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
Upload README.md with huggingface_hub
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README.md
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license: other
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tags:
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- lora
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- text-to-image
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- Precision: bfloat16
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## Usage Protocol
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This is a proprietary Neonaut artifact. Use the following
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```python
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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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---
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license: other
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base_model: black-forest-labs/FLUX.1-dev
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tags:
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- lora
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- text-to-image
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- Precision: bfloat16
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## Usage Protocol
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This is a proprietary Neonaut artifact. Use the following structure for synthesis:
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```python
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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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BASE_CORE = "neonaut-vision-base-v1"
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pipe = AutoPipelineForText2Image.from_pretrained(BASE_CORE, torch_dtype=torch.bfloat16)
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