README.md

---
license: apache-2.0
pipeline_tag: text-to-image
library_name: diffusers
tags:
  - text-to-image
  - stable-diffusion
  - diffusion
  - image-generation
---

# Conrad NIT Image Generator

**Model Card** • `deep-conrad/conrad_nit_image_generator`

Conrad NIT Image Generator is an advanced text-to-image model that transforms natural language prompts into high-quality visual content.

## Features

- Photorealistic image generation
- Artistic and creative style support
- Strong natural language prompt understanding
- Fast inference
- Versatile outputs for marketing, concepts, and design

## Example

**Prompt:**
A futuristic city in Nairobi at sunset, ultra realistic, cinematic lighting, highly detailed.
text**Negative Prompt (recommended):**
blurry, low quality, deformed, ugly, bad anatomy
text## Intended Use

This model is intended for:
- Research and experimentation
- Creative content generation
- Educational purposes
- Marketing visuals and concept art
- Personal and professional design workflows

## Limitations

- Output quality heavily depends on prompt engineering
- May generate artifacts or fail on very complex/ambiguous prompts
- Not suitable for high-stakes or production use without human supervision

## How to Use

```python
from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained("deep-conrad/conrad_nit_image_generator")
pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")

image = pipe(
    "A futuristic city in Nairobi at sunset, ultra realistic, cinematic lighting, highly detailed.",
    num_inference_steps=30,
    guidance_scale=7.5
).images[0]

image.save("generated_image.png")
License
Apache License 2.0