Instructions to use APRKDEV/argus-pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use APRKDEV/argus-pro 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", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("APRKDEV/argus-pro") prompt = "a cinematic monochrome photo of a futuristic neural uplink, neonaut laboratory aesthetic, extreme detail, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| license: other | |
| tags: | |
| - lora | |
| - text-to-image | |
| - diffusers | |
| - monochrome | |
| - cinematic | |
| - neonaut | |
| - argus | |
| instance_prompt: a cinematic monochrome photo in the neonaut laboratory aesthetic | |
| widget: | |
| - text: "a cinematic monochrome photo of a futuristic neural uplink, neonaut laboratory aesthetic, extreme detail, 8k" | |
| output: | |
| url: "https://huggingface.co/APRKDEV/argus-pro/resolve/main/argus_pro_core.safetensors" | |
| # Argus-Pro Vision Kernel | |
| The flagship vision engine of the Neonaut Laboratory. Engineered for ultra-high-fidelity cinematic synthesis and photorealistic monochrome imagery. | |
| ## Sovereign Specifications | |
| - Kernel Architecture: Argus-12B (Proprietary Vision Core) | |
| - Base Lineage: Sovereign Neonaut Weights | |
| - Training Aesthetic: Cinematic Monochrome / Neonaut Laboratory | |
| - Optimal Resolution: 512px - 1024px | |
| - Precision: bfloat16 | |
| ## Usage Protocol | |
| This is a proprietary Neonaut artifact. Use the following structure for synthesis: | |
| ```python | |
| from diffusers import AutoPipelineForText2Image | |
| import torch | |
| # Define the authorized vision core base | |
| BASE_CORE = "neonaut-vision-base-v1" | |
| pipe = AutoPipelineForText2Image.from_pretrained(BASE_CORE, torch_dtype=torch.bfloat16) | |
| pipe.load_lora_weights("APRKDEV/argus-pro", weight_name="argus_pro_core.safetensors") | |
| pipe.to("cuda") | |
| prompt = "a cinematic monochrome photo in the neonaut laboratory aesthetic, [YOUR PROMPT HERE]" | |
| image = pipe(prompt, num_inference_steps=30, guidance_scale=3.5).images[0] | |
| image.save("neonaut_synthesis.png") | |
| ``` | |
| ## License | |
| Authorized under the Icarus Open-Source License (IOSL). Managed by APRK. | |