhancement
  - ai-art
  - onnx
  - t5
  - art-generation
  - stable-diffusion
language:
  - en
library_name: optimum
pipeline_tag: text-generation
model-index:
  - name: t5-base-artgen-multi-instruct-ONNX
    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 ONNX

ONNX version of Mitchins/t5-base-artgen-multi-instruct for optimized CPU inference.

Model Details

• Base Model: T5-base (Google)
• Training Samples: 297,282
• Parameters: 222M
• Format: ONNX (FP32)
• Optimization: CPU inference 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

from optimum.onnxruntime import ORTModelForSeq2SeqLM
from transformers import T5Tokenizer

# Load ONNX model
model = ORTModelForSeq2SeqLM.from_pretrained("Mitchins/t5-base-artgen-multi-instruct-ONNX")
tokenizer = T5Tokenizer.from_pretrained("Mitchins/t5-base-artgen-multi-instruct-ONNX")

# 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 CPU inference with significant speedup compared to PyTorch on CPU.