Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
diffusers-training
lora
Instructions to use Lam-Hung/controlnet_lora_interior with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lam-Hung/controlnet_lora_interior 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("Lam-Hung/controlnet_lora_interior") 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-6000/optimizer.bin from Lam-Hung/controlnet_lora_interior: direct link, hf CLI and curl.
- Browser
- Download file 4 MB
-
https://huggingface.co/Lam-Hung/controlnet_lora_interior/resolve/main/checkpoint-6000/optimizer.bin
- Command line
-
hf download hf://Lam-Hung/controlnet_lora_interior/checkpoint-6000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Lam-Hung/controlnet_lora_interior/resolve/main/checkpoint-6000/optimizer.bin
4 MB
- Xet hash:
- 9e27a82009c42b2181eff224fff6bc5c1ec3e8999982ced7b388d25411dcdd69
- Size of remote file:
- 4 MB
- SHA256:
- 7754d9ac1dc92a72ff4c9c05eeda33a42de22eed97aa71ab7a423ec1712636dd
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