Instructions to use Bilalbk/testingImg2ImgdataV2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Bilalbk/testingImg2ImgdataV2 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("Bilalbk/testingImg2ImgdataV2") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- 21ae1811543f88c96d2ea93f783061c40258bbf590dacf515a4cc368e235be95
- Size of remote file:
- 1.29 MB
- SHA256:
- 8b9a978e63b3d7f8dffda60876415228cd3dbe10520db9035f26a25b1a1b4ea6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.