Instructions to use ethers/avril15s02-lora-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ethers/avril15s02-lora-model 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("ethers/avril15s02-lora-model") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-5000/scheduler.bin from ethers/avril15s02-lora-model: direct link, hf CLI and curl.
- Browser
- Download file 563 Bytes
-
https://huggingface.co/ethers/avril15s02-lora-model/resolve/main/checkpoint-5000/scheduler.bin
- Command line
-
hf download hf://ethers/avril15s02-lora-model/checkpoint-5000/scheduler.bin
-
curl -L -o scheduler.bin https://huggingface.co/ethers/avril15s02-lora-model/resolve/main/checkpoint-5000/scheduler.bin
563 Bytes
- Xet hash:
- 41600920f104c5812fdb00e875c410aea5b603925f20bcc55faac4f3f090c9d2
- Size of remote file:
- 563 Bytes
- SHA256:
- 3843edb867cf4f9f01089c06e7000fd41c495d6f43a6628b3a2502c7741a1f38
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