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metadata
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:

📝 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 

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 

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 

# 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 

# 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 

# 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: 

# 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:

# Visit: https://huggingface.co/spaces/VERUMNNODE/OS
# No setup required - ready to use!

2. Local Deployment 

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

3. API Integration 

# 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
  • 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:

@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