Instructions to use xgemstarx/profile_model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xgemstarx/profile_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/profile_model2") prompt = "a photo of xjiminx" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-2500/optimizer.bin from xgemstarx/profile_model2: direct link, hf CLI and curl.
- Browser
- Download file 79.2 MB
-
https://huggingface.co/xgemstarx/profile_model2/resolve/main/checkpoint-2500/optimizer.bin
- Command line
-
hf download hf://xgemstarx/profile_model2/checkpoint-2500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/xgemstarx/profile_model2/resolve/main/checkpoint-2500/optimizer.bin
79.2 MB
- Xet hash:
- 38b326b379970a1eec41b1b4c5c2d0b6128230db3fd764706657f5d43b3b3bb0
- Size of remote file:
- 79.2 MB
- SHA256:
- 43cd3fa546933ac2a0e80ec51a72632bf4c66e74eaa7d29268f66be3788d740f
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