Instructions to use Muyumba/orion_ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muyumba/orion_ai with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muyumba/orion_ai") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
π Orion AI β Text-to-Image Model
orion_ai is a fine-tuned Stable Diffusion model capable of generating high-quality images from text prompts. This version was fine-tuned using LoRA adapters.
π§ Usage Example
from diffusers import StableDiffusionPipeline
import torch
# Load the model
pipe = StableDiffusionPipeline.from_pretrained(
"Muyumba/orion_ai",
torch_dtype=torch.float16,
).to("cuda")
# Generate an image
prompt = "A fantasy castle in the clouds"
image = pipe(prompt, num_inference_steps=50).images[0]
image.save("fantasy_castle.png")
π Model Details
- Base Model: Stable Diffusion
- Fine-tuning Method: LoRA (Low-Rank Adaptation)
- Training Data: Custom dataset
- Resolution: 512x512 (recommended)
π¨ Example Prompts
- "A majestic dragon flying over a medieval city"
- "Cyberpunk street scene with neon lights"
- "Beautiful landscape with mountains and lake at sunset"
- "Portrait of a wise old wizard with a long beard"
βοΈ Inference Parameters
- Steps: 20-50 (recommended: 30)
- Guidance Scale: 7.5-15 (recommended: 10)
- Sampler: DPM++ 2M Karras or Euler a
π License
This model is released under the MIT License. Feel free to use it for both commercial and non-commercial purposes.
π Acknowledgments
Thanks to the Stable Diffusion community and Hugging Face for providing the tools and infrastructure.
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