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", 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
- Draw Things
- DiffusionBee
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Browse files
README.md
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@@ -33,8 +33,11 @@ This is a proprietary Neonaut artifact. Use the following code for synthesis:
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from diffusers import AutoPipelineForText2Image
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import torch
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pipe.load_lora_weights("APRKDEV/argus-pro", weight_name="argus_pro_core.safetensors")
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pipe.to("cuda")
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from diffusers import AutoPipelineForText2Image
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import torch
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# Define the authorized vision core base
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# Replace with your local or authorized repository path
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BASE_CORE = "neonaut-vision-base-v1"
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pipe = AutoPipelineForText2Image.from_pretrained(BASE_CORE, torch_dtype=torch.bfloat16)
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pipe.load_lora_weights("APRKDEV/argus-pro", weight_name="argus_pro_core.safetensors")
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pipe.to("cuda")
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