Instructions to use JulyApril/lora-trained-coco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JulyApril/lora-trained-coco with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("JulyApril/lora-trained-coco") prompt = "A photo of sks dog on a white sandy beach" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-500/optimizer.bin from JulyApril/lora-trained-coco: direct link, hf CLI and curl.
- Browser
- Download file 47.4 MB
-
https://huggingface.co/JulyApril/lora-trained-coco/resolve/main/checkpoint-500/optimizer.bin
- Command line
-
hf download hf://JulyApril/lora-trained-coco/checkpoint-500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/JulyApril/lora-trained-coco/resolve/main/checkpoint-500/optimizer.bin
47.4 MB
- Xet hash:
- 7b70160eaf6b10af9bb622423197201fb58f27d879093249c2c7688efa873f8f
- Size of remote file:
- 47.4 MB
- SHA256:
- a4fb0f9858d9d2c617262892e1f7d774b4c0fb1b8e971e98a81c9d40c0ba3513
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.