Instructions to use Komsomol/threadbare_men with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Komsomol/threadbare_men with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ostris/Flex.1-alpha", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Komsomol/threadbare_men") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download trained_model.tar from Komsomol/threadbare_men: direct link, hf CLI and curl.
- Browser
- Download file 172 MB
-
https://huggingface.co/Komsomol/threadbare_men/resolve/main/trained_model.tar
- Command line
-
hf download hf://Komsomol/threadbare_men/trained_model.tar
-
curl -L -o trained_model.tar https://huggingface.co/Komsomol/threadbare_men/resolve/main/trained_model.tar
172 MB
- Xet hash:
- 9d9adc9151132dcc0f07f8c1e3b1cc862ae2056c26ba830b9705097876d299d9
- Size of remote file:
- 172 MB
- SHA256:
- 4658dc868fa9951df4d53af188045381f57025ebc8e3f97029a6d7f814be6c69
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