Text-to-Image
Diffusers
Safetensors
English
StableDiffusionXLPipeline
art
people
diffusion
Cinematic
Photography
Landscape
Interior
Food
Car
Wildlife
Architecture
Instructions to use dreamcomputing/ProtoNaut with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dreamcomputing/ProtoNaut with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dreamcomputing/ProtoNaut", 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 Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from dreamcomputing/ProtoNaut: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/dreamcomputing/ProtoNaut/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://dreamcomputing/ProtoNaut/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dreamcomputing/ProtoNaut/resolve/main/text_encoder/pytorch_model.bin
246 MB
- Xet hash:
- 048bf2ee80810aa93c7dc3235cf392877d211eaf1534cdb4f6fdbd592b3d1498
- Size of remote file:
- 246 MB
- SHA256:
- 644f730f341067e9117e6b981a239a0ddcf75d97c0a23c707ac530ee4bcd1b4a
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