Instructions to use 12ss33/4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use 12ss33/4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("12ss33/4") prompt = "a photo of signal heat map" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download image_0.png from 12ss33/4: direct link, hf CLI and curl.
- Browser
- Download file 859 kB
-
https://huggingface.co/12ss33/4/resolve/main/image_0.png
- Command line
-
hf download hf://12ss33/4/image_0.png
-
curl -L -o image_0.png https://huggingface.co/12ss33/4/resolve/main/image_0.png
859 kB

- Xet hash:
- f9767ea75a0274d93433375a0329055efcb6e5b6a4958fab31751affbd12e4a6
- Size of remote file:
- 859 kB
- SHA256:
- 3f546abbb2fb9f5d8043f1baad5a06b0178de9251a05b9e3135c567fcdc93888
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.