import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("deepconradlabs/conrad_nit_image_generator", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]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. textNegative 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
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
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