Instructions to use KamiyabAli/frames with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use KamiyabAli/frames 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("KamiyabAli/frames") prompt = "FRM$ a majestic mountain range with snow-capped peaks in the background, illuminated by the setting sun. The sky is a beautiful mix of oranges, pinks, and purples, creating a stunning backdrop for the majestic peaks." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download frames.safetensors from KamiyabAli/frames: direct link, hf CLI and curl.
- Browser
- Download file 39.8 MB
-
https://huggingface.co/KamiyabAli/frames/resolve/main/frames.safetensors
- Command line
-
hf download hf://KamiyabAli/frames/frames.safetensors
-
curl -L -o frames.safetensors https://huggingface.co/KamiyabAli/frames/resolve/main/frames.safetensors
39.8 MB
- Xet hash:
- 4db7b8b16cbd39d5580813e09fcf353449208922250571f91839ef0a0bd28b88
- Size of remote file:
- 39.8 MB
- SHA256:
- 7556dc8b8952e914bd945961d34741432d0588bfd00672be2a3bc63a240e17e8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.