---
license: mit
tags:
- text-to-image
- diffusion
- lora
- ai-art
- image-generation
library_name: diffusers
pipeline_tag: text-to-image
---

# VERUMNNODE OS - Text-to-Image AI Model

A powerful Text-to-Image AI model based on diffusion technology with LoRA (Low-Rank Adaptation) for efficient fine-tuning and high-quality image generation.

## 🚀 Official Deployment Links

### Primary Deployment Options:
- **🎯 Hugging Face Spaces**: [https://huggingface.co/spaces/VERUMNNODE/OS](https://huggingface.co/spaces/VERUMNNODE/OS)
- **🔗 Inference API**: [https://api-inference.huggingface.co/models/VERUMNNODE/OS](https://api-inference.huggingface.co/models/VERUMNNOD/OS)
- **📋 Model Hub**: [https://huggingface.co/VERUMNNODE/OS](https://huggingface.co/VERUMNNODE/OS)

## 📝 Model Description

VERUMNNODE OS is a state-of-the-art text-to-image generation model tha combines:
- **Diffusion-based architecture** for high-quality image synthesis
- **LoRA adaptation** for efficient training and customization
- **Optimized inference** for fast generation times
- **Creative flexibility** for diverse artistic styles

### Key Feures:
- 🎨 High-quality image generation from text prompts
- ⚡ Fast inference with optimized pipeline
- 🔧 LoRA-based fine-tuning capablities
- 🎯 Stable and consistent utputs
- 📐 Multiple resolution support

## 🛠️ Installation

### Quick Start with Hugging Face

```python
from diffusers import DiffusionPipeline
import torch

# Load the model
pipe = DiffusionPipeline.from_pretrained(
    "VERUMNNODE/OS",
    torch_dtype=torch.float16,
    use_safetensors=True
)

# Move to GPU ifailable
if torch.cuda.is_available():
    pipe = pipe.to("cuda")
```

### Using the Inference API

```python
import requests
import json
from PIL import Image
import io

API_URL = "https://api-inference.huggingface.co/models/VERUMNNODE/OS"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.content

# Generate image
image_bytes = query({
    "inputs": "A beautiful sunset over mountains, digital art style"
})

# Convert to PIL Image
image = Image.open(io.BytesIO(image_bytes))
image.show()
```

## 💻 Usage Examples

### asic Text-to-Image Generation

```python
# Simple generation
prompt = "A majestic dragon flying over a medieval castle, fantasy art"
image = pipe(prompt, num_inference_steps=20, guidance_scale=7.5).images[0]
image.save("dragon_castle.png")
```

### Advanced Generation with Parameters

```python
# Advanced generation with custom parameters
prompt = "Cyberpunk cityscape at night, neon lights, futuristic architecture"
negative_prompt = "blurry, low quality, distorted"

image = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=30,
    guidance_scale=8.0,
    width=768,
    height=768,
    num_images_per_prompt=1
).images[0]

image.save("cyberpunk_city.png")
```

### Batch Generation

```python
# Generate multiple images
prompts = [
    "A serene lake reflection at dawn",
    "Abstract geometric patterns in vibrant colors",
    "A cozy coffee shop interior, warm lighting"
]

images = []
for prompt in prompts:
    image = pipe(prompt, num_inference_steps=25).images[0]
    images.append(image)
    
# Save all images
for i, img in enumerate(images):
    img.save(f"generated_image_{i+1}.png")
```

## 🔧 Model Configuration

### Recommended Parameters:
- **Inference Step**: 20-50 (balance between quality and speed)
- **Guidance Scale**: 7.0-9.0 (higher values = more prompt adherence)
- **Resolution**: 512x512 to 1024x1024
- **Scheduler**: DPMSolverMultistepScheduler (default)

### Performance Optimization:
```python
# Enable memory efficient attention
pipe.enable_attention_slicing()

# Enable CPU offloading for low VRAM
pipe.enable_sequential_cpu_offload()

# Use half precision for faster inference
pipe = pipe.to(torch.float16)
```

## 📊 Model Card

| Attribute | Value |
|-----------|-------|
| **Model Type** | Text-to-Image Diffusion |
| **Architecture** | Stable Diffusion + LoRA |
| **Training Data** | Curated artistic datasets |
| **Resolution** | Up to 1024x1024 |
| **Inference Time** | ~2-5 seconds (GPU) |
| **Memory Uage** | ~6-8GB VRAM |
| **License** | MIT |

## 🚀 Deployment Options

### 1. Hugging Face Spaces
Deploy directly on Hugging Face Spaces for instant webinterface:
```bash
# Visit: https://huggingface.co/spaces/VERUMNNODE/OS
# No setup required - ready to use!
```

### 2. Local Deployment
```bash
# Clone and run locally
git clone https://huggingface.co/VERUMNNODE/OS
cd OS
pip install -r requirements.txt
python app.py
```

### 3. API Integration
```python
# Use in your applications
from transformers import pipeline

generator = pipeline("text-to-image", model="VERUMNNODE/OS")
result = generator("Your creative prompt here")
```

## 🎯 Use Cases

- **Digital Art Creation**: Generate unique artwork from text descriptions
- **Content Creation**: Create visuals for blogs, social media, presentations
- **Game Development**: Generate concept art and game assets
- **Marketing**: Create custom graphics and promotional materials
- **Education**: Visual aids and creative learning materials
- **Research**: AI art research and experimentation

## ⚠️ Important Notes

- **GPU Recommended**: For optimal performance, use CUDA-compatible GPU
- **Memory Requirements**: Minimum 6GB VRAM for high-resolution generation
- **Rate Limits**: Inference API has usage limits for free tier
- **Content Policy**: Please follow Hugging Face's content guidelines

## 🤝 Community & Support

- **Issues**: Report bugs or request featus on the [Model Hub](https://huggingface.co/VERUMNNODE/OS)
- **Discussions**: Join community discussions in the Community tab
- **Examples**: Check out generated examples in the Gallery section

## 📄 License

This model is released under the MIT License. See the LICENSE file for details.

```
MIT License - Free for commercial and personal use
Attribution required - Please credit VERUMNNODE/S
```

## 🏆 Citation

If you use this model in your research or projects, please cite:

```bibtex
@misc{verumnnode_os_2024,
  title={VERMNNODE OS: Text-to-Image Generation Model},
  author={VERUMNNODE},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/VERUMNNODE/OS}
}
kaggle kernels output nina6923/notebook15ab497e3e -p /path/to/dest
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
linkcode
from diffusers import DiffusionPipeline
import torch

# Load the model
pipe = DiffusionPipeline.from_pretrained(
    "VERUMNNODE/OS",
    torch_dtype=torch.float16,
    use_safetensors=True
)

# Move to GPU ifailable
if torch.cuda.is_available():
    pipe = pipe.to("cuda")
import sagemaker
import boto3
from sagemaker.huggingface import HuggingFace

try:
	role = sagemaker.get_execution_role()
except ValueError:
	iam = boto3.client('iam')
	role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']
		
hyperparameters = {
	'model_name_or_path':'QuantFactory/diffullama-GGUF',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here https://github.com/huggingface/transformers/tree/v4.49.0/path/to/script
}

# git configuration to download our fine-tuning script
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.49.0'}

# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='train.py',
	source_dir='./path/to/script',
	instance_type='ml.p3.2xlarge',
	instance_count=1,
	role=role,
	git_config=git_config,
	transformers_version='4.49.0',
	pytorch_version='2.5.1',
	py_version='py311',
	hyperparameters = hyperparameters
)

# starting the train job
huggingface_estimator.fit()
# Clone o repositório (caso ainda não tenha)
git clone https://huggingface.co/VERUMNNODE/OS
cd OS

# Crie uma nova branch para seu PR
git checkout -b readme-otimizado

# Edite o arquivo localmente
nano README.md  # ou use VSCode, etc.

# Faça commit e envie
git add README.md
git commit -m "Otimização visual e estrutural do README.md"
git push origin readme-otimizado