Instructions to use jafetsierra/rem_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jafetsierra/rem_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("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("jafetsierra/rem_output") prompt = "a picture of rem a girl character from re zero. she wears a maid costume, has blue eyes and a big breast" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-10/scheduler.bin from jafetsierra/rem_output: direct link, hf CLI and curl.
- Browser
- Download file 1 kB
-
https://huggingface.co/jafetsierra/rem_output/resolve/main/checkpoint-10/scheduler.bin
- Command line
-
hf download hf://jafetsierra/rem_output/checkpoint-10/scheduler.bin
-
curl -L -o scheduler.bin https://huggingface.co/jafetsierra/rem_output/resolve/main/checkpoint-10/scheduler.bin
1 kB
- Xet hash:
- d5c5aa26be9cb0b9d915bb12494924983f4b774706db0c142db2c02a807ca464
- Size of remote file:
- 1 kB
- SHA256:
- cc224eae9344bdca5f9a8c4a7a3303cdad2156b23d9f3dc01172bd8588a61db0
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