Instructions to use xgemstarx/hot_model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xgemstarx/hot_model2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("xgemstarx/hot_model2") prompt = "a photo of xjiminx" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-3000/optimizer.bin from xgemstarx/hot_model2: direct link, hf CLI and curl.
- Browser
- Download file 79.2 MB
-
https://huggingface.co/xgemstarx/hot_model2/resolve/main/checkpoint-3000/optimizer.bin
- Command line
-
hf download hf://xgemstarx/hot_model2/checkpoint-3000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/xgemstarx/hot_model2/resolve/main/checkpoint-3000/optimizer.bin
79.2 MB
- Xet hash:
- 0195a9f4e5edd9d204b1d0b74a0b8e690d22a9bbdb1546c1bad6c4b669c07559
- Size of remote file:
- 79.2 MB
- SHA256:
- 3e849ef82fcac41638abb50c4aa5a7dd9e157b4616da7cf4d421ed055b3764d0
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