|
|
--- |
|
|
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 |
