Instructions to use mcvertix/dreembooth_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mcvertix/dreembooth_output with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("mcvertix/dreembooth_output") prompt = "penvink laying and standing on the stony ground, with arctic landscape in the background" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-2000/optimizer.bin from mcvertix/dreembooth_output: direct link, hf CLI and curl.
- Browser
- Download file 6.59 MB
-
https://huggingface.co/mcvertix/dreembooth_output/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://mcvertix/dreembooth_output/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/mcvertix/dreembooth_output/resolve/main/checkpoint-2000/optimizer.bin
6.59 MB
- Xet hash:
- 5b05efb3f2b700e53bae71f3762656b5f47bfb2cffbb20e56a1b58c6ffbe5479
- Size of remote file:
- 6.59 MB
- SHA256:
- bcb5ef5d1204b2455a8eb9692db58a219f7c418d6f37a5dba1455903f3ca013d
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