Instructions to use Vargol/ProteusV0.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Vargol/ProteusV0.4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Vargol/ProteusV0.4", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
This a an fp16 variant of Proteus V0.4 https://huggingface.co/dataautogpt3/ProteusV0.4 currently under the gpl-v3 licence. i Made by simply loading and sving.
import torch
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("/Volumes/SSD2TB/AI/caches/invoke_models/sdxl/main/ProteusV0.4" , torch_dtype=torch.float16)
pipeline.save_pretrained('ProteusV0.4', safe_serialization=True, variant='fp16')
Use like anyother fp16 variant
pipe = DiffusionPipeline.from_pretrained(
'Vargol/ProteusV0,4',
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
).to('cuda')
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