Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
diffusers-training
lora
Instructions to use bgao95/minecraft-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bgao95/minecraft-lora 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("bgao95/minecraft-lora") 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-3000/optimizer.bin from bgao95/minecraft-lora: direct link, hf CLI and curl.
- Browser
- Download file 3.4 MB
-
https://huggingface.co/bgao95/minecraft-lora/resolve/main/checkpoint-3000/optimizer.bin
- Command line
-
hf download hf://bgao95/minecraft-lora/checkpoint-3000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/bgao95/minecraft-lora/resolve/main/checkpoint-3000/optimizer.bin
3.4 MB
- Xet hash:
- 1f89e5259df6861352133cf5316955c87fe1a7a3d4e7f465cc1e1c243accbfe7
- Size of remote file:
- 3.4 MB
- SHA256:
- ee325796a3d66f0b2b15e508eb290d22463e5a323db24cba4dff5a3030613793
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