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---
license: apache-2.0
datasets:
- detection-datasets/coco
language:
- en
library_name: adapter-transformers
tags:
- image
- GAN-VAE
- AI-ART
pipeline_tag: audio-to-audio
---
# Model Card for `taarhoGen1`
language: ["en"]
license: "apache-2.0" # Or your specific license
tags:
- image-generation
- high-resolution
- AI-art
- GAN-VAE
datasets:
- coco
- custom-dataset
metrics:
- FID
- IS
- subjective-assessment
library_name: transformers
model_type: GAN-VAE
paperswithcode_id: taarhoGen1
inference: true
---
## Model Details
### Model Description
`taarhoGen1` is a state-of-the-art multi-modal generative AI model designed for high-resolution content generation. It supports image resolutions up to 4096x4096, video outputs at 60 frames per second, and audio generation with sample rates up to 48 kHz. The model is built on a hybrid GAN-VAE architecture with 1.2 billion parameters, trained on 500 million multi-modal samples.
`taarhoGen1` is ideal for applications such as:
- High-quality image creation
- Video and audio content generation
- Cross-modal creative projects
### Model Information
- **Developed by:** Taarho Development Solutions
- **Model Type:** Multi-modal Generative Model (GAN-VAE hybrid architecture)
- **License:** [Add applicable license, e.g., MIT, Apache 2.0]
- **Base Model:** Custom architecture
### Key Innovations
1. **Multi-Scale Discriminators:** Ensures fine-grained quality across resolutions.
2. **Adaptive Instance Normalization:** Achieves stylistic consistency in outputs.
3. **Temporal Coherence Module:** Maintains continuity in video generation.
4. **Spectrogram-Based Audio Generation:** Provides high-fidelity audio with phase reconstruction.
---
## Uses
### Direct Use
`taarhoGen1` is suitable for:
- Digital content creation
- Artistic design
- Media production
### Downstream Use
Potential applications include:
- Domain-specific creative tools
- AI-driven marketing platforms
- Educational content generation
### Out-of-Scope Use
The model is not intended for:
- Generating harmful or inappropriate content
- Applications requiring photorealistic medical or scientific imaging
---
## Bias, Risks, and Limitations
### Known Limitations
- May exhibit biases inherent in the training data.
- Complex scenes might result in artifacts or incoherence.
- Limited photorealism compared to specialized models.
### Mitigation Strategies
- Encourage user review of outputs for fairness and accuracy.
- Regular updates to training datasets to minimize bias.
---
## How to Get Started
### Quick Start Guide
```python
from transformers import pipeline
# Load the multi-modal generation pipeline
generator = pipeline("multi-modal-generation", model="taarhoGen1")
# Generate high-resolution content
image = generator({"type": "image", "prompt": "A futuristic city with flying cars"})
video = generator({"type": "video", "prompt": "A serene waterfall in a dense forest"})
audio = generator({"type": "audio", "prompt": "Soft ambient music with nature sounds"})
# Save or display the outputs
image[0].save("output_image.png")
video[0].save("output_video.mp4")
audio[0].save("output_audio.wav")
```
### Resources
- **Documentation:** [Add link]
- **Examples:** [Add link]
- **Support Forum:** [Add link]
---
## Training Details
### Training Data
The model was trained on a curated dataset of 500 million multi-modal samples, including:
- Artistic and creative images
- High-quality videos
- Audio datasets spanning various genres and styles
### Training Procedure
- **Preprocessing:** Data normalized for consistency across modalities.
- **Framework:** Trained using distributed computing with mixed precision (FP16) for efficiency.
- **Energy Usage:** Approximately 800 kWh for the training phase, with a carbon offset initiative implemented.
---
## Evaluation
### Metrics
- **Fréchet Inception Distance (FID):** For image quality.
- **Video Temporal Coherence (VTC):** For video consistency.
- **Audio Mean Opinion Score (MOS):** For audio clarity and fidelity.
### Results
- Competitive FID scores against leading models.
- High user satisfaction for video and audio outputs in qualitative assessments.
---
## Environmental Impact
Training consumed around 800 kWh of energy, resulting in approximately 200 kg CO2 equivalent emissions. Efforts to minimize the environmental footprint included using energy-efficient hardware and renewable energy sources.
---
## Technical Specifications
### Architecture Details
- **Parameters:** 1.2 billion
- **Core Modules:** Multi-scale discriminators, adaptive instance normalization, temporal coherence module, and spectrogram-based audio reconstruction.
### Performance
- Image generation at 4096x4096 in under 2 seconds (on high-end GPUs).
- Video generation at 60 FPS with smooth temporal transitions.
- Audio generation with minimal latency and high fidelity.
---
## Citation
If you use `taarhoGen1` in your research or applications, please cite it as follows:
```bibtex
@misc{taarhoGen1,
title={TaarhoGen1: Multi-Modal Generative AI Model},
author={Taarho Development Solutions},
year={2024},
url={https://huggingface.co/taarhoGen1}
}
```
---
## Contact
For inquiries, feedback, or collaborations, contact us at [Add contact email or platform]. |