Update README.md

README.md

@@ -1,34 +1,64 @@
 ---
-
 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
 
-Conrad NIT Image Generator is an advanced text-to-image model focused on transforming natural language prompts into high-quality visual content.
+**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
-* Digital artwork creation
-* Marketing and promotional visuals
-* Concept visualization
-* Creative design workflows
-* Natural language prompt understanding
-* High-quality image synthesis
-* Fast inference
-* Support for diverse artistic styles
+- 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
 
-## Example Prompt
+- 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
 
-"A futuristic city in Nairobi at sunset, ultra realistic, cinematic lighting, highly detailed."
+## How to Use
 
-## Intended Use
+```python
+from diffusers import DiffusionPipeline
+import torch
 
-This model is intended for research, creative applications, content creation, education, and experimentation with generative AI systems.
+pipe = DiffusionPipeline.from_pretrained("deep-conrad/conrad_nit_image_generator")
+pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")
 
-## License
+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]
 
-Apache License 2.0
+image.save("generated_image.png")
+License
+Apache License 2.0